Managing manufacturing process innovation – New manufacturing technology adoption as a dynamic capability

D I S S E R T A T I O N of the University of St.Gallen,

School of Management, Economics, Law, Social Sciences

and International Affairs to obtain the title of

Doctor of Philosophy in Management

submitted by

Stefan Schrettle

from

Germany

Approved on the application of

Prof. Dr. Thomas Friedli

and

Prof. Dr. Oliver Gassmann

Dissertation no. 4179

Difo-Druck GmbH, Bamberg 2013

The University of St.Gallen, School of Management, Economics, Law, Social Sciences and International Affairs hereby consents to the printing of the present dissertation, without hereby expressing any opinion on the views herein expressed.

St.Gallen, May 17, 2013

The president:

Prof. Dr. Thomas Bieger

Dedicated to my parents, my family and friends

Acknowledgements First, I would like to thank my referee Prof. Dr. Thomas Friedli and my co-referee Prof. Dr. Oliver Gassmann for introducing me into the academic world and giving me the chance to pursue my own research interests. Their demand for relevance and rigor has been a guiding principle for me and my work.

I also would like to thank Dr. Maike Scherrer for her guidance through the entire dissertation process. Her friendly advice and support encouraged me to overcome obstacles and difficulties on my way.

I am deeply grateful to Prof. Dr. Morgan Swink. It was - and still is - an honor for me to work with a person whose influential work has shaped the academic world in such a way. Dr. Swink was my mentor during my visit at Texas Christian University, who challenged and encouraged me and by doing so, enriched my understanding of research. Moreover, Morgan and his family hosted me, introduced me to local cultural traditions and gave me a sense of home during my stay abroad.

Finally and most important, I would like to say thank you to my family for their unconditional support and love throughout my life. To my parents Konrad und Anna, who have the biggest stake in making me the person I am by providing me with both friendly guidance and the freedom to gain my own experience. To Thomas and Barbara for their warm support and review of parts of this work. And in particular to Verena, for her patience and tolerance as well as all the little things in life.

Stefan Schrettle Friedberg, July 2013

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Table of content

Acknowledgements ..................................................................................................... IV

Table of content ............................................................................................................ V

List of Figures ............................................................................................................. VII List of Tables .............................................................................................................. VII List of abbreviations ................................................................................................ VIII Executive Summary .................................................................................................... IX

1 Introduction and Structure .................................................................................... 1

2 Literature Review ................................................................................................... 4

2.1 Definitions..................................................................................................................... 4 2.1.1 Process Innovation ............................................................................................................. 4 2.1.2 Manufacturing Technology and AMT ............................................................................... 5

2.2 Benefits of New Manufacturing Technologies ............................................................. 7 2.2.1 Operational Performance ................................................................................................... 7 2.2.2 Organizational Performance .............................................................................................. 8

2.3 The Resource-based View and Dynamic Capabilities ................................................ 10 3 Overview of Academic Papers ............................................................................. 12

3.1 Practical Relevance ..................................................................................................... 12 3.2 Theoretical Research Gap ........................................................................................... 17

4 Unlocking the potential of process innovation: a conceptual framework for the exploitation of advanced manufacturing technologies ...................................... 25

4.1 Abstract ....................................................................................................................... 25 4.2 Introduction ................................................................................................................. 25 4.3 The resource-based view and dynamic capabilities¨ .................................................. 26 4.4 Conceptual Framework ............................................................................................... 28 4.5 Discussion and Conclusion ......................................................................................... 36

5 Turning sustainability into action: Explaining firms' sustainability efforts and their impact on firm performance ............................................................................. 37

5.1 Abstract ....................................................................................................................... 37 5.2 Introduction ................................................................................................................. 37 5.3 Decision making regarding sustainability ................................................................... 41

5.3.1 Strategic decision making ................................................................................................ 41 5.3.2 Path dependency in decision making ............................................................................... 42 5.3.3 Towards a decision making process to address the sustainability challenge ................... 43

5.4 Development of a conceptual framework ................................................................... 44 5.4.1 Drivers of Sustainability .................................................................................................. 45 5.4.2 Decision-Making towards sustainability ......................................................................... 48 5.4.3 Components of a sustainability move .............................................................................. 49

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5.4.4 Characteristics of a sustainability move .......................................................................... 53 5.4.5 Knowledge Management ................................................................................................. 54 5.4.6 Firm Performance ............................................................................................................ 56 5.4.7 Moderating Effects .......................................................................................................... 59

5.5 Conclusion .................................................................................................................. 60 6 New Process Technology Investments: The Impacts of Technology Scanning and Centralized Technology Departments on Investment Timing and Business Performance ................................................................................................................ 64

6.1 Abstract ....................................................................................................................... 64 6.2 Introduction ................................................................................................................. 64 6.3 Theory Development .................................................................................................. 67

6.3.1 Technology Scanning ...................................................................................................... 68 6.3.2 Investment Timing ........................................................................................................... 69 6.3.3 Centralized Technology Management ............................................................................. 71 6.3.4 New Process Technologies as a Source of Competitive Advantage ............................... 73

6.4 Research methodology ................................................................................................ 74 6.4.1 Data collection ................................................................................................................. 74 6.4.2 Sample description .......................................................................................................... 75 6.4.3 Measures .......................................................................................................................... 76

6.5 Results ......................................................................................................................... 80 6.6 Discussion and Post Hoc Analysis .............................................................................. 84 6.7 Conclusion and Limitations ........................................................................................ 87

7 Concluding Chapter ............................................................................................. 90

References ..................................................................................................................... 94

Appendix ..................................................................................................................... 112

Curriculum Vitae ........................................................................................................ 113

VII

List of Figures Figure 1: Structure of the dissertation ............................................................................. 3

Figure 2: Share of industrial countries in world manufactures exports ........................ 13

Figure 3: Share of developing economies in world manufactures exports ................... 14

Figure 4: Reasons for process technology failure ......................................................... 16

Figure 5: The adoption process of new manufacturing technologies ........................... 18

Figure 6: Technology adoption and sustainability management .................................. 21

Figure 7: Investment timing of new manufacturing technologies ................................ 23

Figure 8: Assumptions underlying the technology adoption process ........................... 29

Figure 9: Conceptual Framework for Manufacturing Technology Adoption .............. 29

Figure 10: Decision-making framework in the context of sustainability ..................... 45

Figure 11: Research Model Paper 3 .............................................................................. 67

Figure 12: Overview Centralized Technology Departments ........................................ 77

Figure 13: Interaction between Scanning Activities and Presence of a CTD............... 83

List of Tables Table 1: List of technology items ................................................................................... 7

Table 2: Overview of academic papers ......................................................................... 24

Table 3: Constructs and Measurement Items ................................................................ 79

Table 4: Intercorrelation Matrix.................................................................................... 80

Table 5: Regression of Scanning Frequency on Strategic Importance ......................... 81

Table 6: Regression of Early Investment on Hypothesized Drivers ............................. 82

Table 7: Regression of Business Performance on Early Investment ............................ 83

Table 8: Regression analysis, including only firms with a CTD .................................. 85

Table 9: Regression of Business Performance on Relative Investment Timing .......... 87

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List of abbreviations

AMT Advanced Manufacturing Technologies

e.g. exempli gratia (for example)

et al. et alii

HVAT High-volume automation technology

IEPT Information exchange and planning technology

IM Integrated Manufacturing

IP Intellectual Property

JIT Just-in-Time

LVFAT Low-volume flexible automation technology

MPI Manufacturing process innovation

OM Operations Management

PDT Product design technology

RBV Resource-based view

TPM Total Productive Maintenance

TQM Total Quality Management

WTO World Trade Organization

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Executive Summary The competitive landscape for business operations has changed dramatically

during the last decades due to increasing global competitive pressure that has accelerated the pace of technological change. Especially manufacturing firms are facing cost pressure from developing countries and growing product complexity, while quality requirements are increasing and customer demands are rapidly changing. Many industries have experienced a paradigm shift from standardized, big scale manufacturing to low-volume manufacturing with flexible product characteristics. These developments constantly force manufacturing firms to adapt production processes to the latest technological trends and developments, but only few companies successfully manage the implementation of new process technologies in order to improve operational and business performance.

This study helps to improve management capabilities of process technology change by providing fundamental insights into the design of adoption processes and the investment strategy for manufacturing technologies. The first paper identifies relevant determinants that affect the exploitation of new manufacturing technologies’ benefits. We develop a conceptual framework of an effective adoption process that represents a dynamic capability and a potential competitive advantage for a firm.

The second paper analyses technological change in manufacturing units with a special focus on the impact of sustainability management. The importance of the triple bottom line is steadily increasing, in particular for resource intensive manufacturing firms due to rising costs for energy supply and resource inputs as well as stricter governmental regulation. The presented framework supports adequate strategic decision-making to address the challenge of ecological sustainability in order to turn sustainability efforts into superior firm performance.

The third paper analyzes the investment timing in new process technologies and its impact on business performance. We show that early investors can realize a first-mover advantage if certain information processing capabilities are in place. Additionally, our study identifies methods and organizational structures that trigger the beneficial business outcome of technological leadership in process technologies.

To summarize, this piece of work provides numerous theoretical and managerial implications that are of upmost importance for manufacturing managers and addresses some of the most crucial research gaps in the operations management literature.

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Zusammenfassung Im Laufe der letzten Jahre hat sich das globale Geschäftsumfeld dramatisch

geändert. Durch die Globalisierung ist der Konkurrenzdruck merklich gestiegen, was u.a. zu einer Erhöhung der Dynamik des technologischen Wandels geführt hat. Gerade die produzierende Industrie ist einem zunehmenden Kostendruck von Herstellern aus Entwicklungsländern ausgesetzt, während gleichzeitig die Produktkomplexität bei steigenden Qualitätsansprüchen wächst und sich Kundenpräferenzen immer schneller ändern. Viele Industrien erleben dabei einen Wandel weg von standardisierter Massenproduktion hin zu flexibler Fertigung von geringeren Stückzahlen. Diese Entwicklungen stellen produzierende Unternehmen vor die Herausforderung ihre Produktionsprozesse an die neuesten technologischen Erkenntnisse anzupassen. Dabei gelingt es nur den wenigsten Unternehmen, neue Prozesstechnologien erfolgreich einzuführen, um neben der Operational Performance auch das Betriebsergebnis nachhaltig zu steigern.

Die vorliegende Arbeit schafft die Grundlagen, um den technologischen Wandel im Bereich von Fertigungstechnologien besser managen zu können, indem neueste Erkenntnisse zum Design von Technologie-Adoptionsprozessen sowie zur Investmentstrategie für neue Prozesstechnologien zur Verfügung gestellt werden. Im Rahmen des ersten Artikels werden relevante Einflussfaktoren für die Ausgestaltung eines Adoptionsprozesses für Fertigungstechnologien dargelegt. Diese werden in einem Referenzmodell konsolidiert, das zur effektiven Steuerung der Technologieadoption dient. Der zweite Artikel analysiert die Auswirkungen von Nachhaltigkeitsüberlegungen auf die Auswahl strategischer Unternehmensinitiativen. Speziell die produzierende Industrie ist von höheren Energie- und Rohstoffkosten sowie zunehmenden gesetzlichen Auflagen betroffen. Deshalb wurde ein Referenzmodell entwickelt, um optimale strategische Entscheidungen im Hinblick auf die Herausforderungen ökologischer Nachhaltigkeit zu ermöglichen. Der dritte Artikel analysiert die Auswirkungen des Investitionszeitpunkts in neue Proesstechnologien auf das Betriebsergebnis. Es wird aufgezeigt, dass durch die Technologieführerschaft im Produktionsumfeld ein first-mover advantage realisiert werden kann, während gleichzeitig geeignete Methoden zur Erlangung dieses Wettbewerbsvorteils identifiziert werden. Damit liefert die vorliegende Arbeit wichtige wissenschaftliche Erkenntnisse, die speziell für Produktions- und Technologieunternehmen die Realisierung eines strategischen Wettbewerbsvorteils bieten.

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1 Introduction and Structure Global manufacturers experience increasing competitive pressure, due to cost

pressure from manufacturers in developing countries, rapidly changing customer demands, growing product complexity and demanding legal requirements. In addition, the paradigm shift from standardized big scale manufacturing to flexible low-volume manufacturing during the last 20 years is still prevailing and companies have to master the balance between economies of scale and the customers' demand for customized products. Especially firms in industries with shorter product life cycles are confronted with accelerated change of the production processes due to differing requirements for the manufacturing capabilities for new products.

All these factors caused the manufacturing industry to become highly competitive and new market entries from low wage countries have put pressure on management to make changes in their operating business (Idris et al. 2008). One way by which manufacturing plants adapt to those environmental dynamics is the engagement in manufacturing process innovation (Lee et. al 2011). The use of new and advanced manufacturing technologies (AMT) for example has been recognized as valuable weapon for manufacturing companies to address these challenges (Sun 2000). Accordingly, the topic of new manufacturing technologies has attracted both, the attention of academics and practitioners during the last decades. During the last 30 years, AMT has been widely used by manufacturing firms throughout the world and simultaneously, the academic field of manufacturing process technologies has grown remarkably in the Operations Management (OM) literature. However, the literature contains conflicting results and opinions (Swink and Nair 2007). The goal of the present work is to contribute to the vast body of literature to provide a better understanding of new manufacturing process technology. In particular, we aim at raising the awareness for the strategic differentiation potential of innovative and superior manufacturing capabilities, while at the same time, providing methods and instruments that are suitable to realize such a competitive advantage.

This dissertation focuses on three different aspects of new manufacturing process technology. First, a model is developed that structures the findings of existing literature. A theoretical framework is presented to provide a holistic picture of the factors that influence the effectiveness and efficiency of the adoption process of new manufacturing technologies. Second, a decision model for manufacturing managers is presented that considers the growing interest in sustainability issues, which has been

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found in both academia and industry (Linton et al. 2007). Especially ecological sustainability is a highly dynamic phenomena and its relevance for manufacturing firms is constantly growing. Third, we investigate which organizational structures and activities affect the effectiveness of technology adoption processes. In particular, we assess the impact of scanning activities and the existence of a corporate or divisional technology department on the realization of a first-mover advantage due to early investment in new, promising process technologies.

The three papers offer valuable contributions to the literature on new manufacturing technologies and support firms pursuing superior manufacturing capabilities. They aim at increasing our understanding how new technological options can be identified and integrated within the firm. The cumulative dissertation is structured as follows. Chapter 2 gives an overview about different concepts and definitions of manufacturing technologies, explains the corporate and operational benefits and illustrates the resource-based view (RBV) as the relevant theory for this work. Chapter 3 introduces the three papers and gives an overview about the current publications status. In chapters 4, 5 and 6, the relevant papers are presented. Finally, chapter 7 recapitulates the main findings of this work and provides a concluding overview about our contributions to the field of Operations Management (see Figure 1).

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Figure 1: Structure of the dissertation

Introduction and StructureChapter 1

Overview of Academic Papers

Practical Relevance

Theoretical Research Gap

Chapter 3

Literature Review

Definitions

Benefits of New Manufacturing Technologies

The Resource-based View and Dynamic Capabilities

Chapter 2

Paper 1Unlocking the potential of process innovation: a conceptual framework for the exploitation of advanced manufacturing technologies

Chapter 4

Paper 2Turning sustainability into action: Explaining firms' sustainability efforts and its impact on firm performance

Chapter 5

Paper 3New Process Technology Investments: The Impacts of Technology Scanning and Centralized Technology Departments on Investment Timing and Business Performance

Chapter 6

Concluding ChapterChapter 7

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2 Literature Review

2.1 Definitions

It is useful to define the scope of this dissertation by providing precise definitions of key terms that describe the core elements of the study. Various terms are used in the literature for technologies that are used within an industrial production unit and the activity of adopting them. The expressions range from manufacturing process innovation (MPI), manufacturing technology, manufacturing process technology to advanced manufacturing technology (AMT). In the following, all those terms will be defined to develop a common understanding.

2.1.1 Process Innovation

Reichstein and Salter (2006) define process innovation as "new elements introduced into an organization’s production or service operations—input materials, task specifications, work and information flow mechanisms, and equipment used to produce a product or render a service—with the aim of achieving lower costs and/or higher product quality" (Reichstein and Salter 2006, pp. 653). They name two major objectives why managers engage in process innovation on firm level. First, process innovation is an important source of increased productivity. Especially in industrialized countries, the increase of productivity is a major management goal to keep up with developing, cheap labor countries. Second, process innovation enables firms to gain competitive advantage. Thus, a better understanding of the surrounding conditions of process innovation allows a greater appreciation of the means by which firms gain and sustain competitive advantage. The authors also note that it is necessary to separate technological process innovations from organizational process innovations that do not involve technological elements. However, this separation is difficult to sustain in practice as most process innovations involve both organizational as well as technological changes. They name lean production as one example for a major process innovation, which includes the use of a wide range of new material-processing technologies as well as new work practices which represent exclusively the coordination of human resources (Reichstein and Salter 2006).

According to Lee et al. (2011), manufacturing firms need to adapt to environmental dynamics including short product life cycles, growing product complexity, and rapid advances in technologies by continuously engaging in manufacturing process innovation (MPI). They use the term MPI to refer "to all

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activities necessary to design and implement a new manufacturing process, or to change an existing process. MPI projects can address a range of activities, from relatively minor "practice-based" procedural changes to major technological changes, such as the installation of new equipment. Initiatives such as "Lean" manufacturing, Six Sigma programs, new product introductions, and technology upgrades also often motivate such projects" (Lee et al. 2011, pp. 556). This definition takes a perspective that is shared by other authors who distinguish hard-based from soft-based technologies (Sun 2000). Hard-based technologies refers mainly to physical technologies used in engineering, processing and administration, while soft-based technologies covers management practices like Total Quality Management (TQM) and Just In Time (JIT). It is important to note that MPI covers also all activities necessary to adapt existing production processes and therefore, goes beyond other definitions focusing solely on tools and practices that are finally implemented.

2.1.2 Manufacturing Technology and AMT

Sinha and Nobel (2008) define manufacturing technologies as the "master tools of industry that magnify the efforts of individual workers and enable production of all manufactured goods, with production tools including machine tools and other related equipment, their accessories, and tooling" (Sinha and Nobel 2008, pp. 944). This very broad definition includes every technical system that supports the production process, no matter whether the transformation of goods is directly affected, or some kind of data is stored or edited within by any kind of information technology related to the manufacturing process. The definition also makes no distinction between the newness of the invented technology or the extent to which the production process is adapted. The new technology can be a small procedural change, a change of equipment or the total automation of manufacturing processes.

The most extensive body of literature in the area of new manufacturing technologies has been developed with regard to advanced manufacturing technologies (AMT). Noori (1990) applies a very broad definition of AMTs covering all new technologies which are used directly by the firm in the production of a product. In more detail, AMT refers to a broad spectrum of computer-controlled process technology involving new manufacturing techniques and machines embedded in information technology and combined with microelectronics and new organization practices in the manufacturing process (Dean et al. 1992, Idris et al. 2008, Sun 2000, Swamidass and Kotha 1998, Swink and Nair 2007, Zammuto and O’Connor 1992). Although there is a broader conceptualizations of AMT including also soft

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technologies such as JIT and progressive human-resource development techniques, most authors understand AMT to be the application of the latest scientific or engineering discoveries to design production processes with the help of computer integrated technologies (Idris et al. 2008, Swink and Nair 2007, Zammuto and O’Connor 1992).

There are various classification schemas in the literature. Swink and Nair (2007) identify three types of AMT, namely design technologies, processing technologies, and planning (administrative) technologies. Other authors group the various manufacturing technologies into four groups on the basis of the embedded information processing capabilities (Kotha and Swamidass 2000, Swamidass and Kotha 1998). Accordingly, they distinguish the following dimensions:

1. Product Design Technologies (PDT) including technologies such as CAD, CAE and automated drafting technologies that focus primarily on product definition, design, and related information processing functions.

2. Process Technologies (PT). This dimension encompasses technologies such as CNC, CAM, FMS, and programmable controllers that focus on the process related aspects in manufacturing. These technologies are used on the shop floor, generate process related information from the factory floor and can be linked to product related technologies for reciprocal communication.

3. Logistics Planning Technologies (LPT) covering technologies that control and monitor the material flow from the acquisition of raw materials to the delivery of finished products, and the related counterflows of logistical information. It includes both the hardware and software for production scheduling systems, shop floor control systems, and Material Requirements Planning.

4. Information Exchange Technologies (IET). This dimension helps facilitate the storage and ex exchange of information among process, product, and logistics technologies identified above. Technologies that include common databases, data transfer protocols, and intra- and inter-factory networks are essential to this dimension.

Finally, a considerable portion of academic work used the items and classification developed for the Boston University Manufacturing Futures Project (see Idris et al. 2008, Boyer et al. 1997). They categorized 20 technology items into the three variables design technology, manufacturing technology and administrative technology. For more details see Table 1.

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Design technology a) Computer-aided design (CAD) b) Computer-aided engineering (CAE) c) Computer-aided Process planning (CAPP)

Manufacturing technology a) Computer-aided manufacturing (CAM) b) Robotics c) Real-time process control systems d) Group technology (GT) e) Flexible manufacturing systems (FMS) f) Computerized numerical control machines (CNC) g) Automated material handling systems h) Environmental control systems i) Bar coding/automatic identification

Administrative technology a) Electronic mail b) Electronic data interchange c) Office automation d) Knowledge-based systems e) Decision support systems f) Material requirement planning (MRP) g) Manufacturing resource planning (MRP II) h) Activity based accounting systems

Table 1: List of technology items (see Idris et al. 2008, Boyer et al. 1997)

2.2 Benefits of New Manufacturing Technologies

Since the nineties of the last century, manufacturing firms face major challenges. Sun (2000) mentions (1) the reduction of lead time to satisfy consumers, (2) getting new products to market more quickly, (3) flexibility to adapt to changes in the market, (4) improvement of product quality, (5) cost reduction, (6) and increased consumer services as the most important issues to address.

2.2.1 Operational Performance

A vast body of literature has investigated the usage of AMT as an adequate mean to improve operational (manufacturing) performance and its impact on firm performance (Idris et al. 2008, Kotha and Swamidass 2000, Small and Yasin 1997, Swamidass and Kotha 1998). Typical dimensions of manufacturing performance are flexibility, cost, quality and delivery (Ketokivi and Schroeder 2004, Swink and Nair 2007). Many authors highlight the contribution of new manufacturing technologies to strategic priorities like increased flexibility allowing firms to produce a variety of products at low volumes with no additional costs or penalty (Fine and Freund 1990, Lee 1996, Slagmulder and Bruggeman 1992, Swamidass and Kotha 1998, Swink and

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Nair 2007). McDermott et al. (1997) for example found that the adoption of AMT enabled firms to simultaneously pursue volume efficiencies and product flexibility, a central prerequisite to manage the paradigm shift from standardized big scale manufacturing to flexible low-volume manufacturing. Improved changeover and processing speed does not only improve flexibility, it also positively impacts delivery as empirically validated by Swink and Nair (2007).

Additionally, AMT leads to increased productivity by reducing firms' direct labor costs, rework costs and work-in-process-inventories by embedding routine repetitive tasks into AMT hardware and software (Swamidass and Kotha 1998, Swink and Nair 2007, Zammuto and O’Connor, 1992). Finally, firms using AMT achieve higher product quality, because process-related technologies allow for stable manufacturing processes according to product conformance (Swamidass and Kotha 1998, Swink and Nair 2007, Zammuto and O’Connor 1992). Especially automation technologies provide greater consistency with specifications and therefore reduce scrap rates leading to a higher level of quality (Swink and Nair 2007).

Small and Yasin (1997) used responses from 125 US firms to investigate the relationship between the adoption of various AMTs and project performance. Three performance dimensions were measured by a self-assessment of time-based performance items (e.g. time-to-market for a new product, time for major design change in existing product, production changeover time), organizational effectiveness factors (e.g. plant revenues, output rate, product quality) and operational efficiency factors (lead time, turnover rate, ability to change production lot size). The results of the factor analysis suggest that those firms who adopt integrated technologies exert significantly higher levels of performance. With regard to firm size, the study supports the contention that large firms benefit more from new technologies, because firms with annual sales of more than $50 million achieved significantly higher levels of operational efficiency and moderately higher levels of composite performance than firms with lower sales.

2.2.2 Organizational Performance

Although Small (1998) found that operational objectives like improving product quality, reducing manufacturing leadtimes, reducing per unit production costs and improving responsiveness to changing customer needs are the most important goals of AMT adoption, he also found evidence that corporate goals like increasing market share and gaining earlier entrance to market can be realized. His study revealed that

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the 15 investigated benefits can be viewed categorized into three interdependent dimensions: technical/operational objectives, total quality management-based objectives and business or marketing-based objectives.

The impact of manufacturing technology adoption on business performance was investigated by several authors. Idris et al. (2008) conducted an empirical study in the Malaysian manufacturing environment and investigated the impact of various AMT dimensions on performance. They found a significant and strong positive relationship between administrative AMT use as well as advanced manufacturing technology use and return on investment. This supports the results of a prior study by Ariss et al. (2000) who also reported that the adoption of AMT improves return on investment.

Swamidass and Kotha (1998) investigated the relationships between AMT use, firm size, and firm performance in US manufacturing firms. First, they found that AMT use is associated with firm size, meaning that large firms have the resources and the qualified and specialized engineering personnel to implement complex manufacturing systems. Furthermore, a large corporation can take the risk of such a project. Second, they failed to establish a significant relationship between AMT use and performance. AMT does not improve firm performance automatically, but third, size weakly moderates the relationship between AMT use and performance. This finding suggests that larger firms are better able to exploit the benefits of AMTs, maybe due to their superior resource base.

To further investigate the AMT-performance relationship, Kotha and Swamidass (2000) examined the relationships among strategy, a multidimensional view of advanced manufacturing technology and performance. They used survey data from 160 U.S. manufacturing firms and found empirical support that given a strategy fit, AMT use leads to superior performance. They posit that different strategies should be associated with the use of certain AMT dimensions and found a significant relationship for superior performing firms, while the relationship for poor performer stayed insignificant. This validates that the fit between strategy and AMT use is associated with improved performance.

Besides business performance, there is also empirical evidence that new manufacturing technologies and the adoption of manufacturing programs are crucial for the success and survival of manufacturing firms (Doms et al. 1995, Sinha and Noble 2008). Doms et al. (1995) conducted an empirical study on plant level, investigating the relationship between capital intensity, technology use, growth rates and exit probabilities. The central finding is the non-sufficient explanatory power of

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three variables plant size, age and productivity for the growth or exit of a plant. It is rather capital intensity and the adoption of AMT such as robots, lasers, or computer-controlled machinery that explain subsequent growth or failure. Plants that use larger numbers of AMT exhibit higher growth rates and lower failure rates.

Sinha and Noble (2008) analyzed the effect of radical technology adoption, including those technologies that changed competitive dynamics within an industry, on firm mortality. Their study provides strong evidence for the lowering effect of radical manufacturing technology adoption on firm mortality. The timing of adoption is crucial for the likelihood of survival as earlier adoption measured as an absolute measure with regard to the technology maturity as well as earlier adoption relative to other firms has a positive influence on survival likelihood than later adoption.

Finally, literature also provides evidence for a positive relationship between process innovation and product innovation. Reichstein and Salter (2006) conducted a regression analysis and compared incremental process innovators and radical process innovators and compared their sales from products that are new to the market. The data suggests that process innovation is associated with firms with a higher share of sales from products new to the market and the degree of novelty in the process innovation amplifies this relationship. This means that process and product innovations are interdependent implying that radical product innovators are likely to be radical process innovators and vice versa.

2.3 The Resource-based View and Dynamic Capabilities

New manufacturing technologies contribute to the competitive advantage of a firm by extending its resource base and capabilities. Therefore we incorporate the resource-based view (RBV) and the concept of dynamic capabilities to explain superior performance for organizations that adopt new manufacturing technologies in dynamic environments.

The ultimate ambition in the strategy field is to find an explanation why some firms perform better than others (Rumelt et al., 1991). For this attempt, the RBV has become one of the most influential views in the strategic management literature. According to the RBV, competitive advantage can be attained by controlling of unique resource bundles which are valuable, rare, inimitable and non-substitutable (Barney, 1991).

As the RBV approach is inherently static, the explanatory power of this theory has limitations in explaining how firms in dynamic environments can gain sustainable

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competitive advantage over time (Teece, 2007). To overcome this shortcoming, the concept of dynamic capabilities introduces a notion of organizational renewal, as managers need to constantly alter resource and capability configurations within an organization to adapt to environmental change (Helfat & Peteraf, 2003). Hence, dynamic capabilities are defined as "the firm's ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. Dynamic capabilities thus reflect an organization's ability to achieve new and innovative forms of competitive advantage given path dependencies and market positions" (Teece et al., 1997: 516). It is important to mention that superior organizational performance is not caused by dynamic capabilities as such, but from the resource configurations that are resulting from the use of dynamic capabilities. Dynamic capabilities are hence necessary, but not sufficient for competitive advantage and one has to distinguish between dynamic capabilities and their outcomes (Eisenhardt & Martin, 2000). This is in line with Schroeder et al. (2002) who explore the role of resources and capabilities in manufacturing plants and its impact on manufacturing performance. They state that proprietary processes and equipment are created through a path-dependent process of problem solving which mediates between learning and performance. Per se, learning does not lead to superior performance, it must rather be embedded in a physical process or equipment to result in superior performance.

Superior manufacturing competence is a central source of competitive advantage for manufacturing firms and the underlying production technology is an essential part of this competence. Manufacturers can differentiate from competitors by achieving cost efficiency, quality, flexibility, and lead time reduction without trade-offs (Sun, 2000). In the OM literature, manufacturing strategy is defined by capabilities and resources, and their relationship to operational and corporate performance. Schroeder et al. (2002) consider three different types of resources and capabilities that are built within the manufacturing function and are difficult to imitate and transfer: (1) proprietary processes and equipment, (2) internal learning, and (3) external learning. Thus, process innovation by technology adoption is one out of three resources that affect manufacturing performance. This is in line with Reichstein and Salter (2006) who argue that process innovation is a central source of competitive advantage. Rapid technological change and fast shifting customer needs force firms to constantly discover and develop opportunities like the effective and efficient transfer of manufacturing technologies into the value chain of the company (Teece, 2007). With regard to new manufacturing technologies, the selection and development of the

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right technology is an important task to achieve organizational fit with the environment. The ability to manage this integration and to adequately adapt production processes exhibits a substantial dynamic capability of a manufacturing firm, which can be seen as the assembling and orchestrating of difficult-to-orchestrate assets (Teece, 2007). The competitive advantage itself is generated by more efficient and effective production processes caused by the implementation of a new manufacturing technology in combination with complementary assets like manufacturing practices of TQM, JIT, etc. (Cua et al., 2001). The simultaneous organizational transformation (Leonard-Barton, 1988) and the development of routines (Teece, 2007) are major prerequisites to fully exploit the benefits of new production technologies.

3 Overview of Academic Papers

3.1 Practical Relevance

According to the World Trade Organization (WTO 2008), there is no universally agreed definition of the term "globalization". Economists use the term when they refer to international integration in commodity, capital and labor markets (Bordo et al., 2003).

For single enterprises, globalization not only means that new markets for end products arise and plenty of potential new consumers for their goods can be addressed. It also means that internal value chains are changing, moving away from a geographically centralized product plant to world spanning value chains where single production steps or bundles of manufacturing processes are distributed around the world. Especially the production of easy to replicate products and manufacturing processes that are highly labor intensive have been relocated from industrialized countries to developing countries. Furthermore, new competitors arise in regions that for a long time were not even within the horizon of firms in the industrialized world. Those new players accomplished market entry through an aggressive cost leadership, but are developing into a new role of more and more innovative manufacturers.

This evolution of global manufacturing roles has started in the early 1980s and is still going on. Macroeconomic data for this period of time shows that the portion of worldwide manufactured goods and products of the industrial countries has constantly been declining (see Figure 2). While the share of labor-intensive products like textiles and clothing started to decline earlier in the industrialized countries, the ongoing trend throughout the last 40 years remains the same for all industries.

13

There are two main reasons for this development. First, the growth of domestic markets in the developing countries in combination with the long distance of transportation from industrialized countries led to the institution of new plants in those countries. Global enterprises set up a foreign base to master market entry in promising emerging markets. In a first step, this was handled by the establishment of new sales departments. As domestic consumer demand grew, production capacities were added to handle the surplus demand and substituted product imports from the industrialized countries.

Figure 2: Share of industrial countries in world manufactures exports by product group, 1955-2006

(Source: WTO)

The second reason was the exploitation of cost advantages in developing countries compared to traditional plants in the industrialized world by global enterprises. Now, having available a global production network that was supplemented with at least one low cost plant, firms started to substitute production of goods, which were initially produced in target consumer markets in the industrialized world, to their low cost plants. Thus, the export share of developing countries has consistently increased since the early 1980s, almost in the exactly inverse direction than the export share in the industrialized world has declined (see Figure 3).

14

Figure 3: Share of developing economies in world manufactures exports by product group, 1983-2006

(Source: WTO)

Firms and production sites in the industrialized world have to develop competitive strategies that allow for competitive advantage that compensates for the detrimental location factor of high wage level. Many manufacturers chose a strategy in high wage countries that aims at providing modern and flexible production systems, which enable firms to produce customized products in superior quality (Kaluza & Blecker, 2005). A possible way to pursuit this strategy is the adoption of advanced manufacturing technology, which requires a highly educated workforce and enables firms respond to low-cost standardized products from abroad (Gerwin, 1993).

In the past 30 years, almost every manufacturing firm has adopted any kind of advanced manufacturing technology as the information processing capability was a mandatory resource to stay competitive. Information exchange and planning technology (IEPT), product design technology (PDT), low-volume flexible automation technology (LVFAT), and high-volume automation technology (HVAT) characterized the last three decades of manufacturing, especially in industrialized countries of Europe. The paradigm for excellence in manufacturing shifted from standardized big scale manufacturing to flexible low-volume manufacturing. This transformation is still prevailing and urges manufacturing managers to engage in manufacturing process innovation (MPI). MPI includes all relevant activities to design, develop and

15

implement new production processes, or to change existing processes (Lee et al. 2011).

Consequently, the adoption of new and innovative manufacturing technologies is a central challenge for manufacturing firms. Especially in countries in which the high wage level serves as a barrier for a low-cost strategy, the identification and usage of technologies that allow for a flexibility or quality strategy and increase productivity is a mandatory condition for firm survival. Despite the importance of process innovation for long-term firm success, many companies were not able to realize the full potential of new manufacturing technologies. Especially AMTs often did not perform as expected on the company level, lead to a total failure, or worked satisfactory, but did not produce the full benefits. Even if AMTs performed well on the shop floor level and improved manufacturing performance, business performances was often not improved (Sun 2000).

The adoption process for new process technology is complex and often not clearly defined leading to a situation in which unsatisfactory results of technology performance may be traced back to multiple sources. The search for causes may be difficult and rests unresolved in many companies. It remains unclear which process step of the generic technology adoption process was not properly executed and which stakeholders of the process are responsible for failure. The adoption process is characterized by four distinct phases (see also Figure 5). In a first step, numerous potential technology candidates have to be identified, no matter whether candidates are developed in-house or bought from external suppliers. Second, the relevant candidates are screened according to several evaluation criteria which are typically technical and economical in nature. After the decision in favor of a certain technology has been made, it is about implementing the new technology including the challenge minimize the downtime of the existing production process while changing the at least single parts of the production system. Forth, the newly implemented technology is ready to operate and has to proof its superiority in comparison to the old, substituted manufacturing process technology. Most of the time the first period of time of the technology usage phase is characterized by optimizing the system and adapt relevant production parameters to realize excellent manufacturing performance.

The whole process is influenced by the competitive environment of the company reflected in the corporate strategy. The corporate strategy identifies upcoming market trends and future requirements for business success and translates these findings into a set of characteristics for new process technologies. Accordingly, the technology strategy of the firm defines objectives for the use of technology and

16

related means how to realize these goals. In case of manufacturing process technologies, the technology strategy is closely linked to or even part of the production strategy. The technology strategy is often consolidated and disclosed in a technology roadmap which links technological short- and long-term goals with the capabilities of the firm.

Many stakeholders and contingencies have been identified that influence the performance of new manufacturing technologies making the search for critical success factors difficult. Even the major question can not be answered in practice, whether unsatisfactory results can be traced back to an unfavorable decision for a certain technology, or rather the implementation of the optimal technology failed. In an not yet published survey among production and technology managers from 88 European companies, the majority of the respondents indicated that according to their experience, unsatisfactory results of process technology performance is rather an issue of insufficient decision making than implementation of the technology (see Figure 4).

Figure 4: Reasons for process technology failure

Almost 30% of the respondents said that their company does not possess a structured process of manufacturing technology adoption and 20% indicated that the timing of technology adoption is a major barrier for process technology success. These results suggest a practical need to further investigate the effective and efficient implementation of new manufacturing technologies with a focus on innovation processes for the adoption of manufacturing technologies and the right timing of new technology investment.

8%

14%

16%

27%

24%

12%

20%

16%

6%

8%

24%

22%

0% 20% 40% 60% 80% 100%

The failure of technology projects can be traced back to an insufficient implementation of the technology

The failure of process technology projects can be traced back to an insufficient decision for a certain

technology

Totally agree Agree Neutral Do not agree Do net agree at all n/a

17

3.2 Theoretical Research Gap

Almost every manufacturing firm has implemented any kind of advanced manufacturing technology during the last decades. Despite the practical relevance of process innovation, the literature on the sources of technological change focused primarily on product innovation rather than process innovation, which has received much less attention (Reichstein and Salter 2006). This might explain why the results concerning new process technology adoption were surprisingly mixed both, in practice as well as in theory. Accordingly, the academic field of new process technologies and AMT has provided contradictory results and therefore limited evidence of the beneficial impact of process technology innovation (Swink and Nair 2007).

In his 1995 and 1997 studies, Upton found negative relationships between the usage of AMT and both product and production flexibilities. Using data from 54 plants in the fine-paper industry, he found that manufacturing flexibility is negatively related to scale and degree of computer-integrated technology and positively related to newer vintages of mechanical technology (Upton 1997). The findings suggest that the type of technology is an important factor to be controlled for when examining the relationship between process technology usage and manufacturing flexibility.

Boyer et al. (1997) also failed to establish a significant relationship between AMT use and flexibility. Besides operational aspects, the impact of AMT adoption on business aspects of performance is also unequivocal. They found only partial support for their hypothesis that infrastructural improvement programs moderate the relationship between investments in advanced manufacturing technologies and performance. While infrastructural improvement - described as "including workforce and organizational policies, as well as procedural systems such as manufacturing planning and control, and quality assurance" (Boyer et al. 1997: 332) - lead to a higher degree of flexibility, investments in AMTs do not lead an incremental increase in flexibility. On the other hand, the hypothesis is supported if business performance measures like growth and profit are used.

Boyer et al. (1996) investigated whether different patterns of investments in AMT are causal for differences in performance and could not verify a differentiation effect of deviating investment strategies. They identified four different types of companies with regard to their investment strategy in AMTs. While the AMT strategies differ across plant size and integration patterns, there was no difference in terms of industry membership and performance meaning that successful firms can be

18

found in each of the four groups and therefore, that the investment strategy is not the causation for performance effects.

Swamidass and Kotha (1998) found a moderating effect of firm size on the relationship between AMT use and firm performance. This means that AMT use improves performance only in big companies, but not as a general rule. To explain those inconsistencies, several authors have proposed moderating factors for the relationship of process technology adoption and performance (Cagliano and Spina 2000, Das and Jayaram 2003, Swink and Nair 2007, Swamidass and Kotha 1998). The actual operational management literature does not provide a clear understanding how a process of technology adoption for process technologies should be designed to ensure effectiveness and efficiency of technology performance. The picture about relevant contingencies and the related moderating and mediating effects on the relationship between process technology investment patterns and performance is rather vague.

To gain a better understanding of the state-of-the-art literature on AMT and manufacturing process technology, the first paper provides a holistic overview of the OM literature by integrating various findings, moderators and contingencies into one theoretical framework. Furthermore, the separation of a decision-making phase from a technology implementation phase is proposed to improve the understanding of technology adoption. The mediation of the relationship between technology use and organizational performance complements the contributions of the paper.

Figure 5: The first research paper focuses on the adoption process of new manufacturing technologies

(adapted from Wheelwright and Clark, 1994)

TechnologyIdentification

TechnologyChoice

Technology Implementation

TechnologyUsage

Mediating effects

Moderating effects

19

Besides classical operational performance criteria, environmental protection gains more and more importance for the agenda of manufacturing firms. In both theory and practice, a growing interest in sustainability can be found (Linton et al. 2007) and "the need for environmental protection and increasing demands for natural resources are forcing firms to reconsider their business models and restructure their supply chain operations" (Wu and Pagell 2011: 577). Increasing regulation forces managers to invest in green technologies as increasing internalizing of externalities represents an additional matter of expense. For example, carbon trading serves as an incentive to invest in initiatives to reduce CO2 emissions and increasing price levels of raw materials has improves the cost-benefit ratio of resource efficiency programs.

The trend for sustainability was already investigated by many researchers and the positive relationship between green practices and performance has been demonstrated. Zhu and Sarkis (2004) investigated whether green supply chain management (GSCM) practices of 186 respondents from Chinese manufacturing enterprises are related to performance. They found a direct relationship between GSCM practices and economic and environmental performance suggesting that economic and environmental success is rather complementary than substitutes for each other.

Other studies investigated whether consumers reward eco-friendly products by examining the willingness-to-pay (WTP) of consumers for different groups of products. While Anstine (2000) failed to establish such a relationship for the consumer behavior for kitchen garbage bags using recycled plastic, studies in the wood industry (Vlosky et al. 1999) or the agricultural sector (Misra et al. 1991) found evidence for a higher WTP for environmentally friendly products.

The literature on environmental management and its impact on firm performance provides mixed results. It is not clear so far, whether environmental efforts hurt profitability due to high costs of sustainability programs, or whether the growing awareness for the topic offers new growth opportunities that could lead to competitive advantage (Hart and Ahuja 1996, Klassen and McLaughlin 1996). Jacobs et al. (2010) investigated the relationship between environmental performance and firm performance measured as the financial performance at the stock market. They found both statistically significant positive and negative market reactions for corporate environmental initiatives meaning that sustainability per se has no favorable impact on stock market performance. For example ISO 14001 certifications are positively related

20

to firm performance, while voluntary emission reductions are not valued by the market and lead to a decrease in share prices.

These mixed results were also supported by a literature review of 32 studies that analyze the influence of environmental management on financial performance (Molina-Azorin et al. 2009). Although results indicated positive as well as negative relationships, a majority found a positive impact of green management on financial performance.

The management of natural resources is becoming an increasingly important issue to manufacturing firms (Klassen and Whybark 1999). But existing literature provides only limited insights, whether new manufacturing technologies could be a suitable answer for the challenge of ecological sustainability. While the positive effect of green management on performance is well documented (Corbett and Klassen 2006, King and Lenox 2001, Klassen and Whybark 1999, Molina-Azorin et al. 2009, Pil and Rothenberg 2003), it is not clear so far, whether or how environmental issues affect process technology adoption. Sun (2000), for example, indicates the six most important challenges for manufacturing units without mentioning sustainability issues. Based on an extensive literature review, Udo and Ehie (1996) identify 25 benefits of advanced manufacturing technologies (AMT) and Small (1997) list 15 objectives for AMT implementation, both without taking environmental protection into account.

An early study by Klassen and Whybark (1999) with 83 respondents from US manufacturing mangers indicated the beneficial impact of new manufacturing technologies. They found that the composition of a plant's technology portfolio reflecting the investment pattern in environmental technologies, significantly affects manufacturing and environmental performance.

To deepen the understanding of process technology adoption and its impact on performance, paper 2 presents a decision framework for manufacturing firms how to deal with the ecological aspects of the triple bottom line. Various ecological influencing factors are presented to operationalize the sustainability challenge and the knowledge dimension is introduced to explain superior performance of sustainable strategic decision-making. The paper embeds the issue of manufacturing technology adoption into the rising topic of sustainability.

21

Figure 6: The second research paper embeds technology adoption into the topic of sustainability management

The S-curve pattern is a well established concept for the level of maturity of a certain technology (Abernathy and Utterback 1994, Brenner 1996, Flynn et al. 1997). It describes the development of a technology, from the early stages of knowledge-building, when its effects are not well known and only a few plants adopt it, till the mature stage of a technology when it has become a standard within an industry and differentiation is no longer possible due to the high penetration of the technology. Surprisingly, the adoption timing of new process technology has attracted only little attention.

In the OM literature, the adoption of a wide range of different manufacturing technologies is measured by asking whether firms have implemented the respective technology or not, asking the relevance of a technology, the level of usage of a technology, or ratings about the emphasis placed on a certain technology (Boyer et al. 1996, 1997, Kotha and Swamidass 2000, Swamidass and Kotha 1998, Swink and Nair 2007). While in the technology intelligence literature and in the economics literature on technological development, timing is an essential criteria, implications of investment timing in new manufacturing technologies is almost missing (Brenner 1996, Lichtenthaler 2007). The only exception is the study of Sinha and Noble (2008) about the adoption of radical manufacturing technologies and its impact on firm survival. They investigated investment decisions for core manufacturing technologies and found timing to have a significant effect on firm performance. Firms that are early

Sustainability

TechnologyIdentification

TechnologyChoice

Technology Implementation

TechnologyUsage

Mediating effects

Moderating effects

22

adopters of new manufacturing technologies have a significant reduced likelihood of firm mortality.

But the OM literature is still lacking corresponding research about process technology investment timing and how operational or business performance is affected. It is not clear, whether early adopters of process technologies suffer from high risk of failure due to high technological and market uncertainty of an unproven technology, or whether an early investment leads to competitive advantage due to better performance and the potential to differentiate from competitors (Lieberman and Montgomery 1988, Schroeder 1995).

The third paper addresses this gap by introducing the endogenous character of technology adoption by modeling investment timing as both a measure of absolute technological maturity and relative to competitors. Firms that invest in early stage technologies make their investment decision further on the left tail of the S-curve, late adopters are represented on the upper-right part of the S-curve (see Figure 7). Furthermore, organizational structures and activities are presented as a mean to realize early investment. We investigate whether firms that have installed a technology department on divisional or corporate level are more likely to be early adopters of new manufacturing technologies. Furthermore, we investigate the impact of scanning activities on the investment timing of process technologies. Firms that scan their environment might be more likely to identify relevant technological trends and are better positioned to respond to technological change. The relationship of all the constructs is tested as well as the impact of early investment on business performance.

23

Figure 7: The third research paper investigates the investment timing of new technologies and its impact on

performance

Sustainability

Competitiveness of a technology

time

Late adopters

Early adopters

24

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Table 2: Overview of academic papers

25

4 Unlocking the potential of process innovation: a conceptual framework for the exploitation of advanced manufacturing technologies Stefan Schrettle

4.1 Abstract

This paper aims at providing a holistic understanding of the determinants that influence the realization of benefits of new manufacturing technologies. Based on findings from operations management and strategic decision-making literature, we propose a framework that explicitly distinguishes between the phase of decision-making and the phase of implementation of advanced manufacturing technologies. Based on a broad literature review, contingency factors are presented and related to each phase of the process model.

4.2 Introduction

Global manufacturers experience increasing competitive pressure, due to cost pressure from manufacturers in developing countries, rapidly changing customer demands and demanding legal requirements. Additionally, the paradigm shift from standardized big scale manufacturing to flexible low-volume manufacturing during the last 20 years is still prevailing and forces manufacturing divisions to constantly adopt advanced manufacturing technologies (AMT) and new management practices. Several studies have shown that integrated manufacturing (IM), consisting of Total Quality Management (TQM), Just in Time (JIT) and AMT leads to superior performance (Challis et al., 2002, Cua et al., 2001). As for the purpose of this paper more relevant, also the relationship between investments in AMT and performance has been well documented and empirically tested (Idris et al., 2008; Kotha & Swamidass, 2000; Swamidass & Kotha, 1998). The vast body of literature provides evidence that new manufacturing technologies are important for the success of manufacturing companies, but it also recognizes that most firms are not able to fully realize their expected benefits of AMT. Thus, the question is how this shortcoming can be overcome.

The first attempt to do so is done in this paper as it aims at providing a conceptual framework for the effective adoption of new manufacturing technologies.

26

Based on literature review and experiences from discussions with several managers of Swiss manufacturing firms, we try to give a holistic picture of determinants that influence the effective and efficient application of new manufacturing technologies. To do so, we provide theoretical background why new manufacturing technologies contribute to the competitive advantage of a firm by incorporating the resource-based view (RBV) and the concept of dynamic capabilities to explain superior performance in dynamic environments. Our goal is to contribute to the operations management field by extending existing literature on advanced manufacturing technologies. Therefore this paper makes a threefold contribution: First, we propose a conceptual framework of technology adoption that consolidates findings of existing literature on AMT as well as linking the operations management literature to the concept of dynamic capabilities as proposed by Teece (2007). Second, we explicitly take into account the different characteristics of different phases of the technology adoption process. By dividing the process into a decision phase in which the decision about a potential adoption of a certain technology is made, and an implementation phase in which the respective technology is implemented within the firm. We claim that these two phases have distinct characteristics that lead to different determinants for the success of each phase. Researchers as well as managers should take this into account to better understand the influencing factors for technology adoption. Third, we suggest a more detailed picture of performance gains by new manufacturing technologies by explaining how operational performance mediates the relationship between technology adoption and organizational performance.

Therefore, the literature on RBV and dynamic capabilities will be reviewed in the next section with regard to the relevance of the theoretical framework for the application of new manufacturing technologies. Afterwards, we will describe the model and develop a set of testable propositions. The concluding section will shortly discuss the expected results from proposition testing and point to some limitations that are inherent to the paper.

4.3 The resource-based view and dynamic capabilities¨

The ultimate ambition in the strategy field is to find an explanation why some firms perform better than others (Rumelt et al., 1991). For this attempt, the RBV has become one of the most influential views in the strategic management literature. According to the RBV, competitive advantage can be attained by controlling of unique resource bundles which are valuable, rare, inimitable and non-substitutable (Barney, 1991).

27

As the RBV approach is inherently static, the explanatory power of this theory has limitations in explaining how firms in dynamic environments can gain sustainable competitive advantage over time (Teece, 2007). To overcome this shortcoming, the concept of dynamic capabilities introduces a notion of organizational renewal, as managers need to constantly alter resource and capability configurations within an organization to adapt to environmental change (Helfat & Peteraf, 2003). Hence, dynamic capabilities are defined as "the firm's ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. “Dynamic capabilities thus reflect an organization's ability to achieve new and innovative forms of competitive advantage given path dependencies and market positions" (Teece et al., 1997: 516). It is important to mention that superior organizational performance is not caused by dynamic capabilities as such, but from the resource configurations that are resulting from the use of dynamic capabilities. Dynamic capabilities are hence necessary, but not sufficient for competitive advantage and one has to distinguish between dynamic capabilities and their outcomes (Eisenhardt & Martin, 2000).

Superior manufacturing competence is a central source of competitive advantage for manufacturing firms and the underlying production technology is an essential part of this competence. Manufacturers can differentiate from competitors by achieving cost efficiency, quality, flexibility, and lead time reduction without trade-offs (Sun, 2000). Rapid technological change and fast shifting customer needs force firms to constantly discover and develop opportunities like the effective and efficient transfer of manufacturing technologies into the value chain of the company (Teece, 2007). With regard to new manufacturing technologies, the selection and development of the right technology is an important task to achieve organizational fit with the environment. The ability to manage this integration and to adequately adapt production processes exhibits a substantial dynamic capability of a manufacturing firm, which can be seen as the assembling and orchestrating of difficult-to-orchestrate assets (Teece, 2007). The competitive advantage itself is generated by more efficient and effective production processes caused by the implementation of a new manufacturing technology in combination with complementary assets like manufacturing practices of TQM, JIT, etc. (Cua et al., 2001). The simultaneous organizational transformation (Leonard-Barton, 1988) and the development of routines (Teece, 2007) are major prerequisites to fully exploit the benefits of new production technologies.

28

4.4 Conceptual Framework

The aim of the theoretical framework is to provide a holistic picture of the factors that influence the effectiveness and efficiency of adoption processes of new manufacturing technologies. According to Noori (1990), we define AMTs as new technologies which are used directly by the firm in the production of a product. Thus, our definition of AMT is broader than those of many other authors who define AMT as computer-controlled process technology (Idris et al., 2008; Swamidass & Kotha, 1998; Sun, 2000; Zammuto & O’Connor, 1992) or at least a technology that integrates any kind of information technology (Dean & Snell, 1996). Although we recognize that most of today's innovative manufacturing technologies meet this definition, we claim that the involvement of information technology is not a necessary condition as the framework should hold true also for manufacturers in developing countries and manufacturing companies that produce for niche markets, who might deliberately apply production technologies which are not IT-based. Other authors distinguish hard-based from soft-based AMTs (Sun, 2000). Hard-based AMT refers mainly to physical technologies used in engineering, processing and administration, while soft-based AMT covers Total Quality Management (TQM) and Just In Time (JIT). We understand those so-called soft-based AMT rather to be a management practice by which AMTs are accompanied and follow recent operations management literature (Ketokivi & Schroeder, 2004; Cua et al., 2001).

In the past, most manufacturing companies have implemented any sort of AMT. However, research found that "not all AMT perform as expected. Some AMT perform very bad and leads to a total failure. Some AMTs perform “satisfactory”, but did not produce the full benefits. Other AMTs perform well on the shop floor level, while the business performances of the companies were not improved" (Sun, 2000: 632). Although recent work has sharpened the understanding how to successfully implement new technologies, we believe some still existing inconsistencies in research findings could be resolved by providing a more fine-grained model of technology adoption. Dividing the adoption process of technologies in separate process steps of decision-making and an implementation phase copes with differing characteristics of those sub-processes.

Although many contingencies have been identified that influence the performance when firms adopt new manufacturing technology, the central question rests unsolved, namely whether the insufficient benefits should be traced back to an

29

unsatisfying decision for a certain technology, or if the implementation of the right technology failed.

Figure 8: Assumptions underlying the technology adoption process

Research in strategy process has already recognized this problem and developed two separate research streams on strategic decision making (Eisenhardt & Zbaracki, 1992) and strategy implementation (Rapert et al., 2002). A strategic decision is defined as one which is "important, in terms of the actions taken, the resources committed, or the precedents set" (Mintzberg et al., 1976: 246). The underlying assumption is that variations in the decision making process lead to different strategic or technology choices (decision effectiveness). Different technology choices lead to variations in the implementation phase and both, decision making for and implementation of a technology, determine the outcome (performance) of the technology adoption (Dean & Sharfman, 1996). Thus, we hypothesize that a model, which explicitly takes the two different phases of decision making about a technology and implementation of a certain technology into account has more explanatory power than existing models that do not so.

Figure 9: Conceptual Framework for Manufacturing Technology Adoption

Variations in performance

Variations in decision-making

process Different technology choices Variations in

implementation

Change ManagementDecision-Making

Stimulus

Technology Adoption

ImplementationDecision Process

Operational Performance

OrganizationalPerformance

Analysis & Planning

Culture

Communication &Coordination

Human Factors

Complementary assets

Strategy

Assets for AMT

Industry Type

Firm Size

30

The decision-making process requires scanning of the environment, the collection of relevant information, alignment of the technological alternatives with business strategy and the analysis and evaluation of those alternatives, the process shows typical characteristics of strategic decision-making (Hough & White, 2003). In contrast to that, the implementation phase of manufacturing technologies is characterized by typical change management attributes including a straight project management and planning, consideration of human and emotional factor, therefore securing good communication with all stakeholders that are affected by the technological change (Cakar et al., 2007; Siegal et al, 1996). In addition, the decision and the implementation of a technology often involves different people, because varying competencies are needed, which also reflects the different characteristics of both tasks. Therefore we consider the following influencing factors that are divided in four decision-making influencing factors and four change management influencing factors:

Industry type

We propose the industry type to have influence on the decision making process whether to adopt new manufacturing technologies or not. Industries that are characterized by high competitive pressure are more apt to adopt new manufacturing technologies as effective and efficient manufacturing processes are a mandate for firm survival. Additionally, regulatory standards in the pharmaceutical industry or the food industry might restrict certain process innovations and hence, also limit the possibility to introduce new manufacturing technology. Due to this argumentation we propose:

P1a: Firms in high competitive industries have a higher propensity to implement new manufacturing technologies than firms in industries with low competition.

P1b: Firms in highly regulated industries have a lower propensity to implement new manufacturing technologies than firms in low regulated industries.

Firm Size

Reichstein & Salter (2006) found empirical support that firm size has a significant effect on process innovation. Swamidass and Kotha (1998) showed that AMT use and firm size are linearly correlated, which is supported by Sohal et al. (2006) who found a positive and significant direct effect of company size on the adoption of AMT. An interesting finding is presented by Gupta and Whitehouse

31

(2001). First, they take into account that technology might lead to a decrease of plant size caused by increased automation at the expense of labor force. Second, they find support that smaller firms get better performance from technology implementation, but their results also indicate that the size of the firm does interact favorably with the AMT strategy. This corroborates our argument that the phase of technology implementation should be separated from the decision phase as the effect of firm size on the strategic decision to adopt a technology differs from the effect on the implementation of a technology. On the one hand, bigger firms have more resources to identify new technological opportunities and invest in AMT. On the other hand, larger organizations tend to be more complex than small ones which makes the implementation of technology and the integration into complex process more difficult (Swink & Nair, 2007). Hence, we assume:

P2a: Firm Size is positively correlated with the propensity of a firm to implement new manufacturing technologies.

P2b: Firm Size is negatively correlated with the implementation efficiency of new manufacturing technologies.

Strategy

New production technologies need to be in line with the business strategy of the organization and the manufacturing strategy should be linked to the marketing strategy to gain better performance (Small & Yasin, 1997). Reichstein and Salter (2006) found that firms with a clear product development strategy are in a better position to achieve radical process innovation. In contrast to that, firms that pursue AMT mainly for cost reduction purposes will be less apt to invest in AMT. The benefits of AMT like increased flexibility, higher quality, reduced lead time and time-to-market are often rather intangible. Thus, managers who consider AMT purely as a means for continuous improvement and cost savings and do not see a strategic value beyond efficiency gains will underestimate the real value of AMT and thus, will be more reluctant to invest in AMT (Swink & Nair, 2007). This is in line with the argumentation of Kotha and Swamidass (2000) and Sohal et al. (2006) who stress the need to consider a multidimensional view of AMT performance. Managers will only make decisions that require high investments and are often irreversible - like the adoption of AMT, if they are really aware of the real strategic benefit of AMT.

P3a: Companies that recognize the strategic value of AMT and connect manufacturing strategy with business strategy will invest more in AMT.

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Strategic decisions like investments in new manufacturing technologies also depend upon the people who make these decisions and their personal characteristics (risk aversion, enthusiasm). Getting the right people into the decision making process is an important task and is necessary to choose the appropriate technology. Several studies found that the participation of manufacturing managers in the strategic investment decisions of new manufacturing technologies has a positive effect on performance and competitive capabilities (Brown, 2001, Tracey, 1999). Thus, we assume:

P3b: The involvement of manufacturing managers in the decision process is positively correlated with decision effectiveness and consequently with performance.

Assets for AMT

According to Sohal et al. (2006), the "assets for AMT represent the AMT knowledge base of firms". The adoption of AMT is path-dependent on the competences of a firm to adopt new technologies, which requires the accumulation of human assets. Thus, the adoption of AMT is a learning process and correlates with prior efforts of a company to integrate new manufacturing technologies. In line with Sohal et al. (2006) we argue that getting in the position to develop adequate technology alternatives requires firms to allocate a suitable amount of resources to install structures and routines that help to identify available technologies. Furthermore, the effective analysis, evaluation and selection of technology alternatives is a firm specific capability, which is subject to organizational learning. Experience about the right decision criteria and performance measures as well as methods like technology scouting, roadmapping, etc. are capabilities that have to be learned and help firm in the adoption of AMT (Small & Yasin, 1997). This leads us to the following propositions:

P4a: The resource dedication of firms for developing AMT alternatives is positively correlated with decision effectiveness of AMT decisions.

P4b: Firms that already use structures and routines for AMT have an increased propensity to invest in AMT.

Analysis and Planning

Slagmulder and Bruggeman (1992) observed that companies that did not pay enough attention to technical and organizational aspects in the a priori analysis experienced more transition problems during the implementation phase. Small and

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Yasin (1997) provide empirical evidence that firms exerting higher effort on pre-installation and planning activities during the implementation phase achieve higher levels of performance. Udo and Ehie (1996) also mention that system planning and integration are determinants of implementation success. Thus, it seems that the success of the technology implementation phase is rather characterized by analysis and planning efforts, which suggests that the implementation phase is far better predictable compared to the decision phase. Thus, we assume:

P5: Firms that make more effort on analysis and planning during the implementation phase are more successful in AMT implementation.

Communication and Coordination

Udo and Ehie (1996) identified communication as a key element in successful implementation of JIT. They also found that effective communication at the individual, inter-group and intra-group levels can have a major influence on AMT implementation. Sohal et al. (2006) found empirical evidence for a second-order factor adoption of AMT construct that comprises a factor communication and commitment. Boyer et al. (1997: 333) found soft integration, "which focuses on facilitating communication among different functions and workgroups" to be a moderator for increased performance due to AMT. Proper project management, which is part of coordination, is found to be a critical success factor for the implementation process of strategic manufacturing initiatives (Minarro-Viseras et al., 2005). Especially a team-based project management approach, which is appropriate to facilitate cross-functional communication was empirically validated to be the most critical for implementation and performance of AMT (Small & Yasin, 1997). That is why we propose the following:

P6: Communication and Coordination activities are positively correlated with implementation success.

Culture

Besides the physical and organizational closeness, also the cultural closeness between user and producer of a technology is important for the successful implementation of AMT (Gertler, 1995). Several authors have pointed to the importance of organizational culture for technology adoption (McDermott & Stock, 1999; Zammuto & O'Connor, 1992). We adopt the competing values framework from

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McDermott and Stock (1999), which is separated in two dimensions, each with two opposing value orientations. The first dimension is the flexibility-control axis with focus on change or stability, the second dimension is the internal–external axis, separating activities occurring within or outside the organization. Along the whole technology adoption process, a flexibility accentuating culture is favorable as new technologies and processes always require moving away from well established routines. Additionally, especially during the decision phase when scanning of the environment and scouting for technological developments is important, an external oriented culture is supportive, whereas the implementation is rather inside-orientated. This reasoning leads to the following propositions:

P7a: Firms with a flexibility orientated organizational culture have a greater propensity to invest in AMT and are more successful in implementing it.

P7b: An external orientated organizational culture is especially favorable in the strategic decision phase about technology investments.

Human Factors

Although literature always reports of organizations and firms that adopt a technology, it is still the people - worker, managers, etc. who can influence the decision process whether to adopt a technology, which technology alternative to invest in and how to implement it. In the analysis of Minarro-Viseras et al. (2005) respondents ranked the people category first, meaning this to be the most important factor in the process of manufacturing strategy implementation. For example, a project manager’s individual qualities and skills are the most critical factor for the success of the implementation of a strategic manufacturing initiative. Consistent with this finding, Udo and Ehie (1996) report the lack of top management’s continued support and poor commitment to shopfloor employees to be major reasons for unsuccessful technology implementation. They come to the result that firms "should spend time and resources to earn the commitment of the employees through positive belief and trust in AMT" (Udo & Ehie, 1996: 21) to realize the full potential of AMT. This statement is also empirically confirmed by Small and Yasin (1997) what leads us to the following proposition:

P8: The commitment of top management and shopfloor worker are both positively correlated with implementation success.

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Complementary assets

Addressing opportunities like AMT adoption involves maintaining and improving technological competences and complementary assets like complementary management practices to achieve and sustain competitive advantage (Teece, 2007). For example, Boyer et al. (1997) found evidence for the synergistic effect of TQM programs with AMT to affect performance in a positive way, quoting also other authors suggesting that "many of the benefits of a specific technology can be traced back to infrastructural changes" (Boyer et al., 1997: 336). Consistent with that, Dean and Snell (1996) found integrated manufacturing to be correlated with improved performance. These findings suggest that complementary assets moderate the relationship between technology implementation and operational performance. Yet another complementary asset is design-manufacturing integration (DMI), the integration of product design and manufacturing process knowledge (Swink & Nair, 2007). DMI moderates the relationship between AMT usage and manufacturing performance by having a positive influence on performance. Many publications point to the importance of internal and external learning (Boyer et al., 1997; Schroeder et al., 2002; Small & Yasin, 1997). Preparing worker to understand the principles and purposes of AMT is an prerequisite to exploit the full benefits of AMT. Training for example directly results in improved quality, better process control and quicker response to the customer demands (Udo & Ehie, 1996). Due to numerous examples for such a moderating effect of complementary assets, we propose:

P9: The positive relationship between implementation of AMT and operational performance is positively moderated by complementary management practices like TQM, JIT, DMI and Training.

Performance

Many empirical studies on technology adoption prove the positive correlation of AMT adoption on performance. Although performance categories vary in literature, most publications refer to any kind of operational performance (increased flexibility, quality, improved process control, lower lead times, better turnover rates, etc.) or organizational performance (increasing ROI, market growth, etc.). Although this is a legitimate classification, we think that a more accurate understanding of the relationship between technology adoption and performance might have more explanatory power than previous models. AMT has an indirect effect on organizational performance, which is mediated by operational performance. Operational efficiency

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and effectiveness lead to superior processes and products that should result in improved organizational performance. Furthermore, we propose that AMT adoption also has a direct positive effect on organizational performance, which is neglected so far in the present literature. AMT eventually enables firms to realize new product designs due to advanced processing, which leads to predominant product properties. The increased willingness to pay of the customers will result in improved organizational performance, without affecting operational performance. Thus, we assume:

P10: The positive correlation between successful implementation of AMT and organizational performance is partly mediated by operational performance.

4.5 Discussion and Conclusion

A firm's ability to integrate external technological know-how, develop individual technical solutions and therefore reconfigure manufacturing processes is a central source of competitive advantage for manufacturing companies. As many firms fail to fully exploit the benefit that lies in new technological solutions, we developed a holistic framework to understand various contingencies for the adoption process of new manufacturing technologies.

By taking into account the influencing factors that affect the adoption process, firms should be in the position to select manufacturing technologies more effectively and to manage the adoption process more efficiently with respect to the implementation of a certain technology. Because of all, providing a holistic model of the technology adoption process, explicitly distinguishing between a decision-making process and a technology implementation process, and providing a more detailed understanding of the performance side of manufacturing technologies, we aim at overcoming some of the limitations of previous work and extending research on advanced manufacturing technologies.

Of course, our paper holds some limitations. First, we do not consider interconnections between different influencing factors of the technology adoption process. Second, due to the conceptual character of the paper, we have not yet tested our propositions empirically. Thus, future research should validate our model by testing the proposed hypotheses.

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5 Turning sustainability into action: Explaining firms' sustainability efforts and their impact on firm performance Stefan Schrettle, Andreas Hinz, Maike Scherrer, Thomas Friedli

5.1 Abstract

This research seeks to shed more light on how manufacturing firms adjust their strategy according to the sustainability challenge. Strategic decisions are influenced by strategic long-term considerations, which take into account aspects that lie within firms’ boundaries and beyond. Therefore, the first step of this paper is to operationalise the sustainability challenge by identifying relevant drivers for sustainability that firms are exposed to. Second, we develop a framework showing which dimensions affect decisions concerning a sustainability move and which dimensions are affected by these decisions. A sustainability move can contain initiatives emphasising the adoption of new manufacturing technologies, the development of new, sustainable products or the integration of green practices into the supply chain. Next to the influence of sustainability drivers, we explain firms' decisions concerning a sustainability move with past performance, firm size and current level of sustainability action. Depending on whether initiatives are led by strategic or ad-hoc decisions, firms have to explore new knowledge and/or exploit existing knowledge to realise competitive advantage. The goal of this research is to provide an explanation of how decisions of sustainability moves are motivated and which dimensions in the firm are affected by these moves.

5.2 Introduction

The sustainability challenge has increasingly become a key-item on the management agenda of manufacturing firms since global warming and the finiteness of important resources, for instance, have caused different stakeholder groups to adjust their expectations on firms. Wu and Pagell state that "the need for environmental protection and increasing demands for natural resources are forcing firms to reconsider their business models and restructure their supply chain operations" (Wu and Pagell 2011: 577). Growing interest in sustainability has been found in both academia and industry (Linton et al. 2007), especially in the cross-disciplinary field of green supply chain management (GSCM) defined as "integrating environmental concerns into the

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inter-organisational practices" (Sarkis et al. 2011: 3). From a firm's perspective, sustainability can be defined as meeting the needs of a firm's direct and indirect stakeholders without compromising its ability to meet the needs of future stakeholders (Dyllick and Hockerts 2002). The notion of sustainability is rather broad in nature as it entails the three pillars of the triple bottom line, namely environmental, social and economic aspects (Hart and Milstein 2003). We recognise the importance of the triple bottom line for manufacturing firms, however, we focus on the ecological aspect, which we refer to as sustainability in this article. While the focus rests on the ecological aspect, the economic aspect is assumed to be accounted for in any given activity that firms undertake as their main goal is to generate profits and to grow. In line with the notion that all three aspects are integrated in the triple bottom line (e.g., Dyllick and Hockerts 2002, Hart and Milstein 2003), the ecological aspect has an impact on the social aspect as well. For instance, successful measures to reduce emissions at a manufacturing site have a positive impact on the quality of life of the wider community in the neighbourhood. Vice versa, the social aspect (while it is regarded important in its own right) has only a limited impact on the ecological aspect. Therefore, this research does not focus on the social aspect explicitly but solely on the ecological aspect. The interrelations between the three pillars of the triple bottom line are not emphasised in this research. The dominant debate regarding the manufacturing industry's environmental footprint, the likelihood of this trend continuing as well as significant business opportunities that might arise for manufacturing firms (in the form of eco-efficiency and resulting cost-savings, for instance) are the reasons for this emphasis.

Developments in the sustainability arena have significant implications on the strategic decision-making process of the firm as the sustainability challenge requires the revision of current management practices. Managers have to take into account latest developments in the market context of the firm, assess the competences of the firm and anticipate further developments to define strategy (Schweiger et al. 1986, Iaquinto and Fredrickson 1997, Ferrier 2001). Especially manufacturing firms are affected as manufacturing processes are energy intensive and consume significant amounts of resources. Numerous papers investigated the relationship between environmental efforts of a firm and its effects on performance and found mixed results (e.g. Hart and Ahuja 1996, Anstine 2000, Jacobs et al. 2010). On the one hand, studies investigating the relationship between environmental efforts and financial performance, measured as stock market performance, predominantly found a positive relationship (Hart and Ahuja 1996, Klassen and McLaughlin 1996, Jacobs et al. 2010).

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On the other hand, studies focusing on the relationship between sustainability efforts and the consumers' willingness-to-pay (WTP) found no positive relationship (Anstine 2000) or even a negative relationship (Luchs et al. 2010) meaning that consumers value sustainable products less than non-sustainable products. The literature on sustainability provides limited answers to the questions why certain firms adopt sustainability management practices while others do not and under which circumstances firms can realise competitive advantage by the adoption of sustainable practices (e.g., Delmas and Toffel 2004, Etzion 2007, Rivera-Camino 2007). Our assumption is that the answer can be found in different dimensions that drive the development of the firm as well as different moderating effects like the past performance of the firm: Whether firms initiate a higher focus on sustainability is determined by past performance as past success results in greater strategic persistence even after radical changes in the market (Anstine 2000). In contrast, unsatisfying performance leads to re-evaluation of past and current patterns of business and therefore provides motivation for strategic change (Lant et al. 1992, Ferrier 2001). To the best of our knowledge, there is no descriptive model, which supports decision-making of firms facing a sustainability challenge by linking all relevant dimensions in a transparent way. So far, it is not clear how managers should handle the various ambiguous facets of the sustainability challenge in order to turn them into action. Furthermore, it is not clear how firms can control the relevant stock of knowledge, which is necessary to realise sustainable activities. Prior work on knowledge management has pointed out the importance of knowledge exploration and exploitation (e.g., March 1991, Gupta et al. 2006, Jansen et al. 2006, Lichtenthaler and Lichtenthaler 2009). Interestingly, the knowledge perspective has not been used so far to explain mixed results in the relationship between sustainability efforts and performance.

To address the illustrated gaps, we first show possible initiatives firms can engage in to address the sustainability challenge (i.e. product-, process- and supply-chain-related) and explain the decision-making within the firm with the literature on rational-comprehensive strategy development. In doing so, the topic of decision-making is analysed as a response to the sustainability challenge from a managerial perspective. Taking into account various drivers, management is ultimately responsible for the firm's sustainability decisions in order to maintain or increase competitive advantage. We use past performance, firm size and the current level of environmental action as moderators to explain differences in the level of sustainability efforts a firm undertakes. The construct "level of sustainability effort" is used to

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evaluate the volume of a sustainability move, the duration of the move, the complexity of the move, and the unpredictability of a move in sustainability issues in order to consolidate single decisions into an integrated construct. Following Ferrier's (2001) notion of “attack”, we label actions or initiatives undertaken in order to address sustainability as ”sustainability move”. If looking at the business environment of a given industry, efforts to become more competitive can be understood as winning a campaign against relevant competitors. There is a risk that a competitor moves first, which provides that firm with the first mover advantage. By moving into the market environment with a new sustainability initiative, a firm forms the rules for the market environment and, when successful, forces competitors to follow the given direction. With a successful sustainability move, a firm has gained at least a temporary competitive advantage. Furthermore, we illustrate relevant knowledge-related capabilities of the firm to explain how the decisions regarding sustainability need to be implemented to generate competitive advantage. By doing so, we provide fundamental work for future research on sustainability to overcome shortcomings of today's results.

Thus, this study makes theoretical as well as managerial contributions. First, it operationalises the sustainability challenge by defining the relevant drivers of sustainability. By listing and explaining drivers holistically, we want to raise the awareness of practitioners and academics as to how they might be influenced by changing environmental characteristics. Decision makers typically have blind spots (Zajac and Bazerman 1991), although literature suggests that decision comprehensiveness is related to performance (Atuahene-Gima and Li 2004). By reducing those blind spots with regard to sustainability, we aim to convert managers’ limited perceptions of the most salient sustainability drivers into a more objective perception that takes all drivers into account holistically.

Second, this paper provides an explanation of decision-making with emphasis on sustainability by resorting to the decision-making literature. We have not come across conceptual frameworks which comprehensively present relevant drivers of sustainability and indicate the link to the strategic decisions of manufacturing firms. Past performance, firm size and the current level of environmental action are crucial for decision-making and determine the outcome of such decisions (Lant et al. 1992, Audia et al. 2000, Ferrier 2001), because the ability to execute certain strategic decisions is influenced by those factors. To implement the decisions, firms can take action that is of a strategic, radical nature with a long-term perspective. Otherwise, firms take an incremental approach and implement ad-hoc steps to improve current business processes with a rather short-term perspective. The knowledge perspective

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completes our explanatory framework. In order to implement sustainability efforts, firms have to explore and/or exploit knowledge, depending on the ad-hoc or strategic character of the action. Organisational knowledge reflects the view of how resources should be used in order for the firm to benefit (Smith et al. 2005).

5.3 Decision making regarding sustainability

This paper aims to explain why certain firms engage in strategic initiatives in support of sustainability while others do not. We use the literature on strategic process research to explain the phenomena in strategic decision-making. Strategic decisions are defined as "important, in terms of the actions taken, the resources committed, or the precedents set" (Mintzberg et al. 1976: 246). Those decisions are "infrequent decisions made by the top leaders of an organisation that critically affect organisational health and survival" (Eisenhardt and Zbaracki 1992: 17). In our understanding, decisions on how to deal with the sustainability challenge have the characteristics of being strategic. Therefore, the literature on strategic decision-making as one aspect of strategic process research can explain why firms interpret drivers of sustainability differently and consequently start diverging strategic initiatives.

5.3.1 Strategic decision making

Strategic decisions not only have a major impact on the future of a firm, they are also characterised by a high degree of complexity, ambiguity, novelty and open-endedness (Mintzberg et al. 1976). These characteristics lead to the absence of a single right recommendation how to solve a strategic issue. Managers are forced "to draw inferences and assumptions about their organisations and environments from available information and then try to define and solve problems" (Schweiger et al. 1986: 51).

The way how those problems are solved is influenced by the past performance of the organisation. Past firm success creates reliance on past business models and routines and inhibits a firms' aptness for strategic change and renewal (Lant et al. 1992, Miller 1993, Ferrier 2001). Inertia, defined as the level of commitment to current strategy and the tendency to remain with the status quo (Huff et al. 1992) and political processes due to past success stifle innovation (Amatucci and Grant 1993) and lead to a lock-in effect (Burgelman 2002). Audia et al. (2000) revealed that past success results in increased confidence in the correctness of current strategies and less seeking of information, which are reasons for a greater strategic persistence even after radical environmental changes. In contrast, poor past performance gives reason to re-

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evaluate past and current patterns of business and therefore provides motivation for strategic change (Ferrier 2001). Successful firms not only realise environmental change, they are able to link this change to corporate strategy and thus, continuously pursue organisational renewal (Barr et al. 1992). The market context of the firm influences corporate strategy (Lant et al. 1992, Iaquinto and Fredrickson 1997, Ferrier 2001) as reorientation following poor performance is more likely in a stable market (Lant et al. 1992). Market characteristics encompass industry volatility (Lant et al. 1992), stability (Iaquinto and Fredrickson 1997), the intensity of competition (Ferrier 2001) as well as market features, meaning a concentrated versus a dispersed market (Das and Van de Ven 2000). The sustainability challenge is a relatively new phenomenon, which represents a market change with the potential to rearrange industry characteristics. For example, new firms are emerging in the automobile industry with an exclusive focus on electric transportation. In this case, the awareness of sustainability has offered new strategies to compete in this industry, an industry traditionally dominated by large legacy firms, where volatility and intensity of competition were relatively constant. That might change in the future as new players enter the market. Firms take product and market characteristics into account, when defining a product strategy to get their new product technology accepted by the market (Das and Van de Ven 2000). Those characteristics are influenced by the drivers of sustainability, thus leading to a market pressure, which triggers new strategic initiatives.

5.3.2 Path dependency in decision making

Teece et al. (1997) argue that path dependencies play an important role in a firm's choices about domains of competence and are a function of past choices. Firms follow a certain path of competence development and this path affects their stock of competences and their ability to perform certain activities not only in the present but also in the future (Teece et al. 1997). By contrast, Eisenhardt and Martin (2000) argue that paths are not entirely set by a firm's decisions and resulting history but can also be adjusted through fast learning mechanisms, practice, making mistakes and learning from specific experiences. In their distinction between moderately and highly dynamic ("high velocity") markets, Eisenhardt and Martin (2000) argue that existing knowledge might suffice to deal with the former but not with the latter when change becomes nonlinear and less predictable. This is when firms are less concerned with existing knowledge and experience and much more concerned with rapidly creating situation-specific new knowledge (Eisenhardt and Martin 2000). In support of Eisenhardt and

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Martin's (2000) view, Zollo and Winter (2002) note that while a high level of prior experience in heterogeneous contexts has a positive impact on performance of following projects, strong learning mechanisms can enable firms to (quickly) accumulate the required knowledge which suggests a more flexible view on path dependencies. More specifically, Eisenhardt and Martin (2000) argue that, in the context of high velocity markets, the need for stable existing knowledge is replaced by a need for specific new knowledge created closer to the time. This newly created demand-driven knowledge might cause departure from the more linear path trajectory put forward by Teece et al. (1997). Firms' decisions are influenced by past decisions and the stock of acquired competencies (Teece et al. 1997). Learning mechanisms enable firms to overcome the limitations of current knowledge resources with regard to dynamic environments (Eisenhardt and Martin 2000).

5.3.3 Towards a decision making process to address the sustainability challenge

The concept of strategic decision-making explains why firms take different approaches to address the sustainability issue. In order to holistically analyse decision-making with emphasis on sustainability initiatives, several aspects have to be taken into account. First, the drivers pressurise firms to decide whether to adopt sustainability initiatives and if so, which initiatives this includes. A thorough understanding of the sustainability challenge and related exogenous and endogenous drivers it consists of is essential since it helps firms to decide which measures to take (Etzion 2007, Rivera-Camino 2007). However, sustainability not only represents a threat to firms but also an opportunity (DeSimone and Popoff 2000, Machiba 2010). In order to act more progressively with respect to sustainability, ecological issues need to be regarded optimistically as an opportunity for future business success rather than as a threat (Sharma 2000).

Second, several issues regarding decision-making need to be examined. If a firm decides to engage in sustainability initiatives, the question is which initiative is best suited for an individual firm. Firms can adopt new manufacturing technologies to utilise fewer resources and to produce fewer emissions in the production process, develop new and "greener" products that consume fewer resources during the complete lifecycle, or develop and implement sustainable practices throughout their supply chain. Whether those practices can be enforced or have to be jointly developed is affected by the distribution of power within the supply chain (Jassawalla and Sashittal 2002). Firms' decisions to invest resources in various initiatives are influenced by management decisions they took in the past (Teece et al. 1997). Firms that have

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already built competences in the area of one of the potential sustainability initiatives will be more apt to pursue those actions. The amount of additional investment is higher in unknown fields of activity compared to areas where firms already have undertaken action and subsequently have acquired expertise, which lowers barriers of action. Furthermore, if a certain expertise already exists, the probability of success for the knowledge creation is higher than taking a Greenfield approach.

Third, the impact of decisions regarding sustainability on knowledge management needs to be assessed. Once a firm has decided to engage in sustainability initiatives, the required knowledge becomes an issue. Lichtenthaler and Lichtenthaler (2009) note that knowledge plays a key role regarding a firm's ability to drive technology development and the ability to derive a competitive advantage. This relates to manufacturing, product development and the supply chain. Knowledge is particularly important in the context of the sustainability challenge which represents fast-paced change and pressures that firms need to deal with (Sharma and Vredenburg 1998, Huang and Shih 2009). As various authors suggest, knowledge creation and application are critical to address the dynamics of that challenge (e.g., Robinson et al. 2006, Ahmed 2007, Laszlo and Laszlo 2007, Huang and Shih 2009, Melville 2010).

5.4 Development of a conceptual framework

The sustainability challenge currently represents a major challenge which manufacturing units are concerned with. To guide manufacturing managers in decision-making, we derive a conceptual framework from the sustainability literature and the literature on new manufacturing technologies. There are two mechanisms why firms take action towards more sustainability. First, certain external influences such as mandatory legislation may impose pressure upon a firm to kick off sustainability initiatives to prevent disadvantages or penalties. Second, firms see a potential competitive advantage in the realisation of sustainability initiatives leading to a voluntary pursuit of sustainability efforts. The generation of new markets for sustainable products, or cost savings realised through reduced resource consumption within the manufacturing process are both examples for opportunities that arise in the context of the sustainability challenge, which can be used to gain competitive advantage. No matter whether it is because of market pressure or the capturing of opportunities, firms are forced to decide whether they want to take action as an adequate answer on the drivers of sustainability or not. As mentioned, process enhancements due to new manufacturing technologies, new, greener products or the

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application of green practices within the supply chain are three prominent ways we have identified in order to deal with sustainability.

Although we acknowledge that there are even more possibilities for firms to become more sustainable, we focus on these three items as various publications name them to be the most prominent solutions for manufacturing firms (e.g., Sharma and Henriques 2005, Etzion 2007, Linton et al. 2007). Furthermore, the areas of green operations, green-product design and closed-loop supply chains were the most dominant sustainability issues in operations management identified by a literature review of the first 50 issues of Production and Operations Management (Kleindorfer et al. 2005).

Figure 10: Decision-making framework for firms in the context of sustainability drivers and potential outcomes

5.4.1 Drivers of Sustainability

Sustainability drivers can be classified into two groups: exogenous (external) and endogenous (internal) drivers. The following paragraphs will introduce the drivers from literature grouped in these two categories:

Exogenous drivers

In line with stakeholder theory (Freeman 1984, Donaldson and Preston 1995), the following stakeholder clusters are regarded dominant for this work: (1) environmental regulation, (2) societal values and norms and (3) market drivers. Environmental policy

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and regulations issued by governments and supranational organisations are critical sustainability drivers which firms have to comply with unless they are prepared to risk legal consequences and negative effects on reputation and image (Porter and van der Linde 1995, Carroll 1999, Banerjee 2001, Delmas and Toffel 2004, Etzion 2007). Banerjee (2001) suggests that regulatory requirements have a significant impact on firms' environmental approaches and in consequence on growth and profitability. According to Etzion (2007), regulation can take different forms: "It can dictate technologies that must be used, can stipulate specific environmental targets that must be achieved, can create economic frameworks for redistributing environmental costs and benefits and so on" (p. 651). In those instances when management bears personal liability for environmental violations, regulation appears to be a powerful driver (Sharma and Henriques 2005).

Values and norms in society and resulting expectations held by interest groups represent an influence that firms need to be aware of (Bansal and Roth 2000, Wade-Benzoni et al. 2002). Typical interest groups include NGOs, media, politics, local community groups, value-based networks and consumer organisations (Wheeler et al. 2003). In general, dynamic mechanisms can originate from values and norms held collectively by any group of stakeholders (Rivera-Camino 2007). These mechanisms can cause public pressure and have considerably gained power in recent years (Wheeler et al. 2003). It is critical for manufacturing managers to be aware of these mechanisms and to attempt to benefit from them when engaging in sustainable manufacturing initiatives (Wheeler et al. 2003).

Market drivers shape the market context which individual manufacturing firms are exposed to (Rivera-Camino 2007). Stakeholders playing a role in these mechanisms include consumers, suppliers, competitors and shareholders (Rivera-Camino 2007). Based on certain values and norms, consumers can respond favourably to a firm's sustainability initiatives and innovation which creates demand and therefore is of highest importance (Delmas and Toffel 2004, Rivera-Camino 2007). Suppliers might discontinue to deliver inputs for fear of losing their own reputation, if the purchasing manufacturing firm is known for poor environmental practice in its processes (Rivera-Camino 2007). A firm's competition might exert power in that competitors' values and norms may be perceived superior with regards to sustainability (Rivera-Camino 2007). Investors can exert pressure as they can withdraw capital if a firm's specific risk is expected to rise due to poor environmental practices and a resulting damage to its image (Rivera-Camino 2007).

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Endogenous drivers

Endogenous drivers represent internal forces and include three groups: the manufacturing firm's (1) strategy, (2) culture and (3) resource base.

In order to enhance sustainability, a major challenge for managers is the degree of integration of sustainability principles (such as in the form of strategy objectives, vision and mission, for instance) into the overall firm strategy (Schaltegger and Burritt 2000, Labuschagne et al. 2005, Etzion 2007), which needs to include the integration of sustainability considerations in decision-making (Labuschagne et al. 2005). However, Etzion (2007) argues that organisations often tend to see sustainability as a separate aspect of core strategy. Labuschagne et al. (2005) regard this integration - which they refer to as "institutional sustainability" following the UN's "Agenda 21" - as a complementary fourth dimension to the three established dimensions of the triple bottom line aspects (e.g., Hart and Milstein 2003). The authors note that this integration on the strategic level is a prerequisite for sustainable operations. Ramanathan et al. (2010) observe that in some cases firms have integrated sustainability considerations by simply adjusting their processes to meet regulations while others have taken a proactive role (i.e. self-regulation) and that the latter cohort of firms is more likely to succeed by introducing sustainable innovations to their processes. Laine (2005) discusses how the principles of sustainability can be better integrated into industrial activities. He suggests that win-win scenarios whereby economic and environmental benefits can be obtained at the same time support this integration. This notion implicitly hints at the concept of "eco-efficiency" (e.g., DeSimone and Popoff 2000) which allows firms to pursue sustainability (i.e. reduce energy and material consumption) while at the same time deriving economic benefits from these measures (i.e. reduced costs as a result). Russo and Fouts (1997) argue that a proper implementation of a sustainability strategy should become a driver for the development of human resources and organisational capabilities as organisational resources.

Cultural influences such as motivation, information dissemination, management commitment and a longer-term horizon represent important drivers of ecological responsiveness in manufacturing. First, Bansal and Roth (2000) revealed three major motivations that drive sustainability in manufacturing firms: competitiveness, legitimation and ecological responsibility. In addition, they identified three contextual conditions that lead to these particular motivations: field cohesion, issue salience and individual concern. Second, various authors have shown how accurate and timely information dissemination has a positive effect on the implementation of sustainability

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in manufacturing processes (Sharma et al. 1999, Lenox and King 2004). Etzion (2007) sees a considerable potential for manufacturing managers to use a firm's information channels and networks to disseminate information in order to advance sustainability innovation in manufacturing. Third, manufacturing managers’ commitment has a significant impact on how sustainability in manufacturing is approached. López-Gamero et al. (2009) argue that managers' environmental attitude is a significant factor in shaping their firm's sustainable orientation and innovation in manufacturing. Fourth, the time horizon is conducive to sustainability process initiatives (Schaltegger and Hasenmüller 2005). According to Dyllick and Hockerts (2002), "an obsession with short-term profits is contrary to the spirit of sustainability" (p. 9).

The provision of adequate resources drives a firm's operations including sustainability initiatives. Barney (1991) assumes that in order to secure competitive advantage, a firm's resources should be valuable in exploiting opportunities, rare among competitors, imperfectly imitable as well as strategically non-substitutable. According to McGee et al. (1998), the resource-based view (RBV) is a useful concept in this case as ecological strategies and innovations tend to mature over longer periods which makes it more difficult for competitors to comprehend and then imitate these. Barney (1991) mentions physical capital resources including manufacturing technology and equipment as well as human capital resources as important factors for process innovation. Besides physical assets, certain skills and capabilities are part of the resources of the firm which influence the success of sustainability initiative implementation (Huang and Shih 2009; Lichtenthaler and Lichtenthaler 2009; Melville 2010). Firms which have already obtained a track record in sustainability by gaining experience and important capabilities in sustainability management are better positioned to engage in further sustainability initiatives (e.g. Eisenhardt and Martin 2000; Teece et al. 1997).

5.4.2 Decision-Making towards sustainability

Strategic decision-making is complex, multilevel information processing and choices are emergent outcomes of that processing (Corner et al. 1994). Managers have to make assumptions about their organisations and their market from available information and then define an adequate strategy (Schweiger et al. 1986). By integrating the new phenomena of sustainability into strategy making, the strategic alternatives for firms have increased.

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We adapt the notion of rational-comprehensive strategic decision-making (Eisenhardt and Zbaracki 1992). Managers enter a decision-making situation with certain objectives and adapt their actions according to their goals. They acquire appropriate knowledge and information, develop different decision options and select the optimal alternative. The value of different decision alternatives is defined by its contribution to reach the predefined goals. Such a procedural rationality is defined as "the extent to which the decision process involves the collection of information relevant to the decision, and the reliance upon analysis of this information in making the choice" (Dean and Sharfman 1993: 589). There is evidence that the rational-comprehensive decision process leads to more effective decisions (Dean and Sharfman 1996), but the relationship is moderated by market dynamism (Hough and White 2003).

To reduce uncertainty in decision-making, managers need profound knowledge of the drivers of sustainability. Rationality, defined as the extent to which a decision process involves the collection of information and analysis of this information, is positively related to decision effectiveness (Dean Jr. and Sharfman 1996, Elbanna and Child 2007). Accordingly, knowledge of sustainability drivers is a critical precondition for an appropriate decision process in our model. This process contains the phases of the identification of drivers, the development of alternatives and the selection (Mintzberg et al. 1976). These phases do not follow a causal sequence, but occur repeatedly in any order as cycling of different steps is necessary to revisit single parts of a choice when new information is available (Mintzberg et al. 1976, Eisenhardt and Zbaracki 1992). The outcome of the decision-making process is a firms' future action towards more sustainability which, as a whole, defines the level of sustainability efforts. The sustainability efforts of a firm reflect the degree to which firms engage in sustainability issues.

5.4.3 Components of a sustainability move

In order to make progress in terms of sustainability, firms can take different measures. In this regard, we identified three critical focus themes on the agenda of manufacturing firms that are encompassed by sustainability efforts: (1) New manufacturing technologies to make manufacturing processes more sustainable, (2) the development of green products and (3) the integration of green practices in the supply chain. We chose these areas of action as lean and green operations, green-product design and closed-loop supply chains were the most dominant issues in operations

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management on sustainability identified by a literature review (Kleindorfer et al. 2005).

The decision about sustainability initiatives on these three focus themes can be of an ad-hoc as well as a strategic nature, depending on whether a firm takes a more incremental or radical step. On the one hand, ad-hoc decision-making represents a reaction to pressures that need immediate attention (Winter 2003). On the other hand, however, more radical changes call for strategic initiatives in sustainability management that focus on the longer-term. Dyllick and Hockerts (2002), for instance, argue that firms need to focus on longer-term goals and focus less on short-term benefits in order for sustainability initiatives to be successful. Similarly, Henderson and Cockburn (1994) as well as Schaltegger & Hasenmüller (2005) note that firms need to have a longer-term horizon in order for sustainability initiatives to work. Often, this strategic focus coincides with proactive approaches to sustainability (e.g., Delmas and Toffel 2004, Etzion 2007, Delmas and Toffel 2008, Ramanathan et al. 2010). Firms that take a proactive role with regards to sustainability by going beyond what regulation expects, often succeed by introducing sustainability innovations and therefore gain competitive advantage (Rivera-Camino 2007, Ramanathan et al. 2010).

The vast body of literature on New Manufacturing Technologies provides evidence that new manufacturing technologies and manufacturing programs are important for the success of manufacturing firms (Cua et al. 2001, Mora Monge et al. 2006, Sinha and Noble 2008), but existing literature predominantly focuses on rationalisation and cost effects due to automation as well as increases in flexibility and quality. Udo and Ehie (1996) for example list 25 benefits of Advanced Manufacturing Technologies (AMT) including extensive literature sources and Small (1997) mentions 15 objectives for AMT implementation, both without taking sustainability into account. Kaebernick et al. (2003) state that "sustainability in the development and manufacture of new products is a strategy that is widely accepted in principle, although not yet widely practiced (Kaebernick et al. 2003: 461).

Advanced Manufacturing Technologies have been regarded as valuable weapons to address the competitive challenges for global manufacturers including fragmented mass markets, shorter product life cycle and increased demand for customisation (Udo and Ehie 1996). More recently, however, the topic of sustainability has increasingly become dominant as environmental pollution and resource scarcity raised public awareness especially for the sustainability challenge. This issue concerns various firm functions as consumer requirements are shifting and new products and business models are required that meet the needs of sustainable industrial systems. Due to high

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energy and water consumption as well as pollution rates and waste, manufacturing units are especially affected by the sustainability challenge. While generally, the positive impact of green management on financial performance is shown in literature (Molina-Azorin et al. 2009), only little empirical evidence exists, whether new manufacturing technologies in particular might be the suitable answer for manufacturers to address the sustainability challenge and generate superior performance (Klassen and Whybark 1999, Pil and Rothenberg 2003). Klassen and Whybark (1999) found that investments in environmental manufacturing technologies significantly affect both manufacturing and environmental performance. Thus, we argue that new manufacturing technologies are an important lever for firms engaging in sustainability management.

In terms of manufacturing technologies, sustainability enhancements can aim at the use of material and energy as well as the creation of emissions and waste (Rashid and Evans 2009). We distinguish incremental ad-hoc initiatives from strategic manufacturing initiatives. Ad-hoc initiatives, such as the implementation of end-of-pipe technologies, aim to enhance existing manufacturing processes in order to increase resource efficiency and lower emissions. Strategic initiatives have a more radical, long-term character like the changeover of manufacturing capabilities to the ability of remanufacturing (Ijomah et al. 2007).

Measures to become more sustainable can be related to products as eco-efficiency is also relevant to produced goods. DeSimone and Popoff (2000) argue that efficiency is not only applicable to increasing resource productivity in manufacturing but also to the creation of new goods and services that enlarge consumer value while maintaining or reducing environmental inputs. For instance, the Sustainable Product and Service Development (SPSD) approach seeks to support firms to make their products and services more sustainable throughout their entire life cycle including everything from conception to end of life (Maxwell and van der Vorst 2003). More specifically, frameworks such as the Life Cycle Assessment (LCA) can guide firms to make products more sustainable overall. LCA can be defined as "a methodological framework for estimating and assessing the environmental impacts attributable to the life cycle of a product" (Rebitzer et al. 2004: 702). This includes the phases of production, use and end-of-life while the initial phase of design and development is ignored, because its environmental impact is often regarded insignificant (Rebitzer et al., 2004). Nevertheless, Rebitzer et al. (2004) highlight the potential of this phase to determine the environmental impact of the subsequent phases. Going beyond the "basic" LCA, Dreyer (2009) for instance, proposes environmental LCA. This specific

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type of LCA focuses in detail on the environmental impact of the product system by identifying all relevant processes and assessing their individual impact on the environment (Dreyer 2009). The overall goal of such initiatives is to design and manufacture products that are not only environmentally friendly but also meet criteria such as functionality or cost-efficiency. An increasing number of firms introduce product-oriented environmental management systems and have introduced products with superior environmental performance (Boks 2006). Literature also provides guidelines for firms to realise green product design. Kengpol and Boonkanit (2011) for example propose a decision framework helping firms to develop eco design in order to develop the new product more eco-effective than the baseline product.

As noted above, decisions regarding sustainable products can be of an ad-hoc as well as a strategic nature. An example of the former is an improvement in the fuel-efficiency of a conventional combustion engine. By contrast, an example of the radical long-term approach is an entirely new technology such as an alternative electric power train.

Sustainability considerations can be aimed at the supply chain as a whole. In order for the industrial system to be truly sustainable, it is not enough to look at a given firm and its processes in isolation (Etzion 2007). For instance, Vachon and Klassen (2006) propose to extend green practices from the plant out into the supply chain, which affects numerous links among different stages in the supply chain. This means that actors in the supply chain cooperate to minimise the environmental impact of the entire supply chain (Bowen et al. 2001) and build collaborative advantage rather than just competitive advantage (Vachon and Klassen 2006). For example, suppliers and sourcing firms can jointly work out solutions and agree to use more environmentally-friendly modes of transport (i.e. shifting from road to railway transport). Ubeda et al. (2011) show how logistics can become green while simultaneously meeting the efficiency objectives. This can be achieved by a dedicated supply chain design which incorporates the application of new technological solutions. For instance, the use of sensor information to monitor environmental parameters such as temperature and shock gives indications on the condition of transported food stuff. This information can therefore assist players in the supply chain to save energy (by preventing unnecessary onward transportation if the goods have perished already) and time (by replacing perished food stuff sooner). By referring to the importance of the entire supply chain, we intend to put the topic of sustainability into the "wider" perspective which holistically takes into account environmental impact of different supply chain stages.

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Again, decisions regarding sustainable supply chains can be of an ad-hoc as well as a strategic nature. For instance, an ad-hoc decision can be to update the truck fleet with more fuel-efficient models which can be accomplished in a short time frame. A longer-term strategic decision, for instance, can be to develop alternative logistical concepts involving more rail and sea transport versus road and air transport.

In our model, these three themes represent the components of a sustainability move which can all have both, a strategic or ad-hoc character. The model further reveals that each sustainability move changes the endogenous drivers in the sense that alterations in the technology base, the product base or the supply chain base have an influence on either, the firm's culture, its strategy, and/or its resources. This relationship should also be taken into consideration when decisions about changes in one of the three components of a sustainability move are made.

5.4.4 Characteristics of a sustainability move

A sustainability move has certain characteristics (move volume, move length, move complexity, move unpredictability). The more intensively these characteristics are addressed, the higher is the overall effort in a move. Therefore, the sustainability move itself is the sum of actions taken within the three characteristics. Sustainability move volume is defined as the number of sustainability actions a firm undertakes. The more firms engage in new, sustainable manufacturing technologies, green product design and GSCP, the higher the move volume. The duration of a sustainability move is the time elapsed from the beginning to the end of a sequence of sustainability actions (Ferrier 2001). Firms that initiate and sustain moves (or attacks in the words of Ferrier, 2001) over longer, uninterrupted periods of time will be perceived as more aggressive (Ferrier 2001). Sustainability move complexity is the extent to which sustainability efforts comprise different kinds of sustainability actions. Firms that are able to launch sustainability initiatives in breadth combine various ways to be perceived sustainable, thus leading to higher sustainability efforts. Sustainability move unpredictability is the variation of sustainability initiatives. Ferrier (2001) uses optimal matching analysis to measure unpredictability as "the extent to which a firm's sequence of actions carried out in a given time period was or was not similar to that carried out in the preceding time period" (Ferrier 2001: 867).

The fact that a firm actively engages in various sustainability moves can initiate a feedback mechanism on its endogenous drivers. In cases when sustainability is tightly

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integrated in a firm’s activities (Etzion 2007; Schaltegger and Burritt 2000) and when management truly supports sustainability (Etzion 2007; López-Gamero et al. 2009; Melville 2010), the positive impact on the firm’s allocation of resources, its strategy as well as its overall corporate culture is particularly large. To highlight the importance of this point, Etzion (2007) notes that the disciplined implementation of activities in sustainability can drive the development of human resources and organisational capabilities (which can take the form of building a conducive culture or developing focused strategy processes, for instance).

After introducing our understanding of the exogenous and the endogenous drivers as well as sustainability efforts and resulting feedback mechanisms, we derive the following propositions for their relationship:

• Proposition 1a: An increase (decrease) of exogenous sustainability drivers causes a higher (lower) level of sustainability efforts.

• Proposition 1b: An increase (decrease) of endogenous sustainability drivers causes a higher (lower) level of sustainability efforts.

• Proposition 1c: Any sustainability move leads to an increase of one (or several) endogenous sustainability drivers.

• Proposition 1d: Any sustainability move leads to an increase of the moderating effect of current level of environmental actions between sustainability drivers and components of a sustainability move.

5.4.5 Knowledge Management

To be able to execute a sustainability move, firms need to have a certain knowledge base. Especially if new (sustainability) challenges arise, firms need to build up new knowledge. If already existing requirements in terms of sustainability only get tighter (e.g., regulation on CO2 emissions), already existing knowledge can be used and refined to address these new requirements.

Knowledge consists of information and know-how (Kogut and Zander 1992, Helfat et al. 2007). Kogut and Zander (1992) refer to information as "knowledge which can be transmitted without loss of integrity once the syntactical rules required for deciphering it are known" (p. 386). Kogut and Zander (1992) note further that information is composed of facts, (axiomatic) propositions and symbols and that it is often proprietary. Know-how can be defined as the accumulation of skills that enables the work on and completion of a task in a smooth and efficient way (Von Hippel 1988). Kogut and Zander (1992) emphasise the word accumulated implies know-how

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cannot simply be transferred as is the case with information but must be learned. More specifically for the organisational context, Smith et al. (2005) define organisational knowledge as "the validated understanding and beliefs in a firm about the relationships between the firm and the environment" (p. 347). Such organisational knowledge reflects the understanding of how resources should be used by the firm in order to benefit from it (Smith et al. 2005). Thus, a knowledge management capability has direct implications for overall firm performance. Garud and Nayyar (1994) argue that technology is a form of knowledge and consequently, technological change is related to knowledge development and learning. In general, knowledge is created through learning which Teece et al. (1997) attach high importance to. "Learning is a process by which repetition and experimentation enable tasks to be performed better and quicker" (Teece et al. 1997: 520).

In terms of knowledge management, exploration and exploitation are widely discussed in the literature (e.g., March 1991, Henderson and Cockburn 1994, Shane 2000, Rosenkopf and Nerkar 2001, Grant and Baden-Fuller 2004). March (1991) refers to the former as "exploration of new possibilities" and the latter as "exploitation of old certainties" (p. 71). In other words, exploration relates to knowledge creation which can also be regarded as learning while exploitation relates to knowledge application (Grant and Baden-Fuller 2004). This is applicable to processes, products as well as the supply chain. March (1991) suggests that firms should address exploration and exploitation simultaneously and keep an appropriate balance of the two to overcome the limitations they have on their own. More specifically, exploration is only concerned with new knowledge for the firm, while exploitation focuses on the application of existing knowledge without creating anything new (March 1991). Neither exploration which ignores the application of knowledge, nor exploitation which ignores the creation of new knowledge can create sustained performance alone (March 1991). This suggests that the link between exploration and exploitation is critical (March 1991). This link works both ways in that explored knowledge has to be exploited in order to benefit from it and that lessons learned from exploitation have implications on future exploration (because it is only beneficial for firms to explore knowledge that can eventually be exploited at some point).

Lichtenthaler and Lichtenthaler (2009) argue that a given set of knowledge and related capacities will likely help the firm to generate superior innovation performance as well as overall firm performance. However, the authors note further that while this might well hold for a given period, this is not the case for long periods. This means that certain knowledge capacities might support firms to generate performance in one

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period while this might not suffice to sustain that performance over time (Lichtenthaler and Lichtenthaler 2009). To ensure sustained firm performance, firms have to take into account market dynamics (e.g., Eisenhardt and Martin 2000) and engage in exploration and exploitation in order to adjust their knowledge base to meet changing market conditions (March 1991, Lichtenthaler and Lichtenthaler 2009). In other words, a firm's knowledge base needs to be adaptable in order to generate longer-term performance (Azadegan and Wagner, Helfat et al. 2007, Lichtenthaler and Lichtenthaler 2009). Thus, we propose knowledge management capacities as an elementary mediating factor for two things: (1) for the realisation of superior firm performance (which will be discussed explicitly in the section below leading to propositions 4a and 4b and (2) for an increase of a firm’s stock of knowledge and related resources. From this, we derive the following proposition:

• Proposition 2a: An increase of knowledge management causes a higher level of resources.

5.4.6 Firm Performance

A large body of literature investigates the relationship between environmental management and performance. Mixed results were obtained suggesting that environmental efforts are a financial burden which hurts profitability or whether increases in efficiency and the development of new growth opportunities lead to higher profitability and competitive advantage (Hart and Ahuja 1996, Klassen and McLaughlin 1996). Studies that dealt with the relationship between environmental management efforts and firm performance measured as the financial performance at the stock market revealed a positive relationship (Hart and Ahuja 1996, Klassen and McLaughlin 1996, Jacobs et al. 2010), or found negative correlation between bad environmental performance and the intangible asset value of firm (Konar and Cohen 2001). Rao and Holt (2005) find that greening the different phases of the supply chain leads to increased competitiveness and better economic performance measured as new market opportunities, product price increase, profit margins, sales and market share. Furthermore, Green supply chain management (GSCM) not only leads to increased environmental performance, but also to superior economic performance (Zhu and Sarkis 2004). Molina-Azorin et al. (2009) conducted a literature review of 32 studies that analyse the influence of environmental management on financial performance and found - although results were mixed - a predominance of studies providing evidence for a positive impact of green management on financial performance. Many authors investigated the effect of green management on operational performance. Klassen and

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Whybark (1999) found a positive effect between sustainable manufacturing technologies and manufacturing performance (cost, quality, delivery, flexibility). Environmental performance is complementary with lean manufacturing adoption (King and Lenox 2001) and is a significant driver for superior quality (Pil and Rothenberg 2003). Additionally, environmental excellence creates unexpected side benefits like waste reduction and efficiency gains in operations (Corbett and Klassen 2006).

Other authors investigated the willingness-to-pay (WTP) of consumers for certain sustainable products. While studies in the agricultural sector (Misra et al. 1991) and the wood industry (Vlosky et al. 1999) found evidence for an increased WTP for environmentally friendly products, there is no such effect for the buyers of kitchen garbage bags that are made from recycled plastic (Anstine 2000). These ambiguous results are explained by a study about the impact of product sustainability on consumer preferences (Luchs et al. 2010). The authors show sustainable product characteristics are not always perceived to lead to a better product. Although sustainability is regarded as positive, the presence of this characteristic can have "a negative effect on the perception of other product attributes. Consumers are aware that manufacturers operate under budgetary, product development, and manufacturing constraints" (Luchs et al. 2010: 19), thus assuming that the superiority of one product attribute was realised by a trade-off with the inferiority of other product attributes. This means that for product categories in which "strength-related attributes" are valued, consumers imply that sustainable products have the liability of inferior product performance. These findings might explain mixed results of studies on consumers' WTP for green products (Misra et al. 1991, Vlosky et al. 1999, Anstine 2000) and for studies on the impact of environmental management on financial performance (Molina-Azorin et al. 2009). Thus, to investigate the relationship between sustainability efforts and performance, it is essential to control consumer preferences in a certain product market, as different industries are characterised by different product-related attributes. Another possible explanation can be found in the trade-off between additional costs for sustainability efforts and its benefits. While investments in sustainable manufacturing technologies for instance pay off to a certain degree due to higher efficiency and resource savings, this is only true until the additional costs exceed the realised marginal benefits. More efforts in sustainability do not lead per se to better performance, but only until a certain threshold is reached. Firms taking a very proactive approach in sustainability tend to overinvest in green initiatives going beyond the optimal effort-performance-rate. Those firms are characterised by an above average environmental performance

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for which the markets' acceptance and WTP is questionable. The costs of more sustainable technologies applied in processes and products are fundamental characteristics shaping the effort-performance relationship in our model. The costs of technology as well as the consumer preferences for sustainability are the major influencing factors whether an increase in sustainability efforts leads to an increase in firm performance. Hence, we propose the relationship between sustainability efforts and firm performance being a function of customers' and consumers' preferences and costs of technology following an inverted u-shaped curve. Sustainability effort pays off till a certain threshold. Once the threshold is reached, further investments in sustainability initiatives are detrimental to firm performance. Based on these arguments regarding the relationship between a firm's efforts on sustainability and firm performance we propose the following:

• Proposition 3a: The relationship between sustainability efforts and firm performance is moderated by the combination of consumer preferences and costs of technology implementation.

• Proposition 3b: The relationship between sustainability efforts and firm performance is inverted u-shaped.

The model suggests that knowledge management takes different forms with ad-hoc and strategic approaches to improve business processes in the context of sustainability. The former approach is of a more incremental nature which is mediated by knowledge exploitation as existing knowledge is sufficient to fulfill shorter-term goals. However, the latter approach is of a more radical nature which is not only mediated by knowledge exploitation but also knowledge exploration. This is because existing knowledge is insufficient in some instances and needs to be complemented by newly created knowledge in order to be able to take into account newly arising and often game-changing challenges and thereby fulfill longer-term goals. Based on this discussion on sustainability move components, knowledge management and firm performance, the following propositions can be derived:

• Proposition 4a: The relationship between ad-hoc initiatives of sustainability efforts and firm performance is mediated by knowledge exploitation.

• Proposition 4b: The relationship between strategic initiatives of sustainability efforts and firm performance is mediated by knowledge exploration and exploitation.

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5.4.7 Moderating Effects

As we expect the relationship between drivers and decisions to be influenced by moderators, the following paragraphs will focus on possible moderators. Following the above-mentioned literature on strategic decision-making, past firm success influences the extent to which sustainability efforts occur as firms' aptness for strategic renewal towards sustainability and organisational inertia is affected (Huff et al. 1992, Lant et al. 1992, Miller 1993, Ferrier 2001). Those firms, which have already generated superior firm performance, will be reluctant to change their well-developed business model unless they experience urgency towards more sustainability (Lant et al. 1992, Miller 1993). The reliance on existing business models and processes is bigger if firms made decisions in the past leading to success, because proven business practices increase the confidence and persistence in current patterns (Lant et al. 1992, Miller 1993, Audia et al. 2000, Ferrier 2001). In contrast, poor firm performance in the past will increase the probability that firms adapt their business model by incorporating sustainability practices (Lant et al. 1992, Miller 1993, Ferrier 2001). Firms which perform poorly or are at the brink of bankruptcy will be more willing to implement sustainability issues, as they exhibit a higher level of doubt and discussions of current business practices (Miller, 1993) and have less to lose. Poor past decisions leading to poor performance give reason to re-evaluate past and current patterns of business and therefore provides motivation for strategic change (Ferrier 2001). This leads to a sense of urgency and makes them more likely to differentiate themselves from competition by establishing a sustainable firm identity. By doing so, firms compensate other weaknesses being the reason for their poor performance in order to become competitive. Thus, poor performing firms have a higher motivation to engage in sustainability resulting in higher sustainability efforts.

• Proposition 5a: Past performance negatively moderates the relationship between drivers of sustainability and sustainability efforts; as poor (good) past performance causes firms to be more (less) aggressive in their reaction to the presence of sustainability drivers.

The extent to which firms engage in sustainability initiatives is influenced by available resources needed for the implementation of new manufacturing technologies, the development of green products or the application of GSCM. Starting new strategic initiatives is resource intensive, not only financially, but also from a human resources point of view. Especially in smaller firms this can limit sustainability efforts as investments often have to be made in advance of new returns. This becomes even more important when several sustainability initiatives are made in parallel. Large firms

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possess the critical resources to pursue the invention of new products, GSCM and manufacturing process innovation simultaneously, while smaller firms have to focus on the most promising options. Thus, we propose that firm size moderates the relationship between drivers of sustainability and sustainability efforts as bigger firms can engage in higher numbers of sustainability initiatives with a higher duration of a sustainability move.

• Proposition 5b: Firm size positively moderates the relationship between drivers of sustainability and sustainability efforts; as big (small) firms respond more (less) to the presence of sustainability drivers.

The urgency and the willingness to initialise more sustainable actions are influenced by the current firm level of environmental action which is shaped by decisions in the past to engage in sustainability. In line with the notion of path dependency (e.g. Eisenhardt and Martin, 2000; Teece et al., 1997), the current level of environmental action reflects for example past investments in more sustainable technologies, green products and GSCM (King and Lenox, 2001; Pil and Rothenberg, 2003) and can be measured by the environmental performance of a firm. As outlined by Eisenhardt and Martin (2000) and Teece et al. (1997), past decisions to better take into account sustainability considerations and resulting investments influence future decisions and thus the future extent of environmental efforts. Firms with a low level of current environmental action will experience more pressure from sustainability drivers like regulation, customers and competitors than firms with a high level which already meet the required level of sustainability. Even firms with superior past performance, which are reluctant to change their way of making business, have to undertake additional steps towards a more sustainable way of doing business if their low level of current environmental action is not sufficient to meet pressures such as regulatory standards, thus forcing them to address actual sustainability issues.

• Proposition 5c: The current level of environmental action negatively moderates the relationship between drivers of sustainability and sustainability efforts; as firms with a low level (high level) respond more (less) to sustainability drivers.

5.5 Conclusion

In this paper we extend the understanding of decision-making in firms facing the sustainability challenge by providing an explanatory framework to guide future research on sustainability. First, we give a comprehensive overview of exogenous and endogenous sustainability drivers to which firms are exposed. By providing a holistic

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summary, we establish a framework to operationalise the sustainability challenge for future empirical research. This will lead to a better understanding not only of the necessities for a firm to become more sustainable, but also of new opportunities like new markets for sustainable products or efficiency gains due to more sustainable, resource-efficient processes.

Second, we show possible initiatives for firms as a mean to address the sustainability challenge such as adopting new manufacturing technologies, bringing new, greener products to market, and introducing sustainability practices to the supply chain. We propose the construct of sustainability efforts reflecting the integrated actions of a firm to become more sustainable. By doing so, we provide a fundamental component for future work on sustainability from a firms' perspective to be established in the sustainability literature. Additionally, by defining actions and characteristics of a sustainability move we set the foundation to assess the magnitude of the sustainability efforts of a firm. Our framework allows the identification of those sustainability initiatives, which really lead to competitive advantage by integrating the impact of the chosen sustainability actions by firms on the performance dimension.

By integrating the knowledge management perspective as a mediator between sustainability efforts and firm performance into our framework, we accommodate the importance of knowledge-related capabilities. Knowledge capacities have to be aligned according to changing market requirements and market dynamics in order to mediate the creation of sustainable firm performance. Consumer preferences and other requirements to meet the sustainability challenge are new phenomena and not completely understood in every industry. Thus, we inaugurate the knowledge exploration capacities of a firm to develop such new knowledge and the knowledge exploitation capacities of a firm as a critical capability to apply knowledge in a firm’s own products and processes. Both dimensions of knowledge management are inevitable to realise the benefits of superior firm performance due to actions towards more sustainability.

The paper furthermore connects the three main elements (1) drivers, (2) decision categories, and (3) knowledge management dimensions, through propositions. By merging those literature streams, we extend the existing literature on sustainability, decision models, and knowledge management.

Various limitations of this paper merit discussion. First, we do not take any geographical differences into account. While geographical variations in productivity or legislation for instance are reflected by the occurrence of variable priorities within

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sustainability drivers, our model does not account for geographical differences in the relationship between sustainability efforts and firm performance. Lin and Chiang (2011) found country differences impacting the valuation of technology. In our context, this leads to country differences in adoption of new, more sustainable technologies as benefits of higher resource efficiency vary across countries. Consumers as well as political decision-makers in developed countries show higher awareness for sustainability compared to developing countries resulting in higher consumer preferences for sustainable products and processes and subsidies for sustainable operations. We expect the positive relationship between sustainability efforts and firm performance to be stronger in developed countries suggesting this proposition to be tested in future research. Therefore, the degree of generalisation of the findings might be limited.

Second, we acknowledge that there might be interconnections between certain drivers or various sustainability initiatives. For example, the introduction of new and greener products or the available information about more sustainable manufacturing processes might cause increased consumer awareness about sustainability. Thus, neither drivers, nor actions to be taken are fully independent from each other and endogeneity between sustainability drivers and firms' actions towards more sustainability might occur, which is not considered in our model.

Third, this paper does not differentiate the relevance of different drivers. Based on the literature we have argued that regulation and consumers are the most powerful drivers. However, empirical work is required to verify these findings taking the prevalence as well as the importance of sustainability drivers into account. For example, regulation might not be an issue at all in industries which take a very proactive approach regarding sustainability leading to firms being far ahead of laws and norms.

Fourth, our model does not take any marketing or communication efforts into account. A substantial benefit of sustainability initiatives is generated if consumers reward the efforts because of higher consumer preferences for sustainable products or supply chains. This leads to higher market shares or higher sales due to consumers' willingness to pay price premiums. A basic prerequisite is the public perceptions of a firm’s green management efforts. Creating transparency and awareness is a function of a firms' ability to communicate with the market. Thus, the ability to communicate might serve as an additional mediator of the relationship between sustainability efforts and firm performance.

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Finally, this paper is of conceptual nature. The propositions were not empirically tested so far. Further research should test the validity of propositions. In addition, further research should shed more light on the trade-off between extra investments in sustainability initiatives like new manufacturing technologies for instance and the positive impact on sustainability. Better understanding the threshold to which additional investments in sustainability are beneficial and under which circumstances costs of investment exceed expected benefits is a demanding task, which would be of tremendous interest for researchers as well as practitioners who have to justify investments (and related cost) in green initiatives. In line with this, there is a shortcoming in the literature on performance measurements related to the triple bottom line. In this piece of research, we only address financial performance measurements to evaluate the results of chosen initiatives. It would also be interesting to be able to evaluate changes in the three elements of the triple bottom line after conducting a sustainability move and how this is related to firm performance. The development of such performance measurements in relation to the triple bottom line might be worth further research and would help to justify sustainability moves.

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6 New Process Technology Investments: The Impacts of Technology Scanning and Centralized Technology Departments on Investment Timing and Business Performance Stefan Schrettle, Morgan Swink

6.1 Abstract

We assess the direct and interacting effects of a business unit’s scanning activities and the existence of a centralized technology department on the timing of its investments in new, promising process technologies. We further investigate whether investment timing is related to business performance. Our analysis of a survey of 88 business units in Germany, Switzerland and Austria indicates that business units that place strategic importance on new process technologies are more likely to carry out broader and/or more frequent technology scanning activities. Interestingly, however, more extensive scanning leads to early investments only when the firm operates a centralized technology department. Further, our data suggest that process technology investment timing is not significantly associated with business performance, though relative investment timing is. Business units that invest in process technologies earlier than their competitors appear to gain competitive advantages over their competitors. Our study provides insights how firms can benefit from scanning activities in terms of making early process technology investments that create first-mover advantages.

6.2 Introduction

Global manufacturers are experiencing increasing competition, due to cost pressure from manufacturers in developing countries, rapidly changing customer demands, growing product complexity, and demanding legal requirements. Additionally, a shift from standardized, big scale manufacturing to flexible, low-volume manufacturing compels manufacturers to constantly engage in manufacturing process innovation (MPI). Such innovation includes the design, development, and implementation of new processes, as well as the continual improvement of existing processes (Lee et al. 2011). A central aspect of MPI is the integration of new manufacturing technologies into production systems. A large body of literature investigates the usage of advanced manufacturing technologies (AMT) (Gupta and Whitehouse 2001, Sun 2000), AMT implementation patterns (McDermott and Stock

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1999, Small and Yasin 1997, Udo and Ehie 1996), how to realize benefits from AMT (Boyer et al. 1997, Sohal et al. 2006, Swink and Nair 2007, Zammuto and O’Connor 1992) and AMT’s impacts on performance (Idris et al. 2008, Kotha and Swamidass 2000, Small and Yasin 1997, Swamidass and Kotha 1998). Many authors highlight the contribution of new manufacturing technologies to strategic priorities like increased flexibility, allowing manufacturing units to produce a variety of products at low volumes with few added costs or penalties (Fine and Freund 1990, Lee 1996, Slagmulder and Bruggeman 1992, Swamidass and Kotha 1998, Swink and Nair 2007). Additionally, AMTs reduce direct labor costs, rework costs, and work-in-process-inventories by embedding routine repetitive tasks into AMT hardware and software (Swamidass and Kotha 1998, Swink and Nair 2007, Zammuto and O’Connor, 1992). Finally, manufacturing units use AMTs to achieve higher product quality, because process-related automation creates more stable and consistent manufacturing processes (Swamidass and Kotha 1998, Swink and Nair 2007, Zammuto and O’Connor 1992).

Although many empirical studies have investigated the positive impact of new process technology adoption on performance, less information is available regarding how companies gather relevant information about new manufacturing technologies and technological trends. New manufacturing technologies do not create advantages per se; they create opportunities. Turning those opportunities into competitive advantages requires adequate identification, selection and implementation of technologies (Chung 1991, Gouvea da Costa and Pinheiro de Lima 2009). While recent work stresses the importance of proving out the benefits of new process technologies, less is known regarding appropriate methods, tools and organizational structures that promote early identification and successful adoption of new process technologies (Schroeder 1990). Such effective adoptions are thought to create first-mover advantages (FMA), which enable pioneering firms to earn positive economic profits compared to their competitors (Lieberman and Montgomery 1988).

This paper documents a study that examines scanning activities and organizational structures as enablers of early investments and first-mover advantages sourced in process technologies. Past research suggests that early investments in process technologies are risky, because such technologies are unproven in terms of both capabilities and market need (Schroeder 1995). On the other hand, rewards for successful early adoption can be quite high since technological leadership often provides a stronger strategic position within a market (Schroeder 1995).

This study makes several theoretical and managerial contributions. First, it examines whether scanning activities are more intensive in business units whose

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managers deem process technology investments to be strategically important. Researchers argue that businesses need to engage in systematic observations of various markets and environments in order to identify and capitalize upon important technological changes (Lichtenthaler 2007). We examine the degree to which managers recognize and act upon this principle.

Our second contribution is to investigate the extent to which centralized technology departments serve as complementary resources for scanning activities in driving early technology adoptions. The search for new ideas involves scanning and investigation of various sources for technological knowledge (Laursen and Salter 2006). Centralized technology departments that focus exclusively on the adoption of new process technologies bundle resources and knowledge required to identify, evaluate and implement technologies. Thus, a centralized technology department can serve as a hub for knowledge related to manufacturing process technologies, supporting the adoption process by devoting necessary resources to the analysis of the information gathered via scanning activities. We investigate the proposition that business units are more likely to benefit from scanning activities when a centralized technology department is in place, as they are better positioned to make early investments in emerging process technologies. Importantly, our study combines a plant level analysis with a manufacturing network perspective. While new process technologies are deployed at the plant level, scanning activities and the evaluation of this information often take place at a higher organizational level.

The third contribution of our study stems from its more detailed examination of investment timing. While foregoing studies have focused on the extent of advanced process technology adoptions, we explicitly consider adoption timing. Moreover, many studies of FMA have addressed product technologies, rather than process technologies. The theory of FMA is based on the notion of an initial information asymmetry among competitors, enabling one firm to gain a head start over its rivals (Frynas et al. 2006, Lieberman and Montgomery 1988). By modeling the investment timing of adoption decisions, we explore the potential for asymmetries in process technology knowledge to provide FMA.

The remainder of this paper describes the study and its conclusions. Section 2 presents theoretical underpinnings and hypotheses. Section 3 presents the survey and measurement methodologies employed for data collection. Section 4 presents the results of the data analysis. Section 5 discusses the results and offers a post hoc investigation. Section 6 identifies the conclusions and limitations of the study.

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6.3 Theory Development

Superior manufacturing competence is considered a central source of competitive advantage for manufacturing firms, and the underlying process technology is an essential part of this competence. Manufacturers can differentiate themselves from competitors by achieving cost efficiency, quality, flexibility, and lead time improvements with few trade-offs (Sun, 2000; Flynn and Flynn, 2004; Swink and Nair, 2007). Rapid technological change and fast shifting customer needs force businesses to continually discover and develop new technology-based opportunities (Teece 2007). For example, researchers have considered computer-controlled process technologies to be crucial for the survival of manufacturing firms (Doms et al. 1995, Sinha and Nobel 2008). This makes the identification, selection, and development of appropriate technologies an important task; one that is required to achieve and maintain organizational fit with the environment. The ability to manage this integration and to adequately adapt production processes exhibits a substantial dynamic capability of a manufacturing firm, which can be seen as the assembling and orchestrating of difficult-to-orchestrate assets (Teece 2007). This capability is required for long-term firm survival, because staying in business is more about the type and extent of investments in process technologies, rather than about decisions of whether or not to invest in new technologies (Gouvea da Costa and Pinheiro de Lima 2009).

Figure 11: Research Model presents a research model describing relationships among hypothesized antecedents and consequences of early investments in new process technologies. In subsequent sections we describe the theory that underlies and motivates these hypothesized relationships.

Figure 11: Research Model

StrategicImportance of

Process Technology

ScanningFrequency

Technology Department

Early Investment in Process Technology

Business PerformanceH2

H1 H4

Control variables(Firm Size, Industry Process Clockspeed)

H3

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6.3.1 Technology Scanning

Because superior process technology can be a source of considerable competitive advantage, systematic observation of source environments for new, high-potential technologies is an essential activity for aspiring early adopters (Brenner 1996, Lichtenthaler 2007). The literature on technology intelligence provides useful insights how this search for new technologies should be organized. Researchers such as Lichtenthaler (2007) describe the process of systematic acquisition, assessment and communication of information about technological trends and opportunities using terms such as technology scanning, monitoring, assessment, forecasting, and intelligence. A central activity in all these concepts is search of external sources. While some authors distinguish between undirected search called “scanning,” and directed search, called “monitoring” (Ashton et al. 1991, Lichtenthaler 2007, Reger 2001), we recognize that search activities often include both directed and undirected elements. Besides the directed search for concrete information used for problem solving, organizations simultaneously absorb undirected technological information for which the company has no apparent application at the moment, but which might become valuable in the future. Accordingly, we follow the convention of foregoing researchers in using the term “technology scanning” and defining it as the activity of surveying external sources in order to acquire information about technological change (Aguilar 1967, Van Wyk 1997).

Numerous studies have attempted to link technology scanning to innovation performance, with mixed, but overall positive results (Ashton and Stacey, 1995; Clemons, 1997; Frishammar and Hörte, 2005). In this stream, however, few researchers make such distinction between product and process innovation. An exception is Reichstein and Salter (2006), who highlight the importance of scanning suppliers as important sources of new process innovations. The importance of suppliers in this role is echoed by many other researchers (e.g. Leonard-Barton, 1992; Nair and Swink, 2007; Azadegan and Dooley, 2010). Research on “open innovation” practices has also emphasized technology scanning (Chesbrough 2003) as a means for firms to use ideas and knowledge of external actors in their own innovation processes. Proponents of open innovation argue that possibilities of collaboration with external partners and networks is steadily growing, while at the same time becoming more important as a driver of innovation performance; outside-in processes enrich a company’s knowledge base by integrating suppliers, customers, and external knowledge sourcing (Enkel et al. 2009). Similarly, Rosenkopf and Nerkar (2001) explore the role of external technology searches, finding that the impact of explorative

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search is greatest when it spans both organizational and technological boundaries. Laursen and Salter (2006) find that moderate levels of both external search breadth and depth are important for innovation performance. In addition, they show that the relationships of both search factors to innovation performance are inversely u-shaped. Firms who are more open to external sources or search channels are more likely to have a higher level of innovative performance until a certain threshold, after which openness—in terms of breadth and depth—can negatively affect innovative performance, presumably due to time consuming, expensive and laborious search activities (see also Leiponen and Helfat, 2010).

Given that studies like those mentioned above have established the importance of technology scanning for innovation success, our objective is to examine scanning as a factor in explaining why some business units are more likely to make early investments in emerging process technologies. We first note that systematic technology scanning requires intentionality, and it is not cost free. Therefore, it is more likely that business unit managers will invest in scanning regimes and resources when they view manufacturing process technology as an important potential source of competitive advantage. For a number of reasons, certain businesses may consider process technology to be of secondary importance. In mature industries, manufacture processes are sometimes viewed as commodities, or as being easily copied or substituted, therefore lacking the potential for competitive differentiation. Even when the potential for process technology differentiation exists, a business unit’s managers may choose product innovation, product branding, or customer intimacy strategies as the more important avenues for differentiation (Treacy and Wiersema, 1993). Conversely, when process innovation is seen to be strategically important, managers are likely to be more committed to scanning activities, and more willing to devote resources to more frequent and intensive scanning regimes.

H1: Business units that assert the strategic importance of new manufacturing process technologies conduct more frequent external technology scanning activities.

6.3.2 Investment Timing

New product and process technologies are posited to develop according to an S-curve pattern (Abernathy and Utterback 1994, Brenner 1996, Flynn et al. 1997). An emerging new process technology often requires a long period of development and testing in order to establish its capabilities and applications. Early adopters pay the price of these development efforts. As the knowledge base surrounding a new

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technology grows, adoptions increase at an increasing rate, until the innovation begins to diffuse throughout the industry, at which point the new adoption rate declines (Abernathy and Utterback 1994, Brenner 1996, Flynn et al. 1997). Adoption in any of these three stages, emergence, growth, and maturity, involves potential advantages and specific risks associated with each stage. Moreover, these advantages and risks may be influenced by a particular’s organization’s expertise or experience (Stock and McDermott 2001). Flynn et al. (1997) provide strong support for the existence of a strategy of building manufacturing capabilities through process innovations. Further, they show that manufacturing process innovations tend to follow the S-curve pattern, thus indicating the relevance of adoption timing for manufacturing process innovation.

Process technology investment decisions are important because they influence the future success of the firm. Such investments typically involve large amounts of capital, and many technologies (e.g., equipment) have long operating lives. Thus, process technology choices involve non-routine, complex, strategic decisions of high impact on the firm's long-term performance.(Dean and Sharfman 1993; Mintzberg 1976). When decisions have higher magnitudes of impact and consequence, decision-makers tend to act more rationally, engaging in extensive collection of information necessary to form expectations about various alternatives (Papadakis et al. 1998). Accordingly, managers typically make decisions affecting new process technology adoption only after they have collected critical amounts of relevant information. Such information aids decision-makers in reducing blind spots pertaining to both the environment and the operational performance of technology candidates (Zajac and Bazerman, 1991). Collecting a sufficient amount of information can be a challenging task in the early stages of a technology, because accurate signals of the performance of a technology are rather weak and hard to identify (Abernathy and Utterback 1994, Brenner 1996).

Firms are more likely to uncover immature, unproven, or unknown technologies if they engage heavily in scanning, while firms who do little scanning will likely become aware of technologies only after they mature and become well established. Scanning helps to identify and acquire information that may be required for the development of different decision options, and for the selection of optimal technology investments. Scanning therefore promotes earlier investment opportunities, some if which culminate into process technology adoptions (Eisenhardt and Zbaracki 1992; Lichtenthaler 2007).

H2: More frequent process technology scanning leads to earlier investments in new manufacturing process technologies.

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6.3.3 Centralized Technology Management

A systematic technology intelligence process is considered a major factor in reducing the risk of organizational failure in the face of radical technological change. Such a process encompasses not only scanning, but also the assessment and communication of information (Lichtenthaler 2007). It is important to filter and organize the large volumes of information typically produced through scanning to create knowledge that guides proper decisions about investments in new manufacturing technologies (Brenner 1996). Frishammar and Hörte (2005), for example, show that the application of scanned information to product design and launch decisions is significantly correlated with innovativeness. Researchers have not yet shown similar benefits regarding process design and launch.

Some business units create centralized technology departments (CTD) to support technology information processing, technology development, and decision making. A CTD has no direct operational function; it supports manufacturing units on divisional or corporate level to evaluate and implement new process technologies. A CTD serves as a knowledge hub that bundles a firm’s knowledge assets and competences. Such assets include tacit knowledge embedded in human resources and expertise, as well as codified knowledge located in procedures and databases. The effective analysis, evaluation and selection of technology alternatives are firm specific capabilities that emerge when firms develop dedicated resources, structures, and routines to promote organizational learning (Sohal et al. 2006). Learning from study, experimentation, and experience produces organizational capabilities in areas of search strategy, decision criteria, and performance measurement, as well as in methods like technology scouting, roadmapping, and complexity management (Small and Yasin, 1997; Closs, et al., 2008).

When a firm concentrates the development of these capabilities within a CTD, it can take greater advantage of scanning activities. A CTD gives priority and focused attention in applying technology intelligence processes to information collected via scanning, thus enabling a business unit to act faster to capitalize upon technological opportunities. Through developing faster and better intelligence processes, a CTD enables a firm to invest earlier in new process technologies, allowing them to eventually realize a FMA. New process technologies are often in an immature stage, and are in need of further development and adaptation to existing manufacturing processes. This requires in-depth engineering capabilities which are often concentrated within a CTD. Firms without a CTD are often not in the same position to invest in

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early stage technology because they lack the required engineering or technical expertise.

The presence of a CTD indicates that the firm spends considerable amounts of both human and financial resources on developing process innovation competencies. In fact, several studies have found that firm size and resource availability are major factors driving the creation of CTDs. Large firms with considerable resources are able to develop new processes that small firms, operating within greater resource constraints, may be unable or unwilling to develop. Meredith (1987) suggests that only large firms are likely to have the skills and human resources it takes to understand, implement, and manage really new technologies. This technological capability likely explains some of the relationship between firm size and performance that has been established in the literature. Moreover, Swamidass and Kotha (1998) find firm size to moderate the relationship between AMT use and performance. They argue that size enhances the effective use of AMT due to larger firms’ command of knowledge resources embodied by skilled operators and specialized professionals that are less likely to be present in smaller firms. Similarly, Reichstein and Salter (2006) report empirical support for the proposition that firm size has a significant effect on process innovation.

Taking this logic one step further, we argue that larger firms are successful precisely because they possess important specialized process engineering capabilities that are often organizationally housed in a CTD. These focused resources help firms to invest in process technologies earlier because they provide the knowledge required to translate early signals and vague information into concrete opportunities, and because they contain the knowledge needed to further develop and customize early-stage, immature technologies. Firms that lack the concentrated knowledge embodied in CTDs must rely on external markets and other early adopters to develop and prove-out new process technologies, thus they are more likely to be later adopters. In sum, firms that concentrate capabilities for technology intelligence in a centralized technology department achieve greater benefits from scanning activities, because they can process leading information faster and more effectively, because they are better able to capitalize on emerging, unproven technologies.

H3: The existence of a centralized technology department positively moderates the effect of scanning frequency on early investment in new manufacturing process technologies.

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6.3.4 New Process Technologies as a Source of Competitive Advantage

Many research studies provide evidence that new manufacturing technologies are important sources of performance improvements for manufacturing firms (Cua et al. 2001, Mora Monge et al. 2006, Sinha and Noble 2008; Ketokivi and Schroeder 2004). Researchers argue that AMTs are valuable weapons that firms can use to address competitive challenges including fragmented mass markets, shorter product life cycle and increased demand for customization (Udo and Ehie 1996). On the whole, a substantial body of research indicates that AMTs can increase manufacturing process flexibility, speed, and quality with little additional costs (Diaz et al. 2003, Fine and Freund 1990, McDermott et al. 1997, Lee 1996, Slagmulder and Bruggeman 1992, Swamidass and Kotha 1998, Swink and Nair 2007; Stock and McDermott 2001, Zammuto and O’Connor 1992). However, realization of these benefits is challenging. Not all AMTs perform as expected, and some adoptions are even classified as total failures (Sun 2000). Upton (1995, 1997) found negative relationships between AMT use and both product and production flexibilities. Boyer et al. (1997) found no significant relationship between AMT use and flexibility. Dean and Snell (1996) found no significant relationship between AMT and organizational performance.

Several researchers have investigated contingencies that might explain these mixed results, including linkages to TQM and JIT, manufacturing strategy, design-manufacturing integration (Boyer et al., 1997; Dean and Snell, 1996; Swink and Nair 2007), and other factors (see Das and Jayaram, 2003 for a review). While these factors may serve to limit or enhance the adoption process, we posit that timing of adoption is also a salient driver of new process technology’s contributions to competitive performance. Gonzalez-Benito (2005) investigates the relationship between the firm's tendency to implement the most modern and advanced production management practices and business performance. He shows that “manufacturing proactivity” has a positive effect on financial and operational performance, and he attributes the effect to FMA. Although early adopters of process technologies face a high risk of failure due to high technological and market uncertainties (Schroeder 1995), they are also better positioned to realize first-mover advantages (FMA). Lieberman and Montgomery (1988) identify three sources of FMA: technological leadership, preemption of assets, and buyer switching costs. Technological leadership can create advantages through the learning or experience curve, where costs fall with cumulative output, or through succeeding in patent or R&D races (Lieberman and Montgomery 1998; Kerin et al., 1992; Frynas et al. 2006). In line with this literature, we posit that early investors have

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greater chances to realize substantial performance gains by differentiating themselves from competitors with regard to new manufacturing technologies.

H4: A firm's early investment in new manufacturing process technologies is associated with superior business performance.

6.4 Research methodology

6.4.1 Data collection

To test our hypotheses, we developed an internet-based questionnaire, which was reviewed by three manufacturing managers and three academics to correct mistakes, to resolve unclear wording and to improve clarity. To increase the response rate, we used closed-ended questions whenever possible, and created a respondent-friendly format where respondents could see their progress in order to avoid dropout before completion of the survey. We addressed companies with at least 80 employees in discrete and assembled product industries in the German speaking area of Europe (Germany, Switzerland, Austria) with contacts from our own database (ISIC codes Rev. 4: 10-32). Manufacturing plant managers as well as managers from centralized technology departments were invited by phone calls to participate in the survey. If a potential respondent was new to their position, we asked them to redirect us to the responsible person that currently held the relevant position. Our targeted respondents mostly held positions in senior management at the plant level (e.g. plant manager, plant manufacturing manager, process manager) or corporate level (e.g. corporate technology managers, global technology managers, corporate manufacturing process managers). During the call, we explained our aim to the potential respondents, clarified obscurities and explained critical items from our survey to the managers. By talking directly to the respondents, we made sure that we addressed the most knowledgeable persons about our topic of manufacturing process innovation within each company.

Additionally, we created a common understanding between the research team and the respondents with regard to the content of the questionnaire, and verified the correctness of our contact details. If the contact person agreed to participate in the survey, an invitation letter was sent via e-mail, which directed respondents to the internet address that hosted the survey. As an incentive for participation, we promised an executive summary of the research findings and held out the prospect of knowledge

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exchange among responding managers. Two reminders were sent at 4-week intervals within the data collection period between June and October 2011.

6.4.2 Sample description

A total of 609 contacts were identified as potential respondents. We could not reach 50 out of 609 potential respondents (8.2%) by phone during the data collection period. From the remaining 559 respondents, 126 (20.7%) responded that they would not take part in our survey due to insufficient time for filling out the questionnaire, company policy against participating in surveys, or not being interested in the survey. 103 people (16.9%) that we contacted via phone indicated that they were not the right people within their company to fill out the questionnaire, without providing us with necessary contact details of the person in charge. As a result, we sent out 330 surveys, 88 of which were returned, yielding a response rate of 14.4%. To be able to compare our data collection with similar research studies, we computed our response rate as the ratio of potential respondents (609) and actual respondents (88). Other studies use e-mail without pre-telephone calls leading to an obviously higher proportion of actual sent-out questionnaires, because those studies consider contacts that would be sorted out during our data collection process. Taking our response rate of 14.4%, our study compares favorably with similar empirical studies (Lee et al. 2011, Swink and Nair 2007).

We conducted several analyses to investigate potential response biases in the sample. First, we observed no serious response biases in our conversations with non-respondents. Second, we looked for industry-specific characteristics and applied an extrapolation of successive waves of respondents as suggested by Armstrong and Overton (1997) to compare information about early and late respondents. We classified our sample into early and late respondents according to the number of reminders that were sent to the potential respondents. Chi-Square-test showed no significant difference in the distribution of the early and late respondents across ISIC codes of respondents (ISIC Rev.4: 11, 13, 14, 17, 20-32, p = 0.872). Third, we conducted t-tests to compare all waves in terms of company size (p = 0.660), business performance (p = 0.443), strategic importance of manufacturing process innovation (p = 0.444) and found no statistically significant differences. Based on these results, we concluded that our sample is free from demographic and content-related biases.

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6.4.3 Measures

This study investigates the degrees to which firms’ attached strategic importance, scanning frequencies, and dedicated centralized technology departments influence the timing of their investments in new manufacturing process technologies. Furthermore, we examine evidence of performance benefits from early investment in new manufacturing process technologies. We operationalized strategic importance by using a five-point Likert type multi-item scale (1 = strongly disagree; strongly agree = 5). Table 3 lists the two scale items. Scanning activities were measured by asking respondents how often they use certain sources of information and knowledge for manufacturing process innovation (1 = regularly several times a year; 2 = regularly once a year; 3 = incidentally; 4 = not at all). The construct variables and item scales were adapted from Laursen and Salter (2006) and Leiponen and Helfat (2010) and reverse-coded. We dropped three of the seven items due to poor factor loadings; for these items the scores item indicated consistently low frequencies of scanning activity. For the remaining items, we mean-substituted 7% of values that were missing and summed the values to generate the scanning variable.

To assess the existence of a centralized technology department with dedicated resources for MPI, we utilized a binary variable (0,1), indicating whether or not the firm possesses a divisional or corporate technology department as indicated in Figure 12 (this figure was included in the questionnaire). If the responding company had a centralized technology department on either a corporate or a divisional level, we coded the item as a 1 for the respective company. There were no respondents in our sample that had both a divisional and a corporate CTD.

We measured early investment by asking the respondents to specify their investment timing for new manufacturing technology projects over the last 15 years with regard to the maturity level of the adopted technology. Respondents were asked what percentage of their new manufacturing process technology projects was started in each of the maturity levels of technology development indicated in an S-shaped path of technology maturity (Flynn et al. 1997). By doing so, we gained three percentages of technology projects for each maturity level, and calculated a weighted average for each respondent. We calculated the weighted average by multiplying investments in the very early stage with 3, investments in the medium stage with 2, and later investments with 1, added up the scores and divided them by 6.

We collected data on five dimensions of business performance to assess the realization of first-mover advantages. Respondents were asked to rate their firm's

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performance relative to their competitors on a five-point Likert type multi-item scales (1 = significantly lower; 5 = significantly better). The scales were employed and validated by Boyer et al. (1997), Kotha and Swamidass (2000), and Swamidass and Kotha (1998). Two items (see Table 3) were dropped due to poor factor loadings.

Figure 12: Overview Centralized Technology Departments

To narrow our results for the relationship among our constructs to factual relationships, we introduced two control variables: firm size and process technology clockspeed. As expected, a significant correlation in our data indicated that larger firms are more likely to possess the capabilities and resources needed to install a centralized technology department (p < 0.01), making it important to control for firm size. We measured firm size by logging the respondent-reported number of employees in the firm, which is consistent with prior research (Swamidass and Kotha 1998,

CTD on Corporate Level

Executive Board

Management BoardDivision I

Production

R&D

Sales

Marketing

etc.

Management BoardDivision II

Production

R&D

Sales

Marketing

etc.

Management BoardDivision III

Production

R&D

Sales

Marketing

etc.

Executive Board

CTD on Division Level

Management BoardDivision I

Production (…)R&DMarketing

CTD on Division Level

Management BoardDivision II

Production (…)R&DMarketing

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Swink and Nair 2007). Our second control addresses the likelihood that a firm's ability to realize superior economic performance by early investments in new manufacturing process technology is influenced by the clockspeed of process technologies within an industry. The ability to make early substitutions of obsolete technologies is likely more important in high velocity industries, as opposed to stable industries. As suggested by Fine (1996), we control for process technology clockspeed, defined as the time it takes for capital equipment to become obsolete within an industry. Respondents were asked to indicate the number of months until manufacturing process technologies become obsolete and have to be replaced.

We executed a confirmatory factor analysis to evaluate the reliability and validity of the multi-item scales used to measure strategic importance, scanning, and business performance. The measurement model fit indices and factor loadings indicated an acceptable levels of fit and convergent validity, respectively (chi-square = 207.06 (d.f. = 36), p = 0.126; CFI = 0.954; GFI = 0.908; RMSEA = 0.072 (0.000, 0.131)). Cronbach's alpha values indicated acceptable reliability for each of the multi-item scales: strategic importance (α = 0.691), technology scanning (α = 0.712) and business performance (α = 0.875). Discriminant validity is also evidenced by the fact that none of the squared construct intercorrelations exceeded the average variance extracted for any construct.

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Table 3: Constructs and Measurement Items

Table 4 provides the descriptive statistics and inter-construct correlations for the theoretical constructs that we used for our analysis. Strategic Importance is positively and significantly correlated with Scanning Frequency (SFR). Furthermore, the correlation of SFR with Early Investment Timing is positive and significant. This is in line with our expectations indicating predictive validity, except the relationship between Early Investment Timing and Performance. The correlation for these two

Constructs Items

Strategic Importance SIM1 The adoption of new manufacturing technologies is strategically important for our company

SIM2 The collection of information about new manufacturing technologies is strategically important for our company

Scanning Frequency

SFR1 Fairs, exhibitionsSFR2 Professional conferences, meetingsSFR3 (dropped) Dealer for of equipment, materials, components, or softwareSFR4 (dropped) Producer of of equipment, materials, components, or softwareSFR5 Clients or customersSFR6 (dropped) Technical press, trade journalsSFR7 Research organizations, e.g. universities, research institutes

Technology Department TDE1 Does your company possess a technology department on corporate level?

TDE2 Does your company possess a technology department on divisional level?

Early Investment

EIN1 Technology is in an early stage, knowledge about technology and ist diffusion is very low, while risk is high leading to a high potential for differentiation from competitors.

EIN2 Technology diffusion is medium, the effects of the technology are known, but it has not become industry standard yet.

EIN3 Technology is available without technological risk und has diffused throughout the industry, differentiation is low.

BusinessPerformance

FPER1 (dropped) ProductivityFPER2 Profitabil ityFPER3 Sales growthFPER4 Return on salesFPER5 (dropped) Net Profit growth

Controls CTR1 Firm Size, Number of employeesCTR2 Industry process technology ClockspeedCTR3 Compared to your competitors, when does your company start to actively

develop new manufacturing technologies?

With what frequency does your company gather information from the following sources of information and knowledge for manufacturing process innovation?

Taking manufacturing technology implementations of your company during the last 15 years into account, in which maturity stage of the technology did you start your own innovation projects? (Please rate the percentage of implementations for each maturity stage)

Please rate your performance for your most important product group, compared with your competitors in the last two years.

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variables is positive but non-significant, pointing on a relationship that is more complicated than hypothesized. The next section presents the results of hypothesis testing.

Table 4: Intercorrelation Matrix

6.5 Results

We use hierarchical linear regression analysis to test the hypotheses, including moderating effects. We mean-centered the variables of strategic importance, scanning frequency and early investment to reduce the effects of multicollinearity in regression models that contain interaction terms (Cohen et al. 2003).

We begin by testing the left part of our model (H1, H2, H3) to assess the impacts of strategic importance, scanning activities, and the existence of a centralized technology department on the investment timing in new manufacturing process technologies. We employed the analytical framework of Edwards and Lambert (2007) that integrates moderated regression analysis and path analysis and offers some advantage compared to other approaches. First, it exactly pinpoints the paths of a mediated model that are moderated and provides statistical tests of moderation for each path. Second, the form of each moderating effect is clarified with tests of simple paths and corresponding plots of simple slopes. Third, the framework splits the total moderation effect into a direct and indirect effect and allows identification of varying mediating effect for low and high levels of moderation (Edwards and Lambert 2007). To analyze the mediation effect of scanning between strategic importance and early investment, our approach requires several steps. First, a significant relationship

Scales1. Firm Size Lg10(number of employees)2. Process technology clockspeed Number of months3. Strat. Importance 5-Point4. Scanning Frequency (SFR) 4-Point

5. Tech. Dept. (TDE)6. Early Investment Timing7. Business Performance 5-Point (much better than competition - much worse than competition)

Mean SD 1. 2. 3. 4. 5. 6.1. Firm Size 13,452 20,6752. Process technology clockspeed 129 93.6 -.0273. Strat. Importance 3.84 0.78 .203 .1674. Scanning Frequency (SFR) 2.53 0.62 .240* -.099 .376**5. Tech. Dept. (TDE) 0.32 0.47 .368** .094 .124 .1676. Early Investment Timing 27.54 7.51 -.018 .039 .465** .354** .1067. Business Performance 3.43 0.78 .156 .146 .276* .079 .091 .172* p < 0.05; ** p < 0.01

(strongly agree - strongly disagree)(1 = regularly several times a year; 2 = regularly once a year; 3 = incidentally; 4 = not at all) reverse coded

Dichotomous variable (0;1)Three percentages weighted (early, medium or late maturity level of technolog

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between strategic importance and scanning activities must exist. Second, strategic importance must be significantly related to early investment, meaning that there is a direct effect, which, in a third step, must change significantly when scanning activities, the existence of a CTD, and the product term of both are added to the model. This approach tests hypotheses 1 and 2, which as a whole represent the mediating relationship. The integration of the product term of technology department and scanning activities tests the moderating effect of technology department for the relationship between scanning activities and early investment (H3).

Accordingly, we first regressed scanning frequency on strategic importance to test hypothesis 1. Table 5 presents the results of model 1 (control variables) and model 2 (adds strategic importance). Model 2 shows statistically significant results (p < 0.01) and has a high R-squared and F-parameter, which suggests that the effect of strategic importance on the frequency of scanning is positive and statistically significant giving strong support to H1.

Table 5: Regression of Scanning Frequency on Strategic Importance of Technology

Table 6 presents the results of the next steps, represented by models 1-5. First, we included only control variables (model 1) and added strategic importance to test whether it is significantly related to early investment (model 2). The results show a high F-parameter statistically significant relationship as hypothesized (p<0.01). In model 3, 4 and 5, we successively added centralized scanning frequency (model 3), CTD (model 4), and the product term of both, representing the moderation effect (model 5). Because the F-parameter is significant for the 0.01-level with a high R-squared, we receive robust results allowing us to draw conclusions from our model.

Dependent variable

Firm Size .234 ** .160Industry Process ClockSpeed -.093 -.163Strat. Importance .357 ***F 2.870 6.081p-value .062 .001R-squared .065 .182Adjusted R-squared .042 .152Sig. F change .062 .001* p < 0.10; ** p < 0.05; *** p < 0.01 (one-tailed tests)

Scanning Frequency Model 1 Model 2

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Table 6: Regression of Early Investment on Hypothesized Drivers

Model 3 shows that scanning frequency is positive and significant, meaning that a higher frequency of scanning leads to earlier investment in new manufacturing process technologies. This provides support for hypothesis 2 from our model. Though diminished, the coefficient for strategic importance remains significant in Model 3, thus indicating only a partial mediation effect of scanning frequency, at most. To test hypothesis 3, we estimate regression models 4 and 5 by entering the CTD variable and the interaction term of CTD and scanning frequency into our regression. The F-parameter is significant in all our models (p < 0.01) and the change of the F-parameter is significant for model 5, suggesting a significantly better fit over models 3 and 4. The interaction term in model 5 is positive and significant at the 0.05-level, suggesting that there is a moderating effect of CTD, which supports H3. To illustrate the effect, we plot the simple regression lines distinguished by the moderating factor of CTD. Figure 13 shows the regression lines to be consistent with support for H3. While scanning activities are strongly associated with early investment in new manufacturing process technologies in case of the existence of a CTD, this effect is not significant when firms do not possess such a department.

Dependent variable

Firm Size .028 -.056 -.127 -.131 -.089Industry Process ClockSpeed .041 -.034 -.005 -.006 -.033Strat. Importance .451 *** .347 *** .348 *** .349 ***Scanning Frequency (SFR) .288 ** .286 ** .120Tech. Dept. (TDE) .011 -.088SFR x TDE .323 **F .081 5.624 6.008 4.739 5.317p-value .922 .002 .000 .001 .000R-squared .002 .194 .258 .258 .323Adjusted R-squared -.026 .160 .215 .204 .262Sig. F change .922 .000 .017 .922 .014* p < 0.10; ** p < 0.05; *** p < 0.01 (one-tailed tests)

Early Investment Model 1 Model 2 Model 3 Model 4 Model 5

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Figure 13: Interaction between Scanning Activities and Presence of a CTD

In a next step, we evaluate whether early investors in new technologies are able to realize superior business performance, thereby testing hypothesis 4 of our model. Model 2 in Table 7 shows a regression of business performance on early investment, while controlling for firm size and industry clockspeed. The early investment coefficient is not statistically significant; thus hypothesis 4 is not supported.

Table 7: Regression of Business Performance on Early Investment

15

20

25

30

35

40

5 7 9 11 13 15 17 19

Early

inve

stm

ent

Scanning activitites

No CTD CTD exits

Dependent variable

Firm Size .161 .134Industry Process ClockSpeed .194 .190Early Investment .111F 1.544 1.211p-value .224 .316R-squared .062 .073Adjusted R-squared .022 .013Sig. F change .224 .452* p < 0.10; ** p < 0.05; *** p < 0.01 (one-tailed tests)

Business Performance Model 1 Model 2

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6.6 Discussion and Post Hoc Analysis

The integration of external knowledge sources is vital when manufacturing organizations attempt to adopt innovative, leading-edge manufacturing technologies. The research findings support most of our expected relationships between scanning activities, the existence of a centralized technology department, and the investment timing in new process technologies leading to a first-mover advantage. First, we investigated whether the frequency of scanning activities was associated with the strategic importance attached to new manufacturing process technologies. Based on these results, it seems that firms who pursue a process innovator strategy use scanning activities to improve process innovation performance due to better information about technological trends with respect to manufacturing processes.

Our findings associate the strategic importance of new manufacturing process technologies directly to earlier investments in new technologies. The results of model 3 also provide support for hypothesis 2, as there is a positive, significant relationship between scanning activities and investment timing. Interestingly, however, our data indicated no support for a significant role of scanning activities in mediating the relationship between strategic importance and early investments. Thus, our initial analysis provides no indication that scanning is a generative means by which importance leads to early investments. If we take “strategic importance” as a proxy for a priority on innovation, it is curious that scanning seems to play no significant mediating role.

We find an explanation for this non-finding in the significance of the moderation effect that confirmed support for H3. As shown in Figure 13 and Table 6 (Model 5), the scanning effect on early investment is only significant when a CTD is present. Scanning appears to play no significant role in driving early investments with a CTD is absent. As a post hoc analysis, we investigated the relationship between strategic importance, scanning activities, and investment timing using only the data for companies that reported that they had a CTD. Table 8 presents the regression analysis. For this subsample, strategic importance is positively and significantly correlated with early investment (model 2). But in contrast to the result of our previous analysis with the total sample, the significance of strategic importance disappears when scanning frequency is entered into the model. Further, the correlation between scanning frequency and early investment is positive and significant (model 3). These results verify the mediation effect of scanning frequency on the relationship between strategic importance and early investment.

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Our interpretation of these results again points to the importance of a CTD as a complimentary asset to scanning activities. It appears that scanning plays an important role in translating importance to action, but only when a firm possesses the resources to adequately process the collected information gathered through scanning. Only then can decision makers develop the relevant knowledge needed to properly prepare investment decisions. It seems logical that only firms that possess a CTD benefit from scanning by investing earlier in new, innovative technologies. The search for new ideas includes not only scanning a wide number of sources frequently, it also involves drawing knowledge from these sources and processing this knowledge (Laursen and Salter 2006). An important managerial implication is that efforts on the collection of technological data need to be matched by the development of knowledge resources that help to filter, organize, and evaluate the data (Brenner 1996). This capability requires certain skills and competences as well as financial assets that are bundled within a centralized technology department.

Table 8: Regression of Early Technology Investment on Importance and Scanning Frequency, including only

firms with a CTD

Our data analysis yielded no support for hypothesis 4, which states that early investment in manufacturing process technologies is associated with better business performance. In evaluating this result, we wondered if it might be sensitive to the weighting scheme we used to compute the early investment scores. We reran the analyses using two alternative operationalizations. First, we used only the percentage score capturing the proportion of investments made in the earliest technology stage (EIN1 in Table 1). Second, we created scores using a stronger weighting scheme, multiplying investments in the very early stage by 5, investments in the medium stage

Dependent variable

Firm Size -.202 -.156 -.140Industry Process ClockSpeed -.072 -.139 -.114Strat. Importance .378 * .272Scanning Frequency (SFR) .394 **F .497 1.668 2.667p-value .614 .200 .058R-squared .038 .173 .317Adjusted R-squared -.039 .069 .198Sig. F change .614 .060 .038* p < 0.10; ** p < 0.05; (one-tailed tests)

Model 1 Model 2 Model 3 Early Investment Timing

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by 3, and later investments by 1. The results using these two alternative scoring approaches were not different than before. The coefficient relating early investment to business performance remained non-significant in both cases.

An explanation for these results is that positive effects of first-mover advantages are counterbalanced by first-mover disadvantages. Lieberman and Montgomery (1988) name four different effects that reduce or even completely negate those advantages. First, other firms can free-ride on early investments as imitation costs are normally lower than innovation costs. Second, late investors can wait until technological uncertainties are resolved. When technological uncertainty is high and different technological candidates compete to become an industry standard, investors can benefit from later investment timing, especially when firms are not in the position to influence the new standard. Third, a shift in customer needs or process technology enables fast followers to exploit technological discontinuities and displace existing incumbents. It is difficult for early investors to take adequate preventive steps against such a threat when the replacement technology appears while the old technology is still developing, because a considerable amount of resources is deployed to new manufacturing technology projects. This kind of inertia effect is enhanced by several factors that inhibit a firm's ability to respond to environmental change. They represent the fourth factor and include a lock-in effect due to specific sets of fixed assets, a firm's reluctance to cannibalize existing product lines, or the organizational inflexibility caused by established processes.

These arguments notwithstanding, it was surprising to us that investment timing was not significantly associated with business performance. The finding prompted us to test an alternative model that employed a different operationalization of investment timing. In the survey we also included a five-point measurement item (1 = significantly later; 5 = significantly earlier) that asked respondents to rate their investment timing relative to competitors. We repeated the regression analysis using this measure, rather than our measure of investment timing relative to stages of absolute technology maturity. Table 9 provides the results, which confirm that early investments relative to competitors are significantly associated with superior business performance (p < 0.05).

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Table 9: Regression of Business Performance on Relative Investment Timing

The post-hoc analysis revealed that relative early adoption of manufacturing technologies compared to competition is a predictor of superior business performance. These results collectively confirm that investments in very early stages of the technology are risky, and above average failure rate of high risk projects might be an explanation for the non-significant relationship between early investment and business performance. On the other hand, new manufacturing process technologies provide potential to differentiate a firm from its competitors. Being the first adopter of a technology in a competitive industry environment is related to superior business performance, independent of the absolute maturity level of the technology. This finding is congruent with prior results suggesting that the inflection point at the end of the early stage of the S-curve offers the best occasion to adopt a technology (Brenner 1996). At this point, firms can realize operational benefits earlier than competitors and define technological standards, what leads to a pole position among rivals competing for customers and new orders.

6.7 Conclusion and Limitations

The goal of our research was to identify means by which firms invest in new, innovative manufacturing process technologies to realize a potential first-mover advantage. In particular, we tested relationships between strategic importance attached to process technologies, scanning activities, the existence of a centralized technology department, investment timing in new technologies, and business performance. We found that scanning intensity alone is not a predictor of process technological leadership. Scanning activities only explain technology leadership when they are

Dependent variable Business Performance

Firm Size .131 .103 .107Industry Process ClockSpeed .120 .124 .121Relative Investment Timing 0.23 ** .214 **Early Investment .106F 1.350 2.537 2.154p-value .265 .062 .081R-squared .031 .083 .094Adjusted R-squared .008 .050 .050Sig. F change .265 .031 .319* p < 0.10; ** p < 0.05; *** p < 0.01 (one-tailed tests)

Model 1 Model 2 Model 3

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supported by knowledge resources embedded in a centralized technology department. The results suggest that such a department bundles necessary financial resources, knowledge and capabilities to process the information gathered through scanning. Processing of information includes technical and financial risk analysis, cost-benefit analyses, capital budgeting and the comparison of different technology candidates to prepare investment decisions in new technologies. Our findings deepen the understanding of prior literature that found firm size to be positively associated with AMT use and process innovation (Reichstein and Salter 2006, Swamidass and Kotha 1998). We provide an empirical explanation for this relationship by showing that larger firms benefit from specialized resources that are concentrated within a CTD.

In addition, we investigated the effect of early investment on business performance. While we could not establish a significant relationship between early investment in terms of absolute technology maturity and business performance, our data suggests a positive effect of early adoption within a competitive environment on business performance. New manufacturing process technologies offer better operational performance as a means to differentiate firms from their rivals, thus leading to better performance. This has several managerial implications. First, our research provides top management with a strategic option for competitive advantage. While prior literature as well as management attention focuses mainly on the positive effects of product innovation, we provide evidence that early investment in MPI leads to superior business performance. Second, we also assist managers by showing how such an advantage can be realized. We have identified activities and organizational structures that can be set up within an organization to promote earlier investment in process technologies enabling managers to strive for a FMA.

The limitations of this research provide opportunities for future researchers to improve and substantiate our findings. First, we did not ask whether firms rely primarily on internal vs. external knowledge sources for manufacturing process innovation. The literature on open innovation is widely accepted and applicable for our research. While the importance of external knowledge sources for innovation is empirically documented, firms might pursue an innovation strategy based on confidentiality, which is exclusively internal. We are unable to draw conclusions regarding how such a strategy influences the investment timing in new manufacturing process technologies, as our empirical data do not address this particular case.

Second, future studies should investigate the relationship between early investment and performance in more detail. We used business performance to identify a potential first-mover advantage of early investors in new manufacturing process

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technologies. Although we could not establish a significant relationship to support our hypothesis, our control for industry leadership in process technology adoption revealed strong support for a beneficial first-mover strategy. Yet, we do not fully explore how and under which circumstances a FMA is realized. Future research should examine variables that might moderate this relationship, including proper risk evaluation of technology candidates, measures to increase the likelihood of success in adoption projects, and other potential moderators noted in the literature (Das and Jayram 2003).

Third, future research should seek to support our findings with a larger and broader sample. We used 88 respondents from Germany, Austria and Switzerland for our data analysis. Future research should substantiate our findings by using samples representing different geographical and industrial settings.

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7 Concluding Chapter Each of the three research papers addresses an existing gap in the OM literature

on new manufacturing technology adoption. They enhance existing knowledge by contributing to important aspects of process innovation in manufacturing firms. Taken together, they improve the general understanding of how manufacturing managers should design an adoption process for manufacturing technology, how such a process is in particular affected by the increasingly important challenge of ecological sustainability and how the timing of technology investment decisions affects business performance. The main contributions of each research paper are summarized in the following chapter.

Paper 1 provides insights in how to implement an adoption process for new manufacturing technologies. Based on a integral literature review and interviews with manufacturing managers from Switzerland, we identified contingencies that affect implementation success and thus, leading to increased operational performance and firm performance. The existing body of literature underlines the importance of new manufacturing process technologies the success of manufacturing companies. Almost any manufacturing firm has implemented any kind of new technology, mainly AMTs, but most firms are not able to fully realize their expected benefits of AMT. Several determinants that influence the performance when firms adopt new manufacturing technology have already been identified. We summarize these findings and develop a conceptual framework for the effective adoption of new manufacturing technologies. Based on the resource-based view (RBV) of the firm, we argue that an effective adoption process represents a dynamic capability which provides the potential for competitive advantage. Although many contingencies have been identified that influence the performance when firms adopt new manufacturing technology, the literature provides only mixed results and exhibits some inconsistencies. We resolve them by using findings from operations management and strategic decision-making literature and extending the OM literature by explicitly distinguishing between the phase of decision-making and the phase of implementation of advanced manufacturing technologies. We claim that both phases have distinct characteristics that lead to different determinants for the success of each phase. Furthermore, we develop a more fine-grained model of technology adoption by explaining how operational performance mediates the relationship between technology adoption and organizational performance.

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The second paper deals with the increasingly important phenomena of sustainability management. Especially manufacturing firms are influenced by the ecological dimension of the triple bottom line due to increasing energy and resource costs in energy intensive industries like chemicals or metals for example (Hart and Milstein 2003). Increasing governmental regulation forces managers to rethink existing business models and is a major driver for investments in green technologies and products.

The research findings on environmental management and its impact on operational and business performance are mixed. This makes it difficult for manufacturing managers to assess how to manage the ambiguous facets of the sustainability challenge, because benefits are often intangible and hard to quantify in contrast to concrete costs of sustainability initiatives. To enable adequate decision-making with regard to ecological sustainability, we introduce a framework for the selection of strategic initiatives. Existing literature does not provide such a guideline, therefore our framework encompasses past performance, environmental factors and the current level of environmental action to determine suitable actions as a result of a decision-making process (Lant et al. 1992, Audia et al. 2000, Ferrier 2001).

The make this fundamental contribution possible, we first operationalized the trend of ecological sustainability by defining its relevant drivers. By listing and explaining endogenous and exogenous drivers holistically, we reduce blind spots of managers' perception about their market environment, which is a fundamental prerequisite for good strategic decisions (Zajac and Bazerman 1991). We propose several initiatives that are suitable answers for the sustainability challenge. Green management practices like the adoption of new manufacturing technologies, the development of green products and the introduction of sustainability practices to the supply chain are proposed to have a positive effect on performance (Corbett and Klassen 2006, King and Lenox 2001, Klassen and Whybark 1999, Molina-Azorin et al. 2009, Pil and Rothenberg 2003). Finally, our research integrates a knowledge perspective as a mediator between sustainability efforts and firm performance. Knowledge exploration and exploitation are inevitable to respond to changing market requirements like increasingly sustainable customer preferences or increased transparency of global supply chains. Thus, the second paper deepens the understanding of how to deal with the sustainability challenge and provides both, theoretical as well as managerial contributions.

The third paper addresses a theoretical gap of upmost importance in the OM literature. The question whether early investments in new manufacturing process

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technology is highly relevant for production managers, but the research on process innovation did not provide any helpful insights to resolve this question. Therefore, our work helps to clarify, whether early adoption is rather a disadvantage due to problems that might arise from immature, unproven technologies leading to high failure rates, or whether such early investments represent an differentiating factor and thus, an opportunity for competitive advantage (Lieberman and Montgomery 1988, Schroeder 1995).

The timing of process technology adoption has not been on the research agenda so far. Existing work measures the adoption of manufacturing technologies by assessing the current status of the technology portfolio, asking the relevance of a certain technology, or identifying the level of usage of a technology (Boyer et al. 1996, 1997, Kotha and Swamidass 2000, Swamidass and Kotha 1998, Swink and Nair 2007). We were able to show that early adoption compared to competitors is a mean to realize a first mover advantage. In addition, we identified methods and organizational structures that serve as enablers for an FMA. In particular, we found evidence that scanning activities are a predictor of technological leadership in manufacturing for firms that possess a technology department. Firms that scan external knowledge sources systematically are able to identify technological trends on time. The processing capability of such information is concentrated within a technology department and serves as a moderator between scanning activities and early investment patterns of manufacturing firms.

Our study introduces the endogenous character of manufacturing technology adoption showing that actively management of investment timing is a predictor for superior performance. Our results help to better manage the technology portfolio for the manufacturing network of a firm. Divisional and corporate technology departments have a scope that goes beyond the scope of single plants in order to maximize value from technological innovation. While the identification, selection and development of new manufacturing technologies often is supported on the network level, the implementation and integration into existing processes is performed on plant level. By extending the process innovation literature to the scope of research on manufacturing networks, our findings enhance this research stream and reflect the need of today's production managers for global solutions with regard to operational performance maximization.

To summarize, this piece of work provides numerous theoretical and managerial implications. We addressed some of the most important issues for manufacturing managers who deal with the optimization of operational performance, as well as for

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top level executives that define the technology and business strategy of the firm. We hope that our findings are adapted and further developed by both, researchers and practitioners. Our goal is to support practitioners to effectively design adoption processes for manufacturing technologies, adequately cope with the increasing challenge of sustainability for business decisions and finally, better understand the benefits of an early adopter strategy for manufacturing technologies to set up and execute superior technology and production strategies. Finally, we would be honored if our results would serve as the basis for future research to further deepen the understanding about manufacturing process innovation and to gain additional insights into the management of new manufacturing technologies.

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References Abernathy, W.J.; Utterback, J.M. 1994. Mastering the Dynamics of Innovation,

Harvard Business School Press, Boston, MA.

Adler, Paul S. 1988. Managing flexible automation. California Management Review,

Vol. 30, Issue 3, 34-56.

Aguilar, F.J. 1967. Scanning the Business Environment. New York: Macmillan.

Ahmed, A. 2007. Managing knowledge in the 21st century and the roadmap to

sustainability. World Sustainable Development Outlook 2007: Knowledge

Management and Sustainable Development in the 21st Century: Greenleaf.

Amatucci, F.M.; Grant, J.H. 1993. Eight strategic decisions that weakened gulf oil.

Long Range Planning, 26 (1), 98-110.

Andersen, P.H.; Drejer, I. 2008. Systemic innovation in a distributed network: The

case of danish wind turbines, 1972–2007. Strategic Organization, 6, 13–46.

Anstine, J. 2000. Consumers' willingness to pay for recycled content in plastic kitchen

garbage bags: A hedonic price approach. Applied Economics Letters, 7 (1), 35-

39.

Ariss, S.S.; Ragunathan, T.S.; Kunnathar, A. 2002. Factors affecting the adoption of

advanced technology in small firms. SAM Advanced Management Journal

65(2): 14-23.

Armstrong, J. Scott; Overton, Terry S. 1977. Estimating Nonresponse Bias in Mail

Surveys. Journal of Marketing Research, Vol. 14, No. 3, 396-402.

Atuahene-Gima, K.; Li, H. 2004. Strategic decision comprehensiveness and new

product development outcomes in new technology ventures. Academy of

Management Journal, 47 (4), 583-597.

Audia, P.G.; Locke, E.A.; Smith, K.G. 2000. The paradox of success: An archival and

a laboratory study of strategic persistence. The Academy of Management

Journal, 43 (5), 837-853.

95

Azadegan, A.; Wagner, S.M. 2011. Industrial upgrading, exploitative innovations and

explorative innovations. International Journal of Production Economics, Vol.

130, Issue 1, 54-65.

Banerjee, S.B. 2001. Managerial perceptions of corporate environmentalism:

Interpretations from industry and strategic implications for organizations.

Journal of Management Studies, 38 (4), 489-513.

Bansal, P.; Roth, K. 2000. Why companies go green: A model of ecological

responsiveness. Academy of Management Journal, 43 (4), 717-736.

Barney, J. 1991. Firm resources and sustained competitive advantage. Journal of

Management, Vol. 17, 99-120.

Barr, P.S.; Stimpert, J.L.; Huff, A.S. 1992. Cognitive change, strategic action, and

organizational renewal. Strategic Management Journal, 13 (S1), 15-36.

Boks, C. 2006. The soft side of eco design. Journal of Cleaner Production, 14 (15/16),

1346-1356.

Bordo, M. D.; Taylor, A. M.; Williamson, J. G. 2003. Introduction, in Bordo, M. D.,

Taylor, A.M., and Williamson, J.G. (eds.) Globalization in Historical

Perspective, Chicago and London: University of Chicago Press: 1-10.

Bowen, F.E.; Cousins, P.D.; Lamming, R.C.; Faruk, A.C. 2001. The role of supply

management capabilities in green supply. Production and Operations

Management, 10 (2), 174-189.

Boyer, K.K.;Ward, P.T., Leong, G.K. 1996. Approaches to the factory of the future.

An empirical taxonomy. Journal of Operations Management, Vol. 14, Issue 4,

297–313.

Boyer, K.K.; Leong, G.K.; Ward, P.T.; Krajewski, L.J. 1997. Unlocking the potential

of advanced manufacturing technologies. Journal of Operations Management,

Vol. 15, No. 4, 331-347.

Brandenburger, A.M.; Nalebuff, B.J. 1995. The right game: Use game theory to shape

strategy. Harvard Business Review, 73 (4), 57.

96

Brenner, Merrill S. 1996. Technology Intelligence and Technology Scouting.

Competitive Intelligence Review, Vol. 7, Issue 3, 20-27.

Brown, S. 2001. Managing process technology - further empirical evidence from

manufacturing plants. Technovation, Vol. 21, No. 8, 467-478.

Burgelman, R.A. 2002. Strategy as vector and the inertia of coevolutionary lock-in.

Administrative Science Quarterly, 47 (2), 325-357.

Cakar, K.; Baki, B.; Ar, I. M. 2007. Critical Success Factors in ERP Implementations:

The Case of Turkish Manufacturing Companies. Paper presented at the 14th

International Annual EurOMA Conference, Ankara, Turkey.

Carroll, A.B. 1999. Corporate social responsibility: Evolution of a definitional

construct. Business Society, 38 (3), 268-295.

Challis, D.; Samson, D.; Lawson, B. 2002). Integrated manufacturing, employee and

business performance: Australian and New Zealand evidence. International

Journal of Production Research, Vol. 40, No. 8, 1941-1964.

Chesbrough H. 2003. The era of open innovation. Sloan Management Review, MIT

Sloan Management Review, 44, 3, 35–41.

Chung, K.B. 1991. Deriving advantages from advanced manufacturing technology - an

organizing paradigm. International Journal of Production Economics, Vol. 25,

Issues 1-3, 13-21.

Cohen, J.; Cohen, P.; West, S. G.; Aiken, L. S. 2003. Applied Multiple Regression /

Correlation Analysis for the Behavioral Sciences (3rd ed.). Mahwah, NJ:

Lawrence Erlbaum Associates.

Corbett, C.J.; Klassen, R.D. 2006. Extending the horizons: Environmental excellence

as key to improving operations. Manufacturing & Service Operations

Management, 8 (1), 5-22.

Corner, P.D.; Kinicki, A.J.; Keats, B.W. 1994. Integrating organizational and

individual information processing perspectives on choice. Organization Science

5(3), 294-308.

97

Cua, K.O.; McKone, K.E.; Schroeder, R.G. 2001. Relationships between

implementation of TQM, JIT, and TPM and manufacturing performance.

Journal of Operations Management, Vol. 19, Issue 6, 675-694.

Das, A., Jayaram, J., 2003. Relative importance of contingency variables for advanced

manufacturing technology. International Journal of Production Research, Vol.

41, No. 18, 4429–4452.

Das, S.S.; Van De Ven, A.H. 2000. Competing with new product technologies: A

process model of strategy. Management Science, 46 (10), 1300-1316.

Dean, J.W.; Sharfman, M.P. 1993. Procedural rationality in the strategic decision

making process. Journal of Management Studies, 30 (4), 587-610.

Dean, J.W.; Sharfman, M.P. 1996. Does Decision Process Matter? A Study of

Strategic Decision-Making Effectiveness. The Academy of Management

Journal, Vol. 39, No. 2, 368-396.

Dean, J.W.; Snell, S.A. 1996. The strategic use of integrated manufacturing: an

empirical examination. Strategic Management Journal, Vol. 17, No. 6, 459-480.

Dean, James W., Jr.; Yoon, Se Joon; Susman, Gerald I. 1992. Advanced

manufacturing technology and organization structure: Empowerment or

subordination? Organization Science, Vol. 3, No. 2, 203-229.

Delmas, M.; Toffel, M.W. 2004. Stakeholders and environmental management

practices: An institutional framework. Business Strategy and the Environment,

13, 209-222.

Delmas, M.A.; Toffel, M.W. 2008. Organizational responses to environmental

demands: Opening the black box. Strategic Management Journal, 29 (10), 1027-

1055.

Desimone, L.D.; Popoff, F. 2000. Eco-efficiency: The business link to sustainable

development Cambridge, MA MIT Press.

Diaz, Macarena Sacristán.; Machuca; José A.D.; Alvarez-Gil, Maria José. 2003. A

view of developing patterns of investment in AMT through empirical

98

taxonomies: new evidence. Journal of Operations Management 21, Issue 5,

577–606.

Doms, Mark; Dunne, Timothy; Roberts, Mark J. 1995. The role of technology use in

the survival and growth of manufacturing plants. International Journal of

Industrial Organization, Vol. 13, No. 4, 523-542.

Donaldson, T.; Preston, L.E. 1995. The stakeholder theory of the corporation:

Concepts, evidence, and implications. Academy of Management Review, 20

(1), 65-91.

Dreyer, L.C. 2009. Inclusion of social aspects in life cycle assessment of products –

development of a methodology for social life cycle assessment. DTU

Management Engineering.

Dyllick, T.; Hockerts, K. 2002. Beyond the business case for corporate sustainability.

Business Strategy and the Environment, 11, 130-141.

Edwards, Jeffrey R.; Lambert, Lisa S. 2007. Methods for Integrating Moderation and

Mediation: A General Analytical Framework Using Moderated Path Analysis.

Psychological Methods, Vol. 12, No. 1, 1-22.

Eisenhardt, K.M., Martin, J.A. 2000. Dynamic capabilities: What are they? Strategic

Management Journal, Vol. 21, No. 10-11, 1105-1121.

Eisenhardt, K.M.; Zbaracki, M.J. 1992. Strategic decision making. Strategic

Management Journal, Vol. 13, Special Issue, 17-37.

Elbanna, S.; Child, J. 2007. Influences on strategic decision effectiveness:

Development and test of an integrative model. Strategic Management Journal,

28 (4), 431-453.

Etzion, D. 2007. Research on organizations and the natural environment, 1992-present:

A review. Journal of Management, 33 (4), 637-664.

Ferrier, W.J. 2001. Navigating the competitive landscape: The drivers and

consequences of competitive aggressiveness. The Academy of Management

Journal, 44 (4), 858-877.

99

Fine, Charles H. 1996. Industry Clockspeed and Competency Chain Design: An

Introductory Essay. Proceedings of the 1996 Manufacturing and Service

Operations Management Conference, Dartmouth College, Hanover, New

Hampshire June 24-25.

Fine, Charles H.; Freund, Robert M. 1990. Optimal investment in product-flexible

manufacturing capacity. Management Science, Vol. 36, No. 4, 449–466.

Flynn, Barbara B.; Schroeder, Roger G.; Flynn, E. James; Sakakibara, Sadao; Bates,

Kimberly A. 1997. World-class manufacturing project: overview and selected

results. International Journal of Operations & Production Management, Vol. 17,

No. 7, 671-685.

Freeman, R.E., 1984. Strategic Management : A stakeholder approach, Pitman

Publishing, Boston, MA.

Frishammar, Johan; Hörte, Sven A. 2005. Managing External Information in

Manufacturing Firms: The Impact on Innovation Performance. Journal of

Product Innovation Management, Vol. 22, Issue 3, 251-266.

Frynas, Jedrzej George; Mellahi, Kamel; Pigman, Geoffrey Allen. 2006. First Mover

Advantages in international business and firm-specific political resources.

Strategic Management Journal, Vol. 27, 312-345.

Garud, R.; Nayyar, P.R. 1994. Transformative capacity: Continual structuring by

intertemporal technology transfer. Strategic Management Journal, 15 (5), 365-

385.

Gertler, M.S. 1995. Being There: Proximity, Organization, and Culture in the

Development and Adoption of Advanced Manufacturing Technologies.

Economic Geography, Vol. 71, No. 1, 1-26.

Gerwin, D. 1993. Manufacturing Flexibility: A Strategic Perspective, Management

Science, Vol. 39, No. 4, pp. 395-410.

Gouvea da Costa, S.E.; Pinheiro de Lima, E. 2009. Advanced manufacturing

technology adoption: an integrated approach. Journal of Manufacturing

Technology Management, Vol. 20, No. 1, 74-96.

100

Grant, R.M.; Baden-Fuller, C. 2004. A knowledge accessing theory of strategic

alliances. Journal of Management Studies, 41 (1), 61-84.

Gupta, A.K.; Smith, K.G.; Shalley, C.E. 2006. The interplay between exporation and

exploitation. Academy of Management Journal, 49 (4), 693-706.

Gupta, A.; Whitehouse, F.R. 2001. Firms using advanced manufacturing technology

management: an empirical analysis based on size. Integrated Manufacturing

Systems, Vol. 12, No. 5, 346-350.

Hargadon, A.; Sutton, R.I. 1997. Technology brokering and innovation in a product

development firm. Administrative Science Quarterly, 42 (4), 716-749.

Hart, S.L.; Ahuja, G. 1996. Does it pay to be green? An empirical examination of the

relationship between emission reduction and firm performance. Business

Strategy and the Environment, 5, 30-37.

Hart, S.L.; Milstein, M.B. 2003. Creating sustainable value. Academy of Management

Executive, 17 (2), 56-67.

Helfat, C.E.; Finkelstein, S.; Mitchell, W.; Peteraf, M.; Singh, H.; Teece, D.; Winter,

S.G. 2007. Dynamic capabilities: Understanding strategic chance in

organizations Oxford Blackwell.

Helfat, C.E.; Peteraf, M.A. 2003. The dynamic resource-based view: capability life

cycles. Strategic Management Journal, Vol. 24, No. 10, 997-1010.

Henderson, R.; Cockburn, I. 1994. Measuring competence? Exploring firm effects in

pharmaceutical research. Strategic Management Journal, 15 (S1), 63-84.

Hough, J.R.; White, M.A. 2003. Environmental dynamism and strategic decision-

making rationality: an examination at the decision level. Strategic Management

Journal, Vol. 24, No. 5, 481-489.

Huang, P.-S.; Shih, L.-H. 2009. Effective environmental management through

environmental knowledge management. International Journal of Environmental

Science and Technology 6(1), 35-50.

Huff, J.O.; Huff, A.S.; Thomas, H. 1992. Strategic renewal and the interaction of

cumulative stress and inertia. Strategic Management Journal, 13, 55-75.

101

Idris, F.; Rejab, R.; Ahmad, A. 2008. Relationships between Investments in Advanced

Manufacturing Technology (AMT) and Performances: Some Empirical

Evidences. European Journal of Economics, Finance and Administrative

Sciences, Issue 13, 67-78.

Iaquinto, A.L.; Fredrickson, J.W. 1997. Top management team agreement about the

strategic decision process: A test of some of its determinants and consequences.

Strategic Management Journal, 18 (1), 63-75.

Ijomah, W.L.; Mcmahon, C.A.; Hammond, G.P.; Newman, S.T. 2007. Development

of robust design-for-remanufacturing guidelines to further the aims of

sustainable development. International Journal of Production Research 45 (18-

19), 4513-4536.

Jacobs, B.W.; Singhal, V.R.; Subramanian, R. 2010. An empirical investigation of

environmental performance and the market value of the firm. Journal of

Operations Management, 28 (5), 430-441.

Jansen, J.J.P.; Van Den Bosch, F.A.J.; Volberda, H.W. 2006. Exploratory innovation,

exploitative innovation, and performance: Effects of organizational antecedents

and environmental moderators. Management Science, 52 (11), iv-iv.

Jassawalla, A.R.; Sashittal, H.C. 2002. Cultures that support product-innovation

processes. Academy of Management Executive, 16 (3), 42-54.

Kaebernick, H.; Kara, S.; Sun, M. 2003. Sustainable product development and

manufacturing by considering environmental requirements. Robotics and

Computer Integrated Manufacturing, 19 (6), 461-468.

Kaluza, B.; Blecker, T. 2005. Flexibilität - State of the Art und Entwicklungstrends. In

B. Kaluza & T. Blecker (Eds.), Erfolgsfaktor Flexibilität: Strategien und

Konzepte für wandlungsfähige Unternehmen: 1-25. Berlin: Erich Schmidt

Verlag GmbH & Co.

Kengpol, A.; Boonkanit, P. 2011. The decision support framework for developing

ecodesign at conceptual phase based upon iso/tr 14062. International Journal of

Production Economics, 131 (1), 4-14.

102

Kerin, Roger A.; Varadarajan, P. Rajan; Peterson, Robert A. 1992. First-Mover

Advantage: A Synthesis, Conceptual Framework, and Research Propositions.

Journal of Marketing 56 (4), 33–52.

Ketokivi, Mikko A.; Schroeder, Roger G. 2004. Manufacturing practices, strategic fit

and performance- A routine-based view. International Journal of Operations &

Production Management, Vol. 24, No. 2, 171-191.

Ketokivi, Mikko A.; Schroeder, Roger G. 2004. Strategic, structural contingency and

institutional explanations in the adoption of innovative manufacturing

practices", Journal of Operations Management, Vol. 22, No. 2, 63-89.

King, A.A.; Lenox, M.J. 2001. Lean and green? An empirical examination of the

relationship between lean production and environmental performance.

Production and operations management, 10 (3), 244-256.

Klassen, R.D.; Mclaughlin, C.P. 1996. The impact of environmental management on

firm performance. Management Science, 42 (8), 1199-1214.

Klassen, R.D.; Whybark, D.C. 1999. Environmental management in operations: The

selection of environmental technologies. Decision Sciences, 30 (3), 601-631.

Kleindorfer, P.R.; Singhal, K.; Van Wassenhove, L.N. 2005. Sustainable operations

management. Production and operations management, 14 (4), 482-492.

Kogut, B.; Zander, U. 1992. Knowledge of the firm, combinative capabilities, and the

replication of technology. Organization Science, 3 (3), 383-397.

Konar, S.; Cohen, M.A. 2001. Does the market value environmental performance? The

Review of Economics and Statistics, 83 (2), 281-289.

Kotha, S.; Swamidass, P.M. 2000. Strategy, advanced manufacturing technology and

performance: empirical evidence from U.S. manufacturing firms. Journal of

Operations Management, Vol. 18, No. 3, 257-277.

Labuschagne, C.; Brent, A.C.; Van Erck, R.P.G. 2005. Assessing the sustainability

performances of industries. 13 (4), 373-385.

Laine, M. 2005. Meanings of the term 'sustainable development' in finnish corporate

disclosures. Accounting Forum, 29 (4), 395-413.

103

Lant, T.K.; Milliken, F.J.; Batra, B. 1992. The role of managerial learning and

interpretation in strategic persistence and reorientation: An empirical

exploration. Strategic Management Journal, 13 (8), 585-608.

Laszlo, K.C.; Laszlo, A. 2007. Fostering a sustainable learning society through

knowledge-based development. Systems Research and Behavioral Science, 24

(5), 493-503.

Laursen, Keld; Salter, Ammon. 2006. Open for Innovation: The role of Openness in

explaining Innovation Performance among U.K. manufacturing firms. Strategic

Management Journal, Vol. 27, 131-150.

Leiponen, Aija; Helfat, Constance E. 2010. Innovation objectives, knowledge sources

and the benefits of breadth. Strategic Management Journal, 31, 224–236.

Lenox, M.; King, A. 2004. Prospects for developing absorptive capacity through

internal information provision. Strategic Management Journal, 25 (4), 331-345.

Leonard-Barton, D. 1988. Implementation as mutual adaption of technology and

organization. Research Policy, Vol. 17, No. 5, 251-267.

Lichtenthaler, U.; Lichtenthaler, E. 2009. A capability-based framework for open

innovation: Complementing absorptive capacity. Journal of Management

Studies, 46 (8), 1315-1338.

Lichtenthaler, Eckard. 2007. Managing technology intelligence processes in situations

of radical technological change. Technological Forecasting & Social Change,

Vol. 74, Issue 8, 1109–1136.

Lin, W.T.; Chiang, C.-Y. 2011. The impacts of country characteristics upon the value

of information technology as measured by productive efficiency. International

Journal of Production Economics, 132 (1), 13-33.

Linton, J.D.; Klassen, R.; Jayaraman, V. 2007. Sustainable supply chains: An

introduction. Journal of Operations Management, 25 (6), 1075-1082.

Lee, B. 1996. The justification and monitoring of advanced manufacturing technology:

an empirical study of 21 installations of flexible manufacturing systems.

Management Accounting Research, Vol. 7, 95–118.

104

Lee, Jung Young; Swink, Morgan; Pandejpong, Temyos. 2011. The Roles of Worker

Expertise, Information Sharing Quality, and Psychological Safety in

Manufacturing Process Innovation: An Intellectual Capital Perspective.

Production and Operations Management, Vol. 20, No. 4, 556-570.

Lieberman, Marvin B.; Montgomery, David B. 1988. First-Mover advantages.

Strategic Management Journal, Vol. 9, 41-58.

Lieberman Marvin B.; Montgomery, David B. 1998. First-mover (dis)advantages:

retrospective and link with the resource-based view. Strategic Management

Journal, Vol. 19, Issue 12, 1111–1125.

López-Gamero, M.D.; Molina-Azorín, J.F.; Claver-Cortés, E. 2009. Environmental

regulation as a potential to change managerial perception, environmental

management and financial performance 16th International Annual EurOMA

Conference, Göteborg, Sweden.

Luchs, M.G.; Walker Naylor, R.; Irwin, J.R.; Raghunathan, R. 2010. The sustainability

liability: Potential negative effects of ethicality on product preference. Journal

of Marketing, 74, 18-31.

Machiba, T. 2010. Eco-innovation for enabling resource efficiency and green growth:

Development of an analytical framework and preliminary analysis of industry

and policy practices. International Economics and Economic Policy, 7 (2), 357-

370

March, J.G. 1991. Exploration and exploitation in organizational learning.

Organization Science, 2 (1), 71-87.

Maxwell, D.; Van Der Vorst, R. 2003. Developing sustainable products and services.

Journal of Cleaner Production, 11 (8), 883.

McDermott, C.M.; Greis, N.P.; Fischer, W.A. 1997. The diminishing utility of the

product/process matrix. International Journal of Operations and Production

Management, Vol. 17, Issue 1, 65-84.

105

McDermott, Christopher M.; Stock, Gregory N. 1999. Organizational culture and

advanced manufacturing technology implementation. Journal of Operations

Management, Vol. 17, No. 5, 521-533.

McGee, J. 1998. Commentary on 'corporate strategies and environmental regulations:

An organizing framework' by a. M. Rugman and a. Verbeke. Strategic

Management Journal, 19 (4), 377-387.

Melville, N.P. 2010. Information systems innovation for environmental sustainability.

MIS Quarterly, 34 (1), 1-21.

Meredith, Jack. 1987. The strategic advantages of new manufacturing technologies for

small firms. Strategic Management Journal, Vol. 8, Issue 3, 249–258.

Miller, D. 1993. The architecture of simplicity. The Academy of Management Review,

18 (1), 116-138.

Minarro-Viseras, E.; Baines, T.; Sweeney, M. 2005. Key success factors when

implementing strategic manufacturing initiatives. International Journal of

Operations & Production Management, Vol. 25, No. 2, 151-179.

Mintzberg, H.; Raisinghani, D.; Théoret, A. 1976. The structure of "Unstructured"

Decision Processes". Administrative Science Quarterly, Vol. 21, No. 2, 246-

275.

Misra, S., K.; Huang, C.L.; Ott, S.L. 1991. Consumer willingness to pay for pesticide-

free fresh produce. Western Journal of Agricultural Economics, 16 (2), 218-

227.

Molina-Azorin, J.F.; Claver-Cortés, E.; Lopez-Gamero, M.D.; Tari, J.J. 2009. Green

management and financial performance: A literature review. Management

Decision, 47 (7), 1080-1100.

Mora Monge, C.A.; Rao, S.S.; Gonzalez, M.E.; Sohal, A.S. 2006. Performance

measurement of amt: A cross-regional study. Benchmarking: An International

Journal 13 (1/2), 135-146.

Noori, H. 1990. Managing the Dynamics of New Technology. Issues in Manufacturing

Management, Prentice–Hall, New Jersey.

106

Patel, Pankaj C.; Van der Have, Robert P. 2010. Enhancing Innovation Performance

through exploiting Complementarity in Search and Breadth and Depth.

Frontiers of Entrepreneurship Research, Vol. 30, Iss. 9, 1-12.

Pil, F.K.; Rothenberg, S. 2003. Environmental performance as a driver of superior

quality. Production and operations management, 12 (3), 404-415.

Porter, M.E.; Van Der Linde, C. 1995. Green and competitive: Ending the stalemate.

Harvard Business Review, 73 (5), 120-134.

Ramanathan, R.; Black, A.; Nath, P.; Muyldermans, L. 2010. Impact of environmental

regulations on innovation and performance in the UK industrial sector.

Management Decision, 48 (10), 1493-1513.

Rao, P.; Holt, D. 2005. Do green supply chains lead to competitiveness and economic

performance? International Journal of Operations & Production Management,

25 (9), 898-916.

Rapert, M. I.; Velliquette, A.; Garretson, J. A. 2002. The Strategic Implementation

Process - Evoking Strategic Consensus through Communication. Journal of

Business Research, Vol. 55, No. 4, 301-310.

Rashid, A.; Evans, S.H. 2009. Material efficiency: A comparison of theory and

industrial practices. 16th International Annual EurOMA Conference. Göteborg,

Sweden.

Rebitzer, G.; Ekvall, T.; Frischknecht, R.; Hunkeler, D.; Norris, G.; Rydberg, T.;

Schmidt, W.-P.; Suh, S.; Weidema, B.P.; Pennington, D.W. 2004. Life cycle

assessment part 1: Framework, goal and scope definition, inventory analysis,

and applications. Environment International 30, 701– 720.

Reichstein, T.; Salter, A. 2006. Investigating the sources of process innovation among

UK manufacturing firms. Industrial and Corporate Change, Vol. 15, No. 4, 653-

682.

Rivera-Camino, J. 2007. Re-evaluating green marketing strategy: A stakeholder

perspective. European Journal of Marketing, 41 (11-12), 1328-1358.

107

Robinson, H.S.; Anumba, C.J.; Carrillo, P.M.; Al-Ghassani, A.M. 2006. Steps: A

knowledge management maturity roadmap for corporate sustainability.

Business Process Management Journal, 12 (6), 793-808.

Rosenkopf, L.; Nerkar, A. 2001. Beyond local search: Boundary-spanning,

exploration, and impact in the optical disk industry. Strategic Management

Journal, 22 (4), 287-306.

Rumelt, R.P.; Schendel, D.; Teece, D.J. 1991. Strategic Management and Economics.

Strategic Management Journal, Vol. 12, 5-29.

Russo, M.V.; Fouts, P.A. 1997. A resource-based perspective on corporate

environmental performance and profitability. Academy of Management Journal,

40 (3), 534-559.

Sarkis, J.; Zhu, Q.; Lai, K.-H. 2011. An organizational theoretic review of green

supply chain management literature. International Journal of Production

Economics, 130 (1), 1-15.

Schaltegger, S.; Burritt, R. 2000. Corporate sustainability. The international yearbook

of environmental and resource economics. 185-222.

Schaltegger, S.; Hasenmüller, P. 2005. Nachhaltiges Wirtschaften aus Sicht des

"Business case of sustainability" Fachdialog des Bundesumweltministeriums

(BMU), Lüneburg, CSM.

Schroeder, Dean M.; Congden, Steven W.; Gopinath, C. 1995. Linking Competitive

Strategy and Manufacturing Process Technology. Journal of Management

Studies, Vol. 32, Issue 2, 163-189.

Schroeder, Roger G.; Bates, Kimberly A.; Junttila, Mikko A. 2002. A resource-based

view of manufacturing strategy and the relationship to manufacturing

performance. Strategic Management Journal, Vol. 23, Issue 2, 105-117.

Schweiger, D.M.; Sandberg, W.R.; Ragan, J.W. 1986. Group approaches for

improving strategic decision making: A comparative analysis of dialectical

inquiry, devil's advocacy, and consensus. The Academy of Management

Journal, 29 (1), 51-71.

108

Shane, S. 2000. Prior knowledge and the discovery of entrepreneurial opportunities.

Organization Science, 11 (4), 448-469.

Sharma, S. 2000. Managerial interpretations and organizational context as predictors

of corporate choice of environmental strategy. Academy of Management

Journal, 43 (4), 681-697.

Sharma, S.; Henriques, I. 2005. Stakeholder influences on sustainability practices in

the canadian forest products industry. Strategic Management Journal, 26 (2),

159-180.

Sharma, S.; Pablo, A.L.; Vredenburg, H. 1999. Corporate environmental

responsiveness strategies: The importance of issue interpretation and

organizational context. Journal of Applied Behavioral Science, 35 (1), 87-108.

Sharma, S.; Vredenburg, H. 1998. Proactive corporate environmental strategy and the

development of competitively valuable organizational capabilities. Strategic

Management Journal, 19 (8), 729-753.

Siegal, W.; Church, A.H.; Javitch M.; Waclawski J.; Burd, S.; Bazigos, M.; Yang T.;

Anderson-Rudolph, K.; Burke W.W. 1996. Understanding the management of

change. Journal of Organizational Change Management, Vol. 9, No. 6, 54-80.

Sinha, R.K. and Noble, C.H. 2008. The adoption of radical manufacturing

technologies and firm survival. Strategic Management Journal, Vol. 29, Issue 9,

943-962.

Slagmulder, R.; Bruggeman W. 1992. An investment justification of flexible

manufacturing technologies: inferences from field research. International

Journal of Operations and Production Management, Vol. 12, 168–186.

Small, M.H. 1997. Objectives for adopting advanced manufacturing systems: Promise

and performance. Industrial Management & Data Systems, 98 (3), 129-137.

Small, M.H.; Yasin, M.M. 1997. Advanced manufacturing technology:

Implementation policy and performance. Journal of Operations Management,

Vol. 15, No. 4, 349-370.

109

Smith, K.G.; Collins, C.J.; Clark, K.D. 2005. Existing knowledge, knowledge creation

capability, and the rate of new product introduction in high-technology firms.

The Academy of Management Journal, 48 (2), 346-357.

Sohal, A.S.; Sarros, R.; Schroder, R.; O'Neill, P. 2006. Adoption framework for

advanced manufacturing technologies. International Journal of Production

Research, Vol. 44, No. 24, 5225-5246.

Stock, Gregory N.; McDermott, Christopher M. 2001. Organizational and strategic

predictors of manufacturing technology implementation success: an exploratory

study. Technovation, Vol. 21, 625–636.

Sun, H. (2000). Current and future patterns of using advanced manufacturing

technologies. Technovation, Vol. 20, No. 11, 631-641.

Swamidass, P.M.; Kotha, S. 1998. Explaining manufacturing technology use, firm size

and performance using a multidimensional view of technology. Journal of

Operations Management, Vol. 17, No. 1, 23-37.

Swink, M.; Nair, A. 2007. Capturing the competitive advantage of AMT: Design-

manufacturing integration as a complementary asset. Journal of Operations

Management, Vol. 25, No. 3, 736-754.

Teece, D.J. 2007. Explicating dynamic capabilities: The nature and microfoundations

of sustainable enterprise performance. Strategic Management Journal, Vol. 28,

No. 3, 1319-1350.

Teece, D.J., Pisano, G., Shuen, A. 1997. Dynamic capabilities and strategic

management. Strategic Management Journal, Vol. 18, No. 7, 509-533.

Ubeda, S.; Arcelus, F.J.; Faulin, J. 2011. Green logistics at eroski:A case study.

International Journal of Production Economics, 131 (1), 44-51.

Udo, G.J.; Ehie, I.C. 1996. Advanced manufacturing technologies - Determinants of

implementation success. International Journal of Operations & Production

Management, Vol. 16, No. 12, 6-26.

Upton, D.M., 1995. What really makes factories flexible? Harvard Business Review

73, (4) July–August, 74–84.

110

Upton, D.M., 1997. Process range in manufacturing: an empirical study of flexibility?

Management Science 43 (8), 1092–1097.

Vachon, S.; Klassen, R.D. 2006. Extending green practices across the supply chain -

the impact of upstream and downstream integration. International Journal of

Operations & Production Management, 26 (7), 795-821.

Van Wyk, Rias J. 1997. Strategic Technology Scanning. Technological Forecasting

and Social Change, Vol. 55, Issue 1, 21-38.

Vlosky, R.P.; Ozanne, L.K.; Fontenot, R.J. 1999. A conceptual model of us consumer

willingness-to-pay for environmentally certified wood products. Journal of

Consumer Marketing, 16 (2), 122-136.

Von Hippel, E. 1988. The sources of innovation, New York: Oxford University Press.

Wade-Benzoni, K.A.; Hoffman, A.J.; Thompson, L.L.; Moore, D.A.; Gillespie, J.J.;

Bazerman, M.H. 2002. Barriers to resolution in ideologically based

negotiations: The role of values and institutions. Academy of Management

Review, 27 (1), 41-57.

Wheeler, D.; Colbert, B.; Freeman, R.E. 2003. Focusing on value: Reconciling

corporate social responsibility, sustainability and a stakeholder approach in a

network world. Journal of General Management, 28 (3), 1-28.

Wheelwright, Steven C.; Clark, Kim B. 1994. Leading Product Development, New

York, Free Press.

Winter, S.G. 2003. Understanding dynamic capabilities. Strategic Management

Journal, 24 (10), 991-995.

Wu, Z.; Pagell, M. 2011. Balancing priorities: Decision-making in sustainable supply

chain management. Journal of Operations Management, 29 (6), 577-590.

Zajac, E.J.; Bazerman, M.H. 1991. Blind spots in industry and competitor analysis:

Implications of interfirm (mis)perceptions for strategic decisions. Academy of

Management Review, 16 (1), 37-56.

111

Zammuto, R.; O’Connor, E. 1992. Gaining advanced manufacturing technologies’

benefits: the roles of organization design and culture. Academy of Management

Review, Vol. 17, No. 4, 701-728.

Zhu, Q.; Sarkis, J. 2004. Relationships between operational practices and performance

among early adopters of green supply chain management practices in chinese

manufacturing enterprises. Journal of Operations Management, 22 (3), 265-289.

Zollo, M.; Winter, S.G. 2002. Deliberate learning and the evolution of dynamic

capabilities. Organization Science, 13 (3), 339-351.

112

Appendix According to the guidelines of the University of St. Gallen, the authors of the

academic research paper confirm their proportion of contribution. The following table gives an overview about the percentage of work that every author contributed to the completion of each paper.

Paper 1: Unlocking the potential of process innovation: a conceptual framework for the exploitation of advanced manufacturing technologies

Author 1 Stefan Schrettle 100%

Paper 2: Turning sustainability into action: Explaining firms' sustainability efforts and its impact on firm performance

Author 1 Stefan Schrettle 35%

Author 2 Andreas Hinz 35%

Author 3 Maike Scherrer 20%

Author 4 Thomas Friedli 10%

Paper 3: New Process Technology Investments: The Impacts of Technology Scanning and Centralized Technology Departments on Investment Timing and Business Performance

Author 1 Stefan Schrettle 70%

Author 2 Morgan Swink 30%

113

Curriculum Vitae Stefan Schrettle born on April 25, 1981 in Augsburg, Germany EDUCATION: 10/2011 - 12/2011 Texas Christian University, Fort Worth, TX, USA

Visiting Scholar with Prof. Morgan Swink at the Neeley School of Business

07/2008 – 02/2012 University of St. Gallen (HSG), Switzerland Doctoral Candidate at the Institute of Technology Management and Doctoral Program in Business Innovation

09/2002 - 07/2008 University of Augsburg, Germany Graduate Studies in Strategy, Innovation and International Management

09/2001 - 07/2002 TU München, Germany Undergraduate Studies in Mechanical Engineering

09/1993 - 07/2000 Gymnasium Wertingen, Germany Abitur (A-Levels)

PROFESSIONAL EXPERIENCE: Since 05/2012 PricewaterhouseCoopers AG, Frankfurt, Germany Consulting, Valuation & Strategy 07/2008 – 02/2012 University of St. Gallen (HSG), Switzerland

Research Associate at the Institute of Technology Management 08/2007 - 04/2008 SAP AG, Walldorf, Germany

Internship – R&D Controlling 11/2006 - 04/2007 Institute of Management and Environment (IMU), Augsburg,

Germany Student employee – Consulting

08/2001 - 09/2001 Barl Maschinenbau GmbH, Neukirchen, Germany Internship

09/2000 - 06/2001 Military Service 1. Gebirgsjägerdivision / Edelweißkaserne Mittenwald, Germany

2000 - 2006 SGL Carbon AG, Meitingen, Germany Working student