the technology-organization discord

7/30/2019 The Technology-Organization Discord

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Master Thesis 2012

Faculty of Business

Administration & Center

for Innovation Studies

RADBOUDUNIVERSITY

NIJMEGEN

The Technology

O i i Di d


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O i i Di d

Preface

This master thesis is written in the context of completing my Masters degree in Business

Administration. The choice for the topic was easily made because I have always been very

interested in innovation. The innovation topic is very suitable for the specialization of my

master, which is Strategy. In my opinion, innovation is the most important way for

organizations to make a difference and to ensure their competitive advantage. Therefore, it

is really important that research focuses on innovation and the way this concept exactly

works. In this specific research the emphasis lays on determining the optimal configuration

of technological innovations and organizational innovations with regards to better

organizational efficiency. Though the whole process took more time than was expected, this

made sure that I really learned a lot the past year, not only about the specific subject but

also about how to manage a project like this. Although I am very proud on this final product I

definitely can say that it is not about the destination but all about the journey.

On this journey I was accompanied and helped by the following people. Without them I


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Abstract

Purpose This research seeks to explore the effects of temporal sequential configurations

of organizational and technological innovations on the process efficiency of manufacturing

firms.

Design/methodology/approach The effects of the organizational innovation first and

the technological innovation first perspective on organizational process efficiency were

examined by the use of a quantitative analysis of the EMS-2009 survey.

Findings The results of this study revealed that when examining the process efficiency of

manufacturing firms no significant evidence was found in support of any general temporal

sequential configurations of organizational and technological innovations. So in contrast to

what the literature review suggested no strong effects were found. However additional

analyses revealed that the timeframe in which both types of innovations are introduced

seems to have effect on some of the efficiency indicators.


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Content

1. Introduction .................................................................................................................. 7

1.1. General introduction ....................................................................................................... 7

1.2. Structure of the research ................................................................................................ 9

1.3. Problem statement ......................................................................................................... 9

1.3.1. Research problem .................................................................................................. 10

1.3.2. Research questions ................................................................................................ 11

2. Theoretical framework & Conceptual model ............................................................... 12

2.1. Technological innovation & performance ..................................................................... 12

2.2. Organizational innovation & performance ................................................................... 14

2.3. Interrelatedness technological and organizational innovations ................................... 16

2 4 S f 19


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3.3.6. Absolute number of technological and organizational innovations ...................... 31

3.3.7. Production lead time .............................................................................................. 31

3.3.8. Sales growth ........................................................................................................... 31

3.4 Operationalization .......................................................................................................... 31

3.5. Methods of analysis ...................................................................................................... 36

3.6 Conceptual model .......................................................................................................... 38

4. Analysis & results ...................................................................................................... 38

4.1. Descriptive analysis ....................................................................................................... 39

4.1.1. EMS-2009 and participating countries ................................................................... 39

4.1.2. Organizational performance .................................................................................. 40

4.1.3. Technological Process Innovations ........................................................................ 41

4.1.4. Organizational Innovations .................................................................................... 42

4.1.3. Sequential analysis ................................................................................................. 43


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Appendices ..................................................................................................................... 69

Appendix 1 ..................................................................................................................... 70

1.1 General Information EMS-2009 ..................................................................................... 70

1.2 EMS Survey ..................................................................................................................... 71

Appendix 2: Analyses of EMS-2009 data .......................................................................... 72

2.1. Reliability analysis Technological Innovations .............................................................. 72

2.2. Reliability analysis Organizational Innovations ............................................................. 73

2.3. Number of innovations introduced ............................................................................... 74

2.4. Descriptives metric variables ........................................................................................ 75

2.5. Descriptives non-metric variables ................................................................................. 77

2.6. Regression Analyses ................................................................................................. 78

2.6.1. Analysis A - Production Lead Time (PLT) .................................................................... 78

2.6.2. Analysis B - Manufacturing Lead Time (MLT) ............................................................. 86


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1. Introduction

1.1. General introduction

Modern organizations are operating in an increasingly complex and dynamic environment.

Due to environmental factors like globalization and fast moving technological development

firms face increased competition. Besides that manufacturing organizations are confronted

with the upcoming problems of scarce resources which force them to increase the efficiency

of their production processes. In this respect innovation enables organizations to increase

efficiency with regard to the use of raw materials and energy. Given the previous mentioned

developments and environmental factors the need for organizations to innovate is essential

for their growth and survival in the short and long term. Organizations have to develop and

implement new innovations in order to become more sustainable and profitable. Therefore

the study of innovation hardly needs justification as scholars, policy makers, business

executives, and public administrators maintain that innovation is a primary source of

economic growth, industrial change, competitive advantage, and public service (Borins,


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fact that these types of innovations are occurring in almost all manufacturing industries

worldwide since the start of the industrial revolution.

While technological innovations have proven their worth organizations can cope with

environmental changes and uncertainties not only by applying- new technology, but also by

successfully integrating technical or administrative changes into their organizational

structure that improve the level of achievement of their goals (Rosner, 1968; Damanpour &

Evan, 1984, p. 393). This concept of organizational innovation refers to the non-

technological innovations that contribute to the firms performance by for example changing

the organizational structure. The upcoming phenomenon of the multidivisional form can be

seen as a good example of an organizational innovation. Armour and Teece (1978) found

that the adaption of a major administrative innovation (the multidivisional structure) in

petroleum firms increased the rate of return of owners equity (Damanpour & Evan, 1984, p.

395). Non-technological innovations have proved to be of great influence in the success of

multiple organizations. Therefore it can be stated that these organizational innovations

which take place in the social system of an organization, are also important precedents for

i ti l d f


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try whether these temporal sequential configurations are significant indicators for

organizational efficiency, and if so which specific sequence is preferred. Further it isimportant to look to certain factors that also could have an influence on the way

organizations try to increase their performance by introducing new innovations. These

conditional factors could for example be the size of the firm and the specific industry the

firm is operating in. This research will try to answer all these questions by the use of

quantitative research of the data retrieved from the European Manufacturing Survey 2009,

which encompasses data from manufacturing firms from nine European countries with more

than 10 employees.

1.2. Structure of the research

This thesis starts with the problem statement and the corresponding research goal followed

by the presentation of the research questions. The following chapter discusses the existing

theories and findings of previous research conducted about this topic. This theoretical

review led to the conceptual model used within this research. From the results of this

theoretical framework a set of hypotheses is established. Chapter three provides the


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1.3.1. Research problem

The goal of this research is to make a valid contribution to the existing literature about

innovations. Theory development and empirical studies of innovation types have thus far

focused on their antecedents; namely, environmental and organizational conditions that

enhance or hamper the process of generation or adoption of each type (Jansen et al., 2006;

Kimberly and Evanisko, 1981; Tornatzky and Fleischer, 1990; Damanpour, Walker &

Avellaneda, 2009, p.2).However, research about the relation between the interrelatedness

of different types of innovations on the one hand and business efficiency on the other hand

has received considerably less attention. Existing literature describes the role and

importance of both technological developments and organizational concepts regarding their

influence on performance. Yet, important information about how to integrate the use ofboth types of innovations and especially in which temporal sequence and at which specific

conditions is a topic that deserves more attention in the field of innovation research.

Organizations can benefit from a better understanding of the interrelatedness of different

innovations. A deeper understanding helps them to adjust their innovations strategy in such

it t ib t b t t it l Th t l f diff t i ti i t


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1.3.2. Research questions

Main research question:

What is the influence of the different temporal sequential configurations of technological

and organizational innovations on the organizational process efficiency of manufacturing

firms?

Sub research questions:

- What is the effect of the organizational innovation first strategy on the organizational

efficiency of manufacturing firms?

- What is the effect of the technological innovation first strategy on the organizational

efficiency of manufacturing firms?

Additional analysis question:

Wh t i th i fl f th ti f i hi h i ti l d t h l i l


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2. Theoretical framework & Conceptual model

In this chapter the theoretical background underlying this research is presented. To start this

literature review this chapter first elaborates on the concepts of technological and

organizational innovations were the focus lays on their independent influence on

organizational performance. The second part will focus on the interrelatedness and

alignment of these different types of innovation. In this part the existing literature about the

influence of both innovations have on each other will be discussed. Consequently the

different forms of sequence will be presented. These sequences contain the Organizational

Innovation First and the Technological Innovation First perspectives.

2.1. Technological innovation & performance

In the introduction of this thesis the concept of technological innovations has been shortly

explained. In this part the relation between technological innovations and organizational

performance/efficiency is further elaborated on. Technological innovations can be defined

asthe implementation or adoption of technologically new or significantly improved


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can contribute to the performance of an organization can be explained by the so called

rational perspective. Damanpour (2012) explains that technological innovations contributeto the economy of the organization by using the following chain of events: R&D investment

development of new technology introduction of new products or services

performance outcome, which is referred to as the technology push model1. Yet technological

innovations do not only contribute to new products but also within the production process

they can enhance organizational performance in the form of higher production efficiency.

Maybe the most famous technological process innovation is the invention of the assembly

line by Henry Ford2

which had a major effect on labour productivity and quality of the T-Ford

automobile at that time. In figure 1 the different ways an organization can innovate are

clearly displayed.


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Bacharach and French (1980) found that over a ten-year period the average number of

technical innovations proposed in city governments was nearly three times the averagenumber of administrative innovations

3(Damanpour & Evan, 1984, p. 394). The question is

why do a majority of organizations put so much emphasis on these technological

developments? Technical innovations [] are perceived to influence performance more

readily than administrative innovations (Damanpour & Evan, 1984, p. 405). Not only the

business performance effects are accounted for this dominance of technological- over non-

technological innovations. Technical innovations are more observable, have higher

trialability, and are perceived to be relatively more advantageous than administrative

innovations, while administrative innovations are perceived to be more complex than

technical innovations to implement (Damanpour, 1983, p. 45-47; Damanpour & Evan, 1984,

p. 394). Further the findings from Baldwin, Diverty & Sabourin (1995) substantiated the

relation between the use of (new) technology and organizational performance. Because the

relation between innovation and organizational performance is the essence of this research

we will use the following definition for organizational performance, from a systems

perspective, performance is the ability of an organization to cope with all four systematic


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observed in the way innovation is studied. For example the Oslo Manual (2005) started to

refer to organizational innovation and marketing innovation next to technologicalinnovation. Because many researchers use this manual the importance to view innovation

within an organizational context is increasingly recognized in business literature. Because

organizational innovation encompasses multiple possible organizational changes it is

important to define it in a clear way. An organizational innovation is defined as the

implementation of an internally generated or a borrowed idea whether pertaining to a

product, device, system, process, policy, program, or service that was new to the

organization at the time of adoption (Thompson, 1965; Zaltman, Duncan, and Holbek, 1973;

Damanpour & Evan, 1984, p. 393). Yet this definition encompasses a wide range of

innovations. Damanpour & Evan (1984, p. 394) give the following examples: An

administrative innovation can be the implementation of a new way to recruit personnel,

allocate resources, and structure tasks, authority, and rewards (Evan, 1966). It comprises

innovations in organizational structure and in the management of people (Knight, 1967). All

these specific innovations which stand apart from pure technological changes contribute to a

firms performance in a lot of ways. For instance, from the perspective of institution


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to be introduced), and are cognitively less complex (are easier to understand by users)

(Damanpour & Aravind, 2011; Wolfe, 1994; Damanpour, 2012, p.11). These insights providelogical explanations that describe that organizational innovation can positively contribute to

organizational performance. This positive effect can be explained by a rational perspective

for example tested in the study of Evangelista and Vezzani (2010) which shows that the

adoption of new managerial practices has a positive effect on sales growth. Also the findings

of Birkinshaw and Mol (2009) which show that managerial innovations have a positive effect

on productivity indicate the importance and opportunities of these specific non-

technological innovations.

While technological innovations take place in the technical system of an organization

administrative innovations are defined as those that occur in the social system of an

organization (Damanpour & Evan, 1984; pp 394). The social system refers to the

relationships among people who interact to accomplish a particular goal or task (Cummings

and Srivastva, 1977). It also includes those rules, roles, procedures, and structures that are

related to the communication and exchange among people and between the environment

d l (C i d S i 19 & 1984 394) id


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that encompass the use of both organizational and technological innovations in order to

successfully increase performance. The critical aspects of aligning the investments in

technical aspects and investments in the manufacturing infrastructure such as planning,

control, quality assurance and organizational policies is described by multiple conceptual

studies (Meredith, 1987c,d; Ettlie, 1988, Zuboff, 1988; Parthasarthy and Sehti, 1992; Twigg

et al., 1992;Boyer et al., 1997). While these articles stress the importance and the

opportunities of a combined use of technological and non-technological innovations,

organizations still do not take this to a full advantage. The reason for this tendency could be

that the alignment of these concepts is a difficult process because the interaction effects of

diverse innovations differ. So there might be some combinations that enhance performance

but also negative effects by this interaction could be possible. For example, technological

process innovations are expected to have a direct effect on profit margins in the short term.

Schmidt & Rammer (2007 pp. 0) state: What is more, the highest innovation effects on

profit margins are to be found for firms introducing technological innovations without non-

technological ones, indicating that comprehensive innovation activities involving both types

are likely to raise costs stronger than returns. Yet these findings portray the effect on


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higher efficiency. Herbst (1974) describes from a socio-technical systems theory point of

view: From a theoretical standpoint, the sociotechnical systems framework emphasizes the

role of both technical and social systems operating jointly for the effective operation of an

organization, and its suggested that concentration on either the technical or the social

system without due regard to the other would result in low organizational performance and

growth. Damanpour & Evan (1984, p. 407) add to this by stating A balanced

implementation of administrative and technical innovations would help to maintain the

equilibrium between the social and technical systems, which in turn would lead to high

performance. The idea of a social technical system stems from about half a century ago

(Trist et al., 1963) were it was used to describe the influence new technologies had on jobs

and the new organizational forms that had to be developed in order to effectively use these

systems and simultaneously offer employees a quality job. While some argue that this

perspective is dated, the new technological developments in the last years have disrupted

the way employees work. For example, the new communication technologies but also other

technological developments like for example new ICT related innovations in the

manufacturing industry caused an irrevocable change in organizational structures and the


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innovations should be aligned by organizational innovations in order to increase efficiency

levels. The relation between those two distinct forms of innovation is of complementary

nature, and adopting both forms will lead to the highest returns (Armbruster et al., 2006;

Arnal et al,.2001; Evangalista and Vezzani, 2010). Brynjolfsson & Hitt (1998) describe that

these technological innovations have proven to be an essential component of a broader

system of organizational changes which do increase productivity. They found a consistent

positive relationship between the use of technology and a set of work practices that include

for example the use of self-directed work teams, greater levels of individual decision

authority increased investments in training and screening for education and incentive

systems that reward and encourage high team performance (Brynjolfsson & Hitt, 1998, p.

53).

Conclusion can be drawn that a technological innovation necessarily needs organizational

changes to become successful. It has to be acknowledged that these organizational changes

often need a substantial period of time and financial investments in order to be established.

However there are contrasting views about whether they have to be developed or

d d b f h h l l d h h h b


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2.4.1. Organizational innovation first

Although the importance of organizational innovations has been proven, the use of

organizational innovations for preparing organizations that face the implementation of new

technologies should gain more attention. Therefore it is of great importance to further

investigate the role of organizational innovations as driver for the implementation and

success of technological innovations. Earlier research describes the effect of product and

process innovations on non-technological innovation activities, but did not investigate the

opposite direction (Schmidt & Rammer, 2007 pp. 32). The organizational perspective

argues that rather than the adoption of technology, organizational change is fundamental to

the process of innovation and growth (Van de Ven et al., 1999; Vaessen et al., 2012). There

are some arguments that support this view. First we can refer to the role of organizational

innovations as a trigger or even a necessity for the development or acquiring of new

technologies. Damanpour & Evan (1984) find in their study of libraries that organizations

who implement a higher rate of administrative innovations end up with a higher number of

technological innovations in the subsequent period. These results can be explained as

f ll h h l d d ll d b h l


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eventually contribute to new technological changes but also their independent possible

effects on performance within the social system. Damanpour and Evan (1984, 2012, pp.25-

26) relied on a socio-technical perspective and related the introduction of managerial and

technical innovations as a means of introducing change in the organizations social and

technical systems, respectively. Their findings explained that managerial innovations trigger

the introduction of technical innovations more than the other way around.

Further it can be argued that organizational innovations should precede technological ones

because they have an important role in the success of new technology. Damanpour & Evan

state If the social system is not prepared, it cannot adjust to the demands created by the

technical system; therefore, the required match between the two systems for high

performance of the organization will not be achieved (Damanpour & Evan, 1984, p. 396).

Burns, Acar & Datta, (2010) provide a framework that could be used to describe the

influence of this specific temporal sequence. In their exploration of entrepreneurial

knowledge transfers they mentioned two distinct approaches: learning-before-doing and

learning-by-doing in which learning could be seen as an organizational innovation and the


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implemented. An a priori explanation is that learning-before-doing renders the recipient

more capable of learning from experience; in addition, by increasing the credibility of the

knowledge source, it enables smooth knowledge transfer to other recipients in an

entrepreneurial firm (Churchman, 1968, Nass , 1994, Burns, Acar & Datta, 2010).

From these findings it can be concluded that the organizational innovation first perspective

offers many logical opportunities for organizations to enhance their performance. However

not much quantitative analysis is performed within this field. Further they elaborate on

diverse performance indicators and also on product development instead of process

innovations. For that reason the following hypothesis was made.

Hypothesis 1: Generally speaking, manufacturing firms that let organizational innovations

precede technological innovations will have higher organizational efficiency

then organizations that followed the technological innovation first

perspective.

2.4.2. Technological innovation first


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processes) not only depends upon the product and process innovation itself, but also on

accompanying adjustment in the organisation of a firm and - with respect to product

innovation - in adjustments in marketing methods (Schmidt & Rammer, 2007, p. 30). This

quote refers to the idea that organizations use organizational innovations to increase the

chance of success of their technological product and process innovations. These

organizational innovations like for example a new organizational structure, rewarding

systems, HRM and marketing innovations increase the sales for new products and can lead

to cost reductions in the production process. This idea is supported by the findings of

Schmidt and Rammer (2007, p.32) which suggest that firms have an incentive to undertake

non-technological innovation activities if they introduce technological innovations. However

this does not automatically imply that in time these technological innovations precede

organizational ones.

However this specific sequential configuration can be described by the concept of learning-

by-doing (Acar & Datta, 2010). This idea suggest that an organization could choose to learn

from experience, and adjust the organization were needed after the introduction of the


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From the viewpoint of the technological perspective the following hypothesis can be

formulated.

Hypothesis 2: Generally speaking, manufacturing firms that let technological innovations

precede organizational innovations will have higher organizational

efficiency then organizations that followed the organizational innovation

first perspective.

2.5. Conceptual model

In the previous paragraphs the different existing theories about the Technology-

Organization-Discord are described. The examination of different academically writings led

to two hypotheses. All hypotheses can be placed within the conceptual framework that iscentral in this research. At first we have the dependent variable which is organizational

efficiency. This variable is measured by five dependent variables (see chapter 3 Table 1). The

independent variable in this research is the sequence of occurrence which encompasses the

technological innovations (TI) and organizational innovations (OI). The sequence of these


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3. Research Methodology

In this chapter the research methodology is described. In order to examine the relation

between different sequences of innovations and organizational efficiency, quantitative

research will be used. In order to correctly execute this research first the specific type of

research will be described accompanied by a description of the used European

Manufacturing Survey 2009 (EMS-2009). Further the research design will be presented in

which the scope and focus of this research will be treated. Subsequently the variables which

are used in this research and their specific indicators within the EMS-dataset will be

presented. Finally this chapter will present the specific statistical methods that will be used

to test the hypotheses.

3.1. Research type and design

To test the specific hypotheses of this research quantitative research will be used. The data

which is used comes from the European Manufacturing Survey (EMS) from the year 2009.


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of 3204 completed questionnaires. The EMS dataset provides a very comprehensive and

correct measurement of the used concepts and further its entails a high internal validity.

As mentioned above, this research will use quantitative analysing methods. In research three

types of design are differentiated: exploratory, descriptive and explanatory research.

(Churchill et al., 2010) Because this research tests existing theories by an examination of

data we perform explanatory research. Explanatory research is very useful for testing the

hypotheses that are derived from the theories presented in chapter two. By quantitative

research these hypotheses can be validated or rejected and in that way contribute to the

existing knowledge about the use of technological and non-technological in manufacturing

firms in order to increase performance.

3.2. Measurement of concepts

As discussed in chapter two the main concepts which will be measured are the sequence of

occurrence of two distinct forms of innovation namely, technological process innovations

and organizational innovation. The dependent variable in this research is organizational


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non-technological process innovations. The variables technological innovation (TI) and

organizational innovations (OI) therefore only represent technological process innovations

and organizational innovations. (See figure 3)

3.3. Control variables

Within this part the different control variables which could be of influence within this

research are treated. These are in following order: organizational size, type of manufacturing

subsector, product and production characteristics, research & development, product

Figure 3: Focus research: Process innovations:


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more easily [] and employ more professional and skilled workers (Damanpour, 1992).

Others state the opposite (Hage 1980; Utterback, 1974). They argue that large organizations

due to their inflexibility and lower ability to adapt and improve dont innovate as well as

smaller organizations. They further argue that the implementation and acceptance is easier

in smaller organizations. According to Trott (2012, pg. 103) it can be stated that Size is a

proxy variable for more meaning full dimensions such as economic and organizational

resources, including number of employees and scale of operation. The variable

organizational size is therefore expected to influence the process efficiency of manufacturing

firms and for that reason was used as control variable.

3.3.2. Type of manufacturing subsector

This research focusses on the manufacturing industry within eight countries. The

manufacturing industry is an enormous industry which encompasses many different types of

organizations. Because organizations within the manufacturing industry vary in many ways a

closer examination of manufacturing subsectors is needed. It is expected that by using this

variable within this research model a visible difference between different sectors can be


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3.3.3.1. Product development

An organization has to adjust its product development strategy according to the specifics of

the product and its organizational capabilities. For example some manufacturers of very

basic and simple products, like for example manufacturers of paperclips do not have to

develop their product regularly. In the other way manufacturers in for example the car

industry, have to develop their products according to the specific wishes of the customer

and new regulations. Between those extremes there are other ways in which a firm can

organize its product development. Organizations that take an active role in product

development and therefore often have to rearrange the production process rely on other

organizational resources then organizations that do not need this type of innovation.

Therefore it is very plausible product development also has an effect on the efficiency

indicators and therefore it was used as control variable.

3.3.3.2. Production & Assembly

According to the product and the wishes from the customer the way an organization


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3.3.3.4. Product complexity

The topic of high versus low complex products has been explained in the previous three

paragraphs in the form of product development, assembly and batch size. However these

characteristics mostly explain the alignment of the production process according to the

wishes of the customer, the features of the product itself are also important. For very

complex and specific products different results for the efficiency indicators are expected

than for simple products. Therefore product complexity is used as control variable explaining

process efficiency.

3.3.4. Research & Development.

When talking about innovation, many often refer to Research & Development. According to

many studies R&D is a necessity or at least an important precedent of new innovations, on

both technological & non-technological side. Especially in the case of product innovation

R&D plays an essential role. In the case of this specific research it could be possible that R&D

efforts are very closely related to new process innovations, which could have a strong

influence on the process efficiency. Therefore it is importance to use the R&D variable within


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3.3.6. Absolute number of technological and organizational innovationsWithin this research it is important to take the absolute number of innovations into account.

When calculating whether a firm followed a specific sequence, the average year of

implementation is used. However this could lead to wrong conclusions because the

efficiency indicators might be stronger affected by the number of implemented innovations.

Therefore it is important to control our main relations for the absolute number of

innovations, respectively on the technological and organizational side.

3.3.7. Production lead time

Because of the fact that the production lead time is an important part of the manufacturing

lead time (delivery time) it is important to use the variable PLT an additional control variable

in the regression model which analyses the manufacturing lead time.

3.3.8. Sales growth

In the calculation of capital utilization and labour productivity the total amount of sales is an

important predictor. For that reason it is important for those efficiency indicators that are


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value added per employee. These indicators represent the process efficiency of

manufacturing firms within this research.

In order to measure these concepts the following variables will be used (see table 1).

Organizational efficiency

Production efficiency indicators

Indicators Measurement

Production Lead Time (PLT) Average production time Measured in hours (metric)

Manufacturing Lead Time (MLT) Time from order to ready for

shipping (delivery time)

Measured in hours

(metric)

Scrap rate (Scrap) Average percentage of

products that have to be

scrapped or removed after

quality control

Measured in % of total production

(metric)

Capital Utilization Degree of capacity utilization Measured in % of total capacity

(metric)

Labour Productivity Value added per employee Measured in thousands of Euros

(metric)

Table 1: Organizational efficiency indicators within EMS dataset.


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Technological Process

Innovations concepts


Integration of automation - Digital product development

- Industrial robots/ handling systems

- Process integrated quality control

systems

- Radio Frequency Identification (RFID)

- Warehouse management systems(WMS)

Nominal variables

(Y/N)

With starting year

(Interval variable)

Processing and production

techniques

- Use of Laser

- Dry operations

- Rapid prototyping/ Rapid tooling

- Bio-/ gene technological processes /

catalysts

- Processing of new materials

Nominal variables

(Y/N)

With starting year

(Interval variable)

Digital factory / IT networks - Digital exchange production planningwith supply change management

systems of suppliers/customers

- Manufacturing Execution Systems

(MES)

- Virtual Reality of 3D-simulation for

product design

Nominal variables(Y/N)

With starting year

(Interval variable)


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Organizational Innovation

Concepts


Organization of work - Autonomous production task groups

- Integration of Tasks (integration,

planning, executing and control)

- Temporary cross-functional teams

Nominal variables

(Y/N)

With starting year

(Interval variable)

Organization of production - Client/product specific production

units

- Internal Zero-buffer-principle

(Kanban)

- Total Cost of Ownership (TCO)

Nominal variables

(Y/N)

With starting year

(Interval variable)

Standards and audits - Quality circles

- Knowledge Matrix

- Quality management on basis of ISO-

9000 series

Nominal variables

(Y/N)

With starting year

(Interval variable)

Work hours & rewarding systems - Collective regulations for flexibility

work

- Rewarding systems with bonuses for

Nominal variables

(Y/N)

With starting year


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calculated. This metric variable is subsequently recoded into a dichotomous variable in

which the value 1 represents the organizational innovation first perspective and the value 0

represent the technological innovation first perspective.5

This research further uses different control variables. These are: organizational size, Type of

manufacturing subsector, Production characteristics, Research & Development, Product

Innovations, the absolute numbers of respectively technological and organization

innovations, production lead time and sales growth. These different conditional variables

will be measured by using the following variables (see Table 4).

Control Variables Indicators Measurement

Organizational Size Number of employees in 2008 Metric variable

Manufacturing subsector Measured by eight sectors

- Food, Beverages and Tobacco

- Textiles, Leather, Paper and Board

- Construction and Furniture

- Chemistry (energy & non-energy)

Categorical variable

(nominal variable)


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Absolute number of Innovations - Absolute number of

technological innovations

- Absolute number of

organizational innovations

Metric variables

Production Lead Time - Average production time Metric variables measured

in hours.

Sales growth - Growth in sales from 2006-

2008

Measured in percentages

Table 4: Control variables (For more information about the specific questions used see appendix 2 & 3)

3.5. Methods of analysis

In order to start the quantitative analysis all used variables were examined by the use of

descriptive analysis. By providing detailed information about the variables and the use of

different tables and graphs they can be tested for their usability, reliability and validity. Also

the results can tell us more about the dataset and its possible opportunities and limitations.

After performing descriptive analysis, multiple regression analysis will be performed. Given


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Dependent variables

production

time (PT)

Manufacturing

Lead Time

(MLT)

Scrap rate

(Scrap)

Capital

Utilization (CU)

labour

productivity

(LP)

Control variables

Size + + + + +

Manufacturing subsector + + + + +

Product development + + + + +

Production/assembly + + + + +

Batch size + + + + +

Product complexity + + + + +

Research & Development + + + + +

Product innovation + + + + +

Number of implemented TI + + + + +

Number of implemented OI + + + + +


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H1

H2

3.6 Conceptual model

As earlier described this research will test conceptual model that is depicted in figure 2. In

the figure here under (see Figure 4) the same conceptual model is depicted with the

accompanying operationalization and control variables.

Organizational

Innovations FirstAverage year ofintroduction TI

-

Average year of introduction OI

> 0

Organizational

EfficiencyProduction-efficiency

indicators:

- PT,

- MLT,

- SCRAP,

- CU

- LPTechnological

Innovations FirstAverage year ofintroduction TI

-

Average year of introduction OI

< 0


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4. Analysis & results

In this chapter the analysis and results of this research are presented. First it starts with the

descriptive analysis which provides a clear overview of the used dataset. These descriptives

present the variables and discuss their usability within the conceptual model by the use of

multiple tables and graphs. Further the reliability of the measured constructs was tested.

Hair (2005, pp. 3) refers to reliability as the extent to which a variable or set of variables is

consistent in what it is intended to measure. The variables used within this research are

grouped under the constructs of organizational process efficiency, sequence of occurrence

and the different control variables. We will start this paragraph by describing the general

EMS-2009 dataset followed by a description of the dependent variables. Subsequently the

independent and control variables will be further elaborated on. To conclude this descriptive

analysis the results from the correlational analysis are presented.

In the second part of this chapter the analysis of the conceptual model will be performed. By

the use of multiple regression analysis the hypotheses within the model are tested. These


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4.1.2. Organizational performance

In the table below, general descriptive findings are presented for all organizational efficiency

indicators.

0

200400600800

1000120014001600

Frequency of completed questionaires

among participating countries

Figure 5: Absolute number of EMS respondents according to country


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product which is assumed to be correlating with the batch size and complexity. The average

percentage of products that are accounted as scrap or rework is 3,14 % with an standard

deviation of 5,77%. Concerning the Capital Utilization (CU) it can be stated that within the

dataset the mean percentage of CU is 86,64 which implies that on average 86,64% of the

companys assets are used. The standard deviation if CU is 14,92%. The mean added value of

an employee among the different organizations was 102,05 thousand Euros with an SD of

77,78. The minimum of this variable was -291,67 which displays that there are organizationswith negative labour productivity results among the respondents.

4.1.3. Technological Process Innovations

In the following bar chart an overall view of all possible technological innovations is

displayed. In this chart the percentage of each specific technological innovation is displayed.

45,00%50,00%

ns

Technological Innovations introduced


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and Bio- /gene technological processes (+ use of catalysts) are by far the least introduced

technological innovations within this dataset.

Because these different types of technological innovations are recoded into one

variable/construct for technological innovations it is important to test whether these

indicators combined are a reliable measure of the concept of technological innovation. To

test this Cronbachs Alpa () is used. Hair (2005, pp. 102) states that the values of 0.60 to

0.70 are the lower limit of acceptability. The Cronbachs alfa for the construct of

technological innovations is 0,6856

which implies that all indicators used taken together are

above the prescribed border of 0,60. The Cronbachs alfa of this construct can be increased

to 0,687 by removing the variable application of bio-/gene-technology. Because this is a very

small increase all the predefined variables are used in our analysis of technological

innovations.

Finally, the descriptive analysis provides some other important results. The average number

of used technological innovations is 2,60 (from a total of 13 predefined innovations) .This

implies that of all thirteen measured indicators on average manufacturing organization used


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These 13 indicators are combined to one specific variable. Cronbachs alfa is used to test if

these indicators are representative measured for the construct organizational innovations.

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

%oforganizations

Organizational Innovations

Figure 7: Frequencies Organizational innovations


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into technological processes innovations and organizational innovations. Both constructs are

measured by 13 indicators as was earlier explained. Yet with these indicators there are

multiple ways an organization can choose to innovate. First are there the organizations that

dont use any of the technological and organizational innovations, those that either only use

technological on the one hand or organizational innovations on the other hand and those

firms that use both organizational and technological innovations. In table 8 it can be seen

that a large majority of respondents both introduced technological and organizationalinnovations (75%). While on the one hand the percentage of organizations that only used

organizational innovations is (21%) on the other hand the number of firms that only

introduced technological innovations (2%) or even did not even introduced any process

innovation is small (3%).

40,00%50,00%60,00%70,00%80,00%

anizations

Innovation Configurations


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precede technological ones. From the 868 organizations 861 completed the questions about

the years of implementation.

4.1.4. Descriptive analysis metric and non-metric variables

0

10

20

30

4050

60

70

80

90

100

1950

1953

1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

1989

1992

1995

1998

2001

2004

2007

2010

Percentagein

novations

Average year of introduction

Technological Innovations Organizational Innovations

Figure 9: Sequence of occurrence (cumulative frequencies)


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Independent variables control variables Sig. correlates > 0,4 or < -,04

with variable number

Manufacturing subsector1. Metal Not present

2. Textile Not present

3. Construction Not present

4. Chemistry Not present

5. Machinery Not present

6. Electronics Not present

7. Transport Not present8. Organizational size (# of employees 2008) Not present

9. Share employees R&D Not present

10. Share turnover new products Not present

Product development

11.Customer unique 12.(-.,620**)

12.Semi Unique 11.(-,620**)

13.Standard program Not present14.No product development at site Not present

Production/Assembly

15.Make to order 16.(-,566**), 17.(-,600**)

16.Assemble to order 15.(-,566**)

17.Make to stock 15.(-,600**)

18.No production/assembly at site Not present


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4.2. Regression analysis.

In the previous paragraphs a clear overview about the EMS dataset and the used variables

was made. The conclusions were that the data revealed that a majority of organizations let

organizational innovations precede technological ones. However this research focuses on

the question which temporal sequence is better from the viewpoint of organizational

process efficiency. Therefore further analysis is needed. Within this paragraph the results

from multiple regression analysis is used to check which temporal sequence of innovation

types is more effective for gaining better organizational efficiency, which is measured by five

indicators; Production Lead time (PLT), Manufacturing Lead Time (MLT), Rework/Scrap Rate

(Scrap), Capital Utilization (CU) and Labour Productivity (LP). Because these variables were

not all normally distributed some of them were log transferred in order to make regression

analysis possible. For each of them multiple regression analysis was conducted and in the

following table the main results of this analysis are presented in order present and discuss

the findings for all separate efficiency indicators. Subsequently the findings from these

predefined models led to the need for additional analysis. Therefore next to the main


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(log

)

Pro

duction

lea

dlea

dtim

e

(log

)

Manu

facturing

Lea

dTime

)

li

r(L

)

(log

)

Scraprate

(Scrap

)

Capita

l

Uti

lisation

(CU

)

(log

)

La

bour

pro

ductivity

Control Variables

Manufacturing subsector

(reference: Metal)

Food

TextileConstruction

Chemistry

Machinery

Electronics

Transport

(log) Organizational Size

(log)Research & Development

(log) Share of turnover New products

Product development

(reference: no product development at site)

Customer unique

Semi unique

Standard program

Production/Assembly

(reference: Make to order)

Assemble to order

-,123***

-,025-,122***

-,165***

,170***

-,117**

-,026

,107**

,037

-,012

-,039

-,037

-,053

-,078*

,036

,022,067**

-,025

,126***

,058

,037

-,025

-,032

,029

-,046

,003

-,114**

-,108***

-,073

-,017,096*

-,012

-,044

-,038

,015

,058

-,015

,022

-,100

-,086

-,107

-,047

-,105*

-,067-,010

-,106

,173**

,062

-,019

,102

-,070

,034

,217

,217

,110

-,027

,048

-,022-,059

,078

,012

-,017

-,030

,125*

,080

,012

-,069

,018

,003

-,082


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4.3.1. Production Lead Time (PLT)

The first efficiency indicator is the production lead time (PLT). The summarization of this

regression model is as follows [F=12,722, p


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The results showed that concerning the production lead time the temporal sequence of

technological and organizational was only significant at a 90% confidence level for the TI first

perspective when occurring within a small timeframe (1 year). Therefore it is very doubtful

that the temporal sequential configuration is important for achieving lower production lead

times. However the additional analyses revealed an interesting finding. It seemed that

instead of a specific temporal sequence the timeframe in which they are jointly introduced

has an effect on PLT. From the additional models it seems that the smaller this timeframe isthe lower the PLT will be, which can be viewed as a positive sign of process efficiency.

4.3.2. Manufacturing Lead Time (MLT)

The summarization of the regression model for the Manufacturing Lead Time is as follows

[F=42,039, p


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lead time the results found for PLT have possibly an indirect effect in the MLT of

manufacturing firms but this was not confirmed by the findings. When all findings were

taken into consideration it had to be concluded that no evidence for any influence of

temporal sequence of OI and TI on manufacturing lead time was found.

4.3.3. Rework/Scrap rate (Scrap)

After examining the regression analysis of the model representing the efficiency indicator

Rework/Scrap rate it had to be concluded that this model had significant explanatory power.

The summarization of this model is as follows [F=1,185 P>0,10] which correspondents with a

low adjusted R square of 0,014, which implies that this model only can explain 1,4% of the

variance in the variable Rework/Scrap rate. Further no significant beta coefficients are

found. It can be noted that the beta values for OI first are negative and for TI first positive,

which might point to positive efficiency effects of the OI first perspective, however these

findings are not significant and are therefore very doubtful. Also the model with the metric

variable sequence of innovation was not significant when placed into this model as

independent variable. Based on these findings we can conclude that the used models are not


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level, besides the corresponding beta values are very low (,005 & -,005). As explained in

appendix 2.6.4. this could imply two things 1) there is no effect of temporal sequence of OI

& TI on PLT, or 2) there is a significant effect of the temporal sequence of innovations

however only when they are implemented within a short timeframe ( 1 year or 0,5 year).

The results of these additional analyses which examined both perspectives within a one year

timeframe did revealed a significant beta value (-,091**) for the TI first perspective (1

year). From these results it could be concluded that the TI first perspective results in a lowercapital utilization than when occurring in a longer timeframe (>1 year =0,55) this negative

beta within the one year timeframe is a negative sign of efficiency. In combination with the

non-significant beta values for OI first 1 year (-,039) and OI first > 1 year (,032) this

indicates that a small timeframe in which TI and OI are implemented seems to have negative

effects for capital utilization.

Given these results additional analyses were conducted in which specific small timeframes

were tested. These findings revealed that when OI and TI are jointly implemented within one

year this resulted in a significant beta of (-,106**) and when occurring within a 0,5 year

timeframe (-,109**). These findings substantiate the earlier thoughts that when both TI and


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in a significant beta11

in favour of the technological innovation first perspective. To check

this finding additional regression analyses were executed in which the sequence of

occurrence as categorical variable was tested by the use of two dummy variables

representing both OI first and TI first. When examining the beta coefficients it was found

that both temporal sequences were not significant at any reasonable confidence level. As

explained in appendix 2.6.5. this could mean two things 1) there is no effect of temporal

sequence of OI & TI on labour productivity 2) there is a significant effect of the temporalsequence of innovations however only when they are implemented within a short timeframe

( one year or 0,5 year). From these additional analyses it became clear that only

organizational innovation first ( 1 year) provided a significant positive beta (,114**) in

contrast to the negative beta (-,116**) for the OI first (> 1 year) variable. Although the beta

for TI first ( 1 year) was not significant it also provided a positive beta value (,051). From

these results it could be concluded that the positive effect of the organizational innovation

first perspective is only valid when it occurs within a timeframe of maximal 1 year. These

findings are in contrast to the earlier findings from the analysis with the metric variable

sequence of innovation


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forms of innovations interrelate with each other. Subsequently it was described in which

specific temporal configurations these innovations could be introduced. This led to a

detailed description of the organizational innovation first andthe technological innovation

first perspectives. The literature review made clear that there is stronger evidence in

support of the organizational innovation first perspective regarding its influence on

organizational process efficiency. This led to the following hypothesis

Hypothesis 1: Generally speaking, manufacturing firms that let organizational innovations

precede technological innovations will have higher organizational efficiency

then organizations that followed the technological innovation first

perspective.

As described in the literature review much is written about the importance of the

organizational first perspective. By letting organizational innovations proceed technological

ones organizations should be possible to gain higher performance which in this research was

measured by process efficiency. By the use of all tested models the following conclusions

can be made in order to give a substantiated answer to this hypothesis.


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descriptive analyses it became clear that a majority of the manufacturing firms within the

dataset followed this organizational innovation first perspective no strong evidence for

positive effects on process efficiency was found. In conclusion it can be stated that although

the theory review offered many logical reasons for the positive effects of the organizational

innovations first perspective, the hypothesis in support of this literature has to be rejected.

Besides the organizational innovation first perspective the other hypothesis focussed on its

counterpart, the technological innovation first perspective, which was tested by thefollowing hypothesis.

Hypothesis 2: Generally speaking, manufacturing firms that let technological innovations

precede organizational innovations will have higher organizational

efficiency then organizations that followed the organizational innovations

first perspective.

In the literature the concept of technological determinism is widely discussed. Organizations

that focus on technological innovations subsequently followed by organizational changes

could achieve higher process efficiency. By the use of all tested models the following


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these contrasting findings it is not possible to state that the technological innovation first

perspective within a one year timeframe is positive from the viewpoint of process efficiency.

And in comparison to the previous described organizational innovation first perspective no

evidence is found that the technological innovation first perspective if more desirable within

the context of this research. In conclusion it can be stated that although the theory review

offered logical grounds for the positive effects of the organizational innovations first

perspective, the hypothesis in support of this literature has to be rejected in this case.

Additional hypothesis: Generally speaking, organization that jointly introduce

technological and organization innovations within a one year

timeframe will have higher process efficiency than

organizations who jointly introduce these innovations within a

larger timeframe.

In the models representing both the technological innovation first and organizational

innovations first perspective no strong evidence was found in support of efficiency effects of

a specific temporal sequential configuration. However the results from these analyses


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specific temporal sequence which is an interesting finding. However it is important that

more research is performed to substantiate these findings and answer the question why this

time span is of importance. Logical reasons could be that organizations who introduce

technological and organizational innovations in a longer timeframe are the organizations

who manage the configuration by themselves while the organizations implementing them

within a smaller timeframe are those that used external expertise. However this has to be

tested in additional research by investigating the role of corporation with external

organizations. On the other hand the role of these external parties could also be the reason

why no strong evidence was found in favour of one of the temporal sequential

configurations. Because the role of these external partners is not reflected within the used

dataset, because this data assumed that all innovations are executed by the firms

themselves. This is not realistically because with the implementation of for example new

computer- and software systems firms often use external organizations, like for example

consultant companies or the suppliers of the systems. However based on the results of the

tested models it can be concluded that there is some weak evidence in support of this

additional hypothesis.


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5. Discussion & Conclusion

In the previous chapter the results of the quantitative analysis of the EMS dataset were

presented. Within this chapter these findings are used to discuss the central problem

statement within this research and subsequently answer the main research questions. The

conceptual model which guided the corresponding hypotheses will be discussed and

conclusions are drawn. Finally the limitations of this research are discussed and suggestions

for further research will be provided.

5.1. Temporal sequential innovation configurations

The literature study revealed that innovation research has gained much ground in business

literature over the last decade. A huge base of literature describes the innovations that have

changed the way we practice business these days from multiple perspectives. Traditional

research focussed on technological innovations. This technological determinism points to

the importance of new technologies and explains the mechanisms on how new inventions

contributed to organizational performance in the past. On the other side the research about


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from the perspective of organizational learning and knowledge transfer it became clear that

the organizational first perspective strongly relates to the concept of acquisitive learning,

which can be explained as a learning-before-doing approach. By creating a strong knowledge

base and good knowledge transfer organizations can more effectively introduce new

technological changes, and it further also contributes to the effective implementation of

additional organizational changes after the introduction of the a new technology. On

contrary, literature revealed some logical explanations in favour of the technological

innovation first perspective. Based on the theories of technological determinism it could

be argued that new technological developments are the driving force of new organizational

changes. In some cases it would be more effective to implement organizational adjustments

only after a new technology is introduced. When examining this idea from the perspective of

organizational learning and knowledge transfer it could be related to the concept of

experimental learning (experimenting within the production environment). By the use of a

learning-by-doing approach organizations focus on developing their procedural knowledge

(Burns, Acar & Datta, 2010, p. 272) and in that way enhance organizational performance.

This specific approach is especially useful when organizations lack theoretical and practical


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because it suggests that these organizations should possibly reconsider their innovations

strategies in order to achieve better results.

In the examination of the general technological innovations first perspective also no

significant effects on any of the efficiency indicators were found. This implies that there is no

significant evidence that by introducing technological innovations before organizational ones

a higher efficiency can be gained. However when this specific sequential configuration was

examined within a one year timeframe it became clear that there was a prevalent effect on

the production lead time. It seemed that when this configuration was occurring within this

short time span, production lead time was decreased. Further a negative efficiency effect on

the capital utilization was witnessed which implies that in comparison to the positive effect

on production lead time the capital utilization was lower within this specific temporal

sequence.

So when taking all these findings into consideration it can be stated that no strong

unambiguous evidence was found in support of the literature describing the possible effects

on process efficiency. The temporal sequential configuration of innovation efforts did not


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When combining the findings from the literature review and the results from the descriptive

analysis and multiple regression analysis it became clear that in comparison to the theory

which describe possible performance effects for both the organizational innovation first

and the technological innovation first perspectives it became clear that these effects were

not significantly unambiguously supported within the EMS-2009 data.

Yet it can be stated that this research revealed that there are strong opinions about how

organizations should configure their innovation efforts. However the mechanisms that

explain how specific sequences can positively or negatively contribute to organizational

efficiency are very complex, and simply cannot be described by one general success formula.

This research showed that a majority of organizational firms follow the organizational

innovation first strategy, although this did not resulted in efficiency effects within the

specific indicators measured. Further it was found that organizations that pursue an

innovation strategy in which technological innovation precedes organizational adjustment

also did not have unambiguous effects on these indicators. The dynamics of specific

technological and organizational innovations is a complex web of many different relations

which clearly cannot be described by one dominant view or theory. So may some


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investigate how organizations could benefit from an alignment of different innovations. A

better understanding of the mechanisms underlying this alignment between technological

and organizational innovations will eventually provide specific best practices for

organizations in the future.

5.2. Limitations and suggestions for further research

Within this research the Technological-Organization Discords has been explained by thereview of existing literature and new quantitative research. Although this research tried to

encompass all existing perspectives and correctly examine the statistical data of the EMS-

2009 dataset some limitations have to be admitted. In the literature review it became clear

that although the concept of innovation is a hot topic within business literature the last

decades the total base of academically works about the interrelatedness of organizational

and technological innovations is not that extensive as for many other subjects. Many articles

stress the importance of specific innovations, describe the different state-of-the-arts

innovations of the last century and highlight the possible benefits they have to offer, not

that much is written about the strategic choices an organization has to make concerning the


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of technological and organizational innovation encompass a much wider variety of specific

innovations that might provide other results. Therefore the generalisation of results is only

possible to a certain extend. In future research is would therefore be very interesting to

focus on the alignment of specific organizational and technological innovations.

Concluding there are some limitations of the EMS-2009 dataset. Although this dataset is very

substantial and encompasses many organizations within different European countries there

are some points that need to be addresses. First the response rate of the EMS survey is quite

low which may lead to sampling bias. Further the EMS-data only contains information about

organizations within the manufacturing industry, which affect the generalizability of the

findings over other industries. At least some questions were only present in the German

surveys which make the findings less generalizable over the other countries.

Given the previous mentioned limitations and the findings of this research some general

suggestions for further research can be addressed. This research has focussed on the

temporal sequence of both technological and organizational innovations. From the results is

became clear that no strong evidence was found in favour of a specific temporal sequential


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other results are found when examining other indicators of performance like form example:

differentiation indicators or employee satisfaction.


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References

Aiken, M., Bachirach, S.B., and French J. L. (1980) Organizational structure, work process, and proposal making

in administrative bureaucracies.Academy of Management Journal, 20: 631-652.

Aiken, M., and Hage, J. (1971) The organic organization and innovation. Sociology, 5: 63-82.

Anderson, N., & King, N. (1993) Innovation in organizations. In C. L. Cooper & I. T. Robertson (Eds.).

International Review of Industrial and Organizational Psychology, Vol. 8: 1-34. Chicester: John Wiley & Sons.

Armbruster, H., Kirner E. and Lay, G. (2006) Patterns of Organizational Change in European Industry (PORCH).

ISI, Karlsruhe.

Armour, H.O. and Teece D.J. (1978) Organizational structure and economic performance: A test of the

multidivisional hypothesis. The Bell Journal of Economics and Management Science, 9: 106-122.

Arnal, E., Ok, W. and Torres, R. (2001) Knowledge, Work Organization and Economic Growth. OECD Labour

Market and Social Policy Occasional Papers, No 50, OECD Publishing.

Baldwin, J.R., B. Diverty, and D. Sabourin (1995). Technological Adoption and Industrial Transformation, in TheEconomic Impact of ICT; Measurement, Evidence and Implications. (2004) OECD. Published by: OECD

Publishing.

Birkinshaw, J., and Mol, M. J. (2009) The sources of management innovation: When firms introduce new

management practices.Journal of Business Research, 62: 1269-1280.

Borins, S. (1998) Innovating with Integrity: How Local Heroes are Transforming American Government.

Washington, DC: Georgetown University Press.


66/116

Damanpour, F., and Evan, E. (1984) Organizational Innovation and Performance: The Problem of Organizational

Lag.Administrative Science Quarterly, 29 pp. 329-409.

Damanpour, F., Walker, R.M., and Avellaneda, C.N. (2009) Combinative Effects of Innovation Types andOrganizational Performance: A Longitudinal Study of Service Organization. Oxford: Journal of Management

Studies, 46:4 650-675.

Edquist, C., Hommen, C. L., & McKelvey, M. (2001) Innovation and Employment: Process versus Product

Innovation. Edward Elgar, Cheltenham, UK.

Ettlie, J.E. (1988). Taking charge of Manufacturing. Jossey-Bass, Vol. 33, No. 4. San Francisco, CA.

Ettlie, J.E. (2000). Managing Technological Innovation. John Wiley & Sons, Inc.

Ettlie, J.E., Bridges, W.P., and OKeefe, R.D. (1984) Organization strategy and structural differences for radical

versus incremental innovation.Management Science 30: 682-695.

Evangelista, R., and Vezzani, A. (2010) The economic impact of technological and organizational innovations. A

firm-level analysis. Research Policy, 39, pp. 1253-1263

Evan, W. M. (1966) Organizational lag. Human Organizations, 25: 51-53.

Fagerberg, J. (1994) Technology and international differences in growth rates. Journal of Economic Literature,

32 (3): 114775.

Georgantzas, N.C., and Shapiro, H.J. (1993) Viable theoretical forms of synchronous production innovation.

Journal of Operations Management, Elsevier 11: 161-183.

Gera, S. and Gu, W. (2004) The Effect of Organizational Innovation and Information Technology on Firm

Performance. International Productivity Monitor, 9, 3751.


67/116

OECD (2008) OECD Science, Technology and Industry Outlook. OECD Publishing, Paris.

OECD and Eurostat. (2005) Oslo Manual: The measurement of Scientific and Technological activities. Guidelines

for Collecting and Interpreting Innovation Data. 3th edition, OECD publishing, Paris.

Parthasarthy, R., Sehti, S.P. (1992) The impact of flexible automation on business strategy and organizational

structure.Academy of Management Review, 17 (1), 86-111

Pisano, G.P. (1996). Learning-before-doing in the development of new process technology. Research Policy,

Vol. 25 No. 7, pp. 1097-1119

Rosner, Martin M. (1968) Administrative controls and innovation. Behavioral Science, 13: 36-43.

Sanidas, E. (2005) Organizational innovations and economic growth: Organosis and growth of firms, sectors,

and countries. Cheltenham, UK: Edward Elgar.

Schmidt, T., and Rammer, C. (2007) Non-technological and Technological Innovation: Strange Bedfellows?

Zentrum fr Europische Wirtschaftsforschung GmbH.

Schumpeter, J.A. (1934) The theory of Economic Development. Harvard University Press, Boston, MA.

Schumpeter, J.A. (1939) Business Cycles. McGraw-Hill, New York.

Schumpeter, J.A. (1942) Capitalism, Socialism and Democrazy. Allen & Unwin, London.

Skinner, W. (1978) Manufacturing in the Corporate Strategy. Whiley, New York, NY.

Solow, R. (1987) Wed better watch out. New York Times Book Review.

Spencer, J.C. (1996) Making knowledge the basis of a dynamic theory of the firm. Strategic Management

Journal, Vol. 17, special issue, pp. 45-62.


68/116

Utterback, J.M. (1974) Innovation in industry and the diffusion of technology. Science, 183: 620-626.

Van de Ven, A., Polley, D., Garud, S., and Venkataraman, S. (1999) The Innovation Journey. New York: Oxford

University Press.

Vaessen, P. Ligthart, P.E.M., and Dankbaar, B. Technological and organizational innovation and manufacturing

business performance. Nijmegen, Radboud University, Institute of Management research.

Von Hippel, E. (1978) Users as innovators. Technology Review, Vol. 80, No. 80, No. 3, 30.

Von Hippel, E. and Tyre, M.J. (1995) How learning by doing is done: problem identification in novel process

equipment. Research policy, Vol 24 No.1, pp. 1-12.

Whyte, G. (1989) Groupthink reconsidered.Academy of Management Review, vol. 14, no 1: 40-56.

Williamson, O.E. (1975) Markets and Hierarchies. New York: The Free Press.

Wolfe, R.A. (1994) Organizational innovation: Review, critique, and suggested research directions. Journal of

Management Studies, 31: 405-431.

Zaltman, G., Duncan R., and Holbek J. (1973) Innovations and Organizations. New York: Wiley.

Zuboff, S. (1988) In the Age of the Smart Machine: the Future of Work and Power. Basic Books Inc. New York.


69/116

Appendices


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Appendix 1

1.1 General Information EMS-2009

European Manufacturing Survey Partners 2009

Weblink:

http://www.european-manufacturing-survey.eu

Partners:

Germany: Fraunhofer Institute System and Innovation Research

Austria: Division Technology Policy; ARC Systems Research

France: BETA, Universit Louis Pasteur Strasbourg

Switzerland: Institut fr Betriebs- und Regionalkonomie, Hochschule fr Wirtschaft, Luzern
http://www.european-manufacturing-survey.eu/http://www.european-manufacturing-survey.eu/http://www.european-manufacturing-survey.eu/


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1.2 EMS Survey


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Appendix 2: Analyses of EMS-2009 data

2.1 Reliability analysis Technological Innovations


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2.2 Reliability analysis Organizational Innovations


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2.3 Number of innovations introduced

2.3.1 Number of technological innovations introduced

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

%oforganizations

Total number of innovations introduced

Number of used technological Innovations


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2.4 Descriptives metric variables

3

Above the general descriptives of the metric variables within this research are presented.

These findings are important in order to check whether the distributions of the selected

variables are subject to skweness and kurtosis. Within the orange rectangles the variables

that have a certain amount of skewness and kurtosis are depicted. Within this research the

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error

number of technologies used in your factory 2961 ,00 13,00 2,5947 2,26755 1,016 ,045 1,105 ,090

number of organisational concepts used inyour factory

1236 ,00 13,00 4,7071 2,76590 ,404 ,070 -,350 ,139

Av Year Implementation TI - Av YearImplementation OI

861 -41,00000 48,33333 2,5689559 7,15090870 ,991 ,083 7,367 ,166

Production lead time (main product) [workdays at 8 hours]

2846 ,0 10000,0 33,159 202,4129 42,821 ,046 2075,380 ,092

Delivery time (main product) [calendar days] 2920 0 900 40,33 67,970 4,305 ,045 27,905 ,091

Rework/ scrap (main product) [%] 2871 ,0 70,0 3,141 5,7658 5,999 ,046 50,497 ,091

Degree of capacity utilisation 2008 [%] 2344 10,00 150,00 86,6404 14,92081 -,801 ,051 2,825 ,101

Value added (turnover - input per employee[Thsd. Euros])

2322 -291,67 1085,07 102,0512 77,78138 4,095 ,051 32,672 ,102

Number of employees in 2008 3204 10 44000 226,45 1326,149 20,857 ,043 544,104 ,086

Share of personnel: Research anddevelopment [%]

2926 0 90 4,80 7,180 3,804 ,045 24,128 ,090

Share of turnover generated by newproducts [% - only innovators]

1758 0 100 17,18 16,565 2,163 ,058 5,780 ,117

Sales growth 2006-2008 in % 2822 -99,88899 26566,66667 31,2818926 5,01428019E2 52,576 ,046 2783,044 ,092

Valid N (listwise) 313


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Syntax Log transformations

COMPUTE LOG1=LN(V19A+1).VARIABLE LABELS LOG1 'Log Production Lead Time (ln+1)'.EXECUTE.COMPUTE LOG2=LN(V19B+1).VARIABLE LABELS LOG2 'Log Delivery Time (ln+1)'.EXECUTE.COMPUTE LOG3=LN(V19D+1).VARIABLE LABELS LOG3 'Log Rework/Scrap (ln+1)'.

EXECUTE.COMPUTE LOG5=LN(V21B1).VARIABLE LABELS LOG5 'Log Number of employees 2008'.EXECUTE.COMPUTE LOG6=LN(V16B1+1).VARIABLE LABELS LOG6 'Log Share of personal R&D (ln+1)'.EXECUTE.COMPUTE LOG7=LN(v06b+1).VARIABLE LABELS LOG7 'Log Share of turnover new products (ln+1)'.

EXECUTE.COMPUTE LOG10=LN(RCV3+100).VARIABLE LABELS LOG10 'Log sales growth 2006-2008 (ln+100)'.EXECUTE.

Results log transformations


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2.5 Descriptives non-metric variables

Dependent variables

the technology-organization discord

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