international journal of business science and applied management · international journal of...

Int. Journal of Business Science and Applied Management, Volume 10, Issue 1, 2015

The Effects of Collaboration on Logistical Performance and

Transaction Costs

José Geraldo Vidal Vieira

Department of Production Engineering, CCGT-Federal University of São Carlos

João Leme dos Santos (SP-264), Km 110, Sorocaba - SP/Brazil. Code – 18052-780

Phone: +55 15 32296015

Email: [email protected]

Hugo Tsugunobu Yoshida Yoshizaki

Department of Production Engineering. Polytechnic school at University of São Paulo

Av. Almeida Prado 128, travessa 2 - 2º andar. São Paulo – SP/Brazil. Code – 05508-070

Phone: +55 11 30915450, Extension 407


Linda Lee Ho

Department of Production Engineering. Polytechnic school at University of São Paulo

Av. Almeida Prado 128, travessa 2 - 2º andar. São Paulo – SP/Brazil. Code – 05508-070

Phone: +55 11 30915450, Extension 404


Abstract

This paper assesses the effect of supplier–retailer collaboration on logistical performance and transaction costs

from the viewpoint of retail sector suppliers. The methodology consists of an empirical study conducted over

nine months in the logistics department of a large Brazilian supermarket retailer and a survey of 125

representatives of 90 manufacturers. The results show collaboration contributes to an improvement in logistical

performance related to urgent deliveries and deliveries that occur during periods of high demand. Interpersonal

collaboration and joint actions contribute to the reduction of uncertainties among the participants. These joint

actions, together with strategic collaboration, contribute to an increase in investment in specific assets, such as

dedicated production lines or specialised vehicle fleets to serve partners. The study provides an analysis of

logistical performance and transaction cost elements not previously investigated, including urgent deliveries and

deliveries during periods of high demand, contract negotiation and renegotiation, waiting time for agreements to

be reached, contingency logistics planning, and various cultural, psychosocial and geographical aspects of the

supplier–retailer relationship. Managerial implications, research limitation and future research are also

discussed.

Keywords: supplier–retailer collaboration, collaboration, logistical performance, transaction costs, psychosocial

relationship

Acknowledgements: This work was supported by CAPES

mailto:[email protected]

Int. Journal of Business Science and Applied Management / Business-and-Management.org

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1 INTRODUCTION

Studies of collaboration and operational logistical performance have been extensively reported in literature

on supplier–retailer relationships (Sheu, Yen, & Chae, 2006; Simatupang & Sridharan, 2005; Xing, Grant,

McKinnon, & Fernie, 2011; Vlachos, Bourlakis, & Karalis, 2008; Vlachos & Bourlakis, 2006) and supplier–

retailer–distributor–wholesaler relationships (Whipple, Lynch, & Nyaga, 2010; Nyaga, Whipple, & Lynch,

2010). Collaboration and performance in the supply chain are key topics in supply chain management (SCM)

research (Kache & Seuring, 2014). Transaction-specific investments, trust, flexibility, joint actions, and business

performance are also regarded as key elements in traditional buyer–supplier relationships (Rasckovic, Brencic,

Fransso, & Morec, 2012).

According to Whipple et al., (2010), collaborative relationships provide greater advantages than

transactional relationships; they offer improved logistical performance (e.g. fill rate, order cycle time, lead-time,

on-time delivery) due to better information visibility and higher service levels. Our research confirms

collaboration is positively correlated to logistical performance; collaboration positively affects asset specificity

and negatively affects uncertainty. Collaboration reduces transaction costs (Cao & Zhang, 2011) because

specific assets increase with contract frequency and higher levels of interdependence (Bunduchi, 2008).

Negotiated volumes are greater, information exchange is more intense, and contract renegotiation is facilitated.

Previous researchers have investigated many elements related to collaborative relationships, logistical

performance, and transaction costs. Our findings are in line with their qualitative results. For example, they find

trust is an important determinant of the success of a relationship (Vlachos et al., 2008), and leads to

improvement in logistical performance (Whipple et al., 2010). Trust is the unique element that contributes to the

relationship (Vlachos & Bourlakis, 2006). Nyaga et al. (2010) examine a collaboration model from the

viewpoints of both buyers and suppliers. Results show that buyers focus on relationship outcomes and suppliers

focus on collaborative activities; suppliers safeguard their transaction-specific investments through information

sharing and joint actions.

These studies, however, are based on conventional elements of logistical performance (Xing et al., 2011)

and human and physical specific assets (Rasckovic et al., 2012; Heide & John, 1992). In our study, we turn our

attention to elements of logistical performance related to ‘urgent deliveries’ and ‘deliveries that occur during

periods of high demand,’ as well as transaction cost elements related to ‘contract negotiation and renegotiation,’

‘waiting time for agreements to be reached,’ and ‘contingency logistics planning.’ It may be useful to suppliers

and retailers to understand how these elements are modified by degree of collaboration (Xing et al., 2011). We

expect a high level of collaboration results in less time lost to renegotiation and resolution of logistical

contingencies and that it correlates positively with extra deliveries. Simatupang and Sridharan (2005) have

investigated two similar non-conventional elements: ‘agreements on order changes’ and ‘delivery guarantee for

a peak demand.’ We use the collaboration model developed by Vieira, Yoshizaki, and Ho (2009), based on

strategic, tactical, and interpersonal collaboration, to examine the influence of collaboration on the logistical

performance of suppliers serving large retailers and the transaction costs involved.

Performance depends on supply chain integration (SCI) and is strongly based on a culture rooted in

teamwork, cooperation, information sharing, interdependence (Didonet, Frega, Toaldo, & Diaz, 2014), and

interpersonal collaboration (Barratt, 2004). Geography and organisational culture (Hofstede & Hofstede, 2005)

also exert strong influences on management practices (Pagell, Katz, & Sheu, 2005) designed to achieve high

performance (Naor, Goldstein, Linderman, & Schroeder, 2008) in logistical operations. Pagell et al. (2005) find

cultural differences in the manufacturing practices of countries in Europe, Asia and North America. We expect

there are cultural differences not only among national and international firms, but also between regions, that

affect suppliers’ logistical performances and relationships with partners. These differences may be due to large

distances between partners within Brazilian territory that create difficulties in attending meetings, developing

projects, and developing close relationships (De Leeuw & Fransoo, 2009). Cultural differences between local

and foreign partners may also increase instability in development of collaborative business (Meschi & Riccio,

2008).

This paper aims to assess the effect of supplier–retailer collaboration on logistical performance and

transaction costs. It also aims to shed light on cultural, psychosocial, and geographical aspects of the Brazilian

supplier–retailer relationship. We use an in-depth case study of a retailer and its suppliers, as well as multiple

regression analysis, to investigate the effects of supplier–retailer collaboration on logistical performance and

transaction costs, from the viewpoint of suppliers.

We contribute to literature on supplier–retailer relationships by studying logistical performance and

transaction cost elements not previously addressed. These elements include urgent deliveries, deliveries during

periods of high demand, contract negotiation and renegotiation, waiting time for agreements to be reached, and

contingency logistics planning.

José Geraldo Vidal Vieira, Hugo Tsugunobu Yoshida Yoshizaki and Linda Lee Ho

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2 LITERATURE REVIEW

2.1 Collaboration in the supply chain

Barratt (2004) classifies collaboration elements as strategic, cultural, and intrinsic. Strategic and cultural

elements are the most important. Among these elements, the main focus of SCM research has been on

collaborative culture based on trust, information exchange, and effective communication (Whipple et al., 2010).

Simatupang and Sridharan (2005) and Kanter (1994) define complementary attributes and Vieira et al. (2009)

sort them into three dimensions: (1) strategic elements, referring to an integral aspect of the goal of business

partnerships and consisting of sharing inventory information and awareness of the partner’s logistical difficulties

and strategies, (2) elements of interpersonal integration, consisting of trust, reciprocity, flexibility, and

interdependence, and (3) elements of tactical integration, referring to managers, an individual, or a dedicated

team, working on specific projects or joint activities (e.g., working for the resolution of logistical contingencies

or the establishment of information systems for automatic data exchange). Tactical integration elements also

relate to sharing of logistics costs and gains and logistical and commercial information sharing.

The findings of Vieira et al. (2009) indicate elements of interpersonal integration are the most important

factors in collaboration intensity. In line with these findings, our study also examines social relationships.

2.1.1 Cultural, psychosocial and geographical aspects

Cultural and psychosocial aspects are relevant to a better understanding of transactions (Barratt, 2004).

According to Frankel, Goldsby, and Whipple (2002), communication skills reduce interpersonal and inter-

organisational heterogeneity. Our research shows communication skills are important; contacts between

suppliers and retailers happen in the same places and in frequent meetings, balancing logistical indicators in the

face of differing cultures. In accordance with Dyer (1997), when cultural aspects surround the business

environment, rules and operational proceedings should remain unaltered to achieve efficiency in both the

exchange of information and operations (personnel and machinery).

According to Ring and Van de Ven (1994), interpersonal contact among agents establishes positive

conditions for negotiations and facilitates the execution of joint actions. As noted by Zaheer, McEvily, and

Perrone (1998), effective cognitive relationships get incorporated into the transaction environment over time.

Therefore, psychosocial aspects of relationships are studied either at the beginning of the transaction, through

cultural and interpersonal knowledge, or while the relationship is nurtured by the partners. Based on

psychosocial theory, Li (2008) defends the proposition that trust is the main focus of relationships and argues it

is the basis of collaboration.

Although these studies highlight the importance of cultural and psychosocial aspects in achieving

collaboration in the supply chain, it is also relevant to consider the geographical aspects of collaboration,

particularly when partners are located a great distance apart. Geographical proximity (Cannon & Homburg,

2001) facilitates regular meetings and technical visits among partners; it may also result in a significant increase

in knowledge of systems and technology, culture, and standards (Pfhol & Buse, 2000).

2.2 Logistical performance elements

Elements of logistical performance are divided into two theoretical groups: ‘order-winning’ and

‘qualifying’ (Slack, 1994). The qualifying group encompasses common elements of logistical performance, such

as the need to meet high retailer standards (Slack, 1994). It represents the basics of good supplier service, such

as on-time-in-full delivery, availability of products, error-free ordering and delivery, fulfilment of delivery date

and time, frequent delivery, resolution of damaged orders, high order fill rate, and adequate minimum stock

levels. The order-winning group encompasses distinctive logistical performance elements that, according to

Slack (1994), provide a crucial advantage compared to common elements and are the main thrust of

competitiveness (“urgent delivery” and “order fill rate during periods of high demand”).Typically, the order-

winning group refers to deliveries ahead of predetermined delivery dates to satisfy retailer requests. Table 1

provides an overview of the key conceptual definitions of the theoretical factors and their respective elements.


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Table 1. Logistical performance elements – qualifying and order-winning

Concept Measurement References

Qualifying

On-time delivery Percentage of all orders sent on or before the

promised delivery date

Case study; Vlachos and

Bourlakis (2006)

Order fill rate Amount of an order that is filled as compared to the

amount that is requested

Case study; Simatupang and

Sridharan (2005)

Product availability Amount of product available in supplier stocks or

in supplier production line

Case study; Xing et al. (2011)

Error-free delivery Number of SKU ordered and shipped out with

error-free as percentage of total ordered

Case study; Vlachos and

Bourlakis (2006)

Scheduled delivery

fulfilment

Number of times delivery occurs within agreed-

upon delivery window

Case study; Xing et al. (2011)

Damaged orders Number of items returned due to being damaged

during transport

Case study; C Xing et al. (2011)

Rupture Amount of product available on the shelves or retail

stock-out


Sridharan (2005)

Delivery frequency Number of deliveries per week Case study; Xing et al. (2011)

Order-Winning

Delivery of urgent

delivery

Number of deliveries designated as urgent delivery Case study

Delivery of order during

periods of high demand

Number of deliveries in the last week of the month

or immediately before specific sales dates


Sridharan (2005)

In our research model, the order-winning group has the most important logistical performance elements,

due to the low-efficiency logistics of large retail suppliers. The importance of these elements emerges when we

analyse Brazilian retail characteristics (such as the large demand for products concentrated in the last week of

each month), and urban logistical aspects present in large cities (such as lack of unloading areas, restricted

timetable circulation for trucks, security issues, traffic congestion, large numbers of deliveries in small areas,

and restricted access).

2.3 Transaction cost elements

In retail chains, transaction costs may represent huge costs associated with timing of renegotiation (Ellram,

1993) and rework, and costs resulting from specific asset investment (Heide & John, 1992). These costs are

based on three dimensions of the transaction: uncertainty, specific assets, and frequency (Williamson, 1985).

Uncertainty is studied through examination of ex-ante costs (setup costs, the period of agreement-writing and

bargaining, and new contracts) and ex-post costs (contract renegotiation and waiting time required for the

resolution of logistic contingency) (Ellram, 1993). Frequency is analysed through study of delivery

programming for the monthly scheduling of products from suppliers to retailers. Specific assets are associated

with high investment levels in specific physical assets, such as information technology machinery and

equipment, and specific human assets, such as groups committed to a specific project or partner, or staff training

for new logistical agreements. According to Heide and John (1992), in retailer–supplier relationships,

investments made in procedures, equipment, and machines are regarded as specific assets while the transaction

is occurring. Table 2 provides an overview of the theoretical elements studied in our research.

Table 2. Transaction costs elements – uncertainty and asset specificity

Concept References

Uncertainty

Contract negotiation and renegotiation Case study.

Waiting time for contingency logistics planning Case study; Ellram, (1993).

Waiting time for logistic agreements Case study; Simatupang and Sridharan, 2002.

Asset specificity

Human asset specificity Case study; Williamson, 1985; Raskovic et al, 2012.

Physical asset specificity Case study; Williamson, 1985; Dyer, 1997; Heide and

John, 1992; Raskovic et al, 2012

Our study focuses on the concept of uncertainty. The element of ‘contract negotiation and renegotiation’

refers to annual agreements between retailers and suppliers. Typically, large retail suppliers spend a lot of time

on annual contracts and renegotiation due to bureaucratic delays in reaching agreements on prices of products,

volumes, service levels, performance indicators, and cost logistics. The element of ‘waiting time for

agreements’ refers to new logistics projects or joint actions to reduce logistical costs. According to Ghauri and

Roxenhall (2004), bilateral negotiation between partners is facilitated by renegotiation of short-term contracts.


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The element of ‘waiting time for contingency logistics planning’ refers to dyadic transactions such as damaged

orders and inconsistent delivery.

3 CONCEPTUAL MODEL AND RESEARCH HYPOTHESES

The proposed theoretical model (Figure I) combines previously discussed elements of collaboration (based

on Vieira et al., 2009), logistical performance, and transaction costs.

Figure 1. Conceptual model of supplier–retailer relationship

Our model shows the effects of collaboration elements (strategic, tactical and interpersonal) on logistical

performance elements. We study the effects of collaboration elements on order-winner logistics (i.e. high-level

logistical performance indicators) and find collaboration is positively related to logistical performance. We

further examine the effects of collaboration elements on transaction costs of asset specificity and uncertainty and

find collaboration is positively related to transaction costs, with the exception of uncertainty. We analyse the

elements of frequency and qualifier logistical performance in a case study. We also analyse cultural,

psychosocial, and geographical proximity aspect.

3.1 Collaboration and Logistical performance

Van der Vaart and van Donk (2008) have surveyed research related to dyadic buyer–supplier relationships.

They conclude collaborative relationships have a positive impact on performance. According to Lambert and

Pohlen (2001), logistical performance improves when partners are aligned in the search for a common strategy

(Mitra & Bhardwaj, 2010), willing to share their inventory information (Simatupang & Sridharan, 2005) and

plan their stock levels, and open to sharing their logistical difficulties and strategies (Pfohl & Buse, 2000).

Therefore, technical visits to partners’ manufacturing plants and distribution centres are a good strategy for

learning about each other’s practices, systems, cultures, and standards. Tactical collaboration focuses on

providing high-quality services and reducing logistical costs (Stank, Keller, & Daugherty, 2001). Research has

shown several important elements have positive impacts on logistical performance (Simatupang & Sridharan,

2005), including information sharing (Barratt, 2004), suppliers and retailers working together in the distribution

channel on activities such as selection of markets, product assortment planning, promotions (Simatupang &

Sridharan, 2005), sharing customer support information (Kim, 1999), joint participation of both teams in

projects and dyadic logistical activities (Kim, 1999), sharing joint logistical goals (Kanter, 1994), and sharing

costs and benefits of “incentive alignment” (Simatupang & Sridharan, 2002). To achieve optimal logistical

performance, retailers and their suppliers must also share commercial information and information about such

factors as vehicle capacity to deliver, target inventory turnover, order frequency, lead times, product availability,

vehicle availability, and supplier capability in working with urgent deliveries or during peak seasons (Vieira et

al., 2009). With regard to interpersonal collaboration, relationships that include trust, commitment, cooperation,

common interests, and sincerity (Mentzer, Foggin, & Golicic, 2000) lead to improvements in service levels and

cost reductions related to inventory, transportation, and order processing. Accordingly, we derive the following

hypotheses:

H1a: Strategic collaboration has a positive impact on high level of logistical performance.

H1b: Tactical collaboration has a positive impact on high level of logistical performance.

H1c: Interpersonal collaboration has a positive impact on high level of logistical performance.

3.2 Collaboration and transaction costs

Investments in specific assets produce a great desire for establishing closer relationships (Raskovic et al.,

2012) and reducing opportunistic behaviour (Cao & Zhang, 2011); strategic collaboration is positively

correlated with asset specificity. However, the transaction cost structure is based on relational norms between


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partners. Consequently, in these relationships, the effect of asset specificity is not always positive when there is

vertical control (Arshinder & Deshmukh, 2008). An increase in transaction asset specificity increases the

negotiating power of clients and the dominance of clients’ business rules over those of supplier. According to

Arshinder and Deshmukh (2008), control is typically exerted by the client’s dominance over the supplier.

By analysing Japanese and American automotive chains, Dyer (1997) concludes that increasing

collaborative efforts through joint actions and sharing information between partners reduces transaction costs

and increases specific asset investments. However, increasing specific asset investments does not always result

in greater transaction costs, because partners share operational gains and costs.

According to Muckstadt, Murray, Rappold, and Collins, (2001), tactical collaboration reduces uncertainty

in a relationship. Sharing information (e.g. Electronic Data Interchange - EDI) contributes to improvement of

information processing capabilities and thereby reduces uncertainty and transaction costs (Tan, Kannan, & Hsu,

2010). Because there are many suppliers and few large retail chains, the purpose of joint actions (from the

retailer’s point of view) is to gather the players to improve logistical performance and negotiation. Many

companies create a favourable environment for long-term investments and lower uncertainty when they

collaborate. This long-term investment encourages commitment to successful relationships (Ghauri &

Roxenhall, 2004). For example, a large retailer might invite strategic suppliers to participate in collaborative

actions, form synergies, and thereby achieve operational gains. Consequently, they have created specific or even

dedicated transactions. In this way, firms not only share investments and competencies but also risks, losses, and

inefficiencies in the production process. They may also share monitoring of transactions, thereby increasing or

decreasing transaction costs depending on the degree of the relationship among players and the reputation

acquired while trust exists. Trust in a relationship is sufficiently strong to reduce safekeeping (Zaheer &

Venkatraman, 1995) and negotiation costs. We derive a second set of hypotheses as follows:

H2a: Strategic collaboration has a positive impact on high asset specificity.

H2b:. Tactical collaboration has a positive impact on high asset specificity.

H2c: Tactical collaboration has negative impact on uncertainty.

H2d: Interpersonal collaboration has negative impact on uncertainty.

4 METHODOLOGY

The methodology consists of a survey and a case study. Detailed qualitative analysis, combined with the

rigor of quantitative analysis, allows for in-depth investigation (Voss, Tsikriktsis, & Frohlich, 2002). Our

mixed-method approach helps develop the study’s hypotheses and ground the constructs for empirical testing.

We use a case study as a research strategy for exploring the supplier–retailer relationship (Sheu et al.,

2006). We follow well-established methodological guidelines (Voss et al., 2002; Yin, 1994) to increase the

validity of our findings.

During the study, we interviewed all strategic suppliers of the largest Brazilian retailer, along with its

logistics managers. Interviews were transcribed directly to notebook and then categorized according to the

elements identified by our literature review (collaboration, logistical performance and transaction costs

elements). The literature review also guided our construction of the interview protocol used with retailer and

supplier managers during two annual seminars; to increase content validity, we transcribed the interviews and

had respondents review the interview notes.

Previous studies have focused on supplier–retailer performance (Sheu et al., 2006; Simatupang &

Sridharan, 2005). Raskovic et al. (2012) have investigated supply relationship performance from the supplier

perspective using elements based on transaction costs, collaboration, and business performance. However,

further study of a range of companies in the retail channel is required to explain in-depth relationships related to

wasted time in contract renegotiation, waiting time required for the resolution of logistical contingency, and

dyadic logistical problems and measures. To understand these factors, we followed more than 50 meetings and

attended approximately 250 hours of visits with directors, managers, and analysts. To increase reliability of the

study, we conducted interviews within the logistics department of the largest Brazilian retailer; the interviews

gave us a detailed understanding of the retailer’s relationships with strategic suppliers, as indicated by logistical

performance measures and logistical agreements during monthly meetings. We observed discussions among the

partners in relation to the logistical problems and noted the perceptions, psychosocial aspects, and important

nuances revealed by their relationships.

The case study approach gave us the opportunity to collect data based on multiple methods, such as

interviews, observations, logistical performance indicator reports, technical visits, and interviews with more

than one informant from a different culture. This approach increased the validity and reliability of our study .


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4.1 Survey sample and procedures

The survey research1 was conducted with a population based on a list of 90 main suppliers to a large

Brazilian retailer. The suppliers were selected because they met the criteria of having already adopted some type

of collaborative logistic practice and having conducted periodic meetings with the retailer’s logistics team.

Therefore, the analysis unit consists of the supplier companies.

Several pre-tests were run to check the final instrument (Forza, 2002). Next, to take advantage of the

retailer’s fixed physical location, a questionnaire was distributed to a total of 125 respondents (supplier

representatives) following on-site meetings with the retailer. To avoid bias in the study, respondents were asked

to evaluate other large retailers. Opinions came directly from individuals (not from their companies). Each

respondent was asked to choose one of their eight largest customers (measured by sales), and then evaluate the

selected customer according to collaboration, logistical performance, and specific transaction cost elements.

Retailer representatives did not participate in the survey; only supplier personnel took part.

4.2 Survey data analyses

In our model, strategic, tactical, and interpersonal collaboration elements (Vieira et al., 2009) are the

independent variable; logistical performance elements associated with “order winner logistic”, and transaction

cost elements associated with asset specificity and uncertainty are the dependent variables. The dependent

constructs are displayed in Tables I and II.

To measure collaboration and logistical performance indicators and transaction cost elements, we adopted a

0–10 Likert scale (strongly disagree to strongly agree). A 1–5 Likert scale and a 1–7 Likert scale were also

tested. Test results showed interviewees felt more comfortable assessing the construct on the 0–10 scale. Also,

time required to answer was much less than it was for the other scales.

We employed a factorial analysis to reduce the number of original variables of the logistical performance

and transaction costs groups (Hair, Black, Babin, and Anderson, 2009). The collaboration variables resulted in a

reduction of the number of factors analysed by Vieira et al. (2009). In our study, collaboration variables were

comprised of five factors: strategic collaboration (CO1), joint actions (CO2), sharing of logistical costs and

gains (CO3), sharing of logistical and commercial Information (CO4), and interpersonal collaboration (CO5).

The dependent constructs are Higher Logistic Performance Factor (HPLPF), Uncertainty Factor (UF), and Asset

Specificity Factor (ASF).

These factors allowed us to use multiple linear regression analysis to measure the effect of collaboration on

logistical performance and transaction costs. Logistical performance and transaction costs were used as

dependent variables, and collaboration elements as independent variables in the regression. SPSS (Statistic

Package for Social Study, v. 20.0) software was used to obtain the regression models.

5 RESULTS AND DISCUSSIONS

5.1 Case study results

In this section we summarise the results of meetings between the retailer and their suppliers. Our case study

follows van der Vaart and van Donk (2006) in which ‘inter-organisational collaboration’ is measured in six

dimensions: long-term relationships, cooperative behaviour, joint improvement, information planning, physical

integration, and communication.

Table 3 shows the characteristics of suppliers who revealed their perceptions of large retailers (including

the retailer subject of the case study).

1 The questionnaire is available from the authors upon request


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Table 3. Profiles of responding firms

Control Variables Categories Percentage of Firms

in the Sample

Number of employees <500 36.78%

>500 63.22%

Client portfolio Beauty & Health 26.40%

Drinks 15.20%

Electric and Electronic 5.60%

Commodity 10.40%

Grocery 20.00%

Miscellaneous 20.00%

Others 2.40%

Annual revenue ($US) <$US 10 m 24.00%

>$US 10 m and <$US 250 m 24.00%

>$US 250 m and <$US 500 m 24.00%

>$US 500 m 28.00%

Delivery frequency >Twice a week 45.60%

<Twice a week 54.40%

Lead time to delivery <=2 days 52.00%

6 <days >2 31.20%

>6 days 16.80%

Proximity among partners Radius <=100 km 55.20%

Radius >100 km 44.80%

Owner capital Domestic 52.00%

Foreign 48.00%

SKU (Volume) >10% 26.40%

>5% and <10% 20.80%

<5% 52.80%

SKU (Number) National supplier >30 SKUs 25.60%

<30 SKUs 26.40%

Multinational supplier >30 SKUs 40.00%

<30 SKUs 8.00%

Meeting frequency National retailer Month 25.60%

Year/Semester/Trimester 20.80%

Multinational retailer Month 12.00%

Year/Semester/Trimester 41.60%

Technical visit (concurrency) National retailer Regular 21.60%

Never 24.80%

Multinational retailer Regular 18.40%

Never 35.20%

The manufacturers (suppliers) of consumer packaged goods (CPGs) are national and multinational firms;

their sizes range from medium to large. Their annual revenue is drawn from medium and large retail firms, with

10% of total revenue coming from the four largest retailers. Furthermore, large retailers play an important role

in allowing suppliers to show their various products. According to an industry interviewee, “The large retailers

allow our products to be maintained in a showroom; it proves to be a big opportunity to increase our market

share.” At the same time, according to the CEO of a large CPG firm, “The retailer has a high volume of the

purchase, for which we have to provide an efficient logistic more than other channels. It represents high logistic

costs.” This scenario reveals suppliers must maintain high performance logistics at lower costs, have less stock

with the retailer, sustain high delivery frequency, and be able to prove that a good freight distribution system is

available. Our case study reveals direct delivery to final customer has increased by high frequency of order and

the use of small lots. Table 3 shows 55.2% of the distribution centres are located at an average radius of

approximately 100 km from the large consumer centres of Brazil, and 54.4% of deliveries are carried out

frequently (twice a week). Therefore, collaboration is necessary to maintain contact and solve logistical

contingency issues.


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Logistical performance can be analysed by sector. The highest numbers of SKUs are found in the health

and beauty (26.4%) and grocery sectors (20%); the best indicators of logistical performance are also found in

these sectors. This finding may be due to geographical proximity between partners, which allows for better

interpersonal and inter-organisational integration and leads to a higher frequency of logistical meetings and

technical visits. In contrast, the commodity sector, with few SKUs, is characterised by a high turnover ratio and

high storage costs. In addition, commodity products generally originate from an average distance of

approximately 1,000 km (in the south of the country) and are transported by road; distance adversely affects

logistical performance, creating difficulties related to delivery error, delivery delay, damage of orders occurring

during transport, and frequent rescheduling of agendas on the same day. According to a commodity supplier

director, “The delivery freight costs may be over 15% for a large retailer chain due to extra costs with

specialised transporters, new vehicles, and skilled drivers to fulfil the client.”

In the electronic sector, products are transported an average distance of 3,000 km from the north, by boat

and truck, adversely affecting logistical performance. Great distance increases the potential for negative events

such as damage, product theft, and delivery delay. In addition to transport costs, there are further demands from

the large retail chain. According to a supplier, “There may be some transporter that charges more than 40% on

freight costs to satisfy the large retail chain in relation to on-time delivery and to shipment without damage.”

Commodity and electronic products typically originate from long distances. There are no regular logistical

meetings between the partners that involve the managers at both companies (retailer and supplier), and there are

no technical visits between these mainly multinational firms (41.6% commodity and 35.2% electronic). In

contrast to claims in literature that the history of the relationship involved may be important to the transactions,

our research shows this may not always be the case. In our study, according to the retailer’s logistics

coordinator, “The distance increases the safe stock. If the service level of the competitor is better than mine, I

will look for a new alternative in order to reduce my costs with these stocks.” In this case, the commodity

supplier to the retailer has had a good relationship with this retailer for approximately 20 years. Therefore, a

good logistical performance can be independent of the length of the relationship because the retail chain seeks

the lowest price and highest logistical efficiency available. According to the commodity supplier’s manager,

trust, transparency in communication and knowledge among the partners are factors that maintain the

relationship and lead to improvement in logistical performance. This result is consistent with the literature

(Whipple et al., 2010).

Partner meetings provide an opportunity to develop a close relationship, or at least allow the partners to

comprehend each other’s needs. The meetings offer a business environment in which partners may negotiate,

discuss logistical problems and resolutions, and share region-specific logistical information such as delivery

time windows, new product lines, out-of-stock products, availability of vehicle fleets, and demand levels.

According to a supplier informant, “These meetings are relevant to share information, to discuss logistic

problems and respective resolutions, to align objectives with our partner.” In business meetings, new projects

emerge from natural environments where there is trust, previous willingness to make agreements,

interdependence among partners and top management, and involvement in defining logistical agreements. Trust

may be significantly improved by effective communication. In several meetings among the partners, we noted

that cultural and psychosocial aspects are important in the retailer–supplier relationship. If the partners establish

joint social activities, such as inviting each other to attend sporting events and seminars, and if they are open to

each other, willing to hear criticism and express their opinions, their behaviour leads to increased strategic,

tactical and interpersonal collaboration. It results in a streamlining of logistical transactions, transparency in

communication, more information sharing, and more flexibility. Other benefits of openness include fewer costs

related to searches for internal information and greater capacity among partners to solve logistical contingency

issues.

We also noted that relationships based on trust between the retailer manager and different supplier

managers (from the same company) produced different results. According to the retailer manager, “Responding

to this supplier is tranquil, he is polite and trusts me all the time. We established a routine and we have no

dyadic logistical problems. On the other hand, a lot of data that we share with other is not available because we

do not have the same safety procedures.” This asymmetry in the retailer’s relationships with two or more

suppliers was apparent time after time. Hence, trust based on belief and credibility among partners may improve

their relationship. However, a lack of trust may result in a breakdown of the relationship among the people

involved and their companies.

Another example of the importance of culture and trust is the time expended on suppliers who miss

meetings. We noted that time after time, some suppliers were absent from the scheduled meetings and, in some

cases, absences occurred consecutively with the same supplier. The absences increased the cost of telephone

calls, necessitated the resetting of agendas, and wasted time in meeting preparation tasks such as report printing,

searches for information, and team alignment. Absences decrease trust and create other barriers to business.

Our observations support the idea that cultural and psychosocial aspects are important factors in

collaboration.


10

5.2 Survey research results

5.2.1 The effect of strategic, tactical, and interpersonal collaboration on high-level logistical

performance

The results of multiple regression analysis for H1a, H1b, H1c are shown in Table 4. Standardized

coefficients, adjusted R2 and F-test values are provided to describe the results of the analysis.

Table 4. Testing H1a, H1b, H1c – Regression analysis of collaboration and HLPF.

General Results Supplier Revenue Sales Volume Delivery Frequency

Independent Variable Β β Β Β

CO1 0.121* 0.273** 0.249** 0.351**

CO2 0.163* 0.208* 0.257** NS

CO3 0.119* 0.185* NS 0.144*

CO4 0.277** 0.363** 0.364** 0.278**

CO5 0.523** 0.562** 0.676** 0.672**

Adjusted R2 0.38 0.48 0.66 0.70

F 15.67** 13.70** 27.23** 30.70**

Notes: *p < 0.05; **p < 0.01; ***p < 0.001. Standardised coefficients are shown as β values. NS=Not significant

We note for most results, all coefficients are positive and statistically significant. The results confirm H1a,

H1b and H1c, finding that increased collaboration intensity results in improved logistical performance. In

particular, the interpersonal collaboration coefficient (CO5) presents a larger coefficient than the other

coefficients. It may be important for partners to maintain close relationships to avoid a lack of products due to

unexpected demand (urgent requests) or a decrease in service level during periods of high demand. Table 4 also

shows (based on F-tests) that the factors jointly explain the dependent variable HLPF.

The first column of Table 4 shows the general results; the other columns assess the behaviour of the effect

of collaboration in situations where an improvement is expected at the service level. Three other multiple

regressions are obtained using three control variables: (1) ‘supplier revenue,’ indicating quality of logistical

structure, (2) retail ‘sales volume,’ indicating level of interdependence, and (3) ‘delivery frequency,’ indicating

degree of operational interaction.

Comparing the general results for supplier revenue, we observe collaboration coefficients have a greater

effect on logistical performance of companies with revenues greater than $250 million USD. These producers

have a better logistics constitution (assets, professionals, and software) and can abide by collaborative

agreements. Therefore, when reaching a collaborative agreement with large retail chains, they can rely on a

better infrastructure than smaller companies. These larger suppliers can also make extensive use of promotions,

allowing their products to flood the retail chain. They are able to maintain a higher delivery frequency,

guaranteeing better inventory turns for larger retailers. Therefore, large suppliers can meet the requirements of

larger customers more efficiently.

With regard to sales volume to the retailer, higher volume supply by the manufacturer in special situations

leads companies to establish an interdependent relationship, based on trust, reciprocity, and flexibility in

meeting the partner’s urgent needs. This relationship may be more important than other collaborative practices,

such as the sharing of logistical costs.

Our results also show a higher delivery frequency leads suppliers to meet deadlines more accurately.

Therefore, a good logistical performance, particularly during peak periods and responding to urgent requests,

depends on greater commitment to collaboration between partners. As we expected, the interpersonal

collaboration factor coefficient (CO5) is high because an increased delivery frequency requires closer contact

between the partners. Additionally, it verifies (at the 1% level of significance) that increase in delivery

frequency (when the frequency of deliveries is greater than twice a week) during periods of urgent and high

demand increases interdependence and has a positive influence on willingness and commitment to share

information.

5.2.2 The effect of strategic and tactical collaboration on asset specificity

Asset specificity (comprised of elements of human and physical assets) is associated with the degree of

investment that manufacturers have made in the relationship with their partners. Results of the multiple

regression analysis for H2a and H2b are shown in Table 5.


11

Table 5. Testing H2a, H2b – Regression analysis of collaboration and asset specificity

General Results Revenue of Supplier

Independent Variable Β Adj. R2 / F Β Adj. R2 / F

CO1 (strategic) 0.306** 0.21 / 14.95** 0.363** 0.26 / 7.70**

CO2 (joint actions) 0.352** 0.373**

Notes: *p < 0.05; **p < 0.01; ***p < 0.001. Standardised coefficients are shown as β values

The results in Table 5 for all coefficients are positive and statistically significant. They support the

predictions of H2a and H2b that increases in strategic collaboration and joint actions contribute to an increase in

investment in specific assets. Investment tends to result in closer relationships because it causes suppliers to

direct products to larger retailers. Increased investment cost is compensated by increased sales volume, partner

commitment, and ease in adapting to the requirements of the clients. When we use ‘revenue of supplier’ as a

control variable, we see large suppliers have greater potential to make these investments because they can count

on better structures and greater market competitiveness (price versus volume ratio) than small suppliers.

Increased collaboration induces large suppliers to increase their investments in specific assets.

Increased information sharing (by joint actions, CO2) reduces transaction costs and increases investments

in specific assets; constant information exchange and closer contact between players (exchanging of strategic

information, such as inventory and production data, and knowledge of the reality of the logistical partners)

hinder the entrance of new participants into the partnership. Information exchanges and direct contacts act as

safeguards against opportunistic actions by the retailer, such as changing suppliers. Investments in specific and

human assets encourage renegotiations with existing suppliers because the search for new suppliers generates

extra costs for the retailer; it requires new negotiations, training of a new team, and scheduling of meetings with

top management.

5.2.3 The effect of tactical and interpersonal collaboration on uncertainty

The results of multiple regression analysis of H2c and H2d are shown in Table 6.

Table 6. Testing H2c, H2d – Regression analysis of collaboration and uncertainty

Variable Dependent–Uncertainty Factor–CT1

Independent Variables Β Adj. R2 / F

CO2 – Joint actions factor - 0.351** 0.15 / 9.84**

CO5 – interpersonal collaboration factor - 0.191*

Notes: *p < 0.05; **p < 0.01; ***p < 0.001. Standardised coefficients are shown as β values

Table 6 results show all coefficients are negative and statistically significant. These results confirm H2c

and H2d: increased collaboration intensity results in less uncertainty. This finding is explained by the constant

involvement of the partners in the resolution of logistical contingencies (e.g., delays and product returns), and

by direct contact during contract negotiations and new logistical agreements. A manager of a large supplier

claimed, “This constant change of information has contributed to a close approximation of other areas between

both companies. It has improved our logistic performance and there has been less re-work on a daily basis.” Any

delay in contract negotiations or making new logistical agreements increases transaction costs. Because it takes

a long time for the parties to reach agreement, stock-outs may occur. The cost of the contract (i.e. elaboration of

new rules and time spent on meetings) contributes to decreased transaction costs between the retailer and

supplier. This cost also affects the logistical performance of the supplier because the lack of agreement leads to

feelings of untrustworthiness and disinterest in maintaining a good service level. According to a supplier

manager, “We have been dealing with the annual agreement for five months, and during this time, our key

products in the sector were in a stock-out situation. Then, we had to invest in joint actions with other strategic

clients”.

6 CONCLUSION

Our research shows collaboration contributes to an improvement in logistical performance related to urgent

deliveries and deliveries during periods of high demand. Strategic and tactical collaboration have a positive

impact on asset specificity; tactical and interpersonal collaboration have a negative impact on uncertainty.

Enterprise culture and psychosocial aspects are relevant to contract making and the resolution of logistical

problems. Our results show collaboration produces even greater improvement in logistical performance with

larger suppliers, more dedicated supply operations, availability of larger product volume, and increased

frequency of order delivery to the retailer. The findings indicate a closer relationship between partners, resulting


12

in less uncertainty and greater commitment and interdependence, can lead to increased investment in specific

assets. The cost of these investments can be compensated by increased contract volume.

The findings also lead us to conclude that increased interpersonal collaboration and joint actions contribute

to reduced uncertainties among participants and that these joint actions, together with strategic collaboration,

contribute to an increase in investment in specific assets such as dedicated production lines or specialised

vehicle fleets. Moreover, the existence of closer relationships reveals investment in specific assets increases

with contract frequency and transaction costs tend to decrease. The negotiated volumes are greater, information

exchange is intense, and renegotiation of contracts is facilitated.

This research contributes to supply chain literature by studying logistical and transaction cost indicators not

previously addressed, that are strongly influenced by strategic, tactical and interpersonal collaboration (Vieira et

al., 2009). We also find indications that cultural, psychosocial and geographical aspects exert a strong influence

on relationships among partners. Although these aspects are not tested in our research model, their indications

provide a basis for further empirical and theoretical research.

Our study contributes three key managerial implications. First, it suggests good partner relationships are

based on trust, flexibility, transparency in communication, and joint activities inside and outside the work

environment. Second, it demonstrates Information sharing contributes to better logistical performance, by

solving contingency issues, reducing waiting time for agreements, and facilitating contract negotiation and

renegotiation. Third, it shows proximity among partners, more meetings and technical visits and similar cultural

aspects are essential factors to foster closer relationships.

Beyond these contributions, this research contains several limitations that should be taken into

consideration as well. In particular, the data collection involved only suppliers. Most of these respondents came

from an existing list of informants that a single retailer used to develop collaborative agreements. In addition,

though these data relate to the largest retailers, representing approximately 50% of the Brazilian supermarket

industry, the findings cannot be generalised to other retail sectors. Finally, most of the questionnaires were

completed by respondents from within the retailer’s organization. Therefore, it was possible to draw

comparisons between this retailer and competitors that the interviewer was assessing.

Further research should attempt interviews with strategic suppliers of retailers to develop a dyadic

perspective and verify the nature of the close relationships. Such interviews also could include other retail

sectors that were not addressed by this research, such as stationery, building materials, clothing and accessories.

From a methodological perspective, this research is based on a great, in-depth case, but more observations

would allow for analyses by sectors. Joint analyses of the dependent and independent variables across all

constructs (collaboration, logistics performance and transaction costs) also could be applied using structural

equations modelling. Further research might assess psychosocial aspects as basic antecedents of collaborative

behaviour too. Aspects of organisational culture exert a strong influence on collaborative activities (Barratt,

2004), especially in modern globalised markets, and they should be better taken into account in ongoing

research.

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Designing an AHP methodology to prioritize critical elements for

product innovation: an intellectual capital perspective

Ricardo Vergueiro Costa

Research Unit UNICES at University Institute of Maia (ISMAI) and

Research Unit NECE at University of Beira Interior (UBI)

Av. Carlos Oliveira Campos – Castelo da Maia, 4475-690 Maia, Portugal

Phone: +351 229 866 000


Ana Paula Ramos

University Institute of Maia (ISMAI)

Av. Carlos Oliveira Campos – Castelo da Maia, 4475-690 Maia, Portugal

Phone: +351 229 866 000


Abstract

Intellectual capital has for the past decades been evidenced as an important source of competitive advantages

and differentiation at the firm level. At the same time, innovation has become a critical factor for companies to

ensure their sustainability and even their survival in a globalized business landscape. Having in mind these two

crucial concepts for business success, this study intends to build on the relationships between intellectual capital

and product innovation at the firm level. Specifically, we will design and test a model based on the Analytic

Hierarchy Process, whose aim is to allow the prioritization of intellectual capital elements according to their

relative importance for product innovation performance at the firm level. The main goal of this research is to

build a diagnosis and action tool that helps business managers incorporate an intellectual capital perspective into

their product innovation initiatives. This framework will help managers to better understand which intellectual

capital elements are more critical to their product innovation efforts, and thereby systematize actions and clarify

resource allocation priorities to improve their product innovation capabilities. In order to validate the

practicability of this proposal, the methodology was empirically applied to a Portuguese innovative company.

Keywords: intellectual capital, product innovation, Analytic Hierarchy Process, Portugal, AHP, SMEs

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16

1 INTRODUCTION

Competitiveness is nowadays a key concern for nearly every firm, maybe more than ever. The business

landscape has for the past years become more and more demanding, with a widespread economic and financial

crisis adding to an already challenging environment, shaped by complex structural trends such as globalization,

technological evolution, accelerated product cycles and rapid changes in consumers’ needs and expectations

(Daneels, 2002).

Against this backdrop, two critical factors for the competitiveness of firms assume particular relevance:

intellectual capital (IC) and innovation.

In fact, as the “resource-based view of the firm” stream of research began to highlight (Barney, 1991;

Wernerfelt, 1984), resources and competencies of intangible characteristics, as opposed to the traditional “land,

labour and financial capital”, have gradually emerged as critical success factors to corporations. Intangible

assets comply particularly well with the assumption that only valuable, rare, inimitable and non-substitutable

resources are potential sources of sustainable competitive advantages (Barney, 1991; Itami, 1987). It is now

abundantly clear that intangible assets are driving value creation in today’s global economy (Dumay and

Garanina, 2013; OECD, 2012). The recognition that strategic knowledge assets are at the foundation of

company competitiveness needs to be taken into account, both as a critical element in strategy formulation and

as an instrumental lever to achieve strategic outcomes (Lerro et al., 2014).

At the same time, innovation has become one of the most crucial drivers of long term development

(Leiponen, 2005; Lederman, 2010). At the firm level, innovation is a key aspect for business success in the

current competitive arena, representing one of the best ways for reaching competitive advantages (Delgado-

Verde et al., 2011).

As we will argue in our literature review section, several studies have linked intangible assets, and IC in

particular, to the firms’ ability to innovate. It thus seems especially relevant for managers to be able to analyse

and manage this relationship, so that actions can be taken and strategies corrected in order to develop and

improve the firm’s innovation capabilities. The main goal of this article is thus to try to address this need.

Specifically, we will design and test a model to allow the prioritization of IC elements according to their relative

importance to product innovation success at the firm level.

The structure of the paper is as follows: the following section proposes a brief review of the literature

regarding IC, product innovation, the relationships between those concepts, and the benefits of measuring IC.

The next section presents our proposed methodology to prioritize critical IC indicators for product innovation,

using the Analytic Hierarchy Process (AHP). We will then describe the application of the model within a small

and medium enterprise (SME). Finally, some insights and conclusions will be extracted and discussed.

2 LITERATURE REVIEW

2.1 Intellectual Capital

Research on IC gained steam in the mid-nineties as a natural corollary of the resource and knowledge-

based views of the firm. The aim was to understand the implications of those theories for the daily management

of corporations, through the analysis of the intangible assets’ contribution to an organization (Roos et al., 2001).

As stated by Petty and Guthrie (2000), the IC perspective surfaced as a means to better understand what

constitutes the value of the business and to manage more successfully those elements that effectively generate

value.

Descriptions of IC abound in the literature, although there isn’t yet a clear, internationally accepted single

definition. For the purpose of this study, we will thus define IC as “the stocks or funds of knowledge, intangible

assets, and ultimately intangible resources and capabilities, which allow for the development of basic business

processes of organizations, enabling the achievement of competitive advantages” (Martín-de-Castro et al., 2011,

p. 650). IC is thus a multidimensional concept. It is nowadays generally accepted that the main components of

IC can be structured into three dimensions: human capital, structural capital and relational capital (Guthrie et al.,

2012).

Human capital represents those intangible resources that are linked to the individual and generate value to

the company. Human capital includes such diverse elements as individual values and attitudes, aptitudes, and

know-how (Subramaniam and Youndt, 2005). According to Marr (2008), the principal sub-components of an

organization’s human capital are its workforce’s skill sets, depth of expertise, and breadth of experience. Human

resources can be thought of as the living and thinking part of intellectual capital resources. Some examples of

human capital elements include skills and competencies of employees, their know-how in certain fields that are

important to the success of the enterprise, and their aptitudes and attitudes.

Structural capital is the knowledge that the company has managed to internalize and that remains in the

organisation, either in its structure, its processes or its culture, even when the employees leave. Martín-de-Castro

et al. (2011) subdivide structural capital into technological and organizational capital. The first one refers to the

development of the activities and functions of the technical system of the organization, responsible for obtaining

Ricardo Vergueiro Costa and Ana Paula Ramos

17

products and services; the second one can be seen as the combination of explicit and implicit, formal and

informal knowledge which structure and develop the organizational activity of the firm. This includes culture,

structure and organizational learning. Bueno & Salmador (2000) refer to structural capital as the systematized

and explicit knowledge that has been internalized by the organisation, such as its values, culture, routines,

protocols, procedures, systems, technological breakthroughs and intellectual property. In other words, it refers to

the organisation’s intelligence, which, unlike human capital, belongs in fact to the company.

The relational capital concept is based on the consciousness that companies are actively and permanently

connected to multiple external entities. All valuable relationships of this kind, with customers, suppliers and

other relevant stakeholders, represent relational capital (Roos et al. 2001). Marr (2008) argues that although

formalised external relationships tended to be predominant in the past, today informal external relations have a

more important impact on how firms are managed. Brand image, corporate reputation, and product/service

reputation, which reflect the relationships between organizations and their (current and potential) customers,

also fall into this category. Bueno & Salmador (2000) state that relational capital represents the firm’s

“competitive and social intelligence”, while Martin-de-Castro (2011) adds that relational capital provides a

useful external guide for the firm to improve and develop new knowledge.

According to some authors (for example Dumay, 2014; Guthrie et al., 2012), IC research is nowadays

going through a new, emerging stage, characterised by growing calls for a more practice-based IC research,

supported on a critical and more interventionist analysis of IC practices in action.

2.2 Product Innovation

Innovation is in the core of economic change, and its role as the main driver of long term development is

today widely acknowledged (Leiponen, 2005). Both at the macro and at the micro level, policies focused on

employment creation and social welfare have been aiming at strengthening the innovative ability of enterprises

and regions to enhance their competitiveness (Bullinger et al., 2004). At the firm level, innovation is nowadays

considered to be inevitable. To succeed in today’s complex economic environment, or even to remain viable,

corporations must respond with innovation (Govindarajan and Trimble, 2005).

In the Oslo Manual (OECD, 2005), innovation is defined as the implementation of a new or significantly

improved product (good or service) or process, a new marketing method, or a new organisational method in

business practices, workplace organisation or external relations. Different types of innovations are also

distinguished: product innovations, process innovations, marketing innovations and organisational innovations.

Among these distinct types of innovations, product innovation stands out as an element of particular importance

to any business. Companies must develop new products, at least on occasion, to maintain or gain competitive

advantages, and their ability to create new products has been linked to performance and even long-term survival

(De Jong and Vermeulen, 2006; Linzalone, 2008). This study will therefore focus on product innovation,

defined in the Oslo Manual (OECD, 2005) as the introduction of a good or service that is new or significantly

improved with respect to its characteristics or intended uses.

The way firms approach product innovation, and particularly the process of new product development

(NPD), has evolved significantly in the last decades. From a mechanistic and linear approach that focused on

R&D projects ("technology push"), whose success depended essentially on the efficient allocation of resources

to technological research activities, product innovation is now seen as an integrated, multidisciplinary process,

often chaotic and unpredictable, incorporating knowledge which is sometimes tacit, and very sustained in

intangible elements such as creativity, culture for innovation, interaction and knowledge sharing competences,

etc. (Cooper et al., 2004). The characteristics of innovative processes have also become more complex due to

some important trends: the increasing specialization in the production of knowledge, the increasing complexity

of physical products and the technology they use, and the need to accommodate new technological opportunities

with market needs and organizational practices. In this context, two central features of product innovation have

been emphasized: first, that the innovative process includes the coordination and integration of increasingly

specialized knowledge; second, that this process requires continuous learning, in conditions of great uncertainty

(Bullinger et al., 2004; Castellacci et al., 2005).

The increasing strategic importance of new products for companies and the awareness of the high

percentage of failures in their introduction led to the gradual development of formally structured NPD

processes. These are typically defined as a sequence of steps or activities that a company develops in order to

conceive, design, test and market a new product. The literature offers many different proposals and

representations of these steps. One significant example is the "stage-gate" model from Cooper et al. (2002), an

effective conceptual and operational map for moving a new product project from idea to launch.


18

2.3 Relationships between IC and product innovation

Lerro et al. (2014) argue that the resource-based view of the firm helped establish a very clear link between

intangible assets and innovation. Within this body of work, knowledge has emerged as a strategically significant

resource for the firm and has been asserted to play a significant role in the innovation process, as well as in

supporting organizational innovation capacity.

The relationship between intangible assets and innovation has been analysed in several empirical studies,

generally concluding that intangible assets are positively and significantly associated with the firms’ innovative

capabilities. For example, Cañibano et al. (2002) found that innovative, technology-intensive companies are

typically those where intangible assets assume a more critical role. Del Canto and Gonzalez (1999) argue that

intangible resources have a decisive impact on the "absorption" ability of firms, that is, on their ability to

recognize and exploit opportunities abroad (an external perspective), and also on their "transformation" ability,

meaning the aptitude to continuously redefine their product portfolios based on the opportunities created within

the company (an internal perspective). The European Commission (2006) contends that there are strong links

and contingencies between research and development, innovation, human capital and relational capital. Other

studies state that firm-level knowledge is associated with a higher degree of innovation (Thornhill, 2006; Bueno

et al., 2010), and that knowledge assets can play a critical role in the different phases of the NPD process

(Linzalone, 2008).

The specific analysis of the relationship between IC and product innovation is scarcer. However, some

recent studies have shown that the distinct components of IC (human capital, structural capital and relational

capital), either individually or combined, show a significant positive relationship with the outcomes of product

innovation efforts at the firm level (Chen et al., 2006; Costa et al., 2014; Delgado-Verde, 2011; Dorrego et al.,

2013; Fernandez-Jardón et al., 2014; Hsu and Fang, 2009; Santos Rodrigues et al., 2010; Subramanian, 2012;

Subramaniam and Youndt, 2005; Wu et al., 2008).

Regarding the influence of human capital on product innovation, Costa et al. (2011) found various

indications that some employees’ characteristics positively contribute to the firm’s ability to innovate, and

therefore to its product innovation success. Building on an extensive literature review, they structured those

characteristics into three human capital elements: competencies, representing the formal education, professional

experience and specific competencies of managers and employees; values and attitudes, associated to the

orientation towards cooperation and knowledge sharing, risk assumption and creativity, and also to the degree of

commitment to the firm’s values and strategy; and capabilities, representing employees’ learning and team work

abilities and their leadership skills, as well as their understanding of the internal product innovation process.

In what concerns the relationship between structural capital and product innovation, Fernandez-Jardón et

al. (2014) argue that the existence of some organisational intangible and tangible elements, comprising “the

intelligence of a firm”, can enhance creativity and the propensity to innovate, and simultaneously turn

innovation initiatives more focused and effective. The authors divide those elements into four structural capital

categories: corporate culture towards innovation, associated to an organisational structure which permanently

encourages and feels comfortable with concepts such as new ideas, autonomy, entrepreneurship, change, risk-

taking and failure; top management role, related to top management commitment towards product innovation

success; strategy and innovation, representing the level of interaction between the firm’s strategic goals and the

definition of priority areas for product innovation focus; and, finally, new product development management,

comprising the existence of a formal, well organised new product development process.

Dorrego et al. (2013) analysed the impact of relational capital on product innovation performance at

innovative SMEs. The authors state that relational capital, representing the set of channels, contacts and

initiatives that build bridges between the firm and its external environment, can be a critical source of new

knowledge that feeds the firm’s innovative capabilities. They divide those initiatives into two basic relational

capital elements: the existence of vertical and horizontal relationships with the exterior of the firm (including

customers, suppliers, partners, competitors and other stakeholders), and the internal management of relationship

processes.

2.4 Measuring intellectual capital

According to Marr (2004), organizations measure IC for different reasons: to formulate and assess strategy;

to influence people’s behaviour; and to externally validate performance, which includes reporting and

benchmarking. The European Commission (2006) argues that as the future potential of an enterprise lies not

within its financial capital but in its IC, measuring the enterprise’s IC will enable it to manage its intangible

resources better and increase its staff’s confidence and motivation. An IC framework will function as an internal

navigation tool to help develop and allocate resources – create strategy, prioritise challenges, monitor the

development of results and thus facilitate decision-making. Chiucchi (2008) also notes that the implementation

of an IC measurement system positively affects managerial competences since the analysis of company drivers

and cause and effect relationships not only increases the understanding of the business but it also improves the

quality of the company management, making it more rational and professional. Lerro et al. (2014) add that the


19

assessment and management of knowledge assets support the governance of an organization, not only by

improving strategy planning, but most importantly by affecting organizational behaviour.

According to Sveiby (2010), the most interesting reason for measuring intangibles is the learning motive.

The increasing awareness of the benefits of measuring and managing IC is reflected in the growing number

of its measurement frameworks (Marr, 2004). In fact, Sveiby (2010) identifies over 40 models or frameworks

that cover both the financial and non-financial measures of IC. However, none of those models actually tries to

assess the drivers of intangible value creation within a product innovation context. Hence, just as Yu and

Humphreys (2013) state that measuring IC constitutes a learning process and an experience that enhances a

firm’s future earnings potential, we argue that measuring and managing IC within a product innovation context

can enhance the firm’s ability to successfully launch new products and services, and thus become more

competitive and profitable. Yet, most companies do not identify core IC indicators in many areas that directly

influence business value, and those that do frequently use them in an inefficient manner (Kim and Kumar,

2009). The remaining of this article tries to address this issue, by offering some clues regarding the possibilities

of modelling and prioritizing IC indicators within a product innovation context.

3 A METHODOLOGY TO PRIORITIZE CRITICAL IC ELEMENTS

Having argued that product innovation is a key source of competitiveness and that IC can decisively

influence its success, it is only natural for us to stress that these two concepts and their relationships cannot be

ignored by business managers. They should be analysed and managed carefully.

3.1 The AHP method

The Analytic Hierarchy Process (AHP) analysis, proposed by Saaty (2008), is a pair-wise comparison

methodology that results in breaking down a complex problem and then combining the solutions. It has been

broadly acknowledged that the AHP analysis is one of the best methodologies to prioritize various indicators.

Furthermore, the AHP approach needs only a small number of respondents with experience and knowledge

(Kim and Kumar, 2009).

The AHP methodology complies particularly well with the stated goal of this research. In fact, when trying

to put forth a methodological proposal to manage IC elements in a product innovation context, we must keep in

mind that besides listing and classifying a company’s intellectual elements, it is equally important to hierarchize

them, that is, to identify those which have more potential impact on the organization’s strategic goal. Moreover,

the proactive participation of managers in this process is of paramount importance: their experience and

acquaintance with the context is critical in the suggestion of the most relevant intangible elements and

measurement indicators. Management perceptions are thus very important to the preliminary selection and

subsequent evaluation of those intangible assets (Grimaldi and Cricelli, 2009). It is also especially relevant to be

able to identify the specific areas of the organization that demand particular attention, and which IC elements

need to be subject to a more careful and urgent analysis As we will see next, the approach we are proposing

addresses these demands quite thoroughly.

The basic principle of the AHP method lies in analysing several alternatives from different criteria. Thus, a

hierarchy is built where at the top is the problem to be taken into consideration. The next layer consists in the

criteria or strategies to be considered; and the last layer resides in several alternative activities or actions (for

each of the criteria from the second level).

Based on comparative judgments, a positive matrix of choices is derived from these criteria. A ranking

structure is achieved afterwards as a vector of priorities, based on the theory of eigenvectors. The same

procedure is applied for the alternatives considered with respect to every criterion. Then, weights beard by the

criteria are applied to the considered alternatives and lastly, the corresponding totals for each alternative are

calculated. Within the very abstract and fuzzy framework of IC, the step by step approach provided by AHP,

breaking down the problem into smaller parts that can be more easily handled, represents an important

advantage.

The first level of our proposed hierarchical structure encompasses the organization’s goal (in our specific

case, maximizing product innovation performance through the identification and management of critical IC

elements). Second level variables are the basic IC components (human capital, structural capital and relational

capital), as vital drivers of product innovation performance; the particular intangible elements that refer to each

second level component are grouped in third level variables, which are those IC elements considered to be more

critical to product innovation success. At the last level, the specific indicators for each IC element are

established. Although the main goal of this research is not to develop a standardized IC model, but to propose a

methodology to prioritize critical IC elements as perceived by each company, we will materialize our

hierarchical structure with concrete IC elements and indicators, as a way to better explain and exemplify this

proposal. For that purpose, we will resort to the exact same structure and indicators suggested by the

aforementioned work of Costa et al. (2011), Dorrego et al. (2013) and Fernandez-Jardón et al. (2014) when


20

studying the influence of intellectual capital elements on product innovation. Table 1 presents our suggested

hierarchical structure.

Table 1 The AHP model hierarchy: critical IC elements for product innovation

1st Level: organization’s goal

Maximizing product innovation performance through the identification and management of critical IC elements

2nd Level:

IC

components

3rd Level:

critical IC

elements

4th level: specific indicators

Human

Capital

Competencies

*Top managers and technical staff possess high education levels and specialized training

*Top managers and technical staff possess professional experience in different activities

*Top managers and technical staff possess (among them) an heterogeneous academic

education

*Employees possess specific competencies that are adequate to the firm’s product innovation

goals

Values and

attitudes

*Employees cooperate and share knowledge

*Employees take risks, are enterprising and creative

*Employees show interest and participate on idea generation activities

*Employees are committed to the firm’s strategy

Capabilities

*Employees participate on training initiatives related to innovation and successfully apply the

knowledge they acquire

*Employees often develop team work

*Leaders strive to communicate the role of innovation on the firm’s strategy

*Employees know and understand the firm’s new product development process

Structural

Capital

Corporate

culture

towards

innovation

*There is a new product ideas scheme in place, and employees are encouraged to participate

(for instance through economic incentives)

*Entrepreneurs and innovative project leaders are encouraged and rewarded, with no

punishment for failures

*Employees have autonomy and resources to develop their creativity through informal and

parallel projects

Top

management

role

*Innovation metrics represent an explicit and important part of top management’s

performance evaluation

*Top management is strongly committed to the product innovation process

*Top management provides clear support, autonomy and authority to the people involved in

product innovation projects

Strategy and

innovation

*The role of innovation in achieving the firm’s strategic goals is clearly defined

*There is a plan to identify/acquire the skills that are necessary to achieve product innovation

goals

*The areas of strategic focus on which to concentrate the product innovation efforts are

clearly identified

NPD

management

*The characteristics of project teams are a very important feature of the product innovation

process

*There is a system to manage new product development projects

*There is a well organised new product development process

Relational

Capital

Vertical and

horizontal

relationships

*There are vertical relationships (with customers and suppliers) with the specific goal of

strengthening our product innovation capabilities

*There are horizontal relationships (with partners and competitors) with the specific goal of

strengthening our product innovation capabilities

*There are relationships with other institutions (government agencies, external experts, public

and private R&D centres, shareholders, etc.) with the specific goal of strengthening our

product innovation capabilities

Management

of relationship

processes

*The company makes a specific effort to identify and establish relationships with customers

or users who are more receptive to innovative products (lead users)

*The company actively manages formalized relationship processes with clients

*The company actively manages formalized relationship processes with suppliers

*The company actively manages formalized relationship processes with competitors

*The company actively manages formalized relationship processes with institutions,

shareholders and investors

Source: Own elaboration based on Costa et al. (2011), Dorrego et al. (2013) and Fernandez-Jardón et al. (2014).


21

The next step is to compare the relative importance of all variables. For that purpose a questionnaire must

be built, pairing components, elements and indicators, questioning which of each pair is more important with

regards to the objective, and how much more important. In order to help the respondent to assess the pair-wise

comparisons, Saaty created a nine point intensity scale of importance between two elements (Saaty, 2008).

Although this approach has generated some criticisms, the latest research defends against them by

presenting persuasive theoretical works (Kim and Kumar, 2009). According to Saaty (2008), there are numerous

examples validating the use of the 1–9 scale.

The suggested numbers to express degree of preference between two elements are shown in Table 2.

Table 2 The fundamental scale for pair-wise comparisons (Saaty, 2008)

Intensity of importance Definition Explanation

1 Equal importance Two activities contribute equally to the objective

3 Moderate importance Experience and judgment slightly favour one activity over

another

5 Strong importance Experience and judgment strongly favour one activity over

another

7 Very strong or

demonstrated importance

An activity is favoured very strongly over another; its

dominance demonstrated in practice

9 Extreme importance The evidence favouring one activity over another is of the

highest possible order of affirmation

2,4,6,8 For compromise between

the above values

Sometimes one needs to interpolate a compromise judgment

numerically because there is no good word to describe it

The questionnaire is then built and presented to respondents. Next is an example of the pair-wise

questionnaire for level 2 IC components, level 3 elements for human capital and level 4 indicators for the human

capital element “Competencies” (as depicted on Table 1):

Level 2 – IC Components:

How important is “Human Capital” when compared to “Strutural Capital”?

Q1 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Human Capital” when compared to “Relational Capital”?

Q2 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Strutural Capital” when compared to “Relational Capital”?

Q3 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Level 3 - Human capital elements:

How important are “Competencies” when compared to “Values and attitudes”?

Q1 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important are “Competencies” when compared to “Capabilities”?

Q2 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important are “Values and attitudes” when compared to “Capabilities”?

Q3 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Level 4 - Indicators for the human capital element “Competencies”:

How important are “Education levels & specialized training” when compared to “Professional experience”?

Q1 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important are “Education levels & specialized training” when compared to “Heterogeneous academic

education”?

Q2 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9


22

How important are “Education levels & specialized training” when compared to “Specific competencies for

product innovation”?

Q3 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Professional experience” when compared to “Heterogeneous academic education”?

Q4 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Professional experience” when compared to “Specific competencies for product

innovation”?

Q5 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Heterogeneous academic education” when compared to“Specific competencies for


Q6 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

The next step, the calculation of relative weights based on the answers to the questionnaire, can be

conducted using Microsoft Excel. As previous research suggested (Saaty, 2008), three steps are employed. They

are:

(a) Using questionnaire results to insert the data in Excel, building binary comparison matrices for each

level of the hierarchical structure;

(b) Calculating relative weights:

(b.1) Sum of each column of the matrix;

(b.2) Dividing each element of the matrix by the sum of the corresponding column, obtaining a new

standardized matrix;

(b.3) Calculating the average of each line of the standardized matrix (sum and divide by n variables

considered), obtaining the column vector “w” (relative weight). The sum of the vector must equal 1;

(c) Verifying matrix consistency:

(c.1) Multiplying the sum of each column of the original matrix (step b.1) by vector “w” (step b.3),

obtaining a new vector (consistency measure);

(c.2) If the matrix is consistent, the vector calculated in step c.1 will have values ideally equal to 1.

3.2 Testing the AHP methodology: empirical results

Once the conceptual structure of this methodology was completed, an empirical test of its functionality was

in order. More than any kind of frequency count or statistical generalization, our aim was to make sure that the

intended users of this tool (business managers) would understand its purpose and modus operandi, and to get

feedback on those issues as well as on the overall usefulness of the methodology. Considering these goals, an

“action research” approach seemed the most adequate way to fully apprehend how the framework would work

in practice. This methodological choice seeks to bring together action and reflection, theory and practice. The

researcher acts in concert with the host organisation, observes process and outcome, and analyses findings in

view of the relevant literature. Hence, this methodology not only reflects upon the observations of the

researcher, but also on the actual development of the interventions. The main benefit for researchers is the

ability to develop insights into the implementation of new management innovations in organisations; for

practitioners the benefit is to gain the assistance and knowledge of academics in the implementation process

(Demartini and Paoloni, 2013).

This methodological choice also addresses recent calls for an emerging “third stage” of IC research,

characterised by critically studying IC in practice, in search for the managerial implications of how to use IC in

managing a company. According to this perspective, “for IC research to remain relevant, researchers need to

concentrate on research based on managing IC at the operating level of case/field study/interviews rather than

taking a top-down approach to research.” (Dumay, 2014, p. 16).

Hence, in this section the implementation of our proposed model is demonstrated on a real firm, a

Portuguese innovative SME, as part of a larger case-study. The firm is located in northern Portugal and operates

in the chemical industry. Creating innovative products is one of its core strategic aims. It has around 100

employees and an annual turnover estimated at €72 million, thus complying with the European Commission

Recommendation 2003/361/EC from May 6th 2003 in what concerns its SME status. It operates both in the

domestic and international market, with the latter corresponding to 85% of its sales. The research was conducted

with the firm’s CEO, as suggested by the Oslo Manual (OECD, 2005), since he represents the key informant

that better knows the subject of the research and who is most available to communicate it to the researcher.

From the preliminary interview and presentation of our questionnaire it became apparent that the CEO generally

understood the concept of IC, recognizing its importance to the company's product innovation strategy.


23

However, the company had never conducted any kind of structured initiative in order to systematise or measure

in any way the intangible resources that could impact product innovation. This study was therefore labelled as

very pertinent, as the respondent recognized the relevance of building a model that in an intuitive way depicts

the relative importance of each IC element to the firm’s innovation strategy.

Due to time constraints, and also because the CEO generally agreed with the elements and indicators that

were included, it was decided that the hierarchical structure depicted on Table 1 would be utilized without any

changes. The resulting pair-wise comparison questionnaire was thus prepared, as explained on section 3.1, and

fully filled in the course of a few personal interviews. The corresponding results were subsequently introduced

and handled in Microsoft Excel. The exact steps described on section 3.1 were followed in order to build the

binary comparison matrices, the standardized matrices and finally to obtain the intellectual elements’ relative

weights and the resulting hierarchy. Recapping those steps, we started by building the binary comparison

matrices for each level of the hierarchical structure, based on the questionnaire results; then a new standardized

matrix was built from each original matrix, through dividing each cell by the sum of its corresponding column,

so that relative weights were calculated; next, the average of each line of the standardized matrix was calculated,

obtaining the column vector “w” (relative weight).

Some examples of this procedure will be depicted next, including level 2 IC components, level 3 elements

for human capital and level 4 indicators for the human capital element “Competencies”:


Original matrix:

HC SC RC

HC – Human Capital

HC 1 4 1

SC – Structural Capital

SC 0,25 1 0,33

RC – Relational Capital

RC 1 3 1

Sum: 2,250 8,000 2,333

Standardized matrix:

HC SC RC

w

CM

HC 0,444 0,500 0,429

0,458

1,030

SC 0,111 0,125 0,143

0,126

1,011

RC 0,444 0,375 0,429

0,416

0,971

Sum: 1,000 1,000 1,000

1,000

(w=relative weight; CM=consistency measure)


Original matrix:

Comp V&A Cap Comp – Competencies

Comp 1 0,25 0,25 V&A – Values and Attitudes

V&A 4 1 1 Cap – Capabilities

Cap 4 1 1

Sum: 9,000 2,250 2,250


Comp V&A Cap

w

CM

Comp 0,111 0,111 0,111

0,111

1,000

V&A 0,444 0,444 0,444

0,444

1,000

Cap 0,444 0,444 0,444

0,444

1,000

Sum: 1,000 1,000 1,000

1,000



24

Level 4 - Indicators for human capital element “Competencies”:

Original matrix:

ELST PE HAE SC

ELST – Education levels and specialized

training

ELST 1 3 4 1

PE – Professional experience

PE 0,33 1 1 0,33

HAE – Heterogeneous academic education

HAE 0,25 1 1 0,33

SC – Specific competencies for product innovation

SC 1 3 3 1

Sum: 2,583 8,000 9,000 2,667


ELST PE HAE SC

w

CM

ELST 0,387 0,375 0,444 0,375

0,395

1,021

PE 0,129 0,125 0,111 0,125

0,123

0,980

HAE 0,097 0,125 0,111 0,125

0,114

1,030

SC 0,387 0,375 0,333 0,375

0,368

0,980

Sum: 1,000 1,000 1,000 1,000

1,000


After applying the same process to all levels, we were able to illustrate the final results using our original

hierarchical structure, as shown in Figure 1:

Figure 1 - An application of the AHP model hierarchy

Maximizing product innovation performance through the

identification and management of critical IC elements

Level 1 - Strategic Goal:

Level 2 - IC Components:

Human Capital

45,8%

Level 3 - IC Elements:

Competencies

11,1%

Structural Capital

12,6%

Relational Capital

41,6%

Values and

attitudes

44,4%

Top management

role

18,5%

Strategy and

innovation

30,4%

NPD

management

12,2%

Vertical and

horizontal

relationships

50%

Management of

relationship

processes

50%

Corporate

culture towards innovation

39%

Capabilities

44,4%

Level 4 - IC Indicators:

Education

levels &

specialized

training

39,5%

Cooperation &

knowledge

sharing

35,7%

Innovation

metrics part of

performance

evaluation

23,1%

Role of

innovation in

strategic goals

clearly defined

28,6%

Characteristics of

project teams

18,7%

Vertical

relationships

65,5%

Identify and

establish

relationships

with lead users

26,4%

New product

ideas scheme in

place

10,4%

Training

initiatives &

successful

application

47,2%

Professional

experience

12,3%

Take risks, are enterprising and

creative

13,6%

Strongly

committed to

the product

innovation

process

10,4%

Identification of

necessary skills

to innovation

goals

14%

There is a

system to

manage NPD

projects

23,4%

Horizontal

relationships

18,7%

Formalized

relationship

processes with

clients

25,5%

Entrepreneurs

& project

leaders

encouraged &

rewarded

23,1%

Team work

7%

Heterogeneous

academic

education

11,4%

Interest and participation on idea generation

activities

41,1%

Clear support &

autonomy to

product

innovation

projects

65,5%

Identification of

areas of

strategic focus

57,4%

There is a well

organised NPD

process

57,9%

Relationships

with other

institutions

15,8%

Formalized

relationship

processes with

suppliers

27%

Autonomy &

resources to

develop parallel

projects

65,5%

Leaders

communicate the role of innovation

on strategy

20,1%

Specific

competencies

for Product

Innovation

36,8%

Commitment to

the firm’s

strategy

9,6%

Formalized

relationship

processes with

competitors

13%

Knowledge &

understanding of

the NPD process

25,7%

Formalized

relationship

processes with o/

stakeholders

8%


25

This map depicts the hierarchization of all critical IC components, elements and particular indicators in

what concerns their importance to product innovation success, as per the perception of the firm’s CEO. We can

see for example that human capital is considered to be the most important IC component, as opposed to

structural capital which ranked as the least important; the elements ‘capabilities’ and ‘values and attitudes’

assume equal importance within the human capital component; the most valued specific human capital items are

‘training initiatives and their successful application’, ‘interest and participation on idea generation activities’ and

‘education levels and specialized training’. Regarding relational capital, considered the second most important

IC component, the elements ‘Vertical and horizontal relationships’ and ‘Management of relationship processes’

were deemed as equally important, while ‘Vertical relationships’ stands out as the most valued relational capital

specific item. Finally, ‘Corporate culture towards innovation’ was ranked as the most critical structural capital

element, particularly in what concerns the existence of ‘Autonomy and resources to develop parallel projects’.

Building on Kim and Kumar’s (2009) proposal, by considering the relative weight of each element within

the IC components and recalculating the relative importance of each indicator accordingly, we can put together a

second map oriented to the practical envisage of the prioritized elements from an operational perspective, thus

avoiding indiscriminately weighing very distinct intangible elements, or unintentionally neglecting important

ones. Figure 2 shows which areas should be subject to a more careful and urgent attention (core focus areas),

helping the firm to visualize more intuitively the specific IC elements where it should focus its resources and

efforts, in order to improve its product innovation performance.

Figure 2 Focus areas for IC development towards product innovation success

The empirical testing of the proposed AHP methodology was thus, in our opinion, very successful, fully

meeting the goals that were initially set. Not only the company acknowledged its interest and understood its

variables and modus operandi without major difficulties, but also the handling of the responses allowed for the

construction of a preference hierarchy and the identification of focus areas, which was recognized as meaningful

and useful for the company’s product innovation strategy. Ultimately, the company agreed that prioritizing

intangible elements and identifying critical improvement areas can be key to efficiently mobilize IC

management for product innovation.

HC Indicators RC Indicators

SC Indicators

Hig

h

Rel

ativ

e Im

po

rtan

ce

Lo

w

Core

Focus

Areas

General Focus

Areas

Potential

Focus

Areas

*Training initiatives & their

successful application

*Interest and participation on idea generation activities

*Education levels & specialized training

* Cooperation & knowledge sharing

*Specific competencies for Product

Innovation

*Leaders communicate the role of

innovation on strategy

*Knowledge & understanding of the NPD

process

*Professional experience

*Take risks, are enterprising and

creative

*Team work

*Heterogeneous academic education

*Committment to the firm’s strategy

*Vertical relationships

*Identify and establish

relationships with lead users

*Horizontal relationships

*Formalized relationship

processes with clients

*Relationships with other

institutions


processes with suppliers


processes with competitors


processes with other

stakeholders

*Innovation metrics part of top management

performance evaluation

*Role of innovation in strategic

goals clearly defined

*Characteristics of project teams

*New product ideas scheme in place

*Top management committed to the

product innovation process

*Identification of necessary skills to

innovation goals

*System to manage NPD projects

*Entrepreneurs & project leaders

encouraged & rewarded

*Clear support & autonomy to

product innovation projects

*Identification of areas of strategic

focus

*Well organised NPD process

*Autonomy & resources to develop

parallel projects


26

4 CONCLUSIONS

In today’s competitive environment, product innovation should be regarded as a priority by any business.

However, firms in general and SMEs in particular are confronted with two paradoxical issues when it comes to

innovation: intensify innovation efforts to develop new products, and in doing so, become more vulnerable by

engaging in projects characterized by very high levels of risk. Indeed, product innovation projects are very risky

in nature since, generally, they take more time than expected, cost more material resources than those planned,

and do not always produce the anticipated benefits with respect to performance. SMEs are particularly

vulnerable to this dilemma: on the one hand, they usually have a smaller financial capacity and less market

power than larger companies, and as such are even more dependent on innovative dynamics (EC, 2006; Vaona

and Pianta, 2008); on the other hand, the typical scarcity of resources at their disposal dramatically reduces their

margin of error (Rhaiem, 2012). This reality, in our opinion, strongly reinforces the importance of IC

management to enhance product innovation performance at SMEs. In fact, even if their individual ability to

have an impact on their industry is small, the strategic decisions regarding their orientation towards a higher

level of intensity in IC elements is under their control, and that can be an important catalyst for product

innovation success. Moreover, as most SMEs cannot assume the financial risk of conducting a large portfolio of

new product projects (European Commission, 2006), the importance of identifying and prioritizing those factors

that are most critical to the success of each innovation initiative becomes even more apparent. At a time when

there is growing evidence of IC’s relevance for product innovation performance, this dilemma strongly

reinforces the importance of IC management as a means to increase the odds of product innovation success at

SMEs.

Additionally, although the basic relationship between knowledge-based factors, innovation dynamics and

companies’ performance is on the whole convincing, many issues remain to be understood in what concerns

intangible resources exploitation and deployment to improve companies’ innovation dynamics and

organizational performance (Lerro et al., 2014). There is still too little evidence of “IC in action” and its actual

benefits in what concerns product innovation management. Conducting research based on critically analysing IC

management practices in action seems to be the right response to this knowledge gap.

Against this backdrop, this research aimed to address these issues by designing and testing a diagnosis and

action tool to help business managers incorporate an intellectual capital perspective into their product innovation

efforts.

We understand our proposal as a relevant contribution for both the literature and practice of IC and product

innovation, as it stresses the importance of identifying and prioritizing those intangible elements that are

decisive to the success of product innovation initiatives at SMEs. In fact, the proposed AHP methodology

represents a particularly effective way of conducting this process, ultimately allowing managers to concentrate

on the most critical intangible factors that drive product innovation within their firm.

We hope this proposal can contribute to help managers successfully turn IC identification and prioritization

into effective innovation management. As stated by Lerro et al. (2014), the full potential of IC in what concerns

its impact on innovation dynamics is realized when knowledge resources are efficiently identified through easy-

to-use models and frameworks.

Also, as this study was conducted within a Portuguese context, we feel compelled to add a few remarks

regarding our view of the potential usefulness of this type of framework within the Portuguese business

environment. Bloom et al. (2014) developed a project called the “World Management Survey”, which sought to

address the issue of whether management practices were an important factor in understanding the heterogeneity

of firm productivity. Many of the management practices under evaluation at that research can easily be

associated, either directly or indirectly, with the use of our proposed framework (process improvements, human

capital management, etc.). Their general conclusion was that management does indeed appear to be important in

accounting for the large differences in cross-country total factor productivity, as well as within-country

differences. When analysing differences between countries, they show that “average management scores” for

Portugal are well below those of other countries like the US, Japan, the UK, Germany or France. Actually, for

some southern European countries such as Portugal, management accounts for half of the total factor

productivity gap with the US, whereas for other nations like Japan or Sweden that fraction is only one tenth.

Considering that 99.9% of all Portuguese companies are SME, and 95.9% are micro companies under 10

employees (INE, 2014), it is fair to assume that this problem has its roots on SME managers (in fact, the

aforementioned research empirically demonstrates that there is a positive correlation between management

quality and firm size). Portuguese SME managers (as well as many worldwide SME managers in comparable

circumstances) should therefore take this data into serious consideration and try to close this gap, by developing

and adopting new, more innovative and modern management practices. We hope our proposal can contribute to

address this challenge, by suggesting an original and effective way of deploying intangible resources to enhance

product innovation performance.

Finally, we cannot forget that accepting the importance of IC and embracing it as a management priority is

the final result of a learning process within the firm, that involves talking about IC, understanding its


27

contribution to the value creation process, thinking about how and when it impacts corporate phenomena - that

is, “the pragmatic dimension” of IC (Giuliani and Marasca, 2011). The implementation of this methodology can

also contribute to this learning path, as it will inevitably trigger a brainstorming process regarding IC inside the

firm, ultimately helping it to develop a better understanding of how distinct IC elements impact its product

innovation efforts.

The authors acknowledge that this paper has a few limitations, offering possibilities for future research. In

fact, although this tool is conceptually applicable to any firm, the effectiveness of the methodology was tested in

only one SME. In order to generalize the findings, future research should test the model validity on other types

of organizations, ideally in different industries and even countries.

We should also once again stress that our main goal was not to develop a standardized IC model, but to

propose a methodology that can help managers systematize and prioritize critical IC elements that are suitable

for their particular reality. In fact, although we admit that presenting standardized indicators “ex-ante” could

help many organizations to better understand the importance of IC management within their product innovation

strategy, IC is ultimately firm-specific and closely tied to the organization. Therefore, our proposed IC variables

must be understood as a starting base, which can (and should) be subject to adaptations depending on the reality

of each firm.

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APPENDIX I

Pair-wise Questionnaire Please read the following questions carefully and enter your answer in the appropriate place, considering the scale

shown below. If the first attribute is more important in relation to the second, enter your answer in one of the boxes to the

left of the option "1", depending on your preference. Whenever the second attribute is more important than the first, choose

your response from the boxes placed to the right of option "1".

Saaty’s scale for pair-wise comparisons

Intensity of importance Definition Explanation

1 Equal importance Two activities contribute equally to the objective

3 Moderate importance Experience and judgment slightly favor one activity over

another

5 Strong importance Experience and judgment strongly favor one activity over

another

7 Very strong or

demonstrated importance

An activity is favored very strongly over another; its

dominance demonstrated in practice

9 Extreme importance The evidence favoring one activity over another is of the

highest possible order of affirmation

2,4,6,8 For compromise between

the above values

Sometimes one needs to interpolate a compromise judgment

numerically because there is no good word to describe it

http://www.oecd.org/daf/ca/Intangible%20Assets.pdf

http://www.sveiby.com/


30


How important is “Human Capital” when compared to “Strutural Capital”?

Q1 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Human Capital” when compared to “Relational Capital”?

Q2 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Strutural Capital” when compared to “Relational Capital”?

Q3 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9


How important are “Competencies” when compared to “Values and attitudes”?

Q4 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important are “Competencies” when compared to “Capabilities”?

Q5 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important are “Values and attitudes” when compared to “Capabilities”?

Q6 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Level 4 - Indicators for the human capital element “Competencies”:

How important are “Education levels & specialized training” when compared to “Professional experience”?

Q7 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important are “Education levels & specialized training” when compared to “Heterogeneous academic

education”?

Q8 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important are “Education levels & specialized training” when compared to “Specific competencies for


Q9 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Professional experience” when compared to “Heterogeneous academic education”?

Q10 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Professional experience” when compared to “Specific competencies for product

innovation”?

Q11 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Heterogeneous academic education” when compared to“Specific competencies for


Q12 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Level 4 - Indicators for the human capital element “Values and attitudes”:

How important is “Employees cooperate and share knowledge” when compared to “Employees take risks, are

enterprising and creative”?

Q13 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9


31

How important is “Employees cooperate and share knowledge” when compared to “Employees show interest

and participate on idea generation activities”?

Q14 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Employees cooperate and share knowledge” when compared to “Employees are committed

to the firm’s strategy”?

Q15 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Employees take risks, are enterprising and creative” when compared to “Employees show

interest and participate on idea generation activities”?

Q16 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Employees take risks, are enterprising and creative” when compared to “Employees are

committed to the firm’s strategy”?

Q17 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Employees show interest and participate on idea generation activities” when compared to

“Employees are committed to the firm’s strategy”?

Q18 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Level 4 - Indicators for the human capital element “Capabilities”:

How important is “Employees participate on training initiatives related to innovation” when compared to

“Employees often develop team work”?

Q19 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9


“Leaders strive to communicate the role of innovation on the firm’s strategy”?

Q20 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9


“Employees know and understand the firm’s NPD process”?

Q21 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Employees often develop team work” when compared to “Leaders strive to communicate

the role of innovation on the firm’s strategy”?

Q22 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Employees often develop team work” when compared to “Employees know and understand

the firm’s NPD process”?

Q23 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Leaders strive to communicate the role of innovation on the firm’s strategy” when

compared to “Employees know and understand the firm’s NPD process”?

Q24 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Level 3 - Structural capital elements:

How important is “Corporate culture towards innovation” when compared to “Top management role”?

Q25 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Corporate culture towards innovation” when compared to “Strategy and innovation”?

Q26 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Corporate culture towards innovation” when compared to “NPD management”?

Q27 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9


32

How important is “Top management role” when compared to “Strategy and innovation”?

Q28 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Top management role” when compared to “NPD management”?

Q29 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Strategy and innovation” when compared to “NPD management”?

Q30 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Level 4 - Indicators for the structural capital element “Corporate culture towards innovation”:

How important is “There is a new product ideas scheme in place” when compared to “Entrepreneurs and

innovative project leaders are encouraged and rewarded”?

Q31 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “There is a new product ideas scheme in place” when compared to “Employees have

autonomy and resources to develop their creativity”?

Q32 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Entrepreneurs and innovative project leaders are encouraged and rewarded” when

compared to “Employees have autonomy and resources to develop their creativity”?

Q33 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Level 4 - Indicators for the structural capital element “Top management role”:

How important is “Innovation metrics represent an explicit and important part of top management’s

performance evaluation” when compared to “Top management is strongly committed to the product

innovation process”?

Q34 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Innovation metrics represent an explicit and important part of top management’s

performance evaluation” when compared to “Top management provides clear support, autonomy and

authority to the people involved in product innovation projects”?

Q35 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “Top management is strongly committed to the product innovation process” when compared

to “Top management provides clear support, autonomy and authority to the people involved in product

innovation projects”?

Q36 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Level 4 - Indicators for the structural capital element “Strategy and innovation”:

How important is “The role of innovation in achieving the firm’s strategic goals is clearly defined” when

compared to “There is a plan to identify/acquire the skills that are necessary to achieve product innovation

goals”?

Q37 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “The role of innovation in achieving the firm’s strategic goals is clearly defined” when

compared to “The areas of strategic focus on which to concentrate the product innovation efforts are clearly

identified”?

Q38 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9


33

How important is “There is a plan to identify/acquire the skills that are necessary to achieve product

innovation goals” when compared to “The areas of strategic focus on which to concentrate the product

innovation efforts are clearly identified”?

Q39 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Level 4 - Indicators for the structural capital element “NPD management”:

How important is “The characteristics of project teams are a very important feature of the product innovation

process” when compared to “There is a system to manage new product development projects”?

Q40 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “The characteristics of project teams are a very important feature of the product innovation

process” when compared to “There is a well organised new product development process”?

Q41 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “There is a system to manage new product development projects” when compared to “There

is a well organised new product development process”?

Q42 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Level 3 - Relational capital elements:

How important is “Vertical and horizontal relationships” when compared to “Management of relationship

processes”?

Q43 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Level 4 - Indicators for the relational capital element “Vertical and horizontal relationships”:

How important are “Vertical relationships (with customers and suppliers)” when compared to “Horizontal

relationships (with partners and competitors)”?

Q44 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important are “Vertical relationships (with customers and suppliers)” when compared to “Relationships

with other institutions (government agencies, external experts, public and private R&D centres, shareholders,

etc.)”?

Q45 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important are “Horizontal relationships (with partners and competitors)” when compared to

“Relationships with other institutions (government agencies, external experts, public and private R&D

centres, shareholders, etc.)”?

Q46 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Level 4 - Indicators for the relational capital element “Management of relationship processes”:

How important is “The company makes a specific effort to identify and establish relationships with lead

users” when compared to “The company actively manages formalized relationship processes with clients”?

Q47 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9


users” when compared to “The company actively manages formalized relationship processes with suppliers”?

Q48 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9


users” when compared to “The company actively manages formalized relationship processes with

competitors”?

Q49 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9


34


users” when compared to “The company actively manages formalized relationship processes with institutions,

shareholders and investors”?

Q50 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “The company actively manages formalized relationship processes with clients” when

compared to “The company actively manages formalized relationship processes with suppliers”?

Q51 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9


compared to “The company actively manages formalized relationship processes with competitors”?

Q52 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9


compared to “The company actively manages formalized relationship processes with institutions, shareholders

and investors”?

Q53 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “The company actively manages formalized relationship processes with suppliers” when

compared to “The company actively manages formalized relationship processes with competitors”?

Q54 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “The company actively manages formalized relationship processes with suppliers” when


and investors”?

Q55 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

How important is “The company actively manages formalized relationship processes with competitors” when


and investors”?

Q56 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9


Supply chain risk management enablers-A framework

development through systematic review of the literature from

2000 to 2015

Irène Kilubi

University of Bremen, Faculty of General Business Administration, Chair of Maritime Business and Logistics,

Wilhelm–Herbst–Str. 12, 28359 Bremen, Germany

Phone: +49 171 94 38 218


Hans–Dietrich Haasis

University of Bremen, Faculty of General Business Administration, Chair of Maritime Business and Logistics,

Wilhelm–Herbst–Str. 12, 28359 Bremen, Germany

Phone: + 49 421 218 66760

Email: haasis@uni–bremen.de

Abstract

The present paper delivers a robust and systematic literature review (SLR) on supply chain risk management

(SCRM) with the purpose to a) review and analyse the literature concerning definitions and research

methodologies applied, to b) develop a classificatory framework which clusters existing enablers on SCRM, and

to c) examine the linkage between SCRM and performance. The findings reveal that not only is SCRM loosely

defined, but that there are various fragmented supply chain risks enablers and that there is a strong need for a

clear terminology for its building enablers. In addition to that, the review points to a lack of empirical

confirmation concerning the connection between SCRM and performance. This paper contributes an overview

of 80 peer-reviewed journal articles on SCRM from 2000 to the beginning of 2015. We offer an overarching

definition of SCRM, synthesise and assemble the numerous enablers into preventive and responsive strategies

by means of a conceptual framework. Moreover, indicating the social network theory (SNT) as a potential

theoretical foundation for SCRM, we further contribute to the supply chain management (SCM) literature by

providing propositions that guide future research.

Keywords: supply chain risk management, supply chain risk(s), supply chain performance, supply chain

disruption(s), systematic literature review, enabler(s), conceptual framework


36

1 INTRODUCTION

The demands of the business environment and the progression of emerging markets are leading to the

development of dynamic and complex supply chain (SC) networks (Braunscheidel and Suresh, 2009; Manuj and

Mentzer, 2008a; Tummala and Schoenherr, 2011; Spekman and Davis, 2004; Zsidisin et al., 2004).

Consequently, these effects lead to an increase in risk exposure, for instance due to shorter technology and

product life cycles, increased demand for just-in-time (JIT) deliveries, reduced inventory buffers, and e-business

(Brindley, 2004; Fawcett et al., 2011; Giunipero and Eltantawy, 2004; Hallikas et al., 2004; Harland et al., 2003;

Narasimhan and Talluri, 2009).

Fuelled by several well-documented events, such as natural disasters and events (e.g. Tsunami, 2004;

Hurricane Katrina, 2005; Taiwan earthquakes, 1999, 2009, and 2010), diseases (e.g. foot- and mouth disease,

2001 in the UK; SARS-pandemic, 2003/2004; Avian influenza, 2005; Swine influenza 2009), and terrorist

attacks (e.g. New York, 2001; Madrid, 2004; London, 2005; Jakarta, 2009) (Wagner and Neshat, 2012), the

Iceland volcano eruption in 2010, the nuclear disaster in Fukushima, 2011, or Hurricane Sandy in 2012, interest

in supply chain risk (SCR) issues has steadily grown. However, the literature on SCRM is highly fragmented

hindering a throughout understanding of where research lies and what to research next (Pfohl et al., 2010).

Although SCRM has become standard in supply chain management (SCM) research, the term and the concept to

establish beneficial SCRM is still ambiguous and lacks adequate understanding. In general, a well-grounded,

unified, and universally recognised SCRM definition is presently missing. In the same vein, differing concepts

of theory building have headed towards an inconsistent use of terminologies to implement SCRM effectively

using terms such as moderators, activities, antecedents, principles, capabilities, and elements. Furthermore, a

critical still underexplored subject is the relationship between SCRM and performance (Sodhi et al., 2012).

Along with increasing (SCR) due to environmental and economic changes, it is of paramount importance to

answer the question of how to reduce SCRs (Chen et al., 2013). For the purpose of the study we embrace the

following definition of SCRM:

“Supply chain risk management is to [collaborate] with partners in a supply chain apply risk management

process tools to deal with risks and uncertainties caused by, or impacting on, logistics related activities or

resources” (Norrman and Jansson, 2004, p. 436).

We aim to synthesise the existing research findings to provide a clear overview. In addressing this issue,

the study at hand offers an SLR on SCRM by adopting a rigorous research approach. The systematic literature

review methodology has been recently used in other supply chain management literature reviews as well (e.g.

Gimenez and Tachizawa, 2012; Lightfoot et al., 2013; Pilbeam, et al., 2012). Our findings offer essential

theoretical contributions to the SCRM literature on strategic responses to adverse events by synthesising the

inconsistent and fragmented literature. We aim at creating a classificatory framework and indicating existing

gaps to motivate new research that will add to the current knowledge on SCRM. The paper at hand is structured

as follows. The proceeding section deals with the elucidation of the rigorous SLR method and emphasises the

constitutional research questions including the criteria drawn on in selecting and evaluating journal articles.

Next, literature is analysed and synthesised, then succeeded by the description of the principal findings of the

review; resulting in a conceptual framework for the enablers of SCRM. Finally, we work out the underlying

implications for practitioners and point out possible directions for future research. This research makes three

major contributions. A definition of SCRM is established, 12 top SCRM enablers are proposed, and research

propositions are created to guide future research.

2 REVIEW METHODOLOGY AND DATA COLLECTION

The adopted SLR process is inspired by the works of Crossan and Apaydin (2010), Kilubi (2015), Meier

(2011), and Wang and Shu (2010) and follows five distinct stages as shown in Figure 1: (1) database selection,

(2) journal selection, (3) article selection, (4) article classification, and (5) article analysis.

Irène Kilubi and Hans-Dietrich Haasis

37

Figure 1 Research methodology of the present systematic literature review

Source: According to Crossan and Apaydin (2010), Kilubi (2015), Meier (2011), Wang and Shu (2010).

The defined central research trends focused on are: What is the state of SCRM as presented in academic

literature and how mature is the literature? Has a supply chain risk management been adequately defined? What

are the major enablers that lead to increased performance and could this help researchers and practitioners to

build a more grounded case for the implementation? To respond to the questions the following objectives were

defined:

a) Review and analyse the literature regarding definitions and research methodologies applied.

b) Develop a classificatory framework that clusters existing enablers on SCRM based on preventive and

responsive strategies that may be in light of practical advancement, embraced and further developed into

measurement constructs in future research.

c) Examine the relationship between SCRM and performance.

d) Establish a research agenda including research propositions by identifying critical research issues in

areas where further research is required.

The following databases were drawn on for the retrieval of business-related papers published in the English

language in academic journals: Science Direct (Elsevier), SCOPUS, Taylor & Francis Group, Business Source

Complete (EBSCO Host), Springer Link, Emerald Insight, and ABI/Inform Global (ProQuest). These databases

have been applied in previous SLRs, too (cf. Kilubi, 2015; Natarajarathinam et al., 2009; Nijmeijer et al., 2013;

Rashman et al., 2009; Soni and Kodali, 2011). We entered the search phrases “Supply Chain Risk” OR “Supply

Chain Risk Management” in the article title (TI) solely along with ‘Performance’ in the abstract (AB), keywords

(KW) and title (TI) search. The use of the term ‘Supply Chain Risk Management’ is widely acknowledged in the

scholarly literature, which supports the choice.

For the article selection process, top-tier peer-reviewed journals with an ABS

(associationofbusinessschools.org) ranking of 4 or 3 were determined as further inclusion criteria. First of all,

we formed a review panel consisting of one university professor and two research assistants to validate the

review process and to enhance quality. We then scanned the selected electronic databases under the terms of our

defined search strings, with no time restriction. The search resulted in 3963 articles initially; of what 3605

duplicates were removed and 363 references have been identified and screened on title and abstract. Next, 216

references were excluded after the examination of the abstracts and the conclusions because of not meeting the

pre-determined inclusion criteria. The analysis led to an initial article set of 147 articles of which 76 articles

have been excluded as they lay beyond of the research scope of the study. Thus, journal articles had to show a

clear focus on SCRM and the study’s purpose; remaining 71 articles. Besides, while scanning the citations and

bibliographies of every article, the reference check revealed further potential contributions that had not been

Step 1: Database selection

ScienceDirect, (Elsevier) SCOPUS, Taylor & Francis Group, Business Source Complete (EBSC), Springer Link,

Emerald Insight, ABI/Inform (ProQuest’s)

Step 2: Journal Selection

Based on defined search criteria and search strings

Step 3: Article selection

(1) conceptual papers, (2) surveys/questionnaires, (3) simulation/modeling, (4) case studies, (5) secondary

databases, (6) multiple methods, and (7) literature reviews

Step 4: Article classification

Preventive and responsive SCRM enablers

Step 5: Article analysis

Synthesis and evaluation


38

found or overlooked during the initial key search. Here again, their reference lists were in turn scanned; this

process was carried forward until no further journal article was identified. Therefore, nine additional items were

included through this cross-referencing approach. The end result is a database of 80 peer-reviewed journal

articles from 2000 to the beginning of 2015 (first quarter). A detailed schematic presentation of the process

employed for selecting and evaluating studies is shown in Figure 2.

Figure 2: Detailed article selection process

3 STUDY FINDINGS

The synthesis aims at bringing together the findings on a selected topic, the results of which ought to be

used to reach a higher degree of comprehension and attain a level of theoretical or conceptual advance beyond

that accomplished in any single empirical research (Campbell et al., 2003). Indeed, the aim was to examine and

analyse the SCRM literature while synthesising it into a novel format (Denyer and Tranfield, 2009).

3.1 Descriptive features of reviewed SCRM literature

The 80 journal articles identified through the SLR are analysed in this section with respect to the

publication year, journal, and the methodological approach in order to understand the trends in the body of

literature relevant to SCRM. An analysis of the years in which the 80 selected articles were published manifests

that the first articles appeared in 2000. In fact, nearly 89% of the surveyed journal articles (68 out of 80) were

published from 2004 onwards. The years 2004 (13%/ n=10), followed by 2012 (each 11%/ n=9), 2011 (9%/

n=7), 2008, 2009, and 2013 (each 8%/ n=6) marked the peaks, providing evidence that research interest in

SCRM is still further growing (cf. Table 1).

The sample of 80 articles in the present review was published in 27 business-related academic journals.

The highest share of articles (45%) were published in the International Journal of Physical Distribution &

Logistics Management (n=13), International Journal of Production Economics (n = 12), and Supply Chain

Management: An International Journal (n=11).

3968 references identified through database searching:

• ScienceDirect (Elsevier) n = 501

• SCOPUS n = 1331

• Taylor & Francis n = 132

• Business Source Complete (EBSCO Host) n = 312

• ABI/Inform Global (ProQuest) n = 1.057

• Springer Link n = 554

• Emerald n = 81

3605 duplicates removed

363 references identified and screened on title and abstract abstracts

identified and screened

216 references excluded after examination of abstracts and conclusions

not meeting the inclusion criteria

147 full copies retrieved and assessed for eligibility

71 articles included after reading full text

9 additional articles included by reference check

80 articles finally included in the systematic literature review

76 articles excluded after reading the full text

no focus on defined research questions


39

Table 1 Number of articles per year published by the academic journal (appearing at least twice)

Academic Journal 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 No. of

articles %

European Journal of Operational Research

0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 3 3,75%

International Journal of Logistics Research

and Applications 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 2 2,50%

International Journal of Physical Distribution

and Logistics Management 0 0 0 0 7 0 0 0 1 1 0 1 2 1 0 0 13 16,25%

International Journal of Production Research

0 0 0 0 0 0 0 0 0 0 2 0 0 1 1 0 4 5,00%

International Journal of Production

Economics 0 0 1 0 1 0 1 1 1 1 0 2 2 0 0 2 12 15,00%

Journal of Business Logistics

0 0 0 0 0 0 0 0 2 0 0 0 0 1 1 0 4 5,00%

Journal of Operations Management

0 0 0 0 0 0 0 0 0 3 1 1 0 0 0 0 5 6,25%

Journal of Purchasing and Supply

Management 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 3 3,75%

Journal of Supply Chain Management

0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 3 3,75%

Management Science 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 2,50%

Omega 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 3 3,75%

Production and Operations Management

0 0 0 0 0 2 0 0 0 0 0 0 1 0 0 0 3 3,75%

Supply Chain Management: An International

Journal 1 0 0 0 2 0 1 0 1 0 0 2 1 1 2 0 110 13,75%


40

To assess the research methodologies applied in the selected articles, we looked at seven methodological

approaches. Similar to Winter and Knemeyer (2013), we examined methodologies applied in order to compare

the existing SCRM research approaches: (1) conceptual papers, (2) surveys/questionnaires, (3)

simulation/modeling, (4) case studies, (5) secondary databases, (6) multiple methods, and (7) literature reviews.

However, other than Winter and Knemeyer, we chose to leave out delphi methods and include two additional

research methodologies as substitutes — since they seem to be prominent in SCRM research (Agrell et al.,

2004) — namely conceptual and simulation/modeling papers. We found that simulation/modeling was the

primary methodological approach adopted in the reviewed papers (18 out of 80 articles). Furthermore,

researchers adopted the case study research design and the survey/questionnaire design (each 16 out of 80

articles), succeeded by conceptual research design (12 out of 80 articles). However, the multiple research design

along with literature reviews were only used each in 7 out of 80 articles. Besides, only a small number of papers

conducted secondary database research (4 out of 80 articles).

3.2 Defining supply chain risk management

In the present SLR, while we were keen on capturing a broad range of perceptions, we tried to avoid

subjective bias. Hence, a definition of SCRM had to be explicitly stated by the original author, not merely

inferred, for consideration. The findings of this review indicate that the two propositions, namely, coordination

and identification have often been mentioned in SCRM definitions. The elements of coordination and

vulnerability reduction were first referred to in the definition of Jüttner et al. (2003), who took the first steps to

explain risk management within the SCRM context. They posit that the management of risks in the context of

SCM represents “the identification and management of risks for the supply chain, through a coordinated

approach amongst supply members to reduce supply chain vulnerability as a whole” (p. 124). On the one hand,

Norrman and Janssen (2008), as well as Tang (2006), put a primary emphasis on collaboration. On the other

hand, Giunipero and Eltantawy (2004) bring forward the argument that SCRM should have a long-term focus

and follow a continuous approach, requiring dedication from all supply chain members. It is apparent that only

Tang (2006), Manuj and Mentzer (2008b), and the Supply Chain Council included the performance and the cost

savings/ profitability dimension in their definition (cf. Table 2). However, Lavastre et al. (2012) adopt another

perspective on SCRM taking strategic and operational aspects together with short - and long-term assessment

into consideration. Wieland and Wallenberg (2012), likewise, hold a strategic perspective on SCRM, perceiving

it as an approach to implement strategies to reduce vulnerability and ensure continuity. Nonetheless, we have

found that the SCRM definition of Jüttner et al. (2003) is the most frequently used one in journal articles

studying SCRM. Their definition bases on a synthesis of traditional risk management and SCM principles. Next,

Norrman and Jansson (2004), Goh et al. (2007), as well as Lavastre et al. (2012) proposed concise and relevant

SCRM definitions based on research projects. One comprehensive definition of SCRM was suggested by Manuj

and Mentzer (2008b). Their definition builds upon existing literature (cf. Jüttner et al., 2003; Norrman and

Jansson, 2004) and on several in-depth interviews. They describe (global) SCRM as “the identification and

evaluation of risks and consequent losses in global supply chain and implementation of appropriate strategies

through a coordinated approach among supply chain members with the objective of reducing one or more of the

following - losses, probability, speed of event, speed of losses, the time for detection of the events, frequency, or

exposure - for supply chain outcomes that, in turn, lead to close match of actual savings and profitability with

those desired” (p. 205). Closer investigation of the correspondences and dissimilarities revealed that in most of

the papers the elements of coordination, collaboration, and identification across organisations formed part of

several SCRM definitions (e.g. Goh et al., 2007; Tang, 2006; The Supply Chain Council). However, there

seems to be no distinct consensus on the definition of “supply chain risk management” (see Table 2). Indeed,

several authors make evident the predominant heterogeneity by either synthesising earlier SCRM definitions to

create a new one (e.g. Giunipero and Eltantawy, 2004; Tang, 2006; Wieland and Wallenburg, 2012) or referring

to a certain number of different authors (e.g. Tang and Musa, 2011; Trkman and McCormack, 2009; Zhao et al.,

2013). Thus, since no core definition synthesising SCRM understanding has been published yet, we apply our

obtained cognition to convey an overarching definition of SCRM, which will be presented in Section 4.


41

Table 2 Definitions of supply chain risk management (SCRM)

Author(s)

Supply chain risk management definition(s)

Jüttner et al. (2003, p. 202)

SCRM means “the identification and management of risks for the supply chain

through a coordinated approach amongst supply chain members, to reduce

supply chain vulnerability as a whole.”

Giunipero and Eltantawy (2004, p. 703) “Risk management is a continual process that involves long-term dedication of

supply chain members.”

Norrman and Jansson (2004, p. 436)

“Supply chain risk management is to [collaborate] with partners in a supply

chain apply risk management process tools to deal with risks and uncertainties

caused by, or impacting on, logistics related activities or resources.“

Tang (2006, p. 453)

SCRM is “the management of supply chain risk through coordination or

collaboration among the supply chain partners as to ensure profitability and

continuity.”

Goh et al. (2007, pp. 164-165)

SCRM is defined „as the identification and management of risks within the

supply network and externally through co-ordinated approach amongst supply

chain members to reduce supply chain vulnerability as a whole.”

Manuj and Mentzer (2008, p. 205)

“Global SCRM is the identification and evaluation of risks and consequent

losses in global supply chain and implementation of appropriate strategies

through a coordinated approach among supply chain members with the objective

of reducing one or more of the following - losses,probability, speed of event,

speed of losses, the time for detection of the events, frequency, or exposure - for

supply chain outcomes that, in turn, lead to close matching of actual cost savings

and profitability with those desired.“

Thun and Hoenig (2011, p. 243)

”Risk management, in general, is described as the identification and analysis of

risks as well as their control. A main particularity of Supply Chain Risk

Management (SCRM) contrary to traditional risk management is that it is

characterized by a cross-company orientation aiming at the identification and

reduction of risks not only on the company level but rather focusing on entire

supply chains.“

Wieland and Wallenburg (2012, pp.

890-891)

“SCRM is defined as the implementation of strategies to manage both every day

and exceptional risks along the supply chain based on continuous risk

assessment with the objective of reducing vulnerability and ensuring continuity.”

Lavastre et al. (2012, p. 830) “SCRM is the management of risk that implies both strategic and operational

horizons for long-term and short-term assessment.”

Supply Chain Council

“SCRM is the systematic identification, assessment and mitigation of potential

disruptions in logistics networks with the objective to reduce their negative

impact on the logistics network’s performance.”

3.4 Enablers for an effective supply chain risk management

In analysing the enablers to make SCRM work, an inconsistent body of terminologies and wordings in the

literature can be noticed. While some refer to SCRM strategies as antecedents (Braunscheidel and Suresh, 2009;

Jüttner et al., 2003), or moderators (Manuj and Mentzer, 2008b), others call them activities (Sinha et al., 2004),

enablers (Faisal et al. 2006), elements (Christopher and Lee, 2004), capabilities (Chen et al., 2013; Sheffi and

Rice, 2005), or principles (Kleindorfer and Saad, 2005). Taking into account that capability terms have various

definitions in the literature, the present study remains neutral in its descriptive analysis and uses the term

‘enablers’. Hence, our SLR revealed 12 top enablers across various journal articles. The largest number of

studies deal with visibility (n=24) to ensure successful SCRM. Furthermore, researchers also discuss flexibility

(n=17) and relationships (n=15). Next, redundancy (n=13), followed by coordination as well as postponement

(each n=10), and multiple sourcing (each n=9) were considered as vital by several academics. Finally,

collaboration was guided with eight articles (n=8). Table 3 illustrates the top 12 enablers that represent a total

share of approximately 65% of the selected articles.


42

Table 3 Analysis of top 12 SCRM enablers in research from 2000 to the beginning 2015

Enabler(s)

Frequency Author(s)

Visibility 24

Johnson, 2001; Bode et al., 2011; Spekman and Davis, 2004; Finch, 2004;

Kleindorfer and Saad, 2005; Faisal et al., 2006; Sinha et al., 2004; Ritchie and

Brindley, 2007a, b; Lavastre et al, 2012; Christopher and Lee, 2004; Nooraie

and Parast, 2015; Yang and Yang, 2010; Jüttner et al. 2003; Manuj and

Mentzer, 2008a, b; Bandaly et al., 2014; Tang, 2006b; Tang and Tomlin,

2008; Wagner and Bode, 2008; Speier et al., 2011; Zsidisin et al., 2004; Khan

et al. 2008; Norrman and Jansson, 2004

Flexibility 17

Talluri et al., 2013; Knemeyer et al., 2009; Braunscheidel and Suresh, 2009;

Jüttner et al., 2003; Kleindorfer and Saad, 2005; Sinha et al. 2004;

Christopher and Holweg, 2011; Sheffi and Rice, 2005; Zsidisin and Wagner,

2010; Skipper and Hanna, 2009; Johnson, 2001; Khan et al., 2008; Tang,

2006b; Knemeyer et al., 2009; Tang and Tomlin, 2008; Thun and Hoenig,

2011; Wieland, 2013

Relationships 15

Groetsch et al., 2015; Giunipero and Eltantawy, 2004; Faisal et al., 2006;

Ritchie and Brindley, 2007a, b; Lavastre et al, 2012; Tang 2006b; Speier et

al., 2011; Kleindorfer and Saad, 2005; Hallikas et al., 2002; Lavastre et al,

2012; Jüttner et al., 2003; Khan et al. 2008; Spekman and Davis, 2004; Vilko

and Hallikas, 2012

Redundancy 13

Marley et al., 2014; Bode et al., 2011; Talluri et al., 2013; Zsidisin et al.,

2000; Lavastre et al, 2012; Zsidisin and Ellram, 2003; Tang, 2006b;

Kleindorfer and Saad, 2005; Tomlin, 2006; Sheffi and Rice, 2005; Knemeyer

et al., 2009; Schmitt and Singh, 2012; Zsidisin and Wagner, 2010

Coordination 10

Braunscheidel and Suresh, 2009; Ritchie and Brindley, 2007a, b; Knemeyer et

al., 2009; Ellis et al., 2010; Hallikas et al., 2004; Speier et al., 2011; Lavastre

et al., 2012; Sinha et al., 2004; Jüttner et al., 2003

Postponement 9

Manuj et al., 2014; Kleindorfer and Saad, 2005; Yang and Yang, 2010; Jüttner

et al. 2003; Manuj and Mentzer, 2008a, b; Bandaly et al., 2014; Tang, 2006b;

Tang and Tomlin, 2008; Wagner and Bode, 2008

Multiple sourcing 9

Sinha et al. 2004; Jüttner et al., 2003; Knemeyer et al., 2009; Norrman and

Jansson, 2004; Tang, 2006b; Zsidisin and Ellram, 2003; Kleindorfer and Saad,

2005; Ritchie and Brindley, 2007a,b

Collaboration 8

Jüttner et al., 2003; Spekman and Davis, 2004; Khan et al. 2008; Kleindorfer

and Saad, 2005; Christopher and Holweg, 2011; Lavastre et al., 2012; Vilko

and Hallikas, 2012; Chen et al., 2013

Risk Awareness 7 Braunscheidel and Suresh, 2009; Ritchie and Brindley, 2007a, b; Hallikas et

al., 2002; Manuj and Mentzer, 2008a, b

Agility 7

Faisal et al., 2006; Wieland and Wallenburg, 2012; Braunscheidel and Suresh,

2009; Khan et al., 2008; Wieland, 2013; Jüttner et al., 2003; Lavastre et al.,

2012

Avoidance 6 Manuj and Mentzer 2008a, b; Jüttner et al., 2003; Tang, 2006b; Tomlin, 2006;

Knemeyer et al., 2009

Contingency planning 6 Finch, 2004; Kleindorfer and Saad, 2005; Norrman and Jansson, 2004; Ellis et

al., 2011; Skipper and Hanna 2009; Zsidisin et al. 2000

Risk monitoring 6 Finch, 2004; Kleindorfer and Saad, 2005; Hallikas et al., 2004; Norrman and

Jansson, 2004; Hendricks and Singhal, 2005; Hoffmann et al., 2013

Transferring and sharing

risks 5

Li et al., 2015; Peck, 2006; Manuj and Mentzer, 2008a, b; Wagner and Bode,

2008; Knemeyer et al., 2009; Finch, 2004

3.5 Grouping and synthesis of enablers

The controversy and disparity that surrounds the SCRM definitions also surrounds the SCRM enablers. A

good illustration of the discrepancies in expression is the fact that some researchers referred to redundancy,

whereas others spoke about holding surplus inventory, safety stocks, strategic stock, or extra stock — although

all researchers might have meant the same. Thus, the findings designate that a greater agreement on specific

terms regarding key SCRM enablers is required. To group and synthesise the SCRM enablers, we are consistent

with other researchers that make a distinction between preventive and responsive risk mitigating methods (e.g.

Knemeyer et al., 2009; Tomlin, 2008; Wieland and Wallenburg; 2013; Zsidisin et al., 2000). Preventive methods

are cause-related measurement approaches that aim at reducing the probability of risk to occur (Thun and

Hoenig, 2011) and responsive methods are meant to minimise the obstacles of adverse events (Tomlin, 2008).

Within the preventive approach, we are of the opinion that six enablers may help protect against potential SCRs,

http://www.sciencedirect.com/science/article/pii/S147840921300054X#bib43



43

namely visibility, relationships, collaboration, coordination, multiple, postponement, and redundancy.

Responsive methods are effect-oriented approaches that aim at counter measuring the adverse consequence of

incidents; they do not immediately fight the risks but attempt to captivate the impairment caused by risks.

Accordingly, the respective SC should be designed in a way that the consequences of an incurred risk are

moderated (Thun and Hoenig, 2011; Tomlin, 2008). Thus, we propose that the following five enablers for

effective SCRM within the responsive approach to respond to SCRs, namely visibility, flexibility, multiple

sourcing, redundancy, and coordination.

The findings direct that visibility, multiple sourcing, and redundancy are central enablers needed in each

preventive and responsive methods of SCRM. However, while some enablers may be more suited for preventing

SCRs, others are more responsive in nature to immediately react to supply chain risks as soon as they emerge.

Figure 3 demonstrates SCRM enablers and their corresponding sub-level enablers classified into preventive and

responsive strategy. The conceptual framework provides a reference guide for practitioners in considering

SCRM implementation. The framework differs from other SCRM frameworks in terms of its scope as it is

exclusively centered around SCRM enablers.SCRM may be implemented in all or only selected strategies using

each enabler where appropriate, and there are several processes, tools and systems to choose from. Moreover,

SCRM benefits can also be identified at first glance.


44

Figure 3 Supply chain management enablers framework (including sub-level enablers)

45

3.6 The supply chain risk management–performance linkage

In this section, we will present the published contributions that address the relationship between SCRM or

SCRs respectively, and performance. Hendricks and Singhal (2005) illustrate that SC disruptions severely

impact the health of affected organisations concerning their profitability. They further found out that those firms

affected do only slowly recover from the detriments caused by those disruptions. Papadakis (2006) examined

the impact that supply chain disruptions have on the financial performance of companies. His empirical findings

proved the decrease in firm’s stock price due to supply chain risks. He further declares that risk exposure makes

it difficult for companies to anticipate supply chain disruptions, like, for instance, those arising from human-

made or natural disasters. Wilson (2007) analysed the effect of disturbances during transportation on supply

chain (SC) performance. Briefly, a transportation disruption between the 1st tier supplier and the warehouse has

the utmost adverse effect on the supply chain, resulting in high increase in inventory levels and materials in

transit, and in turn leading to unfilled customer orders. Wagner and Bode (2008) executed a large-scale

quantitative research examining the impact of SCRs on SC performance. They revealed that 6% of the variance

of the performance of supply chains was due to the adverse effect of supply chain risks. They concluded that

SCRM was of paramount importance in both managing demand- and supply-side risks. Four measures assess

the impact of SCRs on SC performance: order fill capacity, delivery dependability, customer satisfaction, and

delivery speed. Skipper and Hanna (2009) focused on flexibility and found that top management support,

information technology usage, resource alignment, and external collaboration profoundly contribute to the

flexibility and thus reduce the exposure to supply chain disruption risks. Flexibility has been demonstrated to

enhance the ability to minimise risk exposure in the event of a SC disruption. Braunscheidel and Suresh (2009)

showed that augmenting supply chain agility serves as a critical driver for mitigating supply chain risks.

According to these researchers, agility is of value for both response and mitigation strategies, highlighting fast,

preventive measures when confronted with supply chain risks. Lai et al. (2009) showed that systems efficiency

can be improved through inventory risk sharing within the supply chain. The authors illustrated that with

financial restriction, the combination mode, i.e. sharing inventory risks, delivers the greatest efficiency. In their

study, Thun and Hoenig (2011) revealed that organisations with a more mature level of SCRM implementation

degree yield a superior SC performance and those using the preventive SCRM method show greater flexibility

and are better at planning safety stocks. Kern et al. (2012) have empirically validated the continuous impact of

the three primary risk management steps, 1) risk identification, 2) risk assessment, and 3) risk mitigation on

business performance. Their research demonstrates that firms with high expertise in those three SCRM phases

render excellent performance concerning frequency and impact reduction of supply chain risks. Next, Wieland

and Wallenburg (2012) in their empirical study where survey data from 270 industrial firms had been collected

discovered that SCRM is necessary for the robustness and agility of a firm to improve performance. While

agility has a significantly positive effect only on a SC customer value, robustness has a significantly positive

impact on both performance measurements. Zhao et al. (2013) analysed both the relationship between SCRs

supply chain integration (SCI) and business performance. Their results showed that SCRs have a negative

impact on SCI and in turn on business performance. All in all, their findings advocate that SCRs have an

undesirable impact on internal, supplier, and customer integration, pointing out that supply chain delivery risks

hinder effective SCI. Chen et al. (2013) in their study, examined supply chain collaboration (SCC) as a risk

mitigation strategy with data collected from 203 manufacturing enterprises in Australia. The study shows that

SCC can significantly decrease SCRs. More precisely, their research demonstrated that process risks have the

severest direct effect on SC performance, and that process hazards cause the majority of external hazards, either

from the supply- or demand-side. Schmitt et al. (2015) proved that a decentralised design structure is ideal when

supply and demand uncertainty are both existent, which balances out cost variance via risk modification effect.

Finally, Nooraie and Parast (2015), revealed that increased visibility in SCs offers tremendous cost savings

when SC disturbances occur. The outcomes showed that increased visibility is alluring because it builds

efficiency in a SC and reduces both risks and costs.

4 DISCUSSION

4.1 Supply chain risk management definitions

From the very few definitions that exist, the results of our systematic review indicate that there is no

universal and widely accepted SCRM definition in the extant literature. Given the fact that the foundation of

SCM is the coordination between each entity and interface within a supply chain Thun and Hoenig (2011)

correctly perceived that SCRM in opposition to traditional risk management holds a cross-company perspective

while entire supply chain networks are the centre of attention. Although many authors adopt different views on

SCRM, they put emphasis on the primary focus of SCRM that it extends traditional risk management

approaches by integrating all partners upstream and downstream the supply chain. While there may only be

slight differences between the definitions offered by the authors aforementioned, the central meaning is


46

apparent: Any approach to SCRM should seek to understand, identify, and reduce risks to the SC as a whole

through coordination amongst partners. An in-depth analysis of the literature reveals that although there are

similar definitions of SCRM in existence there is no widely and commonly one available. Considering the

evolution of SCRM definitions, most of the proposals have only slightly been modified, referring to existing

definitions or resulting from theory-building. A plausible explanation for the deviants in defining the elements

inherent in SCRM might be that many researchers only use the SCRM definition as a basis for their research,

thus, merely slightly modifying extant definitions without developing a central and unified definition. Besides,

SCRM is a young cross-disciplinary research field with multiple facets; it should be borne in mind that previous

definitions have primarily been developed based on conceptual views. Thus, SCRM definitions lack the

empirical testing that could impede the creation of an universal definition. The conducted research further

reveals that most researchers focus on defining ‘supply chain risk’ than on proposing a coherent definition on

SCRM. Reviewing the literature it appears that the definition of “supply chain risk” is given more consideration

to by authors than the definition of “supply chain risk management”(cf. Harland et al., 2003; Sinha et al., 2004;

Zsidisin et al., 2004; Zsidisin and Ellram, 2003). A worthy indication of the level of maturity of a discipline is

provided by the attitude of researchers concerning the definition of core concepts (Burgess et al., 2009), which

suggests that definitional consensus does not exist and that SCRM is still in the evolving stage and has not yet

reached maturity. We consequently anticipate that more new or altered definitions will be offered shortly. Thus,

based on the findings and insights of the review, we convey an overarching definition of SCRM:

SCRM implies the identification, assessment, monitoring and evaluation of risks and potential threats within

and outside supply chain networks with all members and entities involved. It supports cooperative and

collaborative management of supply chain risks with the aid of adequate tools, techniques, and strategies as to

mitigate or eliminate risk exposure. SCRM, therefore, aims at ensuring flexibility and agility to deliver

operational excellence and to achieve superior performance and customer value.

4.2 Supply chain risk management enablers

The examination of 80 journal articles reveals that preventive and responsive methods require similar

enablers to make SCRM work. Furthermore, the analysis clearly demonstrates a lack of consistency among

different enablers that may hinder the ability to implement SCRM effectively. Hence, a greater consensus on

particular notions and terms concerning SCRM enablers is undoubtedly required. Consequently, we grouped and

synthesised the different terms into preventive and responsive SCRM methods. For the preventive approach, the

strategy constitutes the enablers of visibility, relationships, collaboration, multiple sourcing, postponement, and

redundancy. In most cases, late differentiation (postponement) works best in situations organisations have to

face a high degree of demand-side risk, while the supply-side risks are fairly small (Manuj et al., 2014). In

contrast, a responsive approach includes visibility, flexibility, multiple sourcing, redundancy, and coordination.

Stank et al. (2001) also comment the benefits of collaboration to include a reduction in resource sharing, greater

response to customer needs, and increased flexibility in adjusting to alterations in the marketplace. In their

study, Wieland and Wallenburg (2013) demonstrate the effectiveness of preventive and responsive strategies for

dealing with unexpected interruptions where they conclude that the design of the SC has a substantial influence

on the suitability of the different SCRM enablers. As a result, we may assume that through combining both

strategy methods organisations can exploit the advantages of both “world”. This is in line with the findings

made by Thun and Hoenig (2011), who found that firms applying preventive methods show higher values in

terms of increased flexibility, reduced stocks, faster reactivity, and cost reduction, whereas firms adopting

responsive methods show higher average values concerning the lessening of the bullwhip effect.

Several authors suggest that an organisation’s ability to capture information for use in planning processes is

critical to exploiting and developing adequate capabilities to deal with SCRs (Fawcett et al., 2000). Firms must

possess that ability to share information to establish contingency plans, to manage planning processes, and to

control daily business operations (Kaplan, 1991). Fundamental to the ability to plan is the exchange of large

amounts of information within and between SC entities. Information and communication systems allow a firm

to implement strategy and planning procedures by making decisions more quickly and increase inter–

organisational and intra–organisational performance levels (Sanders and Premus, 2005). IT usage allows for

efficient communication and information-sharing related to the allocation of resources, which, combined with

adequate resource alignment allows a SC network to respond quickly and in a coordinated manner to SCRs for

maximum operational benefit (Bode et al., 2011; Sanders and Primus, 2005; Wakolbinger et al., 2011).

4.3 Supply chain risk management and performance The findings show a lack of empirical evidence for the linkage between SCRM and its related performance

outcomes; however, there is at least fractional confirmation that SCRM and performance are positively linked to

each other. Nevertheless, only 13 out 80 journal articles conducted empirical research to quantify the impact of

SCRM, for instance, on organisational performance. The analysis point to robustness measures to accommodate

47

sudden, unexpected events and to minimise the potential risk impact on performance. Overall, the contemporary

and emerging studies show that the total SCRM effectiveness can be measured through three fundamental

performance indicators (supply chain performance, financial performance and customer satisfaction), which can

be used to quantify the management of SCRs. Schoenherr et al. (2012) emphasises that the risk topic is a field of

inquiry of paramount relevance in the SCM context and despite the numerous SCRM studies, little focus on

evaluating and measuring the effect of SCRM on performance. Until now, SCRM experiences the absence of

reasonable and sufficient quantitative measures for SCR that considers the attributes of cutting edge supply

chains. Measures principally used in finance and accounting are frequently used as mathematical methods in

SCRM, too. Though, those measures regularly address the deviation from efficiency-based targets (Heckmann

et al., 2014). Consequently, they lack the evaluation of both operational effectiveness and essential performance

indicators like product quality or customer value (Lapide, 2000). Research shows that the impact of a risk is

weakened when companies implement preventive or responsive mitigation strategies (e.g. Knemeyer et al.,

2009, Norrman and Jansson, 2004; Thun and Hoenig, 2011). Therefore, it is s favourable to make the risk

probability and the risk impact comparable; thus, for the risk measurement process, two elements of a risk are

pivotal: likelihood and impact (Norrman and Jansson, 2004). Under the terms of Zsidisin et al. (2004), risk

probability measures how frequently a harmful event emerges and risk impact states the implication of that loss.

In this context, Manuj and Mentzer (2008b) emphasise the importance of speed and group the speed of risk into

three distinct categories:

(a) The rate and frequency of the event that leads to loss,

(b) the rate and frequency losses emerge, and

(c) the rate at which the risk event is detected.

Schmitt and Singh (2012) recommend companies to focus on reducing the duration of disruptions over the

frequency. Tomlin (2006, p. 640) supports the view that the nature of a disturbance, e.g. frequent but short vs.

rare but long, is a crucial determinant of the optimal strategy, when studying mitigation and contingency

methods. The probability of risk and its impact is thus recommended to be applied as decision criteria to select

the right strategy for each particular supply chain (Wieland, 2013). Although the above-mentioned

investigations clearly manifest that SCRM has a positive effect on performance and SCRs negatively impact on

organisational output, the number of studies — only around 16% (13 out of 80 papers) — empirically test that

relationship. While our analysis examined SCRM as the ability to avoid and reduce vulnerability to respond to

uncertainties and risks as well as to analyse and mitigate potential disruptions we identified that SCRM can be

quantified through three essential performance metrics that enable reporting on how severe a SCR impact is and

how a firm’s SCRM performs:

(1) Supply chain performance

(2) Financial performance and

(3) Customer value.

5 MANAGERIAL IMPLICATIONS

The challenges of managing supply chain risks are no less than important than managing other risks faced

by a company. As our world becomes more and more disordered and turbulent, the management of risks will

play a greater role in both global supply chain network design and daily operating decision-making (Fawcett et

al., 2011). SCRM aims at providing approaches and practices for identifying, assessing, analysing and treating

areas of vulnerability, disruptions and risks in supply chain networks (Neiger et al., 2009; Thun and Hoenig,

2011; Jüttner et al., 2003). Practical application of both of responsive and preventive strategy approaches

collectively allows organisations or networks to have their resources properly allocated and positioned for

maximum benefit. SCRM requires top management to take an increasingly preventive role in being well-armed

for several interruptions and necessitates the same rigorous analysis and evaluation of various options and

alternatives for modifying these risks (Silva and Reddy, 2011). Furthermore, measuring SCRM is a crucial

managerial prerequisite that supports an organisation’s knowledge and awareness of handling unexpected risk

events. It also aids organisations to evaluate their SCRM, even in terms of malfunction. Corresponding firms of

SCs thus need to establish a common understanding of SCRs and agree upon on a coherent risk assessment and

evaluation standard, which enables to evaluate the identified risks irrespective of the company 's specific

preparedness to take risks. As a result, our framework with the groupings of SCRM enablers and their

corresponding sub-level enablers provide an excellent managerial guideline to establish an effective SCRM. The

enablers can be applied to design managerial processes to handle SCRs and to identify areas for improvement.

Managers can further apply the identified SCRM enablers in this study to benchmark preventive and responsive

SCRM methods. Practical application of both preventive and responsive approaches collectively allows

organisations or networks to have their resources properly allocated and positioned for maximum benefit. We





48

should note, however, that SCRM requires top management to take an increasingly proactive role in being well-

armed for several interruptions and necessitates a rigorous analysis and evaluation of various options and

alternatives for mitigating risks (Silva and Reddy, 2011).

6 AVENUES FOR FUTURE RESEARCH

Hence, the challenge is first to conduct further studies on that highly relevant topic in a meaningful and

practical context. Those research studies could focus attention on best practices to prevent SCRs with the aim to

meet customer requirements (Lee et al., 2004). Christopher and Lee (2004) prove through the “risk spiral” how

the lack of information leads to a “self-perpetuating descent into chaos” (p. 389). We have reviewed research on

SCRM with a focus on studies that were published in the last decade to date. Subsequent to introducing our

literature review findings we created a definition for supply chain risk management. Furthermore, taking into

account the findings from the literature survey, we have created a unique framework for SCRM with the focus

on it building enablers. In the following we will articulate research propositions to guide future research

prospect aiming at advancing current knowledge on the linkage between SCRM and performance. We will

indicate the social network theory (SNT) as a potential theoretical foundation.

The collected works on social network theory (SNT) are enormous, and especially since around 2000,

rising exponentially (Borgatti and Halgin, 2011); while an immense range of conceptions and methods have

been created (Wynstra et al., 2015). A supply chain may be seen as a value chain of social networks (Gulati,

1998; Gulati et al., 2000). Social network theory is applicable for the analysis of inter-organisational

relationships as firms endeavour to share data, synchronise their plans, and create products conjointly

(Galaskiewicz, 2011). The SNT takes a relational perspective and highlights on the connections a firm has with

different companies, and on how those relationships affect an organisation’s behaviour and performance (Dyer

and Chu, 2000). The SNT views organisational outcomes as a function of the social interrelationships between

firms or individual actors in a network (Jones et al., 1997). A company cannot alleviate risks in isolation. They

are asked to institute viable network structure of clients, suppliers, competitors, university bodies and research

societies, and so forth. The SNT gives an awareness that the benefits and cooperation among firms could be to a

great extent by the grant of network assets and their positionings within the network (e.g., cliques, centrality)

(Chang, 2003). In light of the social network theory, we conclude SCRM is an ongoing process that implicates

long-term dedication of all supply chain members involved (Giunipero and Eltantawy, 2004; Mahapatra et al.,

2010; Manuj and Mentzer, 2008b).

In any case, the relationship between the SCRM and SC performance has seldom been empirically

confirmed (Melnyk et al., 2004 and Ritchie and Brindley, 2007a, respectively). Thus, Hoffmann et al. (2013)

request for more empirical research in SCRM to explain SC performance. Firstly, we recommend researchers

further to study the synthesised findings of the proposed conceptual SCRM framework in a large-scale

quantitative study, in particular with the purpose to quantify the within reported enablers. The enablers may be

in light of practical advancement, embraced and further developed into measurement constructs in future

research.Several authors suggest that an organisation’s ability to capture information for use in planning

processes is critical to exploiting and developing adequate capabilities to deal with SCRs (Fawcett et al., 2000;

Yu and Goh, 2014). Firms must possess that ability to share the information to establish contingency plans, to

manage planning processes, and to control daily business operations (Sanders and Premus, 2005). Hence, we

formulate the first proposition as follows:

P1: The higher the level of supply chain visibility, the greater the ability to mitigate supply chain risks and

the higher the firm’s performance and customer value.

By achieving high levels of external integration through e.g. collaboration firms can increase their agility

and are able to better respond to market uncertainty concerning customer needs and foreseen/unforeseen

disruptions (Lavastre et al., 2012; Sinkovics and Roath, 2004). Some researchers have included collaboration

into SCRM mitigation frameworks (e.g. Christopher and Peck 2004; Chopra and Sodhi, 2004; Hallikas et al.

2004), but they are generally conceptually-based and provide little empirical evidence. In this context, Chen et

al. (2013) claim that collaboration is essential to mitigate SCRs; however this linkage has not been investigated

thoroughly, which leads to the second proposition:

P2: The higher the level of supply chain collaboration, the greater the ability to mitigate supply chain risks

and the better the firm’s performance and customer value.

In uncertain markets, a flexible supply chain can work out its options quicker than its competitors (Manuj

and Mentzer, 2008a). Zhang et al. (2002, 2003) examined the positive relationships between various types of

flexibility in terms of increased performance. Consistent with a study by Swafford et al. (2006), it was

established that flexibility in purchasing, production, distribution and logistics significantly contribute to the



49

achievement of agility. Likewise, Braunscheidel and Suresh (2009) indicate that flexibility is essential in global

SCs because it plays an assisting role in the coordination process and offers a unique ability to support

companies in managing the high levels of uncertainties inherent in global business operations. According to

Fawcett et al. (1996) firms that accomplish higher levels of flexibility leave behind their less flexible

competitors. Hence, we propose the following:

P3: The higher the level of supply chain flexibility, the greater the ability to mitigate supply chain risks and

the higher the firm’s performance and customer value.

Accordingly, in light of the identified SCRM performance metrics, we claim that through smooth exchange

of information, higher level of collaboration, and greater flexibility firms may improve their organisational

performance, and in turn achieve higher customer value. The proposed propositions are in line with suggestions

made by Manuj and Mentzer (2008a) who consider among others for instance flexibility, information systems

and performance metrics as key enablers in the process of risk management and mitigation. A summary of the

propositions is shown in Table 4.

Table 4: Summary of research propositions.

SUMMARY OF RESEARCH PROPOSITIONS

P1 The higher the level of supply chain visibility, the greater the ability to mitigate supply chain risks and the higher

the firm’s performance.

P2 The higher the level of supply chain collaboration, the greater the ability to mitigate supply chain risks and the

better the firm’s performance.

P3 The higher the level of supply chain flexibility, the greater the ability to mitigate supply chain risks and the higher

the firm’s performance.

Secondly, we also suggest investigating best practices for effective risk mitigation. Academics may

generate qualitative and quantitative measures to analyse the effect of SCRM enablers on performance to attain

new valuable insights. Therefore, several scholars advocate the application of mixed methods in SCM (Connelly

et al., 2013; Gammelgaard and Flint, 2012; Taylor and Taylor, 2009). Thirdly, we advise conducting

longitudinal research to monitor and evaluate the long-term performance after implementing SCRM. The

present study contributes to the SCRM literature in several ways. The paper at hand offers an SLR with the

state-of-the-art research with extant knowledge. Its value lies in the ability to synthesise present study and aid

understanding of the SCRM phenomenon to provide a solid basis for further enrichment. Hence, the findings

presented in the present study reveal the potential for future research endeavours in this significant scientific

discipline. We further hope that our SLR will contribute to the adoption of a standard and accepted definition of

SCRM.

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