I

THE DETERMINANTS AND PERFORMANCE EFFECTS OF INTER-

ORGANIZATIONAL COST MANAGEMENT PRACTICES IN THE

SUPPLY CHAIN Word count: 19299 Julien Neven Stamnummer/ Student number: 01609077 Promotor/ Supervisor: Prof. dr. Regine Slagmulder Masterproef voorgedragen tot het bekomen van de graad van: Master’s Dissertation submitted to obtain the degree of:

Master of Science in Business Economics Academic year: 2016 - 2017

II

THE DETERMINANTS AND PERFORMANCE EFFECTS OF INTER-

ORGANIZATIONAL COST MANAGEMENT PRACTICES IN THE

SUPPLY CHAIN Word count: 19299 Julien Neven Stamnummer/ Student number: 01609077 Promotor/ Supervisor: Prof. dr. Regine Slagmulder Masterproef voorgedragen tot het bekomen van de graad van: Master’s Dissertation submitted to obtain the degree of:

Master of Science in Business Economics Academic year: 2016 - 2017

Deze pagina is niet beschikbaar omdat ze persoonsgegevens bevat.Universiteitsbibliotheek Gent, 2021.

This page is not available because it contains personal information.Ghent University, Library, 2021.

IV

Abstract

Companies require effective supply chain management in today’s business environment. This

is due to certain current developments such as rising globalization, vertical disintegration, and

changing competition. Firms, therefore, increasingly rival against their peers on a supply chain

level, instead of the traditional firm level. Furthermore, managers also need to focus on the

concern of cost management, which is gaining importance as a consequence of the financial

crisis of 2007-2008, the ever-increasing costs of logistics and energy, and the rising customer

demands for lower prices. However, this trend does not only require cost management within

firms but also across supply chains. Hence, companies should consider adopting approaches

that allow controlling these supply chain level costs. This research was performed to investigate

the firm performance effects of four such practices. It was found that two of the practices, price

benchmarking and inter-organizational target costing, positively affect firm performance when

applied in isolation. However, when used in combination, they no longer seemed beneficial. In

addition, four factors that precede supply chain-wide cost management were examined. Results

revealed that firms with higher levels of internal cost management, information sharing with

their supply chain partners, and trust and commitment in partner relations experienced higher

inter-organizational cost management involvement. Lastly, the latter two antecedents seemed

to positively moderate the performance effects of some of the interfirm cost management

practices. Particularly, more information sharing made price benchmarking more effective,

while more trust and commitment made both price benchmarking and inter-organizational

target costing more effective.

V

Foreword

This master thesis was written as completion to the Master of Science in Business Economics

with major in Corporate Finance at the University of Gent. The writing of this master thesis

has been a scholarly as much as a personal challenge. It has taught me many things, such as

how to devour and report an abundance of literature, set up a correct research proposal, perform

appropriate tests to analyze this proposal, and formulate relevant inferences and conclusions.

It has been a very valuable experience that has helped me develop my educational as well as

my personal skills.

I would first like to give thanks to my supervisor prof. Dr. Regine Slagmulder for her guidance

and advice that enabled me to write the thesis I had intended to write from the start. I am

thankful for the feedback she has given me throughout the year, which allowed me to perfect

and finalize my work with a satisfied feeling. Also, I would like to show my appreciation to

my commissioner prof. Dr. Sophie Hoozée and all other people that read my dissertation.

Furthermore, I thank my parents, sister, and friends for their endless love and support that

helped me through not only the process of writing this thesis but also through my entire

academic career.

VI

Table of Contents

Abstract .................................................................................................................................. IV

Foreword .................................................................................................................................. V

Table of Contents .................................................................................................................. VI

List of Abbreviations ......................................................................................................... VIII

List of Tables ......................................................................................................................... IX

List of Figures ........................................................................................................................ IX

1. Introduction ...................................................................................................................... 1

2. Literature Review ............................................................................................................ 3

2.1. Supply Chains and Their Management ............................................................................. 3 2.1.1. Supply Chain Management Defined ......................................................................... 3 2.1.2. Supply Chain Management Developments .............................................................. 5

2.2. Cost Management ................................................................................................................ 7 2.2.1. The Importance of Cost Management in Supply Chains ........................................ 7 2.2.2. Inter-Organizational Cost Management Practices .................................................. 9 2.2.3. Antecedents of Supply Chain Management and Inter-Organizational Cost

Management ............................................................................................................................... 16 2.3. Hypothesis Formulation .................................................................................................... 17

3. Methodology ................................................................................................................... 24

3.1. Research Design ................................................................................................................. 24 3.2. Sample Description ............................................................................................................ 26 3.3. Variable Description ......................................................................................................... 28

4. Discussion ....................................................................................................................... 32

4.1. Empirical Results .............................................................................................................. 32 4.2. Managerial Insights and Future Research ...................................................................... 46

5. Conclusion ...................................................................................................................... 52

6. References ........................................................................................................................ X

7. Appendices .................................................................................................................. XXI

7.1. Questionnaire .................................................................................................................. XXI 7.2. Selection of Industries .............................................................................................. XXXIII

VII

7.3. Distribution of Companies Across Industries ........................................................ XXXIV 7.4. Distribution of Companies Across “Other Industries” ........................................... XXXV 7.5. Grouping of Industries ............................................................................................... XXXV 7.6. Distribution of Companies According to Most Recent Annual Gross Sales ....... XXXVI 7.7. Distribution of Companies According to Current Number of Employees ......... XXXVII 7.8. Distribution of Companies According to Use of Intrafirm Cost Management Practices

XXXVIII 7.9. Distribution of Companies According to Use of IOCM Practices ....................... XXXIX 7.10. Correlation Matrices: Antecedents and IOCM Involvement ....................................... XL 7.11. Correlation Matrices: IOCM Practices and Performance ......................................... XLI 7.12. Hypothesis 7b: One-Way ANOVA Results ................................................................. XLII 7.13. Multivariate Linear Regression Results .................................................................... XLIII 7.14. Frequency Distribution of the Score of Trust and Commitment ............................ XLIV 7.15. Frequency Distribution of the Perceived Performance Effects ............................... XLIV 7.16. Frequency Distribution of the Future Outlook .......................................................... XLV

VIII

List of Abbreviations

ABC Activity-Based Costing

ANOVA Analysis of Variance

BVD Bureau Van Dijk

IOCM Inter-Organizational Cost Management

M Mean

ROA Return on Assets

ROI Return on Investment

SCM Supply Chain Management

SD Standard Deviation

IX

List of Tables

Table 1. Stages of ABC Implementation ................................................................................. 14

Table 2. IOCM Involvement Means for Firms with High and Low Intrafirm Cost

Management ..................................................................................................................... 33

Table 3. IOCM Involvement Means for Firms with High and Low Information Sharing ...... 34

Table 4. IOCM Involvement Means for Firms with High and Low Trust and Commitment .. 34

Table 5. IOCM Involvement Means for Firms with High and Low Industry Competitiveness

.......................................................................................................................................... 35

Table 6. Output Linear Regression with Price Benchmarking ................................................ 38

Table 7. Output Linear Regression with ABC ......................................................................... 39

Table 8. Output Linear Regression with Target Costing ......................................................... 40

Table 9. Overview of Tests and Results .................................................................................. 47

List of Figures

Figure 1. Illustration of a Manufacturing Company’s Supply Chain. R. Spekman, J. Kamauff,

and N. Myhr, 1998. ............................................................................................................ 4

Figure 2. Supply Chain Management Antecedents and Consequences. J. Mentzer, W. DeWitt,

J. Keebler, S. Min, N. Nix, C. Smith, and Z. Zacharia, 2001. ......................................... 17

Figure 3. Relation Between Interfirm Cost Management Practices and Firm Performance. .. 24

Figure 4. Scatterplot of Moderating Role of Information Sharing on Performance Effects of

Price Benchmarking. ........................................................................................................ 43

Figure 5. Scatterplot of Moderating Role of Trust and Commitment on Performance Effects

of Price Benchmarking. ................................................................................................... 45

Figure 6. Scatterplot of Moderating Role of Trust and Commitment on Performance Effects

of Inter-organizational Target Costing. ............................................................................ 46

1

1. Introduction

From the beginning of time, it seems that firms have always had an interactive relationship

with their external settings. On the one hand, companies can severely affect the lives of the

communities and wider environments in which they operate with their positive and negative

externalities (Crouch, 2006). On the other hand, these external parties can also impact how

firms function and can impose certain actions such that they can influence the survival of firms.

In fact, what makes or breaks a company nowadays is its ability to be flexible and responsive

to its environment and adapt accordingly if necessary. This environment of many firms is now

demanding greater capacity to contain costs and thereby offer lower prices.

It seems to be a given in today’s business world that the latter is one of managers’ current

primary roles to optimize profits for shareholders by controlling overheads to the best of their

ability (Fayard, Lee, Leitch, & Kettinger, 2014). These outlays can include an abundance of

types such as the ones pertaining to operating the daily business like labor, materials or

administrative costs, or those that are strategic like R&D investments and capital expenditures

in property and equipment. In the past, corporations have attempted to perform their activities

as efficiently as possible by managing costs arising within their own firm boundaries. By doing

so, they implemented practices to measure, track, and control the internal costs. In contrast to

these traditional approaches, the focus these days is slowly but surely shifting from an internal

to an inter-organizational level. This interfirm level implies a wider application of previously

internalized cost management practices by involving supply chain partners of focal firms. This

requires solid supply chain management and cost management capabilities of all participants.

It thus seems crucial to adopt the relevant techniques that allow firms to continue operating

and become responsive to the demands of the environment they operate in.

Before blindly jumping on the bandwagon of interfirm cost management and implementing

these practices, however, it bears to verify whether there are indeed benefits to reap.

Particularly, from a managerial point of view, it appears to be essential that the impact of

interfirm cost management practices is tested on the performance of companies adopting them.

This way, if the effect appears to be positive, it can be said that managers should look into the

possibility of extending their internally focused view and include their partners into the

attempts to become more efficient. This is crucial to learn since companies in supply chains

2

are gradually competing on a higher “supply chain level” (Antai, 2011; Tan, 2001; Wu,

Chuang, & Hsu, 2014). Furthermore, firms in these supply chains are confronted with the

constant pressure of lowering costs (Christopher & Gattorna, 2004; Trent & Monczka, 2003).

Combining both of these developments, the relevance of this study becomes evident. It would

benefit firms to know whether particular interfirm cost management actions that are gaining

attention nowadays aid firms in achieving a reduction in overheads and eventually enable them

to compete successfully.

From an academic perspective, this research also proves relevant as it combines the most

frequently employed cost management approaches and those that are most covered in the

existing literature. It is, therefore, a comprehensive study that will empirically investigate the

question whether certain interfirm cost management approaches are truly valuable to firms’

performance within a supply chain in a Belgian setting. In doing so, the factors that allow this

kind of cost management will be investigated. Additionally, the specific impact of four such

practices on firms’ performance will be examined using different measures. These performance

effects based on an interfirm perspective have not been extensively studied in this specific

design. It is worthwhile to do so since it might potentially provide proof to fill the gap between

the well-known benefits of cost management techniques applied within firms and the claimed

importance of the wider application of these techniques within a supply chain.

The remainder of this text is structured as follows. First, the relevant literature on the topic of

supply chains and cost management within firms and within supply chains will be discussed.

Second, four specific inter-organizational cost management techniques will be defined, namely

price benchmarking, supplier evaluation, inter-organizational activity-based costing, inter-

organizational target costing. Third, certain antecedents to these interfirm practices will be

mentioned briefly. Fourth, the latter antecedents will be elaborated on more in detail to

formulate the first set of hypotheses. After this, the second set of hypotheses will follow. Fifth,

the methodology will describe the design of the study, the sample of companies used for the

study, and the constructs of importance. Sixth, in the discussion, results of the performed tests

will be examined and interpretations will be provided. Lastly, the conclusion will recapitulate

the main findings of the study and state lessons to be learned from this research.

3

2. Literature Review

In order to fully grasp the topic regarding the research question mentioned above, it would be

suitable to frame the study with contemporary literature. This will offer a supportive framework

to comprehend the following analyses and sensibly interpret the results. Therefore, a general

overview of supply chains and supply chain management will be offered to place the theme of

cost management in supply chains. Then, cost management, its importance, and interfirm

practices will be elaborated on more in detail. More specifically, all of these constructs will be

defined and their current developments will be reviewed.

2.1. Supply Chains and Their Management

2.1.1. Supply Chain Management Defined

After the first introduction of the term “Supply Chain Management” (SCM) in 1982, an

abundance of definitions started to appear. For example, an internet search (Google) in January

2005 of the term “SCM definition” lead to 2360 possible options (Gibson, Mentzer, & Cook,

2005). Many researchers say an attempt to attain a clear definition of what SCM is, and also

what it is not, is imperative for understanding the concept and applying it in practice and

research (Cooper, Lambert, & Pagh, 1997). Hence, it will be clarified what SCM exactly entails

to avoid further misconceptions that might negatively impact the study at hand and because it

remains an important element of the topic of this research.

In order to get a consistent and clear exact meaning of the phenomenon of SCM, Mentzer et

al. (2001) studied the available research on the topic, and reviewed, categorized, and

synthesized different definitions of “supply chain” and “supply chain management”. Taking

together several aligning conceptualizations, the authors ultimately define a supply chain as “a

set of three or more entities directly involved in the upstream and downstream flows of

products, services, finances, and/or information from a source to a customer” (Mentzer et al.,

2001, p.4). Figure 1 demonstrates a typical supply chain with a manufacturing company as

focal company and includes activities such as, but not limited to, planning, sourcing and

procurement, scheduling, order processing, manufacturing, inventory management,

4

transportation, customer service, and measuring performance (Lummus & Vokurka, 1999;

Spekman, Kamauff, & Myhr, 1998). Herein, material generally flows downstream to the

customers, cash flows upstream, and information flows in both directions.

Figure 1. Illustration of a Manufacturing Company’s Supply Chain. R. Spekman, J. Kamauff,

and N. Myhr, 1998.

In their attempt to define SCM, Mentzer et al. (2001) looked at the complete picture of and

around SCM to enable a synthesis of three categories of definitions for SCM. This lead to

defining SCM as “the systemic, strategic coordination of the traditional business functions and

the tactics across these business functions within a particular company and across businesses

within the supply chain, for the purposes of improving the long-term performance of the

individual companies and the supply chain as a whole” (Mentzer et al., 2001, p.18). Hence,

SCM can be viewed as the seamless synchronization of business activities within a single firm

and with activities of other firms in the supply chain to enable better overall performance of

the firm and supply chain.

Alternatively, in another study, Gibson et al. (2005) surveyed the Council of Supply Chain

Management Professionals on its members’ views of SCM to reach a consensus for a potential

definition. From the results, the council eventually adopted the definition that SCM entails “the

planning and management of all activities involved in sourcing and procurement, conversion,

and all logistics management activities … [and] also includes coordination and collaboration

with channel partners …” (Gibson et al., 2005, p.22). So, it is the management of the

collaborative activities that transform and bring a product or service from its initial stage to the

final end-consumer. This corresponds to the definition of SCM from another group of Supply

Chain professionals, the Global Supply Chain Forum, an assembly of non-competing firms and

5

academic researchers. They aim to improve the theory and practice of SCM (Lambert &

Cooper, 2000). Combining the latter views, SCM can be understood as managing the activities

within and across firms in a supply chain that bring a product from beginning to end, in an

effort to improve individual and collective performance.

The main goals of SCM according to Dekker and Van Goor (2000) and supporting literature

are twofold. These two goals are in line with the consequences or reasons from Mentzer et al.

(2001). First, SCM is put in place to improve efficiency of all processes taking place in the

chain. Second, improving the effectiveness of these processes and their outcomes is also

crucial. Together these goals permit firms, individually as well as a system, to enhance

performance. However, the authors also mention these two goals are not completely

comprehensive and state that other related objectives exist, such as reducing uncertainty,

reducing inventory, lowering cycle time, increasing visibility, and improving customer

satisfaction, which all ultimately help to improve efficiency and effectiveness (Dekker & Van

Goor, 2000; Tan, 2001). Overall, effective SCM will then be characterized by closely working

and interdependent partners investing in long-term relations, who share information freely,

solve problems cooperatively, plan a future together, and see success as interdependent

(Spekman et al., 1998).

2.1.2. Supply Chain Management Developments

As was mentioned before, the concept of SCM has emerged more than three decades ago, but

it has not always received the attention it deserved. Particularly, over the last 35 years SCM

has gone through many stages of changes in importance (Lancioni, 2000). It was once

considered as “the forgotten management science”, only partially applied to commercial

industries. However, nowadays the topic has been rising in importance for most managers and

their firms.

Firstly, this is exemplified by the increasing amount of universities and colleges covering the

topic and offering courses in this area (Lancioni, 2000). It, therefore, can be posited that it plays

an important role in the business world today and should be understood by all managers in spe.

6

Secondly, the interest in SCM also sparked due to the change in companies’ organization.

Specifically, firms in the 90s and later became more specialized or vertically disintegrated and

therefore realized that from the moment they contracted with other firms, they would both

benefit from others’ success (Lee, Huynh, Kwok, & Pi, 2003; Lummus & Vovurka, 1999).

Thus, SCM practices have become crucial in these instances to handle the interfaces between

cooperating firms to ensure optimality. Third, further evidence of the expanding popularity of

SCM comes from the effects of globalization, whereby firms increasingly source globally,

forcing suppliers and buyers to look for better ways to coordinate the shared flows of

information and materials (Mentzer et al., 2001). This information sharing activity is essential

as it can allow firms to outperform their competitors by, among others, reducing inventories,

lowering costs, and improving the efficiency of flows of goods and services (Samaddar,

Nargundkar, & Daley, 2005). Thus, companies should put more emphasis on SCM to achieve

the latter advantages. Another consequence of globalization is that the increased global

competition leads to greater difficulty for firms to go at it alone and ensure benefits from

solitary activities. Some authors claim that competition has changed in terms of who to

compete with since interfirm competition has changed to inter-supply chain competition (Wu

et al., 2014). Moreover, they say success can no longer be measured based on an intrafirm level

but should be evaluated based on an interfirm level (Tan, 2001). In fact, Spekman et al. (1998)

mention that a firm is only as successful as its (weakest) supply chain partner and requires a

well-developed ability to coordinate with its partners. As such, a coordinated network of

companies and their activities can serve as a source of competitive advantage for the individual

firm as well as the entire chain (Baihaqi & Sohal, 2012).

Fourth, the competition between firms has also shifted in terms of what they compete on

(Mentzer et al., 2001). Customer demands focus more on speed, quality, and low costs beyond

just on-time delivery and damage-free products. This requires closer coordination with supply

chain partners. Lastly, SCM should not be ignored since it improves positions of individual as

well as entire networks of firms. Li, Ragu-Nathan, Ragu-Nathan, and Subba Rao (2004)

investigated the impact of certain SCM practices on organizational performance and

competitive advantage, and thereby outperforming competitors. The article distinguished five

types of SCM practices; strategic supplier partnership, customer relationship, level of

information sharing, quality of information sharing, and postponement. In the results, it is

confirmed that these practices positively influence the organizational performance and

7

competitive advantage of companies (Li et al., 2004). Thus, SCM indeed proves beneficial for

performance.

From these numerous yet non-exhaustive trends, it becomes relatively evident that SCM along

with other critical developments has reached the top of organizations’ attention already a

decade ago. Hence, it might be interesting for companies to explore the topic of SCM further

in order to secure sustainability in today’s globalized world and to improve performance. One

of the principal areas of SCM is collective cost management, which will be explained in more

detail below.

2.2. Cost Management

2.2.1. The Importance of Cost Management in Supply Chains

Cost management has always played a vital role in business managers’ minds, but has enjoyed

even more significance in the wake of the financial crisis of 2007-2008. In fact, for an

abundance of companies, the crisis brought along many negative consequences, including

liquidity and solvency problems, and even risk of bankruptcy (Yap, Mohamed, & Chong,

2014). To combat these issues and maintain their chances of survival, these firms had no other

choice but to alter their operations and search for new ways to achieve their objectives. Thus,

the financial crisis, on the one hand, served as a partial catalyst for attempts to improve

performance, partly by managing costs to increase efficiency. On the other hand, these

initiatives were also commanded by the aforementioned forever increasing customer demands

for higher quality and pressure for lower costs (Christopher & Gattorna, 2004). As a result,

cost management techniques have become quintessential to companies’ strategic activities and

their sustainability and survival. Cost management practices are activities undertaken to gather,

analyze, and utilize cost information to improve the decisions and control of management to

keep costs down ("What is Cost Management? Definition and Meaning", 2016).

It is, however, imperative to notice a difference between firms attempting to manage costs

solely within their own environment and those that aim to manage costs on a larger basis,

particularly within their supply chain environment. The former group is believed to focus only

8

on utilizing intrafirm cost management practices. Contrastingly, the latter group uses interfirm

cost management practices and will most likely outnumber the former one nowadays. This

seems logical as studies claim that joint cost reductions and major cost reductions in general

will only be possible coming from activities within a supply chain context rather than a firm’s

context (Christopher & Gattorna, 2004; Fayard, Lee, Leitch, & Kettinger, 2012). The

distinction made between these two groups of firms will show cost management approaches

employed at different levels since the breadth of focus is different. Some authors even suggest

that firms using only intrafirm cost management approaches produce no supply chain-wide

benefits at all since these approaches only shift costs to other supply chain partners. This is

believed to be the case with practices such as just-in-time practices to reach leaner operations

(Holweg, 2002). Additionally, these ‘egocentric’ firms fail to realize the costs that ought to be

reduced are those that go beyond their own internal costs, particularly those incurred by all

channel partners ranging from suppliers to distributors.

Many authors, however, do recognize the clear and rising need for firms to carry out cost

management practices beyond the scope of their own firm boundaries and across the entire

supply chain (Wagner, 2008). This is in line with the efficiency goal of SCM, one of the two

aforementioned SCM goals. In a sense, cost management on a supply chain level can therefore

be regarded as one of the main goals of SCM as it helps to achieve lower costs and higher

efficiency across the entire supply chain. In order to realize this goal then, it becomes

increasingly important for firms situated in a supply chain to coordinate their activities,

cooperate, and collaborate (Schulze, Seuring, & Ewering, 2012). Even though few empirical

research on the effects of interfirm cost management has been carried out, the available

research reveals that companies do benefit from implementing these practices in terms of firm

and supply chain performance (Carr & Smeltzer, 1999; Dekker & Van Goor, 2000).

Specifically, they will eventually lead to an improved performance and a competitive

advantage for the entire chain. So, it might be fruitful for firms to apply cost management

activities within supply chains.

This is further warranted by the fact that, in all industries, firms are facing relentless pressure

to reduce costs, especially, for example, in the oil industry and air freight industry (Bryan,

2016; Crooks & Adams, 2015). Moreover, according to Trent and Monczka (2003), the ability

to successfully compete internationally requires approaches that can identify and reduce supply

chain costs. For example, in the retail and apparel industry, costs related to supply chains

9

account for a large amount of a firm’s expenditures and the ultimate cost price of the product

or service they are selling. These costs are expected to rise steadily in the future due to

structural factors and globally rising energy costs (Berg & Hedrich, 2014). Urban (2002)

mentions in his article that inventory and transportation costs, both important supply chain

management practices, drive the supply chain costs. Particularly, together they already amount

to 60% to 80% of total costs of a product. Further, Anderson and Putterman (2005) found that

costs related to supporting and transacting with customers represented 10% to 40% of

companies’ revenues. This all reflects the significant impact of supply chain costs on firms’

operations and the relevance of being able to identify and lower them. Looking at the

downstream side of supply chains, one can notice similar costly changes. It is said that

distribution channel partners have an increasingly powerful bargaining position over their

upstream partners (De Pelsmacker, Geuens, & Van den Bergh, 2013). This will lead to an

increase in costs for the latter firms if distributors decide to exercise this power.

Overall, a reasonable point to make is that costs, in this case those related to SCM, are

important to be taken into account and to be managed. Therefore, firms will most likely benefit

from introducing practices that permit them to get a hold of these costs within the firm and

across the supply chain.

2.2.2. Inter-Organizational Cost Management Practices

Approaches that allow supply chain-wide cost reduction, or otherwise called supply chain or

interfirm cost management practices, are characterized by a broader focus beyond the focal

firm’s perspective. Fayard et al. (2012, p.168) define these practices as “strategic cost

management practices that extend beyond the traditional management of internal costs to

include managing costs among supply chain partners”. The authors also mention that the

activities frequently are considered as an inter-organizational application of traditional cost

management activities. However, the essential extension of interfirm cost management

methods lies in the active involvement of both buyers’ and suppliers’ design teams to jointly

contain expenditures (Cooper & Slagmulder, 2004). The activities are used to offer and analyze

information and to support supply chain managers in controlling and making decisions to attain

supply chain goals, more so than traditional single-firm costing systems (LaLonde & Pohlen,

10

1996; Wagner, 2008). Hence, these approaches help lower costs from information asymmetry

(Cooper & Slagmulder, 2004).

In an attempt to leverage these practices the companies involved should collaborate, identify

synergies between the partners, and manage resources required to ensure cost management in

a supply chain-wide setting (Fayard et al., 2012). So, the objective of these activities is to lower

supply chain costs and to improve the strategic performance of all companies in the chain. With

this explanation of interfirm cost management and the abovementioned definition of SCM in

mind, one can assume that this type of supply chain-wide activity is an SCM activity. After all,

SCM was defined as the synchronization of activities within a single firm and with activities

of other firms in the supply chain (Mentzer et al., 2001, cf. supra).

Companies and entire supply chains have a vast array of such practices available to them.

Wagner (2008) classified eighteen interfirm cost management practices into three categories,

namely purchasing, supplier relationship management, and integrated logistics and

investigated the extent to which they were actually used in practice. Overall, he found that the

usage of interfirm cost management approaches was lower than expected. One explanation for

this was the fact that some methods were still too generic and hard to apply to the specifics of

the firm and the supply chain in question, and that companies were not yet willing to share

information with their partners. Moreover, the third category of practices was found to be least

employed because much more information gathering and analysis efforts are needed from a

larger pool of organizations, which is complex in a large chain of partners.

In addition, the author found that there were only two main practices that were used regularly:

purchasing performance (price) benchmarking and supplier evaluation. The first one pertains

to the first category of supply chain cost management approaches that Wagner (2008) identified

relating to purchasing. Some might believe price benchmarking does not constitute as an Inter-

Organizational Cost Management (IOCM) practice. Nevertheless, several studies claim the

opposite. In fact, in the elaborate study of Ellram (2002) on best practices in supply chain cost

management, the author lists price benchmarking or supplier price analysis as the third most

important practice. Its importance as a supply chain cost management technique is also

exemplified by the fact that it is used to support most other techniques of (interfirm) cost

management according to Ellram (2002). The author states that benchmark information is

exploited to help, among others, should-cost analysis, total cost of ownership analysis, etc.

11

Moreover, given the purpose of cost management in SCM to provide and analyze information

and help supply chain managers make decisions and maintain managerial control to attain

supply chain goals, price benchmarking is by definition then a supply chain cost management

practice (Wagner, 2008).

Benchmarking revolves around comparing one’s own operations, abilities, offerings, and so on

against those of (in)direct competitors, evaluating them internally, and then adapting them if

needed be to those of the competitors (Camp, 1989). Hence, price benchmarking or purchasing

performance benchmarking can be seen as the formal process of gathering and analyzing

information on the purchasing process and purchasing performance of other firms to improve

the focal firm’s own and its closest partners’ purchasing process and purchasing performance

(Sánchez-Rodríguez, Martínez-Lorente, & Clavel, 2003). In this definition, purchasing

performance can be seen as the ability of the purchasing department to operate in line with the

corporate strategy of the business and execute its capabilities and practices accordingly (Pohl

& Förstl, 2011). This can be measured by cost savings through lower procurement prices,

increased quality of supplied products or services, improved efficiency, better inventory flow,

adequate customer service, and so on (Sánchez-Rodríguez et al., 2003). Performing well on

these metrics will help bring expenditures down and increase business performance (Wagner,

2008).

The second most employed cost management approach, supplier evaluation, is a supplier

relationship management approach (Wagner, 2008). According to the author, this practice is

central to the purchasing process and takes place before the supplier selection and after the

delivery of the product or service. It involves the provision of the necessary information from

suppliers to and for its buyers to score their suppliers based on criteria such as consistency,

relationship, strategic commitment, flexibility, technological capability, service, reliability,

and price. This will lead to choosing the supplier whose business processes and suggested

solutions provide the best opportunities for consolidation with the processes and solutions of

the buying firm (Agndal & Nilsson, 2009). Simply put, it allows matching supplier capabilities

to buyer needs, where supplier evaluation helps to identify these capabilities. The ultimate

objective of supplier evaluation is then to reduce purchase risk and costs, maximize overall

value to the buyer, and build closeness and long-term relationships between suppliers and their

buyers (Chen, Lin, & Huang, 2006; Monczka, Trent, & Handfield, 1998). In fact, selecting

12

only the best-performing suppliers will most likely lead to improved performance. Moreover,

from the moment that a supplier gets evaluated and selected to become part of the supply chain,

this new relationship will have a lasting impact on the competitiveness of the entire chain (Chen

et al., 2006). Hence, it is relatively evident that supplier evaluation is crucial for founding an

effective supply chain and might influence its and its firms’ performance.

Several researchers believe that firms should not only introduce cost management methods like

the two mentioned above, but should also adopt interfirm cost accounting tools to lower supply

chain costs and improve the strategic performance of the chain (Schulze et al., 2012). The

traditional type of costing systems, designed to determine production costs only, cannot

effectively manage other cost objects like supplier and customer related costs (Slagmulder,

2002). Firms should therefore consider employing an extended version of these traditional cost

management methods to take advantage of potential cost-minimizing synergies that might exist

between supply chain partners. Further, this type of supply chain costing can enhance cost

visibility and strategic management by allocating costs to the activities that consume them on

a supply chain level (LaLonde & Pohlen, 1996). Accordingly, supply chain managers can then

take cooperative action to reengineer costly activities and even remove nonvalue-adding ones,

and continuously support or strengthen those that are value-adding.

Ultimately, it is believed that this type of supply chain costing improves competitiveness and

profitability in three ways: by making the interface between firms more efficient, by helping

to find new and lower-cost ways to design products, and by creating more efficient

manufacturing ways (Slagmulder, 2002). Firstly, making the interface more efficient can be

done by reducing uncertainty through reducing cycle times or sharing more information, by

making suppliers improve performance or change their behavior to lower procurement costs,

and by making buyers change their behavior to reduce service costs. Second, the design stage

can become more efficient when firms coordinate their product development processes

throughout the supply chain. Finally, increased manufacturing efficiency can be attained by

coordinating the manufacturing processes across firms and in some cases by performing these

processes jointly. Thus, it is worthwhile for companies to investigate the possibility of

implementing such interfirm costing tools in their supply chains.

One of the most popular cost management techniques within single firms as well as across

supply chains to achieve the latter objectives is Activity-Based Costing (ABC) (Fayard et al.,

13

2012; Wagner, 2008). ABC is a cost management tool that assigns overhead costs to products

or services based on the resources that these products or services consume. This method was

first developed with the purpose of applying it to a single firm. It allows managers to identify

activities that account for a large part of a firm’s costs and gives them the opportunity to

improve performance by improving these activities accordingly. By employing it on a wider

supply chain basis and detecting all cost drivers in the chain, firms can ensure successful

interfirm collaboration to provide more accurate and detailed up-to-date information and

thereby create chain-wide value (Askarany, Yazdifar, & Askary, 2010; Pohlen & Coleman,

2005). Specifically, it is said that interfirm ABC contributes to SCM by allowing, among

others, greater cost reduction, cost estimation, and performance measurement.

There has been some discussion, however, on how ABC is diffused in firms. In fact, many say

that it is one process but includes several adoption ‘stages’, and is not just adopted or not

adopted (Al-Omiri & Drury, 2007; Askarany et al., 2010; Krumwiede, 1998). Specifically,

some authors have extended the implementation model to 10 stages, which can be seen in Table

1 with their explanations (Brown, Booth, & Giacobbe, 2004; Krumwiede, 1998). These 10

stages can be further grouped into 6 broader phases of implementation, namely: initiation,

adoption, analysis or adaptation, acceptance, routinization, and integration or infusion

(Gosselin, 2006; Krumwiede, 1998). Initiation can be understood as the scanning of problems

and finding solutions (here ABC) accordingly and includes the first 3 stages. The fourth stage

is the adoption phase where rational and political discussions arise to get support for the

implementation of the solution. Next, analysis or adaptation implies the development,

installment, and maintenance of the solution where users are trained and existing procedures

get revised or new ones developed. This phase includes stages 5 through 7. Further, acceptance,

which comprises the similarly named stage, makes users committed to the usage of the solution.

The stage thereafter also forms a phase by itself, namely routinization, and revolves around the

encouragement of employing the solution as a normal activity. The last phase, infusion or

integration, involves comprehensive and integrated usage of the solution on a higher level to

achieve greater effectiveness.

14

Table 1. Stages of ABC Implementation

Stage Explanation

1. Not Considered No serious consideration of ABC

2. Considering Possible consideration of ABC, but no approval of implementation

3. Considered then

Rejected

Consideration of ABC has occurred, but was rejected after

4. Approved for

Implementation

Approval of ABC implementation and spending of the necessary

resources, but analysis has not started

5. Analysis Initiation of the process of determining project scope and objectives,

collecting data and/or analyzing activities and cost drivers

6. Getting Acceptance Analysis is complete, but ABC information is not yet in use for

decision making

7. Implemented then

Abandoned

Implementation and analysis of ABC took place, but it is not in use

anymore

8. Acceptance Occasional use of ABC for decision making to provide more realistic

costs, but it is still seen as a project or model with infrequent updates

9. Routine System Common use of ABC for decision making and seen as a normal part

of information system

10. Integrated System Extensive use of ABC and integrated into financial system, and

brings along well-defined advantages

Note. Stages of ABC Implementation. K.R. Krumwiede, 1998.

Other researchers contend the view that ABC is one process and look at ABC as a set of

different processes that distinguish between full and partial adoption. They then divide the

adoption of ABC into three different ‘levels’ (Askarany et al., 2010; Baird, Harrison, & Reeve,

2004; Gosselin, 1997). Firstly, Activity Analysis is identifying the activities and procedures

carried out to create the final products/services (Gosselin, 2006). Those activities that are not

value-adding can be recognized here and eliminated if necessary to improve speed and product

quality. Second, Activity Cost Analysis regards identifying and analyzing the costs of each

activity and the cost drivers, which cause the costs to vary. This analysis also enables

management to find the interaction between cost drivers and the activities performed.

Specifically, by doing this one will create a better understanding of how a task is performed

and find a way to optimize this task to reduce its costs. This will then lead to cost minimization

15

by mainly focusing on driving out the suboptimal and wasteful efforts (Gosselin, 2006).

Ultimately, complete Activity-Based Costing will then allocate the costs of the activities to the

products/services that needed them. It seems important to recognize that ABC can be carried

out in these different stages or levels, which is why they should not be ignored while testing

the effects of the application of ABC on firm performance.

Another well-covered costing method used in firms and supply chains is target costing. This

management technique was developed by Toyota in the 1960s and is a comprehensive program

to reduce costs (Lockamy & Smith, 2000; Kato, 1993). In his article, Kato (1993) referred to

target costing as “an activity which is aimed at reducing the life-cycle costs of new products,

while ensuring quality, reliability, and other consumer requirements, by examining all possible

ideas for cost reduction at the product planning, research and development, and the prototyping

phases of production” (p.36). This suggests that target costing is mostly used during the design

process of new products. Nevertheless, Afonso, Nunes, Paisana, and Braga (2008) mention that

the term target costing should be viewed much larger and that it includes other techniques as

well, such as Kaizen costing. They thus believe the term covers cost reduction activities in the

product development and design processes (target costing) as well as the manufacturing and

delivery processes (Kaizen costing).

There is, however, no ambiguity regarding the functioning of target costing. Many researchers

agree that it involves a cost management concept based on a long-term and market-driven

perspective (Wagner, 2008). Furthermore, its application involves several steps. First, insights

from market research information are used to set the price customers are willing to pay for the

product taking into account the functionality, quality, and substitutes of the product. Then,

firms decide on the profit margin required by stakeholders and for future use, and subtract this

from the above-calculated price. The result is the “allowable cost” of the product or the

“maximum cost the firm should incur in the manufacture, distribution, service, and disposal of

the product”, or simply put: the target cost (Lockamy & Smith, 2000, p.214). Once the target

cost is set at the first firm in the supply chain, it provides an indication and will help discipline

upstream partners to set their target costs and to meet these while realizing an acceptable profit

margin (Dekker & Smidt, 2003).

Second, the “current cost” or the costs that the product is most likely to produce are calculated

and compared with the allowable cost. If a difference exists, companies should undertake

16

certain actions to reduce the current costs. They can do this using different tools such as value

engineering, which is redesigning the product, the manufacturing, or distribution, and

functional analysis, which is a method to help find technical solutions to fit the target cost

(Afonso et al., 2008; Wagner, 2008). In order for all the partners in the supply chain to meet

the target cost, they must understand and feel the pressure and the importance of reaching it.

This requires superior information sharing and teamwork, which is why target costing is

usually applied on a supply chain-wide basis (Helms, Ettkin, Baxter, & Gordon, 2005).

Moreover, this is also the reason that the supply chains become completely integrated and that

partners will assist each other in attaining their target costs. So, the decision to introduce this

technique should be carefully considered by all participants and should only be implemented

if all of them are willing to coordinate and cooperate.

2.2.3. Antecedents of Supply Chain Management and Inter-

Organizational Cost Management

Several essential precursors for SCM were found to enable the implementation of a supply

chain orientation, which can be regarded as SCM (Figure 2). The first antecedents are trust and

commitment between the partners, which are key since they will encourage managers to

continually invest in the relationship, resist short-term focused relationship harming

alternatives, and avoid high-risk actions (Cooper et al., 1997; Lambert, Stock, & Ellram, 1998).

Other antecedents are the interdependence between companies to create a long-term

relationship orientation; organizational compatibility in terms of corporate culture, vision, and

key processes to improve the relationship effectiveness; the existence of a constructive leader

in the chain; and top management dedication and support (Frazier, 1983; Lambert et al., 1998;

Mentzer et al., 2001). These antecedents will then drive a firm to develop a systemic and

strategic supply chain orientation. However, it is only when all firms in a supply chain dispose

of this same orientation and operate accordingly that SCM can exist. There are also several

reasons for SCM or consequences but these can all be characterized by trying to obtain a

competitive advantage through lower costs and improved customer value and satisfaction.

From the fact that interfirm cost management can be seen as a form of SCM, some of the

antecedents and consequences might also be relevant for this activity. Several authors have

17

studied the application of such interfirm cost reduction activities and have also taken a broader

view to look at what allows this application. Specifically, they investigated the antecedents of

interfirm cost management activities. Some of these will be covered in the next section in

proportion to the attention they received in the existing literature and the extent to which they

correspond to the precursors of SCM mentioned above.

Figure 2. Supply Chain Management Antecedents and Consequences. J. Mentzer, W. DeWitt,

J. Keebler, S. Min, N. Nix, C. Smith, and Z. Zacharia, 2001.

2.3. Hypothesis Formulation

First of all, firms using internal cost management methods or intrafirm practices are expected

to also use IOCM methods like the ones mentioned above. This is believed since companies

will be able to use their understanding of and experience with techniques applied internally to

employ them in a wider context. It is said that the same planning and control skills of internal

cost management can be valuable for the wider application inter-organizationally (Surowiec,

2013). Proof of this is found in the study of Fayard et al. (2014). The authors supposed that a

strong internal cost management capability is a necessary precondition for an IOCM capability.

In addition, Coad and Cullen (2006) say that currently the boundaries between intra- and

interfirm phenomena are blurring. Specifically, they posit that certain intrafirm cost

18

management techniques would spillover from an organizational to an interfirm based level due

to the spillover of capabilities in resource usage, learning, and change. However, these internal

techniques would require some alterations to prepare for their use on an interfirm basis.

Nevertheless, the use of internal cost management methods can be seen as an antecedent for

the use of inter-organizational supply chain-wide cost management methods.

Hypothesis 1: Firms with above average levels of involvement in intrafirm cost management

will be more involved in the use of IOCM practices than firms with below average levels of

involvement in intrafirm cost management.

Second, Coad and Cullen (2006) also mention that information sharing is essential to IOCM.

The authors, along with other researchers, claim that cooperating firms that share cost and

performance information will most likely analyze and adjust interdependent activities, and

share costs and benefits (Dekker, 2003; Seal, Cullen, Dunlop, Berry, & Ahmed, 1999). Further,

they see the role of information sharing as a tool that can help partner firms learn skills and

identify cost reduction and value creating opportunities, reduce uncertainty, and sustain and

renew interfirm relationships that are all crucial for IOCM (Amigoni, Caglio, & Ditillo, 2003).

Also, Krause (1999) found support in his study that effective interfirm communication, or the

frequent and genuine contact or sharing of information, was an important antecedent for a

specific IOCM approach, namely supplier development. Thus, the level of information that

firms in a supply chain are willing to and can effectively share within the chain might be an

antecedent for the use of interfirm cost management approaches.

Hypothesis 2: Firms with an above average level of information sharing will be more involved

in the use of IOCM practices than firms with below average levels of information sharing.

Coad and Cullen (2006) continue by highlighting the importance of trust and commitment

between the partners in a supply chain. Tomkins (2001) defines trust as the expectation, coming

from past experience and frequent interaction, that a partner will not behave in an opportunistic

manner but will act in good faith relative to its partners. In an interfirm context, the concept of

trust is assumed to raise the investment in the relationship, increase the performance of the

partners involved, and widen the scope of firm activities such as cost management activities.

Furthermore, trust also seems to play an essential role in stimulating the level of innovation in

a supply chain (Cooper & Slagmulder, 2004). A higher level of innovation might then be

19

beneficial for all parties in the supply chain as it can make the supply chain more responsive

to its changing environment and more efficient (Flint, Larsson, & Gammelaard, 2008). Also,

Kajüter and Kulmala (2005) mention that trust is a frequently mentioned determinant for the

interfirm cost management approach of open-book accounting (Axelsson, Laage-Hellman, &

Nilsson, 2002). The concept of trust, however, needs sufficient time to be established but will

ultimately help developing effective alliances and allow firms in a supply chain to create strong

IOCM capabilities (Fayard et al., 2014).

Moreover, Krause (1999) found in his study that downstream partners’ investments in the

upstream suppliers, a form of IOCM, was dependent on the perceived commitment of the

buyers from these upstream partners to the relationship. This commitment, or willingness to

make sacrifices to maintain the relationship, made clear that they were in it for the long haul

and that both partners could benefit from the relationship and the investments in it. The latter,

in turn, was a promoting factor for the interfirm cost management activity of supplier

development. Overall, it can be assumed that the buyer-supplier relation characterized by trust

and commitment will affect the use of interfirm cost management practices. In particular, the

level of trust and commitment in the relation of firms with their supply chain partners can be a

promoting factor for the extent to which firms are involved in interfirm cost management.

Hypothesis 3: Firms that have relations that can be characterized by an above average level of

trust and commitment will be more involved in the use of IOCM practices than firms with

relations with below average levels of trust and commitment.

Lastly, the external environment in which firms are situated can also have an impact on the use

of cost management approaches. Particularly, the business intensity can influence the extent to

which these techniques are necessary since times of intense competition tend to elicit certain

pressures on companies. In times characterized by fierce competition, Kajüter and Kulmala

(2005) say it is common for firms to feel the pressure to continuously reduce costs via interfirm

cost management methods. In their study, the authors cover three types of contextual factors

that influence the extent to which open-book accounting is used, namely environmental factors,

network-specific factors, and firm-specific factors. The most important factor from the external

environment according to the authors was the degree of competition. Furthermore, Cooper and

Slagmulder (1997) intended to identify the conditions that favor an interfirm cost management

approach in particular, namely target costing. From their exploratory comparative analysis,

20

they find five factors that influence the target costing process by affecting the number of

benefits firms can gain from the cost management approach. One of the considered factors is

the intensity of competition as it affects the attention paid to cost management. Hence, thinking

in line with these aforementioned studies, it might well be that a higher level of competition in

the industry is an antecedent of greater interfirm cost management usage.

Hypothesis 4: Firms with above average levels of industry competitiveness will be more

involved in the use of IOCM practices than firms with below average levels of industry

competitiveness.

As was posited before, the possible interfirm approaches firms can employ to achieve the goal

of lowering supply chain costs and improving strategic performance are quite numerous.

However, this study will focus on a handful of these IOCM practices, mentioned above. Firstly,

the effect of purchasing benchmarking on firm performance will be investigated more closely.

The popularity of this approach is proven by the fact that there are major independent

organizations that execute purchasing benchmarking studies across different industries (Carr

& Smeltzer, 1999). These organizations provide firms with aggregate data to compare their

individual performance with. Likewise, Sánchez-Rodríguez et al. (2003) state in their article

that benchmarking has gained remarkable consideration in purchasing departments in the 90s

and that they allow firms to adopt world-class standards. In addition, benchmarking suppliers

is one of the fundamental activities of SCM as is the purchasing activity in general of corporate

performance (Choy, Lee, & Lo, 2002). Though the approach has seen much success, the

authors of the latter article along with Yasin (2002) also mention that there is a lack of studies

that clarify the costs and benefits caused by implementing benchmarking. Therefore, it might

be useful to investigate these in the current study.

Carr and Smeltzer (1999) claim in their research that value can arise from learning from

contexts outside the usual frame of reference of companies and from formalizing the process

of benchmarking. They further find a positive relation between usage of price benchmarking

and firm performance in terms of Return on Investment (ROI), profits and percentage of sales,

market share, and net income. In line with these studies, Sánchez-Rodríguez et al. (2003) also

discover a positive indirect effect of purchasing benchmarking on business performance

measures similar to the ones mentioned in the latter sentence. However, a remarkable

21

observation was made where benchmarking showed a negative direct impact on performance,

which ran counter to what they had hypothesized. So, again, it might be relevant to investigate

the effect in the current context. Generally, though, it can be argued that more purchasing

benchmarking will be associated with a higher firm performance.

Hypothesis 5: The extent to which firms in a supply chain use purchasing benchmarking will

be positively related to the performance of those firms using this IOCM practice.

Further, given the aforementioned importance of supplier evaluation as an IOCM, it bears to

examine its effect on firm performance. Two similar studies were carried out in the United

States to gauge the effect of certain supplier selection and evaluation criteria on buying firms’

performance. Both groups of researchers found there to be a positive relation between several

criteria of supplier evaluation, such as soft and hard attributes, and certain performance

indicators like market share, return on assets (ROA), product quality, and competitive position

(Kannan & Tan, 2002; Tracey & Leng Tan, 2001). Moreover, Kannan and Tan (2003)

performed their study once more to compare the importance and the effect of supplier

evaluation in the United States versus Europe. The findings of the latter study confirmed the

ones of the former study in the United States, though showed a more limited impact on buyers’

performance in Europe. It can thus be noteworthy to investigate the extent to which supplier

selection positively affects buying firms’ performance in the current study’s environment.

Overall, the practice of supplier evaluation is believed to ultimately lead to better performance

of the buying firm.

Hypothesis 6: The extent to which firms in a supply chain use supplier evaluation will be

positively related to the performance of those firms using this IOCM practice.

As was mentioned before, not only should firms adopt cost management methods, but they

should also consider implementing interfirm cost accounting tools such as inter-organizational

ABC and target costing. By applying ABC on a wider supply chain basis, firms and their supply

chains will get certain benefits. In fact, interfirm ABC can offer a clearer picture where

customer value is created and where money is made or lost, and can suitably diminish

nonvalue-adding activities (Askarany et al., 2010; Baykasoğlu & Kaplanoğlu, 2008;

Bartolacci, 2004). For example, it allows the calculation of the total costs of contracting with

a certain supplier, and it allows correct estimation of a customer’s profitability (Slagmulder,

22

2002). Thus, ABC can create a better understanding of the cause-effect relationship between

costs and activities such that firms can eliminate redundant tasks and reallocate resources to

more valuable activities (Bartolacci, 2004; Tsai, Lai, Tseng, & Chou, 2008). As a consequence,

pricing, product mix costing, and cost estimation will be more accurate (Kee, 2008; Qian &

Ben-Arieh, 2008). Tsai et al. (2008) also claim that ABC can improve the correctness of

processes and cost data of products, and can allow full control over resources by giving a

clearer overview of these resources. According to SCM research, these benefits can then permit

interfirm ABC to improve cooperation, which increases organizational and supply chain-wide

competitiveness in terms of performance, productivity, and profitability (Bartolacci, 2004).

Hence, inter-organizational ABC can be a smart investment for firms and supply chains.

However, despite the numerous benefits of ABC, evidence from several studies reveals a

surprisingly low level of adoption of this technique within firms (Askarany et al., 2010). For

example, Al-Omiri and Drury (2007) found in their survey study that from firms in the UK

only 15% said to have adopted ABC. Other comparable studies were carried out in Australia

and New Zealand and showed similar results (Askarany, Smith, & Yazdifar, 2007; Cotton,

Jackman, & Brown, 2003). Lack of a common understanding of ABC systems and the variety

of terms that operationalize ABC may have contributed to the mixed adoption rates for ABC,

as many ABC adopters have considered themselves adopters of traditional accounting systems

by mistake (Askarany & Yazdifar, 2011; Baird, Harrison, & Reeve, 2004). Moreover, some

authors even claim no costing system is perfect and mention the drawbacks and shortcomings

of ABC (Bartolacci, 2004; LaLonde & Pohlen, 1996).

As the adoption levels of ABC within firms are surprisingly low, it will most likely also be the

case that interfirm adoption levels of this technique are below what is expected given its

advantages. This, of course, casts doubt on the legitimacy and/or realizable value of the benefits

of ABC. Therefore, it might be worthwhile to verify whether inter-organizational ABC actually

succeeds at increasing performance and profitability. While keeping in mind the different

adoption stages and levels of ABC and the evidence of benefits it brings along, it seems

reasonable that a higher adoption stage/level will be related to a greater number of benefits.

Since the levels and stages of adoption are in line with one another and show significant

overlap, it might be more practical to combine these. Therefore, for practicality reasons, the

remainder of the study will only use levels of adoption of ABC.

23

Hypothesis 7a: The extent to which firms in a supply chain use inter-organizational ABC will

be positively related to the performance of those firms using this IOCM practice.

Hypothesis 7b: Firms in a supply chain with higher levels of adoption of inter-organizational

ABC will have a greater performance than firms with lower levels.

There are also benefits that will accrue to firms and supply chains adopting target costing within

a supply chain. According to Helms et al. (2005) and Wagner (2008), these include improved

internal cost management, cost monitoring, cost accountability, and delivering highest value

products at the lowest price possible to customers, which all will help firms stake out a strong

position and maintain their market share. Further, Dekker and Smidt (2003) found in their study

about the adoption of target costing in Dutch firms that the main benefits included cost

reduction, timely product introduction, higher customer satisfaction, and quality control.

Hence, one can assume all of these advantages will ultimately result in an improved

competitiveness and performance of firms and supply chains.

After the introduction in the 60s, target costing gained tremendous attention in Japan in the

1980s, which lead to an adoption rate of 80% of major companies in the assembly-type

industries (Lockamy & Smith, 2000). Moreover, in a more recent study, Huh, Yook, and Kim

(2008) discovered that 74% of the Japanese firms in their study disposed of an official

department to support their target costing function. Conversely, target costing appears to have

received surprisingly less publicity and is used only by 40% of firms in the United States

(Helms et al., 2005). This finding, however, stands in contrast with the findings of Wagner

(2008), that claim inter-organizational target costing is one of the most employed cost

management approaches by the firms in the study. This study was carried out in Switzerland,

a fairly representative country for other western economies and business cultures according to

Hofstede and Hofstede (2005). One might thus assume inter-organizational target costing will

be similarly applied in other western contexts, such as the one in this study.

Hypothesis 8: The extent to which firms in a supply chain use inter-organizational target

costing will be positively related to the performance of those firms using this IOCM practice.

Figure 3 portrays the implied relationships mentioned above in hypotheses 5 through 8 between

the interfirm cost management techniques used in supply chains (price benchmarking, supplier

evaluation, inter-organizational ABC, and inter-organizational target costing), and the

24

performance measures of firms. This relationship, along with hypotheses 1 through 4, will be

empirically tested in the following sections, which will produce results that will be studied.

Ultimately, interpretations will be made and conclusions will be drawn based on these results.

+

+

+

+

Figure 3. Relation Between Interfirm Cost Management Practices and Firm Performance.

3. Methodology

3.1. Research Design

This study aims to empirically investigate the hypothesized antecedents and the potential

performance effects of four IOCM practices with a quantitative correlational study. In order to

do so, an electronic questionnaire was developed (Appendix 7.1.). A survey of this kind was

employed as it allowed a time- and cost-efficient provision of an abundance of cross-sectional

data for this study, regardless of the possible limitations of this instrument for collecting data

(Afonso et al., 2008; Askarany & Yazdifar, 2011; Birnberg, Shields, & Young, 1990; Runkel

& McGrath, 1972; Young, 1996).

Price benchmarking

Firm performance

measures

Supplier evaluation

Inter-organizational

ABC

Inter-organizational target costing

25

The questions of the questionnaire were designed to allow the measurement of descriptive

information of respondents’ companies and of the core constructs. Specifically, the survey

consisted of 23 questions, of which some had multiple statements. These questions and their

statements were used to gauge respondents’ opinions on the antecedents, the extent of usage of

IOCM, and the relative performance of their firms. All of the questions were formulated in a

way to avoid the problem of endogeneity and ensure temporal precedence; respondents were

asked about the antecedents two years ago, about the IOCM practices one year ago, and about

performance this year (Van de Ven, 2007). The majority of the questions were based on

questions used in surveys from previous empirical research as they proved successful, relevant,

and accurate. When some questions’ wording did not reflect the desired aspect of the

constructs, changes were made to make them more aligned with the purpose of the study at

hand (Fayard et al., 2012).

The questionnaire was set up with the program Qualtrics and was sent out via email. In this

email, it was urged that respondents were personnel with the experience and expertise

necessary to answer the questions correctly, such as employees with functions like operations-

, supply chain-, purchasing-, logistics manager, or Chief of Operations. Surveys were sent out

to email addresses obtained through the Bel-first database of Bureau Van Dijk (BVD), the

website trendstop.knack.be, and via LinkedIn. For some firms in the BVD database that did

not identify employees’ email addresses occupying the aforementioned functions, surveys were

sent to the head of the operations department and/or the head of the financial department

(Dekker & Smidt, 2003). In BVD, the relevant email addresses were found by making a

selection according to certain criteria, such as geographical location, minimum number of

employees, date of incorporation, and industry choices in addition to the default selection

criteria.1 Further, only firms that were reachable by email were considered since this would

allow obtaining their responses.

1 Legal status: Active companies, File in a provisional legal situation, Unknown.

26

3.2. Sample Description

The population of interest for this study is the collection of companies in Belgium that make

use of IOCM practices to be able to investigate the research question at hand. To achieve a

sample that is fairly representative of this population, firms were contacted based on certain

selection criteria.

The specific criteria with regards to location, number of employees, incorporation date, and

industry were the following. First, all companies were considered in the Brussels capital region,

the Flemish region, and the Walloon region to get a representative view of Belgium as a

country. Second, the minimum number of employees or minimum size was set at 20 employees

to avoid bias towards small companies since such firms do not generally make use of cost

management practices, such as ABC (Abusalama, 2008; Bjornenak, 1997; Clarke, Hill, &

Stevens, 1999; Innes & Mitchell, 1995, 1998; Krumwiede, 1998; Van Nguyen & Brooks,

1997). The initial categorization in the questionnaire of the size of firms in terms of number of

employees was in line with the six suggested classifications in previous studies (Askarany et

al., 2010; Berryman, 1993; McMahon, Forsaith, Holmes, & Hutchinson, 1993; Nooteboom,

1994; Watson & Everett, 1993).2 However, taking into account the feedback received after

pretesting the survey (cf. infra) and reviewing other past research, it was decided to use the

second lower bound (after 0) used in the suggested classifications to categorize firms

(eventually reduced to 20). Third, companies that were incorporated within the period of 1950-

2014 were included. This was done to ensure a wide and representative sample, and to

guarantee the availability of information regarding the antecedents that are measured two years

before the moment of responding to the survey. Lastly, a selection of sectors or industries was

made as for some it was highly unlikely that they would employ IOCM practices, such as “arts,

artists, and trade”, “bookkeepers, accountants, tax consultants”, “child day-care”, “(interior)

architects”, etc. (Tan, Handfield, & Krause, 1998; Tan, Kannan, & Handfield, 1998) (Appendix

7.2.). By surveying respondents in many different industries it is possible to get a fairly

generalizable view of the topic of the study, and the findings can therefore be regarded as

relatively representative in Belgium.

2 (1) up to 25 employees; (2) from 26 up to 50 employees; (3) from 51 up to 100; (4) from 101 up to 200 employees; (5) from 201 up to 500 employees; and (6) more than 500 employees.

27

The survey was pretested to ensure content validity of the constructs, to increase suitability and

correctness of the questions, and to gain feedback in order to improve possible misconceptions

or ambiguities (Kannan & Tan, 2003; Tan, Kannan, Handfield, & Ghosh, 1999). This pretest

was conducted by sending the survey to 60 knowledgeable professionals, who were able to

give reasoned advice and insights, at different companies via LinkedIn to obtain 8 responses,

which equals an initial response rate of 13.33%. As a result, some questions were rephrased

and removed to further improve the quality of the questionnaire. After the pretest, the survey

was sent out 9838 times for a total amount of 346 responses, which means a response rate of

3.52%. Responses were obtained by sending out the questionnaire, and by sending a reminder

one week after the first invitation. From these responses, several surveys had to be deleted

because they were unusable, which led to 266 usable surveys. For multiple questions, however,

some of these 266 respondents did not answer, which is why the number of respondents (N) in

several tests performed below is lower than 266.

To get a better overview of the sampled firms, the descriptives were examined. Particularly,

the composition of the sample was investigated by looking at questions 1 through 3 indicating

the industry type of the firms and their size. The majority of firms in the sample are

manufacturing firms (22.22%), which is expected as a significant amount of different

manufacturing industries were included in the sample and aggregated in the division of

“Manufacturing” (Appendix 7.3.). Other substantial industries are the food and beverage

industry (10.9%) and the service industry (10.5%). When firms belonged to another industry

than mentioned in the list of choices they were given the opportunity to formulate an answer.

Overall, only 14 (5.3%) firms did not operate in the provided industries, but operated in the

“construction”, “entertainment”, “environment”, “real estate”, or “steam industry” (Appendix

7.4.). In order to simplify the statistical tests below, certain industry types were grouped

together to form six industry groups, namely Primary Industries, Manufacturing, Services,

Retail Trade & Wholesale, Pharmaceuticals, and Other Industries (Appendix 7.5.).

Looking at question 2 about size in terms of annual gross sales, firms generally fell into the

category of sales ranging from “€10 million to €50 million” (24.8%), after which followed the

range of “€100 million to €500 million” (16.5%) (Appendix 7.6.). This means that, generally,

the firms in the sample were on the rather larger side, except for the second to last range (“€500

million to €1 billion”) which seems fairly underrepresented (4.5%). Regarding size in terms of

employees, this view does not entirely coincide with the latter size findings. In fact, the

28

majority of firms belonged to the first category of “Less than 100 employees” (37.2%), whereas

the rest of the categories are relatively similarly represented (Appendix 7.7.). So, the companies

in the sample have a fairly low headcount but large revenues.

By reviewing the descriptives for question 4 and 13, one might be able to get an idea of how

involved the firms are in the management of costs internally as well as inter-organizationally.

One way of doing this is by summing the number of intrafirm cost management practices used

(question 4) by a firm as well as the inter-organizational practices (question 13). This shows

that 61 firms (22.9%) use no single intrafirm cost management practices, while 205 (77.1%)

use one or more different practices to manage costs internally (Appendix 7.8.). The distribution

for the use of IOCM practices, however, shows that relatively few companies made use of these

practices (Appendix 7.9.). In fact, 46.2% of the companies used no such practices at all. This

seems to be in line with the findings of Wagner (2008) that revealed a surprisingly low adoption

rate of IOCM practices.

3.3. Variable Description

In order to accurately execute the study at hand and perform the relevant statistical tests, several

questions or items were used to measure each construct in the research. These items were

combined to form one indicator or score for each construct. Measurement scales of the latter

kind were used to gauge the rather unobservable constructs (antecedents, IOCM practices, and

performance measures) (Fayard et al., 2012). Thus, all constructs are multi-item constructs,

which should lead to better measurements, and each construct is determined by the

respondents’ answers to the measurement scale questions (Dekker & Smidt, 2003). Most of the

questions used were formulated using a five-point Likert scale, in addition to the option “not

applicable” in the case an answer was not applicable in the company, to allow a measured value

for each construct.

Firstly, the involvement of the companies in internal cost management and the extent of their

use of internal cost management practices was measured with questions 4 through 6

(Abusalama, 2008; Ellram, 2002; Fayard et al., 2012). Questions 5 and 6 were measured on

five-point Likert scales, and were used to quantify involvement of firms in internal cost

29

management. Specifically, the answers for each respondent were transformed into values:

“Strongly disagree” was assigned a “1”, “Disagree” a 2, “Neither agree nor disagree” a 3,

“Agree” a 4, and “Strongly agree” a 5, and “Not applicable” was left out of the analysis as

these values represented firms with no such antecedent. The latter was applied to all questions

with a “Not applicable” option. The construct of internal cost management practices for a firm

was then composed by calculating the mean score of the summed values of the answers to the

two five-point Likert scale questions. Firms’ level of information sharing with supply chain

partners was gauged by question 9 and its accompanying statements, all measured on a five-

point Likert scale (Krause, 1999; Li et al., 2006; Monczka, Petersen, Handfield, & Ragatz,

1998). Taken together, the three statements were able to offer an indication of how high or low

the level of information sharing is in one company. Here again, answers were converted into

values that ranged from “1” through “5” for “Strongly disagree”, “Disagree”, “Neither agree

nor disagree”, “Agree”, and “Strongly agree” respectively. These values were then summed

and averaged to obtain the construct of level of information sharing.

Trust and commitment of relationships that firms have with their supply chain partners was

evaluated with question 10. The answers of respondents to the four statements of this question,

also measured on a five-point Likert scale, were again converted, scored, and averaged to give

an idea of the general trust and commitment level in the relationships (Krause, 1999). The last

antecedent of competitiveness level of the industry was measured using questions 7 (whose

statements are reversed) and 8 with a five-point Likert scale, whose responses were also

converted, combined, and averaged into a score to provide a competitiveness level indication

of the industry (Krause, 1999; Tan et al., 1999). One might claim that this variable is on another

level (industry level) than the aforementioned variables (firm level), and that this could

influence the subsequently performed statistical tests by affecting the level of IOCM

involvement and performance. The potential influence of the industries was examined using

two linear regressions with IOCM involvement and performance as dependent variables and

industries as dummy variables, essentially control variables (Vanacker, 2016). Both tests

revealed that the regression model was not significant and that none of the coefficients of the

predictors were significant. Hence, one can posit that there is no confounding effect of industry

on the following tests.

The extent to which companies used IOCM practices was measured with the use of questions

11 through 14 (Abusalama, 2008; Ellram, 2002; Fayard et al., 2012; Wagner, 2008). Questions

30

11, 12, and 14 were used to form a score of involvement in IOCM of the companies by adding

and averaging the sum of the values in the same way as above. Level of involvement was used

to avoid being limited to one specific measure of the application of IOCM, and to allow the

combination of items into a more inclusive indicator of IOCM usage. The measurements of the

specific IOCM practices were done in line with the aforementioned converting into values,

adding, and averaging techniques. Particularly, purchasing performance benchmarking was

measured via question 15 and its four statements (Sánchez-Rodríguez et al., 2003).

Respondents were asked to indicate the extent to which they agreed to the statements based on

a five-point Likert scale. The IOCM practice supplier evaluation was measured with questions

16 and 17 (Kannan & Tan, 2002; Tan, Handfield, & Krause, 1998; Tan, Kannan, & Handfield,

1998).

The use of inter-organizational ABC was examined by the four statements of question 18, of

which the last three investigated the level of adoption of inter-organizational ABC, all

measured on a five-point Likert scale (Baird et al., 2004; Fayard et al., 2012). Specifically, the

second statement gauged the extent of level 1 adoption of ABC, the third statement measured

the level 2 adoption, and the fourth measured the level 3 adoption. The grouping of firms into

the different levels of ABC adoption was done in the following way. Companies that responded

with “Strongly disagree” and “Disagree” to the second statement were regarded as non-

adopters of ABC or level 0 adopters. Firms that responded “Neither agree nor disagree”,

“Agree”, and “Strongly agree” to the second statement but only answered “Strongly disagree”,

“Disagree”, or “Neither agree nor disagree” to the third statement were considered as level 1

adopters. Level 2 adopters were then considered as those answering “Agree” and “Strongly

agree” to the third statement and “Strongly disagree”, “Disagree”, and “Neither agree nor

disagree” to the last statement. Respondents who answered “Agree” or “Strongly agree” to the

last question were considered as level 3 adopters. This division of respondents proved to be

valid as only a handful of firms did not answer the three statements consistently.

In order to measure the use of the last IOCM practice, inter-organizational target costing,

questions 19 and 20 were asked on a five-point Likert scale (Afonso et al., 2008; Dekker &

Smidt, 2003; Fayard et al., 2012). Respondents were requested to disclose whether they used

IOCM practices matching the definition mentioned in question 19. The advantage of

employing this fairly broad definition is evident as “it enables to identify firms that have

31

developed and use costing practices similar to target costing, which would not be identified by

focusing on ‘‘target costing’’ per se, or by setting narrow boundaries on the system’s

characteristics. On the other hand, using such a definition has limitations as well, as less detail

is captured about the exact content and use of the costing practice and the extent to which it

deviates from the description of target costing systems in the literature” (Dekker & Smidt,

2003, p.297).

As a last construct, performance was operationalized using question 21 with the measures of

ROI, ROA, growth of market share, and growth of sales that reflect financial, market, and

product performance respectively (Kannan & Tan, 2002). Specifically, respondents were asked

to indicate their firm’s performance relative to that of their competitors. These subjective

performance measures are frequently used in empirical studies since respondents oftentimes

are unwilling to reveal sensitive “hard” data, and since managerial assessments are consistent

with objective performance indicators (Vickery, Droge, & Markland 1993; Vickery, Droge, &

Markland, 1994; Vickery, Jayaram, Droge, & Calantone, 2003). Respondents’ answers were

again translated into values, added, and averaged. Control variables were also included in the

model of this study. In fact, in the survey respondents were asked to indicate their industry and

size in terms of number of employees, with questions 1 and 3 respectively. The measure of size

in terms of number of employees was employed as a control variable as this is the most popular

in the literature (Askarany et al., 2010; Gosselin, 1997). All of the mentioned constructs were

used in subsequent tests performed in the software package SPSS for statistical analysis. Such

tests included independent samples t-tests, Mann-Whitney U tests, Spearman’s rank-order

correlations, uni- and multivariate linear regressions, and One-Way Analysis of Variance

(ANOVA), which will be elaborated on in the next paragraph.

Before applying these tests, one needs to critically think about the data and verify the reliability,

representativeness, and validity. Firstly, the internal consistency reliability of the scales of the

items used in the questionnaire to create the constructs was tested using Cronbach’s Alpha.

The scale reliability is the proportion of the variance of a scale item that is due to the true score

of the latent factor (DeVellis, 1991). This measure equaled .746, which is higher than the

frequently used cutoff value of .6. The latter indicates that all items share a high degree of

common variance and that the reliability is sufficient (Krause, 1991). Second, to ensure

representativeness of the sample, one should make sure there is little or no nonresponse bias.

Thus, one can test for significant differences between the responses of early respondents and

32

late respondents (Armstrong & Overton, 1977; Krause, 1999; Lambert & Harrington, 1990).

This method is legitimate based on the assumption that the opinions of late respondents are

relatively similar to those of non-respondents. For the study at hand, 20 survey items were

randomly selected and tested with independent samples t-tests across two groups of the first 70

respondents and the last 70 respondents. These t-tests revealed that none of the 20 items was

significantly different between the two groups on a 5% significance level. Although this does

not rule out nonresponse bias, it suggests that it does not pose a significant problem given that

late respondents’ answers are representative for non-respondents (Krause, 1999). Lastly, by

using a questionnaire as research instrument with multiple questions, construct validity is

enhanced. Moreover, the possibility of replicating the questions and hence comparing and

analyzing the results is provided (Afonso et al., 2008; Foster & Swenson, 1997).

4. Discussion

4.1. Empirical Results

Starting with the hypotheses 1 through 4, these were all tested in a similar fashion. In fact, to

enable investigating the exact hypothesized statements (i.e. comparing means across two

groups) it was most appropriate to employ independent samples tests. Therefore, all four

hypotheses were tested with an independent samples t-test. This proved legitimate as the

assumption of independent groups was respected given that a firm was assigned to one group

and was not part of the other at the same time (Sharpe, De Veaux, & Velleman, 2012). Further,

according to Sharpe et al. (2012), the normal distribution assumption can be disregarded with

a certain degree of confidence since the Central Limit Theorem starts to take effect in groups

with sample sizes larger than 40, which was the case in all groups in this study (the minimum

group size was 52). Hence the distribution matters less and violations should not cause major

problems (Ghasemi & Zahediasl, 2012; Sharpe et al., 2012).

Firstly, hypothesis 1 claims that firms with above average levels of involvement in intrafirm

cost management portray higher levels of involvement for IOCM. Firms who scored above the

average of Mean (M) = 3.98 (Standard Deviation (SD) = 0.76) on the score of intrafirm cost

management were considered to have an above average level of involvement in intrafirm cost

33

management and were part of group 1. In contrast, firms with values below the average were

considered having below average levels of this antecedent and formed group 0. It was found

that firms with more intrafirm cost management involvement had a mean score of involvement

in IOCM (M = 3.19, SD = 0.67) that was higher than those firms with lower involvement (M =

2.99, SD = 0.6) (Table 2). This result is statistically significant, t(148) = -1.77, p = .04 (one-

sided), and hence supports hypothesis 1. Thus, firms with above average levels of intrafirm

cost management have a higher involvement in IOCM than firms with below average levels.

The effect size of this difference can be measured by Cohen’s d and was estimated at .31,3

which is regarded as medium (Cohen, 1992).

Table 2. IOCM Involvement Means for Firms with High and Low Intrafirm Cost Management

Intrafirm Cost Management N Mean Std. Deviation IOCM Involvement Group 0 52 2.9917 .59812

Group 1 98 3.1878 .66919

Hypothesis 2 posits that firms with above average levels of information sharing would have

higher levels of IOCM involvement than those firms with below average levels. Firms that had

a value higher than the mean information sharing score of M = 3.53 (SD = 0.76) were

considered to have above average information sharing levels and belonged to group 1, and

those below this to have below average levels who were group 0. The results denote that firms

with more information sharing do have a higher mean (M = 3.29, SD = 0.54) than firms with

less information sharing (M = 2.78, SD = 0.65) (Table 3). This difference is statistically

significant t(144) = -5.098, p = .000 (one-sided), supporting hypothesis 2. It means that firms

with higher information sharing levels are, on average, associated with more IOCM

involvement. Cohen’s d, measuring the size of the difference or effect size, was .84, which

indicates the difference in means was significant and rather large (Cohen, 1992).

3 𝐶𝑜ℎ𝑒𝑛&𝑠𝑑 = +,-+.

(01,-01.)3

Independent Samples Test

t-test for Equality of Means

t df Sig. (2-tailed)

Mean Difference

Std. Error Difference

IOCM Involvement

Equal variances assumed

-1.770 148 .079 -.19609 .11076

34

Table 3. IOCM Involvement Means for Firms with High and Low Information Sharing

Information sharing N Mean Std. Deviation

IOCM Involvement Group 0 52 2.7782 .65495 Group 1 94 3.2943 .54414

Independent Samples Test

t-test for Equality of Means

t df Sig. (2-tailed)

Mean Difference

Std. Error Difference

IOCM Involvement

Equal variances assumed

-5.098 144 .000 -.51612 .10124

The third hypothesis revolves around the fact that firms with above average levels of trust and

commitment would have higher levels of IOCM involvement than firms with below average

levels. Companies that scored above the average of M = 3.61 (SD = 0.72) were considered

group 1 and as having above average levels of trust and commitment while those with lower

scores as group 0 with below average levels. Here, the mean of IOCM involvement of firms

with higher trust and commitment (M = 3.17, SD = 0.61) was not significantly higher than the

mean of firms with lower trust and commitment (M = 3, SD = 0.67) (Table 4). The results

indicate that the standardized difference t(145) = -1.618, p = .054 (one-sided) is on the edge of

significance, but insignificant. Therefore, hypothesis 3 is not supported and firms with higher

trust and commitment do not seem to be, on average, more involved in IOCM.

Table 4. IOCM Involvement Means for Firms with High and Low Trust and Commitment

Trust and Commitment N Mean Std. Deviation IOCM Involvement Group 0 52 2.9978 .66635

Group 1 95 3.1746 .61470 Independent Samples Test

t-test for Equality of Means

t df Sig. (2-tailed)

Mean Difference

Std. Error Difference

IOCM Involvement

Equal variances assumed

-1.618 145 .108 -.17680 .10925

Hypothesis 4 claims that firms with above average levels of industry competitiveness would

have higher levels of IOCM involvement than firms with below average levels. The cutoff

value between the two groups was the average of M = 3.37 (SD = 0.66) where firms with

35

values above belonged to group 1, and firms with values below to group 0. The mean (M =

3.23, SD = 0.61) for firms with above average levels of industry competitiveness was different

and higher than the mean (M = 3.02, SD = 0.68) for firms with below average levels. This

difference is significant with t(147) = 1.967, p = .03 (one-sided) (Table 5). Hypothesis 4 is

therefore supported since firms with higher industry competitiveness show more IOCM

involvement than firms with lower industry competitiveness. This difference has an effect size

of d = .33, which again is relatively medium according to Cohen (1992).

Table 5. IOCM Involvement Means for Firms with High and Low Industry Competitiveness

Industry Competitiveness N Mean Std. Deviation IOCM Involvement Group 0 89 3.0202 .67727

Group 1 60 3.2347 .61463 Independent Samples Test

t-test for Equality of Means

t df Sig. (2-tailed)

Mean Difference

Std. Error Difference

IOCM Involvement

Equal variances assumed

-1.967 147 .051 -.21450 .10905

For all four antecedents, according to the Shapiro-Wilk test, the assumption of normality of the

distribution of the dependent variable in both groups was violated. This test is the preferred

method for verifying the latter assumption (Ghasemi & Zahediasl, 2012). Thus, the Mann-

Whitney U test, a non-parametric version of the independent samples t-test, was performed in

order to verify the above hypotheses and results (MacFarland & Yates, 2016). These tests

confirmed the conclusions for every independent samples t-test. As such, hypotheses 1, 2, and

4 are supported while hypothesis 3 is not. From the abovementioned, results it well seems that

intrafirm cost management involvement, information sharing, and industry competitiveness

appear to matter for the ultimate involvement of firms in IOCM. In fact, companies with more

intrafirm cost management, information sharing, and industry competitiveness have higher

involvement levels in the interfirm management of costs than those with lower levels. This

might be due to the fact that these higher levels of antecedents elicit an effect on the level of

IOCM involvement. An additional test was therefore carried out to reveal this potential cause-

effect relation between the dependent variable of IOCM involvement and the four antecedents.

36

Van de Ven (2007) focuses on three criteria for inferring a causal relation between variables,

namely correlation between variables, temporal precedence of the cause occurring before the

effect, and absence of spurious factors influencing the cause-effect relation. Firstly, a

Spearman’s rank-order correlation test, used since certain variables were not normally

distributed, was performed for each antecedent as independent variable with the dependent

variable of IOCM involvement to investigate the first criterion (Appendix 7.10.). These show

significantly positive correlations (one-sided) of IOCM involvement with intrafirm cost

management, information sharing, and trust and commitment, rs(150) = .190, p = .01, rs(146)

= .501, p = .00 rs(147) = .241, p = .002 respectively, while the correlation with industry

competitiveness is insignificant. Secondly, temporal precedence can be assumed given that the

antecedents (cause) were asked in a way that specifies a time (t-2) taking place before the

timing (t-1) of the dependent variable of IOCM involvement (effect). Thirdly, a spurious effect

of a potential confounding variable on the relationship between the dependent and independent

variables was minimized by considering the most plausible and actual factors in the available

research (Van de Ven, 2007). However, controlling for every other possible and relevant factor

is outside of the scope of this study.

The rather surprising result of the lack of correlation between IOCM involvement and industry

competitiveness might have been caused by accident or by another confounding variable.

Another potential explanation could be that firms in highly competitive industries focus less

on actively reducing their costs, but more on increasing their revenues through spending on

marketing initiatives and product improvements. In an attempt to verify this, a Spearman’s

rank-order correlation between the level of industry competitiveness and firms’ most recent

annual gross sales was carried out. This revealed that there is a significant positive correlation

between these two variables, rs(194) = .160 (one-sided).

Hypotheses 5 through 8 posit that the performance of firms is positively influenced by the

extent to which these firms make use of the four IOCM practices mentioned in this study: price

benchmarking, supplier evaluation, inter-organizational ABC, and inter-organizational target

costing. These hypotheses were tested using a linear regression given that the cause-effect

relationship is of interest, and the dependent variable of performance as well as all predictor

variables are continuous variables. The assumptions of linearity, normality and independence

of the error terms, multicollinearity, and homoscedasticity were examined and satisfied.

37

However, prior to running the regressions, it proved relevant to investigate whether the variable

of performance positively correlated with the four IOCM practices (Van de Ven, 2007). The

Spearman’s rank-order correlations, again used since certain variables were not normally

distributed, reveal that there are significantly positive correlations between price

benchmarking, inter-organizational ABC, and inter-organizational target costing with

performance (Zar, 1998). Particularly, a weak positive correlation was found between

performance and price benchmarking of rs(127) = .152, p = .044 (one-sided), between

performance and inter-organizational ABC of rs(127) = .156, p = .039 (one-sided), and between

performance and inter-organizational target costing of rs(129) = .203, p = .011 (one-sided)

(Appendix 7.11.). Given that supplier evaluation is not correlated with performance, hypothesis

6 is by default not supported (Van de Ven, 2007). However, one can still investigate the causal

effects of the IOCM practices of price benchmarking, inter-organizational ABC and inter-

organizational target costing on performance. In the regression models, control variables of

industry and firm size (in terms of number of employees) were included to account for the

variability of firms in these respects and to neutralize the effects of these discrepancies

(Vanacker, 2016). This can be regarded as legitimate as certain industries and certain firm sizes

will perform better than other industries and sizes (Houthoofd & Hendrickx, 2012; Lee, 2009).

For hypothesis 5, a linear regression function was tested with performance as dependent

variable and price benchmarking as predictor, along with the control variables. When carrying

out this and the following regression models, the independent variables IOCM practices were

added to the initial model of performance as dependent variable and solely the control variables

as predictors. This allows observing whether adding the IOCM practice as predictor brings any

improvements to the model and can predict performance better than only the control variables

(Vanacker, 2016). The outcome reveals that the model is significant. In fact, adding the

independent variable of price benchmarking is value adding given the significant R Square

Change of .027, p = .033 (one-sided). Thus, the model significantly predicts performance, F(7,

117) = 1.909, p = .037 (one-sided) (Table 6). The prediction equation is:

𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 3.09 + .12 ∗ 𝑃𝑟𝑖𝑐𝑒𝐵𝑒𝑛𝑐ℎ𝑚𝑎𝑟𝑘𝑖𝑛𝑔 − .07 ∗ 𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑖𝑒𝑠 −

.05 ∗ 𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑠 − .42 ∗ 𝑅𝑒𝑡𝑎𝑖𝑙&𝑊ℎ𝑜𝑙𝑒𝑠𝑎𝑙𝑒 + .52 ∗ 𝑃ℎ𝑎𝑟𝑚𝑎𝑐𝑒𝑢𝑡𝑖𝑐𝑎𝑙𝑠 − .27 ∗

𝑂𝑡ℎ𝑒𝑟𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑖𝑒𝑠 + .02 ∗ 𝑆𝑖𝑧𝑒,

38

with the Manufacturing industry as base case. This means that with each unit increase in the

score of price benchmarking, the performance score rises with a value of .12. This result

supports hypothesis 5. The model, however, only accounts for 10.3% of the variance in the

dependent variable (R Square), which might suggest there are more explanatory variables to

take up in the model. Regardless of the abovementioned lack of correlation between

performance and supplier evaluation, hypothesis 6 was also tested using a univariate linear

regression. However, this rendered an expected nonsignificant result for this hypothesis.

Table 6. Output Linear Regression with Price Benchmarking

ANOVAa

Model Sum of Squares df F Sig. Regression 4.436 7 1.909 .074b Residual 38.832 117 Total 43.268 124

Note a. Dependent Variable: Performance b. Predictors: (Constant), Price Benchmarking, Primary Industries, Services, Retail & Wholesale, Pharmaceuticals, Other Industries, Size Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig. B Std. Error Beta (Constant) 3.093 .222 13.904 .000

Price Benchmarking .117 .063 .169 1.866 .065 Primary Industries -.074 .142 -.049 -.517 .606 Services -.053 .147 -.035 -.361 .718 Retail & Wholesale -.423 .168 -.234 -2.515 .013 Pharmaceuticals .519 .417 .111 1.245 .216 Other Industries -.270 .235 -.105 -1.150 .253 Size .024 .029 .074 .836 .405

Note a. Dependent Variable: Performance

Hypothesis 7a was also tested with a linear regression. This delivered a nonsignificant model

F(7, 117) = 1.403, p = .11 (one-sided), with a nonsignificant change in R Square of .013, p =

.11 (one-sided), going from model 1 to model 2 (Table 7). Therefore, hypothesis 7a is not

supported and no valid conclusions can be made regarding the coefficient of the predictor

variable of inter-organizational ABC in the prediction equation:

39

𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 3.11 + .09 ∗ 𝐴𝐵𝐶 − .04 ∗ 𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑖𝑒𝑠 − .11 ∗ 𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑠 −

.37 ∗ 𝑅𝑒𝑡𝑎𝑖𝑙&𝑊ℎ𝑜𝑙𝑒𝑠𝑎𝑙𝑒 + .53 ∗ 𝑃ℎ𝑎𝑟𝑚𝑎𝑐𝑒𝑢𝑡𝑖𝑐𝑎𝑙𝑠 − .18 ∗ 𝑂𝑡ℎ𝑒𝑟𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑖𝑒𝑠 + .04 ∗

𝑆𝑖𝑧𝑒.

Additionally, hypothesis 7b was investigated. The latter states that firms with higher levels of

inter-organizational ABC adoption will have higher performance levels. This can be examined

using a One-Way ANOVA to compare the means of a continuous variable, performance, across

more than two groups or levels. Prior to applying the test, the assumptions were checked. Here,

the sample size assumption of minimally 30 cases per group was violated as well as the one of

balanced groups in case of not normally distributed variables. This means inferences from the

results are not extremely robust (Field, 2009; Sharpe et al., 2012). The results reveal that the

mean differences between the levels of adoption are insignificant. Hypothesis 7b can therefore

not be supported, potentially due to the assumption violation (Appendix 7.12.).

Table 7. Output Linear Regression with ABC

ANOVAa

Model Sum of Squares df F Sig. Regression 3.665 7 1.403 .211b Residual 43.673 117 Total 47.338 124

Note a. Dependent Variable: Performance b. Predictors: (Constant), ABC, Primary Industries, Services, Retail & Wholesale, Pharmaceuticals, Other Industries, Size Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig. B Std. Error Beta (Constant) 3.105 .252 12.313 .000

ABC .085 .067 .115 1.259 .211 Primary Industries -.040 .151 -.025 -.262 .794 Services -.112 .153 -.071 -.733 .465 Retail & Wholesale -.368 .179 -.194 -2.050 .043 Pharmaceuticals .526 .443 .107 1.187 .238 Other Industries -.184 .246 -.069 -.749 .455 Size .035 .031 .101 1.122 .264

Note a. Dependent Variable: Performance

40

Further, hypothesis 8 was tested with a univariate linear regression with predictor inter-

organizational target costing. The regression provided a significant R Square Change of .034,

p = .019 (one-sided), and a significant model F(7, 119) = 1.815, p = .045 (one-sided) with the

prediction equation:

𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 3.02 + .14 ∗ 𝑇𝑎𝑟𝑔𝑒𝑡𝐶𝑜𝑠𝑡𝑖𝑛𝑔 − .02 ∗ 𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑖𝑒𝑠 − .08 ∗

𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑠 − .32 ∗ 𝑅𝑒𝑡𝑎𝑖𝑙&𝑊ℎ𝑜𝑙𝑒𝑠𝑎𝑙𝑒 + .51 ∗ 𝑃ℎ𝑎𝑟𝑚𝑎𝑐𝑒𝑢𝑡𝑖𝑐𝑎𝑙𝑠 − .28 ∗

𝑂𝑡ℎ𝑒𝑟𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑖𝑒𝑠 + .02 ∗ 𝑆𝑖𝑧𝑒.

The interpretation of this is that performance rises with .14 with each unit increase in inter-

organizational target costing (Table 8). Thus, hypothesis 8 is supported.

Table 8. Output Linear Regression with Target Costing

ANOVAa

Model Sum of Squares df F Sig. Regression 4.592 7 1.815 .090b Residual 43.010 119 Total 47.602 126

Note a. Dependent Variable: Performance b. Predictors: (Constant), Target Costing, Primary Industries, Services, Retail & Wholesale, Pharmaceuticals, Other Industries, Size Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig. B Std. Error Beta (Constant) 3.024 .213 14.228 .000

Target Costing .137 .065 .196 2.102 .038 Primary Industries -.024 .148 -.015 -.162 .871 Services -.084 .148 -.054 -.571 .569 Retail & Wholesale -.319 .179 -.168 -1.785 .077 Pharmaceuticals .510 .436 .104 1.170 .244 Other Industries -.276 .247 -.103 -1.114 .267 Size .018 .031 .054 .601 .549

Note a. Dependent Variable: Performance

41

To further investigate the causality of the two significant IOCM practices of price

benchmarking and inter-organizational target costing, a multivariate linear regression was

performed with the prediction equation:

𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 𝛽Z + 𝛽[ ∗ 𝑃𝑟𝑖𝑐𝑒𝐵𝑒𝑛𝑐ℎ𝑚𝑎𝑟𝑘𝑖𝑛𝑔 +𝛽3 ∗ 𝑇𝑎𝑟𝑔𝑒𝑡𝐶𝑜𝑠𝑡𝑖𝑛𝑔 + 𝛽\ ∗

𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑖𝑒𝑠 + 𝛽] ∗ 𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑠 + 𝛽^ ∗ 𝑅𝑒𝑡𝑎𝑖𝑙&𝑊ℎ𝑜𝑙𝑒𝑠𝑎𝑙𝑒 + 𝛽_ ∗

𝑃ℎ𝑎𝑟𝑚𝑎𝑐𝑒𝑢𝑡𝑖𝑐𝑎𝑙𝑠 + 𝛽` ∗ 𝑂𝑡ℎ𝑒𝑟𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑖𝑒𝑠 + 𝛽a ∗ 𝑆𝑖𝑧𝑒.

The additional independent variables do not appear to be extremely value adding as the R

Square Change of .037 is barely significant, p = .048 (one-sided), and the coefficients of price

benchmarking and inter-organizational target costing prove insignificant (Appendix 7.13.).

This means that, implemented in combination, price benchmarking and inter-organizational

target costing no longer have a(n) (positive) impact on performance.

Having mapped out the aforementioned relationships between the four IOCM practices and

firm performance, it might be interesting to discover whether there remain certain concepts

that can influence this relationship. Specifically, one might be able to find certain constructs

that moderate this relation. However, this will only be examined for the IOCM practices that

were suggested to have a significant effect on performance, namely price benchmarking and

inter-organizational target costing. This is reasonable as potential moderators cannot have an

effect on a nonsignificant relation (Vanacker, 2016).

First, it might seem logical that the level of information sharing can positively interact between

the effect of IOCM practices on performance. As the overall business environment nowadays

is characterized by being highly dynamic and uncertain, firms should find merit in the degree

to which they exchange valuable information in their relations with other firms (Fiala, 2005).

Such disseminating of input is regarded as crucial to remain viable and competitive in the

global economy of today (Lotfi, Mukhtar, Sahran, & Zadeh, 2013). For example, the bullwhip

effect, which describes the growing variation upstream in the supply chain and causes poor

supply chain performance and high costs, can be minimized through sharing information (Fiala,

2005; Wu et al., 2014). Reducing information asymmetry between partners can thus render

supply chain practices more effective (Jeong & Jorge Leon, 2012). Furthermore, Baihaqi and

Somal (2012) propose information sharing to be one of the main factors to support supply chain

42

performance through increasing efficiency. The authors claim that information sharing does

this by creating a high level of integration between the members. This again, in turn, allows

firms to coordinate their actions more effectively, such as interfirm cost management actions

(Wu, Yeniyurt, Kim, & Cavusgil, 2006). In fact, Lin, Huang, and Lin (2002) found that more

information sharing positively influenced cost reduction abilities of the entire supply chain.

Therefore, one might posit that the level of information sharing could moderate the effect of

the two significant IOCM practices, price benchmarking and inter-organizational target

costing, on performance.

In order to empirically test this, two linear regressions were carried out. On the one hand, a

regression was formed with the relevant control variables, performance as dependent variable,

price benchmarking as independent variable, and the interaction term of price benchmarking

with information sharing, as well as the variable of information sharing. On the other hand, the

second regression equation was formed in a similar way but with inter-organizational target

costing as independent variable and where the interaction term consisted of this variable and

information sharing. The interaction term was included to account for the effect that the level

information sharing could have on the slope of the IOCM practice (Sharpe et al., 2012). This

way, it can be investigated whether more information sharing could positively influence the

effect of price benchmarking or inter-organizational target costing on performance. The

interaction term was created as the product of the variables of information sharing and price

benchmarking, and inter-organizational target costing (Sharpe et al., 2012). These were all

centered to avoid multicollinearity when using them in one regression equation. Before running

the regressions, the assumptions of normality and independence of residuals, linearity,

multicollinearity, and homoscedasticity were examined and were respected.

The results of the first regression with price benchmarking show that the model is significant

F(9, 111) = 2.962, p = .003. Moreover, the coefficient of the interaction term shows a

significant and positive sign, p = .004 (one-sided). This runs in line with the existing literature

on the importance of information sharing to supply chain performance and practices, such as

cost management techniques. In an attempt to understand this phenomenon more in detail, a

scatterplot was created of price benchmarking against performance (Figure 4). In this plot, the

sample is separated into three levels of information sharing, from low (1) to high (3). The figure

shows that higher levels of information sharing elicit a steeper slope of the line through the

dots between price benchmarking and performance. Thus, this means that higher levels of

43

information sharing do indeed positively interact in the relation between price benchmarking

and performance. This then seems to support the theory about the interacting impact of

information sharing on the effectiveness of cost management activities.

Figure 4. Scatterplot of Moderating Role of Information Sharing on Performance Effects of

Price Benchmarking.

The results of the second regression, with inter-organizational target costing as independent

variable, just appeared insignificant with F(9, 113) = 1.948, p = .052. The coefficient of the

interaction term also appeared insignificant, p = .204 (one-sided). Therefore, the

abovementioned reasoning about the interacting role of information sharing does not hold in

the case of inter-organizational target costing. In this regression, information sharing does not

operate as a moderator.

Secondly, it may seem evident that the level of trust and commitment between supply chain

partners can have a certain impact on the extent to which practices employed by firms

effectively improve performance. Particularly, it might be expected that the more the partners

trust each other and show commitment towards one another, the better they will cooperate and

thus will experience bigger effects of their joint actions to reduce costs on performance. Wu,

44

Chiag, Wu, and Tu (2004) found in their study that more commitment and trust have a positive

impact on supply chain integration. This integration of business processes, including efforts to

reduce costs, they claimed, was associated with higher levels of performance (Wu et al., 2004;

Yu, Yan, & Edwin Cheng, 2001). Moreover, Min et al. (2005) concluded that idiosyncratic

investments, a form of commitment, lead to more effective cost reduction actions and jointly

created economies of scale. Hence, it can be postulated that more trust and commitment can

render cooperative efforts of companies to reduce costs inter-organizationally more effective.

Here, this would mean that trust and commitment would have a positive moderating role in the

relationship between the significant IOCM practices and firm performance.

To test this, two linear regressions were performed similarly as before but with, in the first one

an interaction term of the product of the centered variables price benchmarking with trust and

commitment, as well as the centered variable of trust and commitment. In the second regression

equation, the interaction term consisted of the product of the centered variables inter-

organizational target costing and trust and commitment, and the centered variable of trust and

commitment was added. Here, the interaction term was used to check the effect of the level

trust and commitment on the slope of the IOCM practice (Sharpe et al., 2012). The assumptions

of normality and independence of residuals, linearity, multicollinearity, and homoscedasticity

were, once more, examined and met.

For the first regression with price benchmarking, the results indicate the model is significant

F(9, 113) = 2.298, p = .021. Moreover, the coefficient of the interaction term proved positive

and significant, p = .007 (one-sided), implying that with higher trust and commitment the effect

of using price benchmarking on performance was greater. Furthermore, as in the case of

information sharing, the sample was segregated in three levels of trust and commitment, from

low to high, and the independent variable of price benchmarking was plotted on performance

(Figure 5). The figure shows that the slope of the predictor increases with the level of trust and

commitment. So, trust and commitment in a relationship between firms clearly positively

moderate the effect of price benchmarking on performance.

45

Figure 5. Scatterplot of Moderating Role of Trust and Commitment on Performance Effects

of Price Benchmarking.

The second regression with inter-organizational target costing as independent variable also

uncovered some significant results. In fact, the model was significant, F(9, 114) = 2.237, p =

.024, as well as the coefficient of the interaction variable between inter-organizational target

costing and trust and commitment, p = .009 (one-sided). Dividing the sample once again into

three levels of trust and commitment and creating a scatterplot in the same manner as was done

above, it becomes clear that higher levels of trust and commitment stimulate the impact of

target costing on performance (Figure 6). This is translated into the fact that higher levels have

steeper slopes. Therefore, it appears that trust and commitment does have a moderating role on

the performance effects of inter-organizational target costing. Both results, then, regarding the

interaction effect of trust and commitment on the performance impacts of collaborative actions,

coincide with the theory. Specifically, more trust and commitment can magnify the benefits

that the two IOCM practices have on performance.

46

Figure 6. Scatterplot of Moderating Role of Trust and Commitment on Performance Effects

of Inter-organizational Target Costing.

4.2. Managerial Insights and Future Research

Some caution should be taken when interpreting the abovementioned outcomes. In fact, the

results apply to the companies studied in this research. This means that the inferences of the

statistical tests cannot be generalized to industries that are not represented in the study, and to

companies that do not fit the selection criteria with regards to location, size, date of

incorporation, etc. Moreover, surveys are only as trustworthy as the respondents’ expertise

(Sekaran & Bougie, 2013). Hence, there remains a certain degree of error when analyzing the

data since less knowledgeable people might have answered the surveys regardless of the

instructions in the invitation emails. Also, the antecedents and IOCM practices used here are

non-exhaustive. Potential future research might, therefore, explore the effects of other types of

antecedents and/or IOCM approaches to provide a more holistic view of the research topic of

interfirm cost management. Furthermore, the performance measures used in this study are not

completely exhaustive. There might remain other interesting performance indicators that future

research might include. Moreover, the questions in the survey regarding performance do not

specifically state that respondents must evaluate the effects of the practices on these

47

performance measures. As such, assigning the performance levels directly to the IOCM

practices implemented should be done with some caution. Table 6 provides an overview of

some of the tests and their results.

Table 9. Overview of Tests and Results

Tests Antecedents

Independent Samples T-Tests

Linear Regression with Price Benchmarking

Linear Regression with Inter-

organizational Target Costing

Intrafirm cost management involvement

Hypothesis 1 supported*

Information sharing Hypothesis 2 supported**

Moderator effect found**

No moderating effect found

Trust and commitment

Hypothesis 3 not supported

Moderator effect found**

Moderator effect found**

Industry competitiveness

Hypothesis 4 supported*

Tests IOCM Practices

Univariate Linear

Regressions Multivariate Linear

Regressions

Price Benchmarking

Hypothesis 5 supported* No significant effect

found

Supplier Evaluation

Inter-organizational ABC

Hypothesis 7a (and 7b)

not supported

Inter-organizational Target Costing

Hypothesis 8 supported* No significant effect

found

Note * and ** indicate the one-sided significance of the performed tests/coefficients on a 5%

and 1% significance level.

Combining the results of the independent samples t-tests, and its non-parametric counterpart,

and the correlation matrices, there seem to be some important insights. Firstly, sampled firms

that are more involved in the management of inter-organizational costs are those that also

focus, more than the average company, on intrafirm cost management. Additionally, intrafirm

cost management involvement seems to positively associate with interfirm cost management

involvement, and potentially even cause the latter since they correlate positively and have a

time lag. Hence, these results could confirm the aforementioned statements that abilities and

48

techniques used to keep costs in check within the firm can be extrapolated and used for firms’

efforts to reduce costs on a wider basis. So, firms that might have an outstanding track record

of or a competitive advantage in internal cost management, might consider broadening this to

an interfirm dimension. Hereby, they could reap the benefits of their cost reduction abilities.

The cause-effect relation for this antecedent as well as for the other three antecedents on IOCM

involvement can also be examined using a linear regression to further clarify such a

relationship. Hence, this can be a possible avenue for future research to investigate. Also, in

this study, no distinction was made between which specific capabilities or techniques were

most valuable for IOCM. Therefore, future research might go into more detail and uncover

what exact internal cost management methods can ensure effective interfirm cost management.

Second, firms in the sample that are more involved in IOCM practices are characterized by

higher information sharing levels in the supply chain. This sharing of information also seems

to positively associate with and influence the extent to which firms use techniques to manage

costs inter-organizationally. The results reveal a potential takeaway for managers. In fact, firms

that have established information exchange channels should consider adopting IOCM

practices. The reason for this is that the results seem to indicate that firms with more

information sharing manage their supply chain costs more actively. In line with previously

stated literature, a potential reason for this might be that information sharing makes managing

interfirm costs more effective. This would explain why the variables of information sharing

and IOCM involvement correlate positively.

The latter was partly confirmed by the tests performed to verify the moderating influence of

information sharing on the performance effects of the significant IOCM practices. However,

this effect only existed for price benchmarking. One might have expected the same result for

inter-organizational target costing as for price benchmarking since the two IOCM practices

have a certain overlap (cf. infra). However, price benchmarking, compared to inter-

organizational target costing, might require a slightly greater amount of information shared

between supply chain partners. This can then imply that higher levels of information sharing

benefits firms who apply price benchmarking, more so than those applying inter-organizational

target costing. This outcome, however, might be due to the fact that the study at hand is not a

representation of the population at large, but only of the sample used in the research.

Nevertheless, in order to maximize the advantages in terms of performance from price

benchmarking, managers should therefore invest time and effort to exchange sufficient

49

information. Regardless of the insignificant interaction effect of information sharing for inter-

organizational target costing on performance, it seems rather logical that relations in which

information is disclosed can only be beneficial. Accordingly, companies that update each other

and disclose vital information with their up- and downstream partners can also be considered

good candidates for implementing practices to reduce costs in the supply chain.

Third, an insignificant result was found when comparing IOCM involvement between firms

from the sample with high and low trust and commitment in the relations with their supply

chain partners. This might be a consequence of the fact that most respondents, when asked

about their trust and commitment, score in close proximity to the mean (Appendix 7.14.). This

implies that dividing the sample into two groups naturally leads to groups whose means lie

closely to each other and where no significant difference can be found. The independent

samples t-test can therefore be regarded as rather inconclusive. Consequently, one might regard

the correlation test as more representative. The latter suggested a significantly positive,

possibly cause-effect, relation between trust and commitment in relations between firms in

supply chains and the involvement in inter-organizational management of costs. Thus,

companies could learn, as in the case of the latter antecedent, that trust and commitment might

allow for more effective management of inter-organizational costs.

This can, again, be confirmed by the outcome of the tests examining the moderator role of trust

and commitment. Trust and commitment in relationships between supply chain partners

seemed to have a significant influence on the performance effects of price benchmarking and

of inter-organizational target costing. So, established and appropriately maintained

relationships are valuable. In case firms have yet to create these relations, they should consider

doing so as soon as possible as trust and commitment are only achieved over time (Dwyer,

Schurr, & Oh, 1987; Gulati, 1995; Stanko, Bonner, & Calantone, 2007). Thus, it may benefit

firms to create solid relationships with their supply chain partners where opportunistic behavior

is minimized and where they act in good faith. This will then allow for better cost reduction

efforts applied in interfirm environments.

In line with hypothesis 4, firms in the study who faced greater competitiveness in their industry

were more involved in IOCM practices than those with lower industry competitiveness.

However, counterintuitively, the industry competitiveness did not associate with, or influence,

IOCM involvement. As a result, one cannot claim that the difference in IOCM scores is

50

completely due to the difference in industry competitiveness. Therefore, the results run counter

to the theories used as basis for the hypothesis. Alternatively, one could state that higher

competitiveness levels in the industry can positively influence the sales of firms given the

outcome of the correlation analysis and temporal precedence of industry competitiveness. This

might be due to the fact that companies in more competitive industries tend to concentrate more

on ways to boost revenues at the expense of managing costs that arise on an interfirm level,

which may cause those greater sales volumes. This might then explain the lack of a significant

relation between industry competitiveness and IOCM involvement. One could, however,

advise these companies to equally emphasize the importance of revenue creation and cost

containment as both help improve performance.

The linear regressions provide some proof for the latter statement. Certain IOCM practices

seemed advantageous to the performance of the sampled firms. This leads to additional insights

for firms and their managers. Price benchmarking and inter-organizational target costing both

appeared to positively affect the performance of firms in the sample. The latter practice seemed

to have the greatest effect. Thus, managers should opt to implement inter-organizational target

costing to obtain the greatest impacts on their firm performance. When applied together,

though, the two techniques did not show any (positive) effects on performance. This might be

explained by the fact that the two IOCM practices have, to some extent, a certain overlap. In

fact, both processes are situated in the early stages of creating the product and partly focus on

similar objectives. Price benchmarking, as mentioned above, ensures among others low

procurement prices. Inter-organizational target costing, in turn, concentrates on reducing costs

in the early stages of product development, which includes the purchasing of necessary

resources for the ultimate creation of products (Afonso et al., 2008). As such, this might be a

reason for the fact that, applied together, the one IOCM practice does not seem to generate

additional benefits in terms of performance effects beyond the other’s benefits. Companies

should keep in mind, however, that their company can be different from the ones sampled in

this study, and that not every company is fit to introduce whichever interfirm cost management

approach. Prior to adopting any IOCM practice, the decision should therefore be carefully

considered and analyzed to ensure an optimal choice and result.

The outcome of the regression results also discloses the rather surprising lack of performance

effects of supplier evaluation and inter-organizational ABC. The insignificant effect of supplier

evaluation seems to agree with the aforementioned findings of Kannan and Tan (2003) where

51

they found that the IOCM practice showed a more limited impact on buyers’ performance in

Europe when compared to the United States. However, the insignificant effect of ABC stands

in stark contrast with some of the literature studied above. Further, the fact that hypothesis 7a

was not supported might have caused hypothesis 7b not to be supported. If ABC is not

positively related to performance, then higher levels of ABC will most likely not be able to

bring about substantial differences in the means of performance of these levels. Another reason

for this contrasting outcome might be the violation of the assumptions. Alternatively, the way

in which firms were divided into levels of adoption might also have biased the results. This

was an ad hoc categorization and, even though inconsistencies were rather small, might have

influenced the representativeness of the test. However, one must remember when interpreting

the results that this only applies to the sample at hand and that this does not disregard the

relevance of these two IOCM approaches. It merely is an approximate reflection of the situation

at the sampled companies. Hence, additional research could elaborate on supplier evaluation

and inter-organizational ABC in detail and examine more profoundly whether they can be

employed to boost performance.

The two last questions of the questionnaire investigated companies’ opinions on the

performance effects and their future outlook. The first one asked respondents about the

perceived effects of implemented IOCM practices on performance. The distribution of

respondents’ answers shows that the majority of firms believe that employed IOCM practices

only moderately influenced performance (Appendix 7.15.). This seems to coincide with the

results of the linear regressions that reveal only two IOCM practices to have a weak positive

but significant impact on performance. It is therefore questionable whether the practices

covered in the study do create advantages for firms that have implemented them. Future

research could show the true impacts of these IOCM practices more profoundly. The second

question aimed to form an idea of the future outlook of companies regarding their prospective

involvement in IOCM. Reviewing the distribution of answers unveils most firms agree with

the statement that predicts adoption to take place within three years’ time (Appendix 7.16.).

Given this outcome, one might suppose there to be certain benefits accruing to firms

undertaking IOCM. If this were not the case, the majority of companies would not agree with

the latter statement.

52

5. Conclusion

This study was carried out to empirically test conjectured relations and effects in the area of

cost management in the supply chain. It was posited that firms with above average levels of

certain antecedents would be more involved in interfirm cost management than firms with

below average levels of these antecedents. Moreover, it was theorized that the performance of

firms was positively influenced by the extent to which they had adopted certain IOCM

practices.

The research was performed in response to the increasing importance of effective supply chain

management for the survival of companies situated in networks of organizations. Due to several

developments in the business world, such as rising globalization, vertical disintegration, and

changing competition, firms nowadays have been facing critical times. Specifically, they are

forced to contend with others on a gradually higher level, a supply chain level, instead of the

traditional firm level; interfirm competition has been replaced by inter-supply chain

competition (Wu et al., 2014). Also, in these supply chains, a growing trend is the concern of

cost management. This is a consequence triggered by the destructive consequences of the

financial crisis of 2007-2008, the ever-increasing costs of logistics and energy, and the rising

customer demands for lower prices. However, this trend does not solely prompt cost

management practices within firms, but also commands actions to manage costs across supply

chains. After all, supply chain costs also account for a large part of firms’ total charges. As

such, companies would benefit from approaches allowing them to contain these costs.

Though scarce previous research has been performed on the latter topic, it has mostly focused

on individual types of inter-organizational cost management practices and their performance

effects in isolation of other practices. This study, however, aimed to combine several of the

most popular IOCM practices in one analysis and map out their performance effects

individually as well as applied in combination. In addition, an attempt was made to examine

the factors or antecedents that allow for such management initiatives. This all could bring

knowledge to managers such that they can learn what to focus on to create an optimal

environment for interfirm cost management practices to take place, and which IOCM practices

are most beneficial for firm performance. Particularly, the antecedents of intrafirm cost

management involvement, level of information sharing, level of trust and commitment, and

53

industry competitiveness were chosen as they were most prominent in the available research.

The IOCM practices included purchasing performance (price) benchmarking, supplier

evaluation, inter-organizational ABC, and inter-organizational target costing.

The results of the research revealed several important insights. Companies are most likely to

have a higher level of involvement in managing costs on an inter-organizational level when

they manage their own firm costs more actively, when they share information more fervently,

and when they have greater trust and commitment in the relations between their partners.

Higher levels of industry competitiveness in which firms operate did cause significant

differences in the level of involvement in IOCM but cannot completely be considered as an

influencing factor. Companies in such industries were shown to put more emphasis on revenue

enhancement than cost containment. The former, however, should not be carried out at the

expense of the latter as they are equally crucial for affecting performance. Generally, when

firms are considering implementing actions to reduce interfirm costs, they should also focus

on managing costs within their firm, sharing sufficient information, and establishing trust and

commitment as they allow for more successful inter-organizational cost management.

Further, it appears that only certain IOCM practices prove advantageous for company

performance. Particularly, when applied in isolation, price benchmarking and inter-

organizational target costing seem to positively influence firm performance. However, when

applied in combination these two practices no longer showed favorable for increasing

performance. Therefore, it can be said that companies should concentrate on employing only

one of these two studied techniques. Preferably the latter as it exhibited the largest effect on

performance. Moreover, the performance effects of price benchmarking were found to be

positively moderated by the level of information sharing. Additionally, the performance effects

of both price benchmarking and inter-organizational target costing were influenced by the trust

and commitment in the relations between supply chain partners. In fact, more information

sharing amplified the effect of price benchmarking on performance, and so did trust and

commitment for the effects of price benchmarking and inter-organizational target costing on

performance. Hence, when applying these specific IOCM practices it is essential to ensure that

companies communicate and invest in their relationships as it makes these practices more

effective in terms of performance effects.

54

Overall, one might stand to reason that companies can benefit from certain interfirm cost

reduction practices in terms of improved performance. While respecting some of the

contradicting findings, it is therefore recommended that managers confer with their up- and

downstream partners to assess the possibility of implementing such performance enhancing

approaches. Though, future research might prove valuable to find additional answers to the

question at hand about the true relevance and proceeds of interfirm cost management.

X

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7. Appendices

7.1. Questionnaire

Dear participant, This questionnaire is designed to study the antecedents and the possible performance effects of cost management practices applied across the supply chain. Because you are the one who can help create a correct understanding of this topic, I request you to respond the questions as honestly as possible. Your responses will only be used in aggregated form and will be kept strictly confidential. Thank you very much for your time and cooperation. I greatly appreciate the help of your company and yourself. Cordially, Julien Neven Master Student Business Economics at UGent 1. What main industry is your company in?

• Chemicals • Communication • Educational • Electronic equipment • Food & Beverage • Furniture • Mining • Manufacturing • Paper • Pharmaceuticals • Primary metal • Printing • Retail trade • Rubber/plastics • Textile • Transportation • Wholesale • Services • Other (please specify) ____________________

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2. What was your company’s most recent annual gross sales? • Less than €1 million • €1 million to €5 million • €5 million to €10 million • €10 million to €50 million • €50 million to €100 million • €100 million to €500 million • €500 million to €1 billion • Over €1 billion

3. What is your company's current number of employees?

• Less than 100 employees • 101 up to 200 employees • 201 up to 500 employees • 501 up to 1000 employees • 1001 up to 5000 employees • Over 5000 employees

4. Please indicate which of the following internal cost management practices were used within your company two years ago? (indicating more than 1 is possible) q Standard costing q Total Quality Management (TQM) q Six Sigma q Inventory Management q Kaizen costing q Job costing q Process costing q Budgeting q Target cost planning q Payback period q Cost-Volume-Profit analysis (CVP) q Return On Investment (ROI) q Activity-Based Costing (ABC) q Activity-Based Management (ABM) q Activity Cost Analysis (ACA) q Net Present Value (NPV) q Quality Cost Analysis (COQ) q Other (please specify) ____________________

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5. How important was internal cost management (= undertaking activities within your firm to gather, analyze, and use cost information for budgeting, improving decisions, and monitoring costs to keep costs down) within your company two years ago?

• Extremely important • Very important • Moderately important • Slightly important • Not at all important • Not applicable

6. Please mark the extent to which you agree with the following statements regarding your company two years ago?

Not applicable

Strongly disagree Disagree

Neither agree nor disagree

Agree Strongly agree

Your company actively undertakes activities to manage costs internally

o o o o o o

7. Please mark the extent to which you agree with the following statements regarding your company two years ago?

Not applicable

Strongly disagree Disagree

Neither agree nor disagree

Agree Strongly agree

- Your company rarely changes its marketing practices

o o o o o o

- Actions of competitors in your main industry are easy to predict

o o o o o o

8. Please rate the overall competition level of your main industry two years ago?

Very high High Average Low Very low Not applicable

o o o o o o

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9. Please mark the extent to which you agree with the following statements regarding your company two years ago?

Not applicable

Strongly disagree Disagree

Neither agree nor

disagree

Agree Strongly agree

- In the relationship with your supply chain partners, any information that might help a supply chain partner, such as but not limited to financial information, strategic information, tactical information, customer information, product information, etc. is be provided to this partner

o o o o o o

- In the relationship with your supply chain partners, exchange of information with your supply chain partners takes place frequently

o o o o o o

- In the relationship with your supply chain partners, it is expected that your company and its partners keep each other informed about events or changes that may affect you or the other partners

o o o o o o

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10. Please mark the extent to which you agree with the following statements regarding your company two years ago?

Not applicable

Strongly disagree Disagree

Neither agree nor

disagree

Agree Strongly agree

- In the relationship with your supply chain partners, the partners have a strong sense of loyalty to your company

o o o o o o

- In the relationship with your supply chain partners, the partners are willing to make a long-term investment in helping your company

o o o o o o

- Your company and your supply chain partners see your relationship with them as a long-term alliance

o o o o o o

- In the relationship with your supply chain partners, it is expected that your company and its partners act in good faith and do no behave opportunistically relative to each other

o o o o o o

11. How involved were your supply chain partners in the process of managing and controlling the costs of doing business with them two years ago?

Extremely Very Moderately Slightly Not at all o o o o o

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12. Please mark the extent to which you agree with the following statements regarding your company last year?

Not applicable

Strongly disagree Disagree

Neither agree nor

disagree

Agree Strongly agree

- Your company takes a total supply chain view of cost management and takes into account costs that arise at its supply chain partners when managing its costs

o o o o o o

- Your company and its supply chain partners share common assets (can include staff) with each other to coordinate activities and collaborate to reduce costs

o o o o o o

- Your company is planning to implement more Inter-Organizational Cost Management practices (IOCM) in the next five years (IOCM = strategic cost management that does more than traditional management of internal costs, and includes managing costs among supply chain partners)

o o o o o o

- Your company regards IOCM practices (such as supplier evaluation, purchasing performance benchmarking, Activity-Based Costing, target costing, etc.) as a strategic function in your organization? (A function that is respected, whose input is valued, that participates in high-level decisions)

o o o o o o

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13. Please indicate which of the following IOCM practices were used within your company last year? (indicating more than 1 is possible) q Functionality-Price-Quality (FPQ) tradeoffs q Inter-organizational cost investigations q Concurrent cost management q Supplier evaluation q Price/purchasing performance benchmarking q Activity-Based Costing q Target costing q Value Analysis (VA) q Minimum Cost Investigations (MCIs) q Supply chain costing q Total Cost of Ownership (TCO) q Value Chain Analysis (VCA) q Balanced Scorecard (BSC) q Supplier Lifetime Value (SLV) q Supply Chain Operations Reference (SCOR) q Open book accounting q Process Benchmarking q Other (please specify) ____________________ 14. Please mark the extent to which you agree with the following statements regarding your company last year?

Not applicable

Strongly disagree Disagree

Neither agree nor disagree

Agree Strongly agree

Your company manages costs beyond the traditional management of internal costs to include managing overall supply chain costs

o o o o o o

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15. Please mark the extent to which you agree with the following statements regarding your company last year?

Not applicable

Strongly disagree Disagree

Neither agree nor

disagree

Agree Strongly agree

- Your company gathers information about prices and levels of quality of supplier purchases of other companies in your industry

o o o o o o

- Your company analyzes the purchasing process of other companies in your industry to improve your own company’s purchasing process

o o o o o o

- Your company compares the supply price charged by your suppliers for a given products or service with the prices your suppliers charge to other industry players

o o o o o o

- There is a formal procedure to compare your purchasing performance with the purchasing performance of other companies (i.e. purchasing performance benchmarking) (purchasing performance = relates to the ability of the purchasing department to operate in line with the corporate strategy and achieve its goals)

o o o o o o

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16. Last year, how frequently did your company...

Very frequently Frequently Sometimes Seldom Almost

never - make use of criteria (such as on-time delivery of purchases, quality level, flexibility, reliability, price, etc.) to evaluate your suppliers’ performance (i.e. supplier evaluation)?

o o o o o

- visit suppliers’ facilities for inspection?

o o o o o

17. Did your company have and employ a quality-assurance program for your suppliers’ products and performance last year?

• Yes • No

18. Please mark the extent to which you agree with the following statements regarding your company last year?

Not applicable

Strongly disagree Disagree

Neither agree nor disagree

Agree Strongly agree

- Your company measures inter-organizational costs as a function of the activities that drive the costs (i.e. you apply Activity-Based Costing to inter-organizational costs)

o o o o o o

- Your company identifies activities and procedures performed in their organizations to make the final products/services (Activity Analysis)

o o o o o o

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19. Last year, how frequently did your company...

Very frequently Frequently Sometimes Seldom Almost

never

employ cost management practices matching this definition in cooperation with your supply chain partners? “A costing method that calculates the maximum allowable cost price for newly developed products by subtracting a required profit margin from the expected selling price.” (i.e. target costing)

o o o o o

- Your company identifies and calculates the costs of the various activities involved with providing services or producing goods, for the purpose of identifying the factors which influence costs and allocating costs to cost pools (Activity Cost Analysis)

o o o o o o

- Your company identifies and calculates the costs of the various activities involved with providing services or producing goods for the purpose of allocating costs of activities to products/services and enabling a more accurate assessment of product costs (Activity-Based Costing)

o o o o o o

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20. Please mark the extent to which you agree with the following statements regarding your company last year?

Not applicable

Strongly disagree Disagree

Neither agree nor disagree

Agree Strongly agree

- Your company uses target costing in cooperation with your supply chain partners to meet market prices for your product while providing a profit margin to your firm and partners

o o o o o o

- Your company changes the product with the help of your supply chain partners during the design process in order not to exceed the predetermined maximum production cost

o o o o o o

- During the product development process, product attributes that are considered too costly when compared with the predetermined maximum production cost are reduced/eliminated (e.g. package, warranties, after-sales service, etc.)

o o o o o o

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21. Please indicate the most recent position of your company with respect to its competitors on the following metrics (this year)?

Much higher

Moderately higher

About the same

Moderately lower

Much lower

- Return On Investment (ROI) o o o o o

- Return On Assets (ROA) o o o o o

- Growth of market share of your company

o o o o o

- Growth of sales of your company o o o o o

22. Please mark the extent to which your company believes the performance of your company this year is influenced by the IOCM practices implemented?

Extremely Very Moderately Slightly Not at all o o o o o

23. Please mark the extent to which you agree with the following statement?

Not applicable

Strongly disagree Disagree

Neither agree nor disagree

Agree Strongly agree

In case your company has not adopted IOCM practices, it anticipates that it will adopt some of these practices in the next three years

o o o o o o

This is the end of this questionnaire. Please make sure that you have not skipped any questions inadvertently. Thank you for your time and cooperation.

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7.2. Selection of Industries

1 - Crop and animal production, hunting and related activities, 2 -Forestry and logging, 3 -

Fishing and aquaculture, 5 - Mining of coal and lignite, 6 - Extraction of crude petroleum and

natural gas, 7 - Mining of metal ores, 8 - Other mining and quarrying, 10 - Manufacture of food

products, 11 - Manufacture of beverages, 12 - Manufacture of tobacco products, 13 -

Manufacture of textiles, 14 - Manufacture of wearing apparel, 15 - Manufacture of leather and

related products, 16 - Manufacture of wood and of products of wood and cork, except furniture;

manufacture of articles of straw and plaiting materials, 17 - Manufacture of paper and paper

products, 19 - Manufacture of coke and refined petroleum products, 20 - Manufacture of

chemicals and chemical products, 21 - Manufacture of basic pharmaceutical products and

pharmaceutical preparations, 22 - Manufacture of rubber and plastic products, 23 -

Manufacture of other non-metallic mineral products, 24 - Manufacture of basic metals, 25 -

Manufacture of fabricated metal products, except machinery and equipment, 26 - Manufacture

of computer, electronic and optical products, 27 - Manufacture of electrical equipment, 28 -

Manufacture of machinery and equipment n.e.c., 29 – Manufacture of motor vehicles, trailers

and semi-trailers, 30 - Manufacture of other transport equipment, 31 - Manufacture of furniture,

32 - Other manufacturing, 33 - Repair and installation of machinery and equipment, 35 -

Electricity, gas, steam and air conditioning supply, 36 - Water collection, treatment and supply,

37 - Sewerage, 38 - Waste collection, treatment and disposal activities; materials recovery, 39

- Remediation activities and other waste management, 41 - Construction of buildings, 43 -

Specialized construction activities, 45 – Wholesale and retail trade and repair of motor vehicles

and motorcycles, 46 - Wholesale, except of motor vehicles and motorcycles, 47 – Retail trade,

except of motor vehicles and motorcycles, 49 - Land transport and transport via pipelines, 50

- Water transport, 51 - Air transport, 52 - Warehousing and support activities for transportation,

53 - Accommodation, 56 - Food and beverage service activities, 61 - Telecommunications, 95

- Repair of computers and personal and household goods.

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7.3. Distribution of Companies Across Industries Main Industry

Frequency Percent Valid Percent Cumulative

Percent Valid Chemicals 17 6.4 6.4 6.4

Communication 6 2.3 2.3 8.6 Electronic equipment 14 5.3 5.3 13.9 Food & Beverage 29 10.9 10.9 24.8 Furniture 3 1.1 1.1 25.9 Mining 4 1.5 1.5 27.4 Manufacturing 59 22.2 22.2 49.6 Paper 2 .8 .8 50.4 Pharmaceuticals 8 3.0 3.0 53.4 Primary metal 3 1.1 1.1 54.5 Printing 3 1.1 1.1 55.6 Retail trade 19 7.1 7.1 62.8 Rubber/plastics 5 1.9 1.9 64.7 Textile 19 7.1 7.1 71.8 Transportation 21 7.9 7.9 79.7 Wholesale 12 4.5 4.5 84.2 Services 28 10.5 10.5 94.7 Other (please specify) 14 5.3 5.3 100.0 Total 266 100.0 100.0

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7.4. Distribution of Companies Across “Other Industries” Other Industries

Frequency Percent Valid Percent Cumulative

Percent Valid 252 94.7 94.7 94.7

Construction 6 2.3 2.3 97.0 Entertainment 2 .8 .8 97.7 Environment 2 .8 .8 98.5 Real Estate 3 1.1 1.1 99.6 Steam Industry 1 .4 .4 100.0 Total 266 100.0 100.0

7.5. Grouping of Industries

• Primary Industries: Chemicals, Mining, Paper, Primary Metal, Rubber/Plastics, Textile

• Manufacturing: Electronic Equipment, Food & Beverage, Furniture, Manufacturing

• Services: Communication, Educational, Services, Transportation

• Retail Trade & Wholesale

• Pharmaceuticals

• Other Industries

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7.6. Distribution of Companies According to Most Recent Annual Gross

Sales

Most Recent Annual Gross Sales

Frequency Percent Valid Percent Cumulative

Percent Valid Less than €1 million 9 3.4 3.5 3.5

€1 million to €5 million 23 8.6 9.1 12.6 €5 million to €10 million

26 9.8 10.2 22.8

€10 million to €50 million

66 24.8 26.0 48.8

€50 million to €100 million

35 13.2 13.8 62.6

€100 million to €500 million

44 16.5 17.3 79.9

€500 million to €1 billion

12 4.5 4.7 84.6

Over €1 billion 39 14.7 15.4 100.0 Total 254 95.5 100.0

Missing System 12 4.5 Total 266 100.0

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7.7. Distribution of Companies According to Current Number of

Employees

Current Number of Employees

Frequency Percent Valid Percent Cumulative

Percent Valid Less than 100

employees 99 37.2 38.1 38.1

101 up to 200 employees

30 11.3 11.5 49.6

201 up to 500 employees

42 15.8 16.2 65.8

501 up to 1000 employees

17 6.4 6.5 72.3

1001 up to 5000 employees

32 12.0 12.3 84.6

Over 5000 employees 40 15.0 15.4 100.0 Total 260 97.7 100.0

Missing System 6 2.3 Total 266 100.0

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7.8. Distribution of Companies According to Use of Intrafirm Cost

Management Practices

Sum Intrafirm Cost Management Practices Frequency Percent Valid Percent Cumulative Percent Valid 0 61 22.9 22.9 22.9

1 26 9.8 9.8 32.7 2 29 10.9 10.9 43.6 3 42 15.8 15.8 59.4 4 32 12.0 12.0 71.4 5 15 5.6 5.6 77.1 6 25 9.4 9.4 86.5 7 9 3.4 3.4 89.8 8 7 2.6 2.6 92.5 9 5 1.9 1.9 94.4 10 6 2.3 2.3 96.6 11 5 1.9 1.9 98.5 12 1 .4 .4 98.9 13 2 .8 .8 99.6 16 1 .4 .4 100.0 Total 266 100.0 100.0

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7.9. Distribution of Companies According to Use of IOCM Practices

Sum of IOCM Practices

Frequency Percent Valid Percent Cumulative

Percent Valid 0 123 46.2 46.2 46.2

1 23 8.6 8.6 54.9 2 21 7.9 7.9 62.8 3 31 11.7 11.7 74.4 4 25 9.4 9.4 83.8 5 13 4.9 4.9 88.7 6 11 4.1 4.1 92.9 7 5 1.9 1.9 94.7 8 4 1.5 1.5 96.2 9 2 .8 .8 97.0 10 3 1.1 1.1 98.1 11 2 .8 .8 98.9 12 2 .8 .8 99.6 17 1 .4 .4 100.0 Total 266 100.0 100.0

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7.10. Correlation Matrices: Antecedents and IOCM Involvement Correlations

IOCM

involvement Intrafirm cost management

Spearman's rho

IOCM involvement Correlation Coefficient

1.000 .190*

Sig. (1-tailed) . .010 N 152 150

Intrafirm cost management

Correlation Coefficient

.190* 1.000

Sig. (1-tailed) .010 . N 150 206

*. Correlation is significant at the 0.05 level (1-tailed). Correlations

IOCM

involvement Information

sharing Spearman's rho

IOCM involvement

Correlation Coefficient

1.000 .501**

Sig. (1-tailed) . .000 N 152 146

Information sharing

Correlation Coefficient

.501** 1.000

Sig. (1-tailed) .000 . N 146 180

**. Correlation is significant at the 0.01 level (1-tailed). Correlations

IOCM

involvement Trust and

commitment Spearman's rho

IOCM involvement

Correlation Coefficient

1.000 .241**

Sig. (1-tailed) . .002 N 152 147

Trust and commitment

Correlation Coefficient

.241** 1.000

Sig. (1-tailed) .002 . N 147 170

**. Correlation is significant at the 0.01 level (1-tailed).

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Correlations

IOCM

involvement Industry

competitiveness Spearman's rho

IOCM involvement Correlation Coefficient

1.000 .134

Sig. (1-tailed) . .052 N 152 149

Industry competitiveness

Correlation Coefficient

.134 1.000

Sig. (1-tailed) .052 . N 149 199

7.11. Correlation Matrices: IOCM Practices and Performance Correlations

Price

benchmarking Performance Spearman's rho

Price benchmarking

Correlation Coefficient

1.000 .152*

Sig. (1-tailed) . .044 N 142 127

Performance Correlation Coefficient

.152* 1.000

Sig. (1-tailed) .044 . N 127 130

*. Correlation is significant at the 0.05 level (1-tailed). Correlations

Supplier

evaluation Performance Spearman's rho

Supplier evaluation

Correlation Coefficient

1.000 .091

Sig. (1-tailed) . .153 N 147 130

Performance Correlation Coefficient

.091 1.000

Sig. (1-tailed) .153 . N 130 130

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Correlations ABC Performance Spearman's rho

ABC Correlation Coefficient 1.000 .156*

Sig. (1-tailed) . .039 N 142 127

Performance Correlation Coefficient .156* 1.000 Sig. (1-tailed) .039 . N 127 130

*. Correlation is significant at the 0.05 level (1-tailed). Correlations Target costing Performance Spearman's rho Target costing Correlation Coefficient 1.000 .203*

Sig. (1-tailed) . .011 N 135 129

Performance Correlation Coefficient .203* 1.000 Sig. (1-tailed) .011 . N 129 130

*. Correlation is significant at the 0.05 level (1-tailed).

7.12. Hypothesis 7b: One-Way ANOVA Results Descriptives Performance

N Mean Std.

Deviation Std. Error

95% Confidence Interval for Mean

Lower Bound Upper Bound Level 0 21 3.3095 .56405 .12309 3.0528 3.5663 Level 1 19 3.1711 .85818 .19688 2.7574 3.5847 Level 2 6 3.5000 .44721 .18257 3.0307 3.9693 Level 3 77 3.4740 .56053 .06388 3.3468 3.6013 Total 123 3.4004 .61503 .05546 3.2906 3.5102

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ANOVA Performance

Sum of Squares df Mean Square F Sig.

Between Groups 1.650 3 .550 1.471 .226 Within Groups 44.498 119 .374 Total 46.148 122

7.13. Multivariate Linear Regression Results

ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 4,591 8 ,574 1,785 ,087b

Residual 36,978 115 ,322 Total 41,569 123

a. Dependent Variable: Performance b. Predictors: (Constant), Price Benchmarking, Target Costing, Primary Industries, Services, Wholesale & Retail, Pharmaceuticals, Other Industries, Size. Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig. B Std. Error Beta (Constant) 2.993 .236 12.689 .000

Price Benchmarking .076 .072 .112 1.065 .289 Target Costing .081 .072 .123 1.127 .262 Primary Industries -.040 .141 -.028 -.287 .775 Services -.020 .146 -.014 -.140 .889 Wholesale & Retail -.357 .170 -.201 -2.096 .038 Pharmaceuticals .503 .411 .110 1.225 .223 Other Industries -.282 .234 -.113 -1.207 .230 Size .011 .029 .035 .383 .702

a. Dependent Variable: Performance

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7.14. Frequency Distribution of the Score of Trust and Commitment

7.15. Frequency Distribution of the Perceived Performance Effects

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7.16. Frequency Distribution of the Future Outlook