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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