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The Impact of Interactive Use of Performance Measurement Systems on Procedural Fairness Perception, Cooperation, and Performance in Supply Alliances

Habib Mahama*

Associate Professor Research School of Accounting and Business Information Systems

Australian National University Canberra, ACT0200, Australia

Phone: +612.6125.4857 Email: habib.mahama@anu.edu.au

Alex Zhichao Wang

Associate Lecturer Research School of Accounting and Business Information Systems

Australian National University Canberra, ACT0200, Australia

Phone: +612.6125.7374 Email: alex.wang@anu.edu.au

First draft: please do not quote with the permission of the authors

20th May 2013

*Corresponding author

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Abstract

This study investigates the relationships among interactive use of performance measurement systems (PMS), procedural fairness perception, and cooperation and how that translates into alliance performance in the context of supply alliances. Drawing on the existing literature, we develop a model that shows the predicted relationships among our theoretical constructs. To test the predicted relationships, we conducted an online survey on supply relationships managers of United States (US) based firms that engage in supply alliances. We analysed the responses using partial least squares (PLS) approach to structural equation modelling. The results show that the interactive use of PMS is positively and directly associated with procedural fairness perception and three dimensions of cooperation (i.e. information sharing, joint problem solving, and willingness to adapt to changes). The interactive use of PMS also has positive indirect association with all four dimensions of cooperation through procedural fairness perception. It is also indirectly related to alliance performance through procedural fairness perception and three dimensions of cooperation (i.e., information sharing, restraint from use of power, and joint problem solving). The results also indicate that procedural fairness perception have positive direct effects on all four dimensions of cooperation and positive indirect effects on alliance performance through three dimensions of cooperation ((i.e., information sharing, restraint from use of power, and joint problem solving). Finally, we find a positive direct relationship between three dimensions of cooperation ((i.e., information sharing, restraint from use of power, and joint problem solving) and alliance performance. We discuss the implications of these results and provide suggestions for future research.

Keywords: Interactive use of performance measurement systems (PMS), Procedural fairness perception,

cooperation, Alliance performance, Supply alliances

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The Impact of Interactive Use of Performance Measurement Systems on Procedural fairness perception, Cooperation, and Performance in Supply Alliances

Background

This paper examines the extent to which the interactive use of performance measurement

systems (PMS) affects procedural fairness perception, cooperation among supply alliance partners

and alliance performance. In recent times, organisations have sought to expand their business

through alliances. This has led to a proliferation of many forms of inter-firm alliances among

which supply alliances have become a popular strategy. Alliances are said to be vehicles through

which organizations gain competitive advantage and by which they improve efficiency and

effectiveness (Ring and Van de Ven 1994; Cousins and Spekman 2003). Whilst alliances are said to

have the potential to improve organisational performance, existing studies have documented a

high failure rate of these alliances (Spekman and Isabella 2000; Ireland et al. 2002). This high

failure rate is partly attributable to fairness perceptions and lack of cooperative behaviour among

alliance partners (DeRond and Bouchikhi 2004; Luo 2005). We focus on how, in supply alliances,

the interactive use of PMS influences fairness perceptions and cooperation and the impact of that

on alliance performance.

Many academic researchers and commentators consider fairness perceptions to be a key

element in a successful interfirm alliance equation (Tyler et al. 1997; Pinder 1998; Konovsky 2000;

Mitchell and Daniels 2003; Luo 2005; Hartner et al. 2008). Fairness perception is argued to be the

foundation of all types of economic transactions, especially for interfirm alliances where fairness

perceptions are instrumental in guiding parties behaviours and contribution to mutual outcomes

(Luo 208). Fairness perception is critical to alliance performance as it has been found to be

associated with alliance partners’ positive attitudes such as commitment, trust, reciprocity and

social harmony (Pinder 1998; Konovsky 2000; Mitchell and Daniels 2003). It has also been found

to decrease conflicts and opportunism (Lind and Tyler 1988; Konovsky and Organ 1996), to

ameliorate the negative reactions when outcomes for alliance partners are unfavourable (Brockner

and Siegel 1996; Smith and McDonough 2001), and to be associated with alliance partners’

motivation to act in the alliances’ best interests (Nauman and Bennett 2002; Birnberg et al 2007).

These prior studies call for an examination of the factors that can improve fairness perceptions

among alliance partners.

Cooperative behaviour is also held to be crucial to successful alliance performance

(Osborn and Hagedoorn 1997; Luo 2002; Mahama 2006). Cooperation is said to facilitate the

exchange of resources among alliance partners, joint decision-making, collective actions taking and

mutual benefits sharing (Mahama 2006). Boddy et al. (2000, p.1004) argue that “cooperation may

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enable partners to achieve a stronger position together than they could alone”. Cooperation is said

to facilitate the flow of critical and proprietary information among alliance partners (Parkhe 1991)

and this is considered essential for maintaining alliances. For example, cooperation enables

partners to gain access to each other’s information, technology, marketing, experience and

resources (Hitt et al. 2001d; Ireland et al. 2001a) thereby creating positive synergies and mutual

benefits for alliance partners (Chung et al. 2000). Prior studies also suggest that cooperation

improves coordination of action, real-time resolution of conflicts and equitable distribution of

benefits among alliance partners (Browning et al 1995; McAllister 1995; Mahama 2006).

Overall the existing alliance research suggests that successful alliance performance is partly

a function of fairness perceptions and cooperation among alliance partners. Prior management

accounting research suggests that management control systems have a role in enhancing fairness

perception (Libby 1999; Wentzel 2002). Libby, for example, examine procedural fairness

perceptions in the context of participative budgeting and show that results improve significantly

when budgeting processes are perceived to be fair. Wenzel (2002) examines fairness in the context

of budgeting and concludes that increased participation during budgeting processes fosters fairness

perception and performance. Other studies also suggest that management control systems may be

implicated in cooperative behaviours within and among organisation (Baiman and Rajan 2002;

Coletti et al. 2005; Mahama 2006). Theoretically, these prior studies suggest a role for management

control system in fairness perception and cooperation among alliance partners. This study seeks to

extend the existing literature by empirically investigating how the interactive use of PMS is

implicated in enhancing fairness perception and cooperation in supply alliances.

To accomplish the aim of this paper, we conducted a cross-industry online survey of

supply alliance in the United States (US). We predicted that the interactive use of PMS would be

positively associated with procedural fairness perception and cooperation among alliance partners,

as well as, alliance performance. The results provide support for the association between the

interactive use of PMS and procedural fairness perception. The interactive use of PMS was found

to be positively and directly associated with three dimension of cooperation (information sharing,

joint problem solving, and willingness to adapt to changes) and significantly indirectly related to

three dimensions of cooperation (information sharing, restraint from use of power, and joint

problem solving) through procedural fairness perception. We found that the interactive use of

PMS has both direct and indirect positive effects on alliance performance. We also predicted that

procedural fairness perception would be positively associated with cooperation and alliance

performance. A positive direct relationship was found between procedural fairness perception and

all four dimensions of cooperation. Procedural fairness perception were found to be positively and

indirectly associated with alliance performance through three dimensions of cooperation

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(information sharing, joint problem solving, and restraint from use of power). Cooperation was

predicted to be positively associated with alliance performance. The results show positive direct

relationships between three dimensions of cooperation (information sharing, restraint from use of

power, and joint problem solving) and alliance performance.

The study makes several contributions to the literature. First, the results show that the

interactive use of PMS is an important antecedent to procedural fairness perception and

cooperation in supply alliances. Second, the paper shows the significant intervening role of

procedural fairness perception and cooperation in the relationships between the use management

control systems (such as PMS) and alliance performance. The significance of the intervening roles

of procedural fairness perception and cooperation is demonstrated by the indirect impacts of the

interactive use of PMS on alliance performance. Third, the results also show that procedural

fairness perception is an important antecedent to cooperative behaviour in supply alliances. Finally,

the paper provides additional empirical evidence on the need for alliance partners to cooperate; as

cooperation is shown to yield positive performance benefits.

The rest of the paper is structured as follows: the next section focuses on the literature

review and hypothesis development. The third section reports the research methods and this is

followed by a section on results. The final section concludes the study.

Literature review and hypothesis development

Management control systems and interfirm alliances

Supply alliances occur when “separate legal entities, constituting themselves into buyers

and suppliers, adopt a high level purposeful exchange to maintain inter-organizational

relationships over an extended period of time, in which both parties may have the power to shape

its nature and future direction” (Mahama (2006, p.317). Supply alliances involve recurrent

exchanges of resource, technology, expertise, and experience among partners with the aim of

achieving mutual benefits (MacNeil 1980; Tan 2002; Cousins and Spekman 2003). They are

beyond traditional discrete exchanges in the market (Stuart 1993; Benton and Maloni 2005); as this

requires exchange partners to rely on each other and build a close cooperative relationship (Boddy

et al. 2000) over a long period. The interdependent and relational nature of supply alliances has

implications for the use of management control systems.

A growing number of accounting research has examined the role of management control

systems in inter-firm alliances generally and supply alliances in particular (Van der Meer-Kooistra

and Vosselman 2000; Langfield-Smith and Smith 2003; Dekker 2004). The earlier accounting

studies that examined interfirm alliances, focused on the effective design of management controls

systems and/or the interaction of management controls and trust in managing alliances ((Van der

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Meer-Kooistra and Vosselman 2000; Langfield-Smith and Smith 2003; Dekker 2004). For instance,

based on transaction cost economics, Van der Meer-Kooistra and Vosselman (2000) build a

structural model which is made up of three management control patterns for managing control

problems in alliances. They argue that the effective choice of these control patterns depends on

transaction contingency factors such as transaction characteristics, transaction environment

characteristics and transaction party characteristics. Building on Van der Meer-Kooistra and

Vosselman’s (2000) management control model, Langfield-Smith and Smith (2003) contend that

trust develops progressively and control patterns change gradually in different alliance lifecycle

stages. Dekker (2004) identifies two control problems (i.e. appropriation problems and

coordination problems) and develops a model of management control systems involving formal

and informal controls in alliances. Formal controls are useful in mitigating appropriation problems

and coordination problems and informal controls moderate the association between control

problems and formal controls.

In subsequent accounting research the focus has tended to be on the specific informational

role of accounting leading to significant attention being paid to the role of open-book accounting

in performance management of interfirm alliances (Kajuter and Kulmala 2005; Mouritsen et al.

2001; Dekker 2003). These studies have found that open-book accounting is effective in

enhancing alliance relationship through the facilitation of cost management, the enhancement of

controls, and augmentation of trust (Kulmala 2002; Kajuter and Kulmala 2005; Caglio and Ditillo

2008). Other studies have used case studies to go beyond exploring statistical association between

management control systems and alliance performance and to embrace the array of practical

activities through which these control systems are embedded in alliance processes and become

implicated in the effects generated in the name of interfirm alliances (Mouritsen et al 2001;

Mouritsen and Thrane 2006; Chua and Mahama 2007).

Despite prior research effort, few accounting studies have investigated the behavioural

effects of management control systems in alliances (for an exception see Mahama 2006). Yet

research in management and organisational studies suggests the importance of behavioural

research in the alliance context (Parkhe 1993; Das and Teng 1998; Luo 2007). We seek to extend

prior accounting research by investigating the behavioural effects of management control systems

in alliances. More specifically, this paper examines how the interactive use of PMS affects two

behavioural factors (i.e. procedural fairness perception and cooperation).

The interactive use of controls

According to Simons (1991, p. 52), a control system is regarded as being used interactively

if “top manager reported that his personal, regular, and frequent use of a system was a top priority

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both for himself and for his subordinates, and that this system was used to set agendas for regular

interlocking meetings with direct subordinates and others to review data and resulting action

plans”. Any control system can be made interactive when management personally and

continuously use the controls in an active and regular dialogue with peers and subordinates and

when it is also used as the basis of challenging underlying data, assumptions and action plans

(Simon 1990; 1994). Mainly, the objective of interactive use of control systems is achieved through

frequent interactions between top managers and their peers/subordinates where information can

be gathered, shared and spread through the whole organisational system (Simons 1995; Abernethy

and Brownell 1999; Bisbe and Otley 2004; Winder 2007). In an alliance context, the interactive use

of controls may involve alliance partners using the control system to personally and regularly

engage in alliances activities and with other alliance partners.

According to Henri (2006) the interactive use of controls focuses managers attention and

forces dialogue throughout an organisation system by reflecting signals sent by top managers.

Similarly, Bisbe and Otley (2004) argue that the interactive use of controls places pressure on

managers at all levels of an organisational system and motivate information gathering, face-to-face

dialogue and debate. Bisbe et al (2007) have identified five interrelated properties of using

controls interactively. First, it is manifested in the degree to which top managers devote a

significant amount of their limited attention and time to issues emanating from the input, process

and output of the management control system. In an alliance context, this will be reflected in the

regular and frequent personal involvement of alliance partners in the activities of the alliances

through the use of the control system. Second, the intensive use of the controls by operating

(subordinate) managers as reflected by the degree to which these operating managers are actively

and frequently involved in the use of the control systems. Third, both senior and subordinate

managers (in our context, alliance partners) regularly meet and interact resulting in face-to-face

challenge, dialogue and debate. Fourth, involve the collection and generation of information that

relates to the effects of strategic uncertainties on business strategy. Finally, senior managers’ non-

invasive, facilitative and integrative role that encourages, facilitates, and inspires others. Overall,

the crucial issue about the interactive use of controls is using the control system to create a

positive informational environment that stimulates dialogue and enhances information sharing

(Simon 1995; Bisbe et al 2007).

In the present study, we focus on the interactive use of one specific type of management

control systems; namely performance management systems (PMS). We focus on PMS because it is

the most commonly used management control system in supply alliance. We expect that using

PMS interactively will ensure that alliance partners are intensively and frequently involved in

activities of the alliance. The interactive use of PMS requires partners to pay frequent attentions to

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alliance performance and become involved in the performance-related decision-making process.

This requires that performance-related information is interpreted and communicated with alliance

partners through regular and recurrent dialogue. The recurrent dialogue requires face-to-face

meeting among alliance partners and that will enable discussion and debate among alliance

partners about performance-related information, assumptions, and action plans. We expect that

the interactive use of PMS will enhance procedural fairness perception and generate corporative

behaviour among alliance partners. This expectation is represented by the theoretical model shown

in Figure 1.

Insert Figure 1 here

Interactive use of PMS and procedural fairness perception

The organisational justice literature suggests two types of fairness perceptions: distributive

and procedural fairness perceptions. Whiles distributive fairness focuses on the fairness of the

amount of rewards/outcomes allocated relative to input; procedural fairness is about the fairness

of the procedures and dynamics of the decision processes that led to the outcome (Niehoff and

Moorman 1993; Luo 2007). In the alliance context, Luo (2007) argue that because alliance payoff

is very difficult to predict ex ante, given both internal and external uncertainty, they will rely more

on the fairness of observable procedures (procedural fairness) in forming their fairness beliefs than

on the distributive fairness. Also, Scminke et al (2000) argue that distributive fairness is a less

efficacious predictor of behaviour and suggest that attention in behavioural research should focus

on procedural fairness perception. On this, Hartner et al. (2008, p.138), argues that “people desire

fair procedures because they believe that this would lead to fair distributions”. Thus, alliance

partners’ perceptions of the fairness of the decision-making process serves as a basis that drives

alliance partners’ behaviour (Tyler et al. 1997; Hartner 2008). Consequently, in this study, we focus

on procedural fairness perception.

In an alliance setting, procedural fairness perception has been defined as the extent to

which the decision making processes and alliance procedures that affect each party’s gains and

interest are impartial and fair as perceived by the parties or by the boundary spanners who

represent each party (Luo 2005; Prasad et al 2011). This is said to occur when parties, perceive the

procedures applied in decision making and execution to be representative, transparent, adjustable

and correctable by alliance partners (Luo 2007; Prasad et al 2011). Prior studies have articulated

three properties that encapsulate procedural fairness perception; namely, engagement; explanation;

and clarity of expectations (Kim and Mauborgne 1998; Prasad et al 2011).

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Engagement refers to the involvement of each party in decisions that affect the alliance

(Prasad et al 2011). Such involvement is said to trigger interactions among partners in alliances and

provides them with the opportunity to both influence decisions by voicing their ideas and also by

challenging the merits of the ideas and assumptions of other parties during decision making and

implementation processes. An opportunity to voice one’s ideas create a feeling that one’s interests

and opinions are represented in the decision processes (Kim and Mauborgne 1998; Schminke et al

2000). Also, the opportunity to challenge the ideas of others generates the belief that decisions are

adjustable and correctable (Kim and Mauborgne 1998). Explanation means “everyone involved

and affected should understand why final decisions are made as they are and why individuals’ ideas

and input may have been overridden in ultimate decisions” (Kim and Mauborgne 1998, p. 325).

This implies each partner is provided with a clear understanding of the input, processes, and

output of decision making and this understanding revolves around questions related to what, how

and why particular decision processes have been followed and not others (Kim and Mauborgne

1998; Prasad 2011). Kim and Mauborgne (1998) argue that such explanations grants individuals a

more comprehensive understanding of of the cognitive processes of decision making and also

serves as a feedback loop to inform and educate. This enhances perceived transparency of the

decision-making process and reduces partners’ fear of cheating behaviours (Jassawalla and Sashittal

1998; Smith and Mcdonough 2001). Finally, clarity of expectations is about how parties have

definite appreciation of what is expected of them before, during, and after decisions are made in

the alliance. These three properties are said to capture the domain of procedural fairness

perceptions (Kim and Mauborgne 1996; Prahad et al 2011).

We expect that the interactive use of PMS will alliance influence partners’ procedural

fairness perceptions. Our expectation is premised on the idea that interactive use of PMS involves

the use of PMS to create a platform for alliance parties to engage each other in decision making

processes that affect their interest and payoffs. The interactive use of PMS stimulates frequent

engagement among partners through day-to-day dialogues and face-to-face meetings; where all

parties have the ability influence decisions and have the opportunity to challenge the ideas, data

and assumptions of other parties. Through this, parties will develop the belief that their views are

represented in alliance decisions and procedure and thus perceiving the procedures to be fair. Also,

the informational environment created by the interactive use of PMS is expected to stimulate

information sharing and debate through which questions are raised and by which explanations are

provided regarding how and why certain decisions and actions are taken. Such explanations are

expected to enhance perceptions of transparency and by extension procedural fairness. Also, as

Simon (1990) notes, the interactive use of controls provides signals that reveal parties preferences

and expectations. In the dialogic process associated with the interactive use of PMS, partners are

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afforded the opportunity to clarify their expectations of themselves and others and to demand

same from others. This is expected to positively influence the alliance partners, procedural

fairness perception.

Following from the above, we expect that the interactive use of PMS in alliances will

enhance procedural fairness perceptions among partners. Thus, we predict that:

Hypothesis 1: The extent of interactive use of PMS is positively associated with the level of

procedural fairness perception of alliance partners.

Interactive use of Performance Measurement Systems and Cooperation

Cooperation exists when parties to a transaction jointly undertake to interact for mutual

benefit (Smith et al 1996; Mahama 2006). This occurs when parties “comply with, help, and

contribute energy to the group and its representatives” (Cremer and Tyler 2007). Prior studies

suggest that cooperation a multidimensional construct comprising information sharing, restraint

from the use of power, joint problem solving, and willingness to adapt to changes (Tjosvold 1988;

Argyle 1991; Heide and Miner 1992; Mahama 2006).

Information sharing refers to the communication of critical and proprietary information

among parties to a transaction (Monczka et al. 1998; Li and Lin 2006). Sharing information

facilitates the sharing of knowledge and the understanding of the capabilities and mutual

expectations of partners (Cannon and Perreault 1999). Mahama (2006) argue that the information

shared may include product design, open book arrangements, cost data, and the discussion of

future plans. The predisposition to share information is said to be a crucial indication of

cooperation. Also, power is said to be always present in interfirm alliances. It is “the ability to

impose one’s will on others irrespective of or by manipulating their wish” (MacNeil 1980, p.32).

Restraint from the use of power is said to be an important indication of a parties’ disposition

cooperate despite the advantages such power may confer. Economic theory suggests that power

imbalance arises in the presence of information asymmetry. Increased availability of information

therefore reduces this imbalance. Also, people restrain from the use of power when they have the

requisite information about their interest and payoffs and those of others and when they have the

opportunity to challenge the ideas of other.

Joint problems solving can be described as “treating problems as joint responsibilities and

working collaboratively towards resolving those problems” (Mahama 2006). It is argued that joint

problem solving is a reflection of partners feeling of attachment and empathy towards each other

(Detush 1949; Janis 1982). Such attachment and empathy may manifest in the partners working

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harmoniously in joint planning and action for the mutual fulfilment of their needs. Willingness to

adapt to changes is the “degree to which partner firms are able to modify the structural

arrangements in the alliance in order to adapt to changing conditions” (Das and Teng 2000, p.87).

It is argued that in alliances, willingness to adapt to changes requires partners’ interactions with

each other (Heide and Miner 1992) that creates transparency about each other’s preferences and

opportunities for negotiation (Mahama 2006). The need for adaptability arises because of

uncertainties about the future of the alliance. A critical factor in the willingness to adapt to changes

is the availability of information cause-effect relationships and on payoff of alternative causes of

action when adaptability becomes necessary.

We expect that the interactive use of PMS will have positive effects on cooperation.

Interactive uses of PMS provide a channel for regular and open discussion among partners

(Winder 2007; Bisbe and Otley 2004). Through regular and open discussion, partners are able to

share critical and relevant information about the alliance (Wathne et al. 1996). Also, the dialogue

and communicative practices of using PMS interactively will reduce information asymmetry and

provide parties with information on the payoffs of restraining from the use of power. Moreover,

the interactive use of PMS requires frequent face-to-face meetings among partners (Simons 2000).

This creates appropriate occasions for partners to listen to each other’s concerns and provides to

jointly identify problems, develop shared meanings of these problems and provide supporting

information and resources for solving these problems (Lang et al. 1978; Mahama 2006). In

addition, as the interactive use of PMS focuses on collecting and generating information relating to

the effects of strategic uncertainties on alliance performance; it is able to communicate the need to

adapt to changes by signalling the impact of these changes on alliance performance and the mutual

benefits of exchange participants.

All the above signals that interactive use of PMS can motivate information sharing, joint

problem solving, adapting to changes and power restricting among partners. Thus, we expect that

the interactive use of PMS will affect all four dimensions of cooperation. More formally:

Hypothesis 2: The extent to which a PMS is used interactively is positively associated with the level of

cooperation in alliances.

Procedural fairness perception and Cooperation

Prior research has suggested that procedural fairness perception have positive effects on

cooperation in team situations (Tyler and Blader 2000; Luo 2005; Luo 2008). Cremer and Tyler

(2007) argue that when fair procedures are perceived to exist, people are willing to cooperate by

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investing resources, time and energy in the collective interest. Such cooperative behaviour are said

to be reciprocal actions taken in reaction to collective action that are deemed fair (Cremer and

Tyler 2007). For instance engagement allows partners to voice their ideas in alliance decision

making and to challenge those of others. This does not only make the parties feel valued and

respected but provide a supportive atmosphere in the alliance, reducing the motivation for self-

seeking behavior and encouraging greater cooperation and information sharing (Nakos and

Brouthers 2008). Explanation also offers partners the opportunity to challenge other partners’

ideas where explanations can be given. A satisfied explanation reduces partners’ fear of

opportunism, which in turn restrains them from the use of power (Luo 2008; Killing 1983; Pisano

1989). Also, prior research offers evidence that restraint from using power reflects partners feeling

of satisfaction and equity about each other (Skinner et al. 1992; Gaesan 1994; Maloni and Benton

2000). This is supported by Celly and Frazier (1996) who state that procedural fairness perception

is effective in guarding against the use of power. Also, through engagement, individuals develop a

strong feeling of support for their group decision-making process and for the decisions resulting

from that process (Kim and Mauborgne 1998, p.507). This has a positive effect on joint problem

solving and increases partners’ flexibility of adapting to changes. In addition, clarity of

expectations facilitates partners’ understanding of role specificity, responsibility and expectation of

mutual goals. This enhances satisfaction among partners and motives partners’ willingness to share

information, input cooperative efforts, and accept adjustments in reaction to unexpected

environment changes.

Overall, procedural fairness perception implies that the parties view the criteria and

procedures used in the decision-making process to be unbiased, representative and transparent

(Luo 2008). Unbiased, representative and transparent procedures reduce partners’ fear of

exploiting behaviour, align partners’ interests and catalyses their willingness to share information,

solve problem jointly, adapt to changes, and restraints them from the use of power. We therefore

predict that alliance partners’ procedural fairness perceptions are more likely to promote

cooperation. Thus, we hypothesised as follows:

Hypothesis 3: The level of procedural fairness perception is positively associated with the level of cooperation

among alliance partners.

Interactive use of Performance Measurement Systems and Performance

Prior research suggested that the interactive use of PMS leads to better performance

(Bruining et al. 2004; Chong and Mahama 2009). For example, Henri (2006) investigated found

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interactive use of PMS to enhance organisational capability such as market orientation,

entrepreneurship, innovativeness, and organisational learning. He further argues that these

enhanced organisational capabilities have positive effects on performance. Widener (2007) found

that interactive use of control systems enhanced firm performance through learning and

management attention. Also, Tuomela (2005) found that interactive use of PMS helped

organisation maintain competitive advantages by facilitating the learning of strategic uncertainties

and elaborating responses to changing environment. Competitive advantages are argued to

enhance organisational performance (Hitt et al. 1997; Russo and Fouts 1997). Following these

prior studies, we expect that the interactive use of PMS will influence alliance performance. As the

interactive use of PMS is said to play facilitative and integrative roles, we expect that it will provide

the basis for coordinating action and also encourage, facilitate and inspire parties to perform as

expected. We also expect that, the interactive use of PMS will demands regular attention and

action from all parties thereby focusing their effort on performance related matters. Therefore, we

hypothesise that:

Hypothesis 4: The extent to which PMS is used interactively is positively associated with the level of

alliance performance.

Procedural fairness perception and Performance

Prior research suggests that procedural fairness perception can improve alliance

performance. Partners who engage actively in decision-making process have low resistance to the

associated procedures and a high level of feeling involved (Erez and Kanfer 1983; Renn 1998).

These feelings are more likely to focus partners’ attention and effort on achieving goals (Kanfer et

al. 1987; Earley and Shalley 1991). Saxton (1997) found that engaging in decision-making process

reduced information asymmetry among alliance partners and enhances commitment to alliance

performance and involvement in alliance activities. The enhanced feeling of commitment and

involvement motivated partners to act for mutual benefits. Luo (2006, p.11) also argues that

procedural fairness perception is perceived as the best means for ensuring best personal efforts.

Such effort leads to better alliance performance. This view is supported by Johnson et al. (2002)

who found that partners who perceive there to be fairness in the decision-making processes and

procedures in their alliance are more likely to direct and expend extra efforts to achieve alliance

goals. Procedural fairness perception elicits a sense of group harmony, which encourages partners

to ignore their own interests (Naumann and Bennett 2002) for the pursuit of collective interests.

Prior research also suggests that people who feel that they have been treated fairly by their group

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are motivated to strive for and achieve the best group outcomes (Baumeister and Leary 1995;

Pickett et al. 2004; Zadro et al. 2004). As a result, it is expected that procedural fairness

perceptions are more likely to enhance alliance performance. Thus:

Hypothesis 5: The level of procedural fairness perception is positively associated with the level of alliance

performance.

Cooperation and Performance

Prior research suggests that cooperation has a positive impact on performance. First,

information sharing among partners can save total costs and enhances product/service quality,

which may lead to better performance (Korsgaard et al. 1995; Zhao et al. 2002). This is supported

by Zhou and Benton (2007), who provide evidence to suggest that information sharing improves

performance because partners obtain more manufacturer, customer, and technology information,

through which they could save costs of collecting these information in the market and elaborate

less costly strategy. Second, through joint problem solving, partners are able to complement each

other by contributing their own expertise and experience (Yang et al 2008). This improves

efficiency, which may enhance performance. Cai et al. (2009) provide evidence of the positive

relationship between joint problem solving and alliance performance in a study of Chinese

manufacturing firms involving buyer-supplier relationships. They found that joint problem solving

enabled “two parties work together to troubleshoot problems and negotiate mutual adaptations

for resolving the difficulty” (Cai et al. p.662), which enhanced performance. Third, adaptation to

changes may help alliances maintain competitive advantages by meeting customer needs (Huang

and Gangopadhyay 2004). Lee and Cavusgil (2006) found that flexibility in contracts enabled

alliance partners to overcome unexpected consequences arising from technological and market

condition changes through renegotiating original clauses and procedures, which had a positive

effect on alliance performance. Finally, Maloni and Bention (2000, p.5) argue that “inter-firm

power retains the potential to upset the mutuality of supply chain relationships and subsequently

presents a barrier to the win-win integration process”. Restraint from use of power reflects the

reduction of the fear of exploitation behaviour, which promotes genuine commitment among

partners (Brown et al. 1995). This stimulates partners to exert extra efforts, which has a positive

effect on alliance performance. In addition, Bucklin and Sengupta (1993) found that restraint from

use of power created balance and reduce vulnerability among alliance partners. They argue that

enhanced balance among alliance partners will retrain partners from engaging in negative

behaviour (e.g. subtle efforts to diminish the role of its partner with customers and failure to

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employ all of the resources required) and enlarge the reach of agreement, which leads to better

alliance performance. Therefore, all four dimensions of cooperation are expected to have a

positive effect on performance. More formally:

Hypothesis 6: The level of cooperation is positively associated with the level of alliance performance.

Methods

Sample selection and data collection

Data for this research was collected from a sample of firms involved in supply alliances

across different industries in the US. As suggested by Lillis and Mundy (2005), cross-industry

sample was used to enhance the generalisability and external validity of the results. The choice of

US supply alliances was based on two reasons. First, the large economic size of the US enhances

the probability of obtaining sufficient number of target sample firms. Second, there is a

professional marketing research organisation (EmPanel Online) that has a rich and well-

established database for US firms involved in supply alliances. We had access to that database,

which facilitated data collection process from supply alliances based in the US. The use of the

EmPanel Online database for data collection is consistent with the method used in several other

accounting papers.

We identified 2,350 potential participants from EmPanel Online B2B database. Given that

this study focuses on supply alliances, the target sample were supply/procurement/purchasing

managers who were currently responsible for/involved in managing supply alliances. The choice

of supply/procurement/purchasing managers was based on the fact that this type of managers

were responsible for managing supply alliance(s) within a firm and they had access to more

resources and information in relation to the supply relationship.

The 2,350 potential participants were invited by email to participate in the survey. The

email explained the background and aim of this research. We assured them of the confidentiality

of their responses and that the study met the ethics requirements of our university. They were

assured that only aggregate data would be used for further analysis. A link was provided so that

those who were willing to participate in this study could go to the survey. The online survey was

presented in a similar format to those of paper-based self-administered questionnaire. Each screen

only contained one separate section of the survey to ensure participants could read easily and

complete all questions before proceeding to next screen. A reminder message appeared to

encourage participants to finish unanswered questions if any screen was incomplete. The survey

was created with the goal of keeping the completion time to less than 15 minutes. The aim of this

14

was to boost responses and let the respondents feel like their opinions were important while not

taking too much time for them to complete the survey.

To ensure that participants of the survey fit the target sample that we were looking for,

four screening questions were asked before participants could proceed to the actual survey

questions. These four screening questions included (a) “Is your firm based in the United States”,

(b) “Does your firm have at least one supply alliance1”, (c) “Are you involved in the management

of one of the supply alliances”, and (d) “If you are involved in the management of more than one

supply alliance, please nominate only one supply alliance and answer the survey based on that

nominated supply alliance”. Participants who answered “No” to any of these four questions were

screened out and those who answered “Yes” to all four questions were allowed to proceed to the

actual survey questions.

Of the 2,350 potential participants who were invited to participate in the survey, 483

viewed the survey site and 353 managers actually started the survey. Among the 353 people who

started the survey, 195 respondents were screened out by the four screening questions. Of the

remaining 158 managers who started the survey and were allowed to continue the survey after the

four screening questions, two people did not complete the survey. This resulted in a final sample

of 156 responses. Table 1 (Panel A) provides a summary of the data collection process.

Insert Table 1

Measurement of constructs

All theoretical constructs of interest to this study could not be observed or measured

directly. This demonstrated the latent nature of these constructs. These constructs were measured

indirectly using observable multi-item scales. We developed reflective measures for all the latent

constructs in the theoretical model of this study (see Appendix A for the measures of each

construct). To enhance the reliability and validity of data, measures for all constructs were adapted

from prior literature to suit the context of this study. Each measure was anchored on a seven-

point Likert scale. The survey instrument was pilot tested on two accounting academics. They

suggested further improvement in terms of wording and clarity of the questions. They also

commented on the format and presentation of the scales. We implemented all these suggestions.

The measures for each theoretical construct are discussed below:

1 The concept of supply alliances included ‘long term supply agreement or contract’, ‘outsourcing or contracting out’, ‘long-term buyer-supplier contracting’, ‘long-term service agreement’, ‘buyer-supplier partnership’, ‘cooperative buyer-supplier relationship or contracts’, ‘buyer-supplier collaboration’, ‘relational contracting’.

15

The interactive use of PMS is measured as a seven-item scale. This scale was adapted from

Henri (2006). Participants were asked to indicate a score from 1 (not at all) to 7 (great extent) on

the seven-item scale, which indicated the extent to which PMS was used to enable discussion,

continual challenge and debate, common view and vocabulary, and unifying partners. Cronbach’s

alpha for the scale was 0.866. A principal component analysis of the measures resulted in a single

factor solution with eigenvalues exceeding 1 and factor loadings ranged from 0.659 to 0.794. This

factor explained 55.760% of the total variance.

Procedural fairness perception is measured by a seven-item scale was adapted from Luo (2008).

Participants were asked to score on the seven-item scale within a seven point range (with 1

representing very unfair and 7 meaning vary fair), which indicated the level of fairness perceptions

in relation to the procedures used in alliance decision-making process, in formulating and

structuring the supply alliance, in governing knowledge or resource sharing among alliance

partners, in executing strategic decisions, in administrating and monitoring contract execution and

implementation of strategic decisions. Cronbach’s alpha for the procedural fairness perception

measures was 0.863. A principal component analysis of the scale items revealed a single factor

solution with eigenvalues exceeding 1 and factor loading ranged from 0.669 to 0.776. This factor

explained 55.276% of the total variance.

Cooperation: As indicated earlier, existing literature suggests that cooperation is a multi-

dimension construct; comprising information sharing, restraint from use of power, joint problem

solving, and willingness to adapt to changes. Hulland (1999) suggests that when a construct is

multi-dimensional, it is more appropriate to represent each dimension separately in a statistical

model. Therefore, separate scales were used to measure each of the four dimensions of

cooperation. Three-item scales were used to measure each dimension of cooperation. For each of

the three-item scales, two items were adapted from Mahama (2006) and one item was developed

based on existing cooperation literature. Cronbach’s alphas for all four dimensions of cooperation

ranged from 0.700 to 0.744. For the information sharing dimension, a principal component

analysis of the measures yielded a single factor solution with eigenvalues exceeding 1 and factor

loading ranged from 0.688 to 0.850. This factor explained 63.625% of the total variance. Also, a

principal component analysis of these measures for the restraint from use of power led to a single

factor solution with eigenvalues exceeding 1 and loadings ranged from 0.724 to 0.884. This factor

accounted for 66.538% of the total variance. Further, for the joint problem solving dimension, a

principal component analysis of the measure revealed a single factor with eigenvalues exceeding 1

and factor loadings ranged from 0.760 to 0.857. This factor accounted for 63.260% of the total

variance. Finally, a principal component analysis of these measures for willingness to adapt to

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changes dimension yielded a single factor solution with eigenvalues exceeding 1 and factor

loadings ranged from 0.714 to 0.855. This factor explained 64.335% of the total variance.

Alliance performance was measured based on an eight-item scale developed by Mahama

(2006). The scale measured the participants’ perceptions of service quality, product quality, cost,

time and improved decision-making Participants were asked to rate each performance criteria

based on their expectation with a score ranging from 1 (strongly disagree) and 7 (strongly agree).

Cronbach’s alpha for this measure was 0.911. A principal component analysis of these measures

yielded a single factor solution with eigenvalues exceeding 1 and factor loadings ranged from 0.699

to 0.790. This factor accounted for 55.742% of the total variance.

Given that the data collected in this survey is self-reported, there is the likelihood of the

responses being affected by common method bias. We performed Harman’s single factor test

wherein measures representing the constructs in our model were entered into an unrotated

exploratory factor analysis (Podsakoff et al. 2003). The results show that common method bias is

not a serious problem in our study.

Demographic Information and Control Variables

Besides the primary theoretical constructs of interest to this study, we also collected

information about the industry groupings of the respondent firms, the size of nominated supply

alliances, the history and expected life of the nominated supply alliances, past experience of

cooperation among partners of the nominated supply alliances, the respondent’s experience with

the respondent firm and the nominated supply alliances, and respondent’s job title and gender.

Consistent with Drake et al. (2007), we examined the extent to which these factors were

statistically associated with our primary constructs of interest with the intention of including those

with statistical significant association in our model. As discussed in the following paragraphs, all

these factors were not statistically significantly associated with our primary constructs of interest

and we excluded them from the final model.

The industry grouping of respondent firms is shown in Table 1 (Panel B). For the industry

grouping factor, Industrials and Information Technology had the highest representation among

the survey respondent firms. Independent sample t-test was conducted to determine whether there

were significant association between industry and the primary theoretical constructs. We coded

Industrials as 1 and other industry groups as 2, which was followed by the comparison of these

two groups’ means across all primary theoretical constructs. We repeated this procedure for

Information Technology industry grouping. There were no significant statistical differences in

17

means for these two groups across the primary theoretical constructs for each of these two sets of

test.

We used contract sum to proxy for the size of the nominated supply alliances. The size

grouping of respondent firms is shown in Table 1 (Panel C). To test whether there was significant

association between size of the supply alliances and the primary theoretical constructs, we treated

all the supply alliances with contract sum below the median size ($34.99 million) as small and

coded these supply alliances 1 and all those with sizes equal or greater than the median size were

coded 2. We tested the difference in means of these two groups across all the primary theoretical

constructs. There were no significant statistical differences in means for these two groups across

all primary theoretical constructs.

On average, the supply alliances nominated by the respondents had a history of 15.6 years

(S.D.=39.6). Independent t-test was conducted to determine whether history of the nominated

supply alliances was significantly associated with primary theoretical constructs. We separated the

156 supply alliances into two groups, with 1 representing those with below-mean history and 2

representing those with above-mean history. After testing the differences in means of these two

history groups, we found no significant statistical differences across all the primary theoretical

constructs. The nominated supply alliances had an average expected life of 26.8 years (S.D.=175.0).

We used the mean expected life of the 156 nominated supply alliances to split them into two

groups. The nominated supply alliances whose expected lives were less than the mean were coded

1 and those above the mean were coded 2. There were no statistically significant differences

between the two groups across all the primary theoretical constructs.

Of the 156 respondent firms, 108 had past experience of cooperation with the partner

firm(s) of the nominated supply alliances and 48 had no such experience. To determine whether

there was significant association between past alliance experience and the primary theoretical

constructs, we coded the 48 respondent firms with no past experience with partner firm(s) of the

supply alliances 1. The other 108 respond firms with past experience with partner firm(s) of the

sample supply alliances were coded 2. There were no statistically significance differences in means

across all the primary theoretical constructs, as shown by the results from independent sample t-

test.

The 156 respondents had a mean of 8.3 years (S.D.=4.6) of employment with the

respondent firm. We used the mean employment history with the respondent firm to split the

respondents into two groups with the aim of determining whether significant association existed

between employment history of respondents and the primary theoretical constructs. Respondents

with less than the mean history of employment with the respondent’s firm were coded 1 and those

above the mean were coded 2. Our test revealed no significant statistical differences between these

18

groups across all the primary theoretical constructs. In addition, to test whether there was any

significant association between the experience of managing the supply alliances and the primary

theoretical constructs, we used respondents’ mean length of managing the supply alliances

(mean=6.6 years; S. D.=4.6) to split the respondents into two groups. Respondents with less than

the mean were coded 1 and those above the mean were coded 2. The independent sample t-test

showed no statistical significant differences in means between the two groups across all primary

theoretical constructs. Finally, among all respondents, 50 respondents were females and 106 were

males. To test whether gender was significantly associated with the primary theoretical constructs,

male respondents were coded 1 and female respondents were coded 2. The differences in means

of these two gender groups were tested across all primary theoretical constructs. The results from

independent-sample t-test suggested there were no significant differences in means of these two

groups across all the primary theoretical constructs.

Given that none of the above factors were statistically significantly associated with the

primary theoretical constructs, they were excluded from the final model.

Statistical Analysis

The partial least squares (PLS) approach to structure equation modelling was used in this

study to analyse data obtained from the survey. PLS is a component-based modelling approach,

with the aim of maximising the variance explained and minimising errors (Chin 1998a). PLS

facilitates the creation of latent constructs scores on the basis of cross products involving multi-

item measures (Fornell 1982). Therefore, it enables the test of path models involving latent

constructs that are indirectly measured by multiple measures (Chin et al. 2001). PLS also enables

the simultaneous examination of both theory (measurement model) and measures (measurement

model). According to Diamantopoulos and Winklhofer (2001 p.272), “under reflective

measurement, multicollinearity is not an issue because only simple regressions are involved”.

Given that all our measures were reflective in nature, multicollinearity was not an issue in our data

analysis.

It is advantageous to use PLS approach for small sample size as it makes no distributional

assumptions about the data. According to Chin and Newsted (1999), the minimum sample size for

PLS modelling should be ten times the largest regression in the model. In this paper, the construct

having largest regression is alliance performance, which has six independent constructs leading

into it. This suggests a minimum sample size of 60. Our sample size of 156 is therefore adequate

for PLS modelling. Further, fit indices associated with covariance-based structural equation

modelling are not applicable in PLS modelling because of no requirement for distributional

19

assumption. Instead, R2 is considered the most appropriate statistic to evaluate the stability and

overall predictiveness of a model. In addition, traditional parametric-based technique for

significance testing is not possible due to the distributional free assumption of PLS (Chin 1998).

Hence, the significance of factor loading and path coefficients are tested using bootstrapping

resampling as suggested by Chin (1998).

Smart PLS Release 2 (Ringle et al. 2005) was used to simultaneously estimate the

measurement model and structural model. The R2 values for all the dependent variables in our

model range from 0.41 to 0.52; indicating a good predictive model. Hulland (1999) suggests that

analysis and interpretation of measurement model and structure model be separated.

Results

Results of PLS measurement model

We used the measurement model to evaluate the relationship between measures and

constructs through the assessment of reliability (individual item and composite) and validity

(convergent and discriminant) of the scale measures. Individual item reliability is the extent to

which multi-item measures reflect the latent constructs they are assumed to represent relative to

error. Individual item reliability is examined using the factor loading of the items to their

respective constructs. All measures that show loadings 0.7 or higher are said to be reliable,

indicating that the variance of measures are more related to variance of construct than variance of

errors (Nunnally 1978; Carmines and Zeller 1979). Hulland (1999) suggests that measures with a

loading of less than 0.7 should be excluded in the final measurement model because they add very

little explanatory power to the model.

Of the seven measures for interactive use of PMS, all measures (except interact3) loaded

higher than 0.7 with p value less than 0.001, demonstrating adequate reliability. Interact3

(“performance measures are used to provide a common view of the supply alliance”) had a loading

less than 0.7 and was therefore excluded from the final measurement model. Of the seven

measures of procedural fairness perception, six of them had loadings greater than 0.7 with p value

less than 0.001. The measure “The procedures used to govern knowledge or resource sharing

between supply alliance partners” (Fairness3) had a loading of 0.671, which was dropped in the

final measurement model. All measures for all four dimensions of cooperation and alliance

performance were above the 0.7 loading level. The individual item loadings, means, standard

deviations, and t-statistics of the final measurement model are shown in Table 2.

Insert Table 2

20

Composite reliability is the extent to which “the items assumed to measure each latent

variable measures them and not measuring another latent variable” (Aibinu and Al-Lawati 2010,

p.720). According to Nunnally (1978), the constructs with composite reliability coefficient greater

than 0.7 have acceptable reliability. Composite reliability scores for all constructs were above 0.8,

which demonstrates adequate composite reliability (see Table 3).

Convergent validity is the extent to which multiple measures of a construct are related with

one another, where each measure represents different aspect of the construct (Campbell and Fiske

1959). In PLS, convergent validity is assessed via average variance extracted (AVE), which

measures the average variance shared between a construct and its measures (Hulland, 1999).

According to Chin (1998), AVEs of 0.5 or above indicates appropriate convergent validity. In this

study, AVEs for all constructs were greater than 0.5 (see Table 3), providing evidence of adequate

convergent validity.

Discriminant validity refers to “the distinctness of the construct components” (Lewis,

2005, p.396). Discriminant validity of constructs can be examined by the cross loadings of factors

and also by comparing the square root of AVE to the correlations among the latent constructs

(Chin 1998; Hulland 1999). In using cross loadings, adequate discriminant validity is demonstrated

if the measures for a construct load higher on the constructs they intend to measure than they load

on other constructs. As shown in Table 2, all the measures loaded higher on their intended

constructs than on any other constructs; thus providing some evidence of discriminant validity. In

comparing the square root of AVE to correlations among constructs, adequate discriminant

validity exists if the square root of AVE is larger than the respective correlations between the

constructs. The square root of AVE for each construct are shown in Table 3 in bold on the

diagonal; the correlations between constructs are also shown in the off-diagonal. It can be

concluded that discriminant validity for each construct was sufficient because the square roots of

AVE for each of the constructs were larger than the respective correlation between the constructs.

Insert Table 3 here

Overall, the PLS results provides confidence in the reliability and validity of the measurement

model.

Common Method Bias

As data for this study was collected through survey (and with perceptual measures),

common method bias could potentially affect the results reported in this study. To examine and

21

address any possible concerns with common method bias, we relied on two approaches suggested

by Podsakoff et. al. (2003). We first performed Harman’s single-factor test by entering all our

scale-items (measures) into an unrotated exploratory factor analysis. We analysed the results to see

whether (a) a single factor emerged from the factor analysis or (b) one factor accounted for the

majority of the covariance among the variables (Podsakoff and Organ, 1986). The test did not

reveal any evidence of common method bias given that a multi-factor rather than a single factor

solution and the first factor explained only 24.7% of the total variance.

In the second approach, we implemented the single unmeasured method factor design in

our PLS model (Liang et al 2007; Mahama and Cheng 2013). We created a common method factor

by using all the scale-items for the substantive (main) constructs in our model. This common

method factor was then included in our PLS model in order to partial out any error variance in the

measurement model (for details see Liang et al 2007 and Mahama and Cheng 2013). In examining

whether common method bias is a concern, we assessed (a) the statistical significance of factor

loadings on both the method factor and the substantive constructs, and (b) compared the

percentage variance (measured as the squared values of the factor loadings) of each scale-item

explained by its substantive construct and by the common method factor. The results reported in

Table 4 show insignificant loadings (except for two items) on the method factor. The percentage

variances of the scale-items explained by the substantive constructs (average variance = 0.634) are

substantially greater than the percentage variances explained by the common method factor

(average variance = 0.011). The ratio of substantive variance to common method variance is

about 57:1. These results provide evidence that common method bias is unlikely to be a serious

concern for this study (Liang et al., 2007).

Insert Table 4 here

In addition, the results of the PLS structural model (when the common method factor is

included) are very similar to those of our substantive model; thus providing further evidence that

our results are unlikely to be affected by common method bias. The results of the PLS structural

model (when the common method factor is included) are reported in Appendix B.

Results of Structural Model

The hypothesised relationships among constructs in the theoretical model were tested

through the PLS structural model. Table 5 summarises the results of the structural model. In Table

22

5, Panel A shows the results for direct relationships and Panel B shows the results for indirect

relationships.

Insert Table 5 here

Hypothesis 1 predicted a positive relationship between the interactive use of PMS and

procedural fairness perception. The PLS results for the link between the interactive use of PMS

and procedural fairness perception show a significant (p<0.001) positive (0.719) direct relationship

between interactive use of PMS and procedural fairness perception, supporting H1.

The interactive use of PMS was hypothesised (Hypothesis 2) to be positively associated

with all four dimensions of cooperation. This hypothesis was partially supported. The results for

the path linking the interactive use of PMS and information sharing was significant (p<0.001) with

a positive path coefficient (0.366). This shows that there is a direct relationship between interactive

use of PMS and information sharing. This is consistent with Bisbe and Otley’s (2007) argument

that interactive use of PMS facilitates information sharing. In addition, there is a significant (at the

99% confidence level) positive (0.254) indirect relationship between interactive use of PMS and

information sharing through procedural fairness perception (see Panel B of Table 5). The direct

path leading from interactive use of PMS to restraint from use of power was was not statistically

significant. Though this suggests that there is no positive direct relationship between interactive

use of PMS and restraint from use of power, we explored the possibility of an indirect relationship

and found that interactive use of PMS had a significant (at the 99% confidence level) positive

indirect effect (o.460) on restraint from use of power through procedural fairness (see Panel B,

Table 5). The structural path coefficient between interactive use of PMS and joint problem

solving was statistically significant (p<0.05) and in the hypothesised direction (0.240). There was

also a significant (at the 99% confidence level) positive indirect relationship (0.322) between

interactive use of PMS and joint problem solving (see Panel B of Table 5). Further, the path

coefficient from interactive use of PMS to willingness to adapt to changes was positive (0.215) and

statistically significant (p<0.05) (see Panel A of Table 5). The results in Panel B of Table 5 also

show a significant (at the 99% confidence level) positive indirect relationship (0.114) between

interactive use of PMS and willingness to adapt to changes through procedural fairness perception.

Overall, hypothesis 2 is partially supported.

Hypothesis 3 predicted a positive relationship between procedural fairness perception and

all four dimensions of cooperation. This hypothesis is fully supported as shown by the results in

Panel A of Table 5. There was a significant (p<0.001) positive direct relationship between

procedural fairness perception and information sharing (0.354); restraint from use of power

(0.640); joint problem solving (0.449); and willingness to adapt to changes (0.530).

23

Hypothesis 4 predicted a positive relationship between the interactive use of PMS and

alliance performance. We found a significant (p<0.10) positive direct relationship (0.148) between

interactive use of PMS and alliance performance (see Panel A of Table 5). We also found

significant positive indirect relationship between interactive use of PMS and alliance performance

through the procedural fairness and information sharing dimension of cooperation (0.052; at the

95% confidence level); through procedural fairness perception and restraint from use of power

(0.164; at the 99% confidence level); and through procedural fairness perception and joint problem

solving (0.047; at the 90% confidence level). Overall, the total indirect effect of the interactive use

of PMS on alliance performance is 0.262.

In Hypothesis 5 we predicted a positive relationship between procedural fairness

perception and alliance performance. Although there was no support for a positive direct

relationship (see Panel A of Table 5), we found that procedural fairness perception has significant

positive indirect effect on alliance performance through three dimensions of cooperation:

information sharing (0.073; at the 95% confidence level); restraint from use of power (0.228; at the

0.99% confidence level); and joint problem solving (0.065; at the 90% confidence level). The total

indirect effect of procedural fairness perception and alliance performance was 0.365.

Hypothesis 6 predicted a positive relationship between all four dimensions of cooperation

and alliance performance. The results in Panel A of Table 5 partially support this hypothesis.

There was a significant (p<0.05) positive direct relationship (0.206) between information sharing

and alliance performance. This result is consistent with findings in alliance literature that also show

a positive relationship between information sharing and alliance performance (Korsgaard et al.

1995; Zhao et al. 1996; Huang and Gangopahay 2004). In addition, there was a significant

(p<0.001) positive direct relationship (0.355) between restraint from use of power and alliance

performance. Brown et al. (1995) argue that restraint form use of power promotes trust, genuine

commitment, and partners’ willingness to exert efforts, which in turn enhances alliance

performance. Further, joint problem solving was directly and significantly (p<0.05) associated with

alliance performance in the predicted direction (0.144). This provides further support for prior

research that argues that joint problem solving strengths mutual support among alliance partners,

encourages them to contribute their expertise and experience to alliance activities and thus

improves performance (Wang and Wei 2007; Yang et al 2008). However, no significant positive

direct relationship between willingness to adapt to changes and alliance performance was found.

Figure 5 presents the PLS structural model and the associated path coefficients.

Insert Figure 2 here

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Conclusion

This study was motivated by the high failure rate of alliance and the attribution of this

failure to fairness perceptions and lack of cooperative behaviour. We argued that management

control systems may have a role to play and hence investigated the relationships among the

interactive use of performance measurement systems (PMS), procedural fairness perception, and

cooperation and how that translates into supply alliance performance. We developed a theoretical

model to reflect our prediction of the relationships among these constructs and tested this model

using partial least squares approach to structural equation model. We found support (direct

and/or indirect relationships) for the hypothesised relations. The results have importance in the

accounting and alliance literature as they highlights the significant role of using PMS interactively

in improving conditions (e.g. procedural fairness perception and cooperation) that lead to

enhanced alliance performance.

The study makes theoretical and practical contributions. Theoretically, this study

demonstrates the importance of interactive use of PMS in enhancing procedural fairness

perception and cooperation in the context of supply alliances. It also shows that procedural

fairness perception and cooperation play an intervening role in the relationship between the

interactive use of PMS and alliance performance. This intervening role is represented by the

significant indirect effects of the interactive use of PMS on alliance performance through these

two variables. In addition, the results also show that procedural fairness perception is an important

antecedent to cooperation in supply alliance. Further, the results provide additional support for

prior research that documents the importance of cooperation in the successful performance of

alliances. This study is anticipated to have practical contribution by providing managers

knowledge about how PMS is used in alliances. Given that the ultimate objective of organizations

engaged in supply alliances is to maintain a fair and stable cooperative environment and to

maximise firm performance, the results of this study shows that using PMS interactively is

important in realising these potential benefits. Thus, this study offers managers who are dealing

with supply alliances a management control approach that can improve procedural fairness

perception, cooperation, and alliance.

Although the above results provide useful information about the role of interactive use of

PMS on partners’ behaviours and how that translates into alliance performance, the results need to

be interpreted with the following limitations in mind. There is inherent limitation of using survey

as the method of data collection. Self-rated survey instruments are subject to common method

bias. Though we have conducted tests that suggest that common method bias is unlikely to be a

serious treat in this study, we are unable to conclusively rule out its effects. Also, this paper

25

investigates supply alliances across various industries with the aim of maximising the

generalisability of the results. However, the internal validity of the results may be compromised.

Future research can extend the findings of this paper by using more objective performance

measures such as revenues, gross profits, and customer feedback instead of perceived performance

measures. Further, this paper tests the role of management control systems by focusing on a

particular type: PMS. There are other types of management control systems including budgets,

incentive systems/reward structures, strategic planning, group norms, and socialisation (Dekker

2004; Collier 2005). Future research should explore the effects of these other management control

systems in alliances. Also, the hypothesised relationships among the theoretical constructs were

tested in supply alliances in this study, while there are other types of alliances including joint

ventures, licensing agreements, and franchising. It is possible for researchers to test the

hypothesised relationships in those other alliance contexts. Finally, this study is based on a single

firm point of view in alliances. It would be very interesting if future research can draw upon all

counterparties (buyers and suppliers)’ point of views and match their perceptions to examine the

effects of shared perceptions in alliances.

26

Appendix A Survey Questions and Their Descriptors

Interactive Uses of Performance Measurement System The response scale for the following items ranged from 1 (not at all), to 7 (to a great extent) To what extent do you and your alliance partner currently use performance measures to: Interact1: Enable discussion in your supply alliance meetings. Interact2: Enable continual challenge and debate underlying data, assumptions and action plans

related to supply alliance. Interact3: Provide a common view of the supply alliance. Interact4: Tie the supply alliance partners together. Interact5: Enable the supply alliance partners to focus on common issues. Interact6: Enable the supply alliance partners to focus on critical success factors. Interact7: Develop a common vocabulary in the supply alliance. Procedural Fairness Perceptions The response scale for the following items ranged from 1 (very unfair), to 7 (very fair) How would you rate the fairness of the following procedures in the supply alliance: Fairness1: The procedures used in supply alliance meetings and decision-making processes in

these meetings. Fairness2: The procedures the partners use in formulating and structuring the supply alliance. Fairness3: The procedures used in planning, organizing, and managing supply alliance activities. Fairness4: The procedures used to govern knowledge or resource sharing between supply alliance

partners. Fairness5: The procedures for executing strategic decisions, including the clarity of their

definition and consistency of their performance. Fairness6: All partners’ administration and monitoring of contract execution. Fairness7: All partners’ administration and monitoring of the implementation of strategic

decisions. Cooperation The response scale for the following items ranged from 1 (strongly disagree), to 7 (strongly agree) To what degree do you agree with the following statements about your alliance relationship? Information Sharing Inform1: In this relationship, any information that might help other partner(s) will be provided

to them. Inform2: We keep each other informed about events or changes that may affect the other

supply alliance partners. Inform3: We share information about the supply alliance.

27

Joint Problem Solving Solve1: In most aspects of this supply alliance we are jointly responsible for getting things

done. Solve2: We treat problems that arise in the course of this supply alliance as a joint rather than

individual responsibility. Solve3: We jointly solve problems when they arise in this supply alliance. Willingness to Adapt to Changes Adapt1: When some unexpected situation arises, we would rather work out a new deal than

hold each other to the original terms of the contract. Adapt2: It is expected that we will be open to modify our agreement if unexpected events

occur. Adapt3: We are willing to adapt to changes if unexpected events occur. Restraint from Use of Power Restraint1: We feel it is important not to use any proprietary information to other partner(s)’

disadvantage. Restraint2: We expect that neither party will make demand that might be damaging to the other. Restraint3: We restrain from taking advantage of each other in this supply alliance. Alliance Performance The response scale for the following items ranged from 1 (strongly disagree), to 7 (strongly agree) Relative to your expectations, your relationship with the supplier has led to: Perf1: Improved service quality. Perf2: Improved product quality. Perf3: Cost savings. Perf4: On time delivery. Perf5: High quality decision making.

28

Appendix B

PLS Structural Model (with Latent Common Method Factor Included) Results

Latent Construct

Path to: R2

Procedural Fairness

Perception

Information sharing

Restraint from use of power

Joint Problem solving

Willingness to adapt to

changes

Alliance Performance

Interactive use of PMS

0.715****

(14.5715)

0.360****

(3.5200)

ns 0.228***

(2.3748)

0.217**

(1.9831)

0.144*

(1.3765)

Procedural fairness perception

0.347***

(2.9560)

0.640****

(6.9657)

0.451****

(4.8929)

0.530****

(5.5195)

ns 0.51

Information sharing

0.199**

(2.0669)

0.43

Restraint from use of power

0.346****

(3.5906)

0.45

Joint problem solving

0.145**

(1.6741)

41

willingness to adapt to changes

ns 0.49

Alliance performance

0.44

n = 156; ****p<0.001 (one-tailed); ***p<0.01 (one-tailed); **p<0.05 (one-tailed); *p<0.1 (one-tailed)

29

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35

Figure 1: Theoretical model

H1

H5

Procedural Fairness Perceptions

H3

Alliance Performance Interactive uses of Performance

Measurement Systems (PMS) H4

H2 H6

Cooperation

36

Willingness to Adapt to Changes

R2 = 0.49

Restraint from Use of Power

R2 = 0.45

Alliance Performance

R2 = 0.45

Figure 2: PLS structural model results

n = 156; ***p<0.001 (one-tailed); **p<0.05 (one-tailed); *p<0.1 (one-tailed)

Interactive Uses

of PMS

Information Sharing

R2 = 0.45

Procedural Fairness

Perceptions

R2 = 0.52

Joint Problem Solving

R2 = 0.41

0.719***

0.366***

0.215**

0.148

*

0.240**

0.640***

0.530***

0.449***

0.354***

0.355***

0.144**

0.206**

37

Table 1: Survey Participants

Panel A: Summary of data collection processes

Initial list of invited people 2,350

No. of people who viewed the survey 483

No. of people who started the survey 353

No. of people who started the survey but were screed out from by screening questions 195

No. of people who started the survey and were allowed to continue after screening questions 158

No. of people who continued the survey but not completed the survey 2

No. of people who continued the survey and completed the survey 156

Panel B: Industry Grouping of Respondent Firms

Industry Frequency Percentage

Consumer Discretionary 21 8.27%

Consumer Staples 24 9.45%

Energy 14 5.51%

Financial Services 28 11.02%

Health Care 23 9.06%

Industrials 40 15.75%

Information Technology 39 15.35%

Materials 29 11.42%

Metals and Mining 11 4.33%

Telecommunication Services 10 3.94%

Utilities 2 0.79%

Other 13 5.12%

Total 254* 100%

*some respondent firms indicate that they involve multiple industries.

Panel C: Size Grouping of Respondent Firms

Contract Sum Frequency Percentage

Below $ 5 million 20 12.82%

$ 5 million - $14.99 million 25 16.03%

$ 15 million - $ 24.99 million 22 14.10%

$ 25 million - $ 34.99 million 26 16.67%

$ 35 million - $ 44.99 million 27 17.31%

$ 45 million - $ 54.99 million 19 12.18%

Above $ 55 million 17 10.90%

Total 156 100%

38

Table 2: Measurement Model – Factors, Descriptive Statistics, Cross-Loadings, and T-statistics

Latent Construct Scale items Mean Std dev Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 t-statistics*

Interactive uses of PMS

Interact1 5.84 0.980 0.772 0.621 0.575 0.370 0.478 0.440 0.425 17.793

Interact2 5.808 1.114 0.746 0.567 0.478 0.382 0.403 0.447 0.400 18.052

Interact4 5.769 1.077 0.729 0.524 0.436 0.381 0.425 0.443 0.356 16.848

Interact5 5.917 1.022 0.779 0.468 0.503 0.348 0.397 0.481 0.349 19.894

Interact6 5.994 0.905 0.772 0.495 0.420 0.382 0.427 0.435 0.330 19.508

Interact7 5.878 1.043 0.807 0.618 0.435 0.435 0.455 0.502 0.414 29.881

Procedural fairness perceptions

Fairness1 5.737 0.944 0.559 0.764 0.556 0.544 0.436 0.605 0.387 21.455

Fairness2 5.692 1.105 0.570 0.768 0.434 0.496 0.448 0.517 0.450 23.359

Fairness3 5.801 1.000 0.530 0.716 0.532 0.489 0.513 0.481 0.399 17.006

Fairness5 5.699 1.050 0.596 0.769 0.417 0.508 0.485 0.501 0.374 21.396

Fairness6 5.853 0.983 0.513 0.774 0.420 0.472 0.540 0.493 0.393 17.943

Fairness7 5.872 0.914 0.517 0.785 0.453 0.548 0.423 0.531 0.349 19.750

Information sharing

Inform1 5.827 0.965 0.488 0.496 0.823 0.517 0.525 0.534 0.420 24.915

Inform2 5.923 1.019 0.424 0.418 0.810 0.440 0.506 0.400 0.454 25.555

Inform3 5.744 1.147 0.554 0.545 0.752 0.412 0.506 0.557 0.449 19.705

Restraint from use of power

Restraint1 5.821 1.081 0.450 0.458 0.378 0.712 0.423 0.529 0.413 10.824

Restraint2 5.904 1.064 0.401 0.586 0.507 0.876 0.520 0.565 0.461 38.387

Restraint3 5.891 1.013 0.385 0.582 0.507 0.848 0.537 0.547 0.566 28.528

Joint problem solving

Solve1 5.763 1.066 0.446 0.419 0.570 0.385 0.754 0.429 0.424 14.903

Solve2 5.744 1.077 0.439 0.565 0.485 0.499 0.845 0.437 0.385 28.038

Solve3 5.814 0.989 0.456 0.492 0.490 0.558 0.783 0.465 0.480 22.343

Willingness to adapt to changes

Adapt1 5.641 1.107 0.390 0.517 0.370 0.434 0.328 0.705 0.289 9.808

Adapt2 5.865 0.991 0.502 0.598 0.538 0.546 0.473 0.852 0.364 33.659

Adapt3 5.8462 1.023 0.531 0.536 0.590 0.615 0.525 0.841 0.483 28.977

Alliance performance

Perform1 37.981 9.220 0.431 0.465 0.501 0.448 0.393 0.432 0.810 22.543

Perform2 37.301 9.444 0.449 0.431 0.494 0.529 0.469 0.380 0.824 25.074

Perform3 36.417 9.226 0.395 0.443 0.390 0.481 0.456 0.379 0.813 23.678

Perform4 37.859 8.991 0.326 0.370 0.441 0.481 0.425 0.319 0.810 26.018 Perform5 38.289 8.591 0.418 0.389 0.438 0.481 0.465 0.444 0.824 22.526

All Significant at the 0.001 level (one-tailed)

39

Table 3: Inter-construct correlations and the square root of AVE

Latent Construct Composite Reliability

AVE Willingness to adapt to

changes

Procedural fairness

perception

Information sharing

Interactive use of PMS

Alliance performance

Restraint from use of

power

Joint problem solving

Willingness to adapt to changes 0.843 0.643 0.802 Procedural fairness perception 0.893 0.582 0.685 0.763

Information sharing 0.838 0.633 0.631 0.617 0.795 Interactive use of PMS 0.896 0.590 0.597 0.719 0.620 0.768

Alliance performance 0.909 0.666 0.479 0.514 0.556 0.496 0.816 Restraint from use of power 0.855 0.665 0.669 0.669 0.574 0.500 0.594 0.815

Joint problem solving 0.837 0.632 0.559 0.621 0.646 0.562 0.542 0.609 0.795 n = 156 Diagonal elements: square root of AVE; off-diagonal elements: correlations between constructs.

40

Table 4: Common Method Bias Analysis with Latent Common Method Variance Factor Scale Items Substantive

Factor loading (R1)

Variance explained by Substantive Factor (R12)

Method Factor loading

(R2)

Variance explained by

Method Factor loading (R22)

Interactive use of PMS INTERACT1 0.643****

0.413 0.147* 0.022

INTERACT2 0.693****

0.480 0.061 0.004

INTERACT4 0.726****

0.526 0.005 0.000

INTERACT5 0.894****

0.799 -0.132 0.017

INTERACT6 0.894****

0.800 -0.139 0.019

INTERACT7 0.763****

0.582 0.049 0.002

Procedural fairness perception

FAIR1 0.660****

0.435 0.115 0.013

FAIR2 0.742****

0.551 0.028 0.001

FAIR3 0.569****

0.324 0.159 0.025

FAIR5 0.786****

0.617 -0.017 0.000

FAIR6 0.884****

0.781 -0.118 0.014

FAIR7 0.930****

0.866 -0.160 0.026

Information sharing INFORM1 0.878****

0.771 -0.046 0.002

INFORM2 0.988****

0.977 -0.195 0.038

INFORM3 0.500****

0.250 0.264**

0.070

Restraint from use of power

REST1 0.692****

0.479 0.036 0.001

REST2 0.944****

0.891 -0.078 0.006

REST3 0.797****

0.635 0.049 0.002

Joint problem solving SOLVE1 0.776****

0.603 -0.018 0.000

SOLVE2 0.918****

0.842 -0.083 0.007

SOLVE3 0.684****

0.468 0.107 0.011

Willingness to adapt to changes

ADAPT1 0.791****

0.626 -0.100 0.010

ADAPT2 0.880****

0.774 -0.032 0.001

ADAPT3 0.741****

0.549 0.116**

0.014

Alliance performance PERF1 0.770****

0.592 0.054 0.003

PERF2 0.764****

0.584 0.067 0.005

PERF3 0.826****

0.682 -0.012 0.000

PERF4 0.895****

0.801 -0.106 0.011

PERF5 0.828****

0.685 -0.003 0.000

Average 0.788 0.634 0.001 0.011

n = 156; ****p<0.001 (one-tailed); ***p<0.01 (one-tailed); **p<0.05 (one-tailed); *p<0.1 (one-tailed)

41

Table 5: PLS Results

Panel A: Path Coefficients (directs effects), t-statistics and R2

Latent Construct

Path to: R2

Procedural Fairness

Perception

Information sharing

Restraint from use of power

Joint Problem solving

Willingness to adapt to

changes

Alliance Performance

Interactive use of PMS

0.719****

(15.5614)

0.366****

(3.7177)

ns 0.240**

(2.2880)

0.215**

(1.9197)

0.148*

(1.4010)

Procedural fairness perception

0.354****

(3.2027)

0.640****

(6.9657)

0.449****

(4.5736)

0.530****

(5.4082)

ns 0.52

Information sharing

0.206**

(2.1474)

0.45

Restraint from use of power

0.355****

(3.5131)

0.45

Joint problem solving

0.144**

(1.7250)

41

willingness to adapt to changes

ns 0.49

Alliance performance

0.45

n = 156; ****p<0.001 (one-tailed); ***p<0.01 (one-tailed); **p<0.05 (one-tailed); *p<0.1 (one-tailed)

Panel B: Indirect effects and bootstrap confidence intervals (in parenthesis)

Path to:

The effects of Interactive Use of PMS Through:

Information sharing

Restraints from the use of

power

Joint Problem solving

Willingness to adapt to changes

Alliance Performance

Procedural fairness 0.254***

(0.061 – 0.485)

0.460***

(0.285 – 0.629)

0.322***

(0.150 -0.536)

0.114***

(0.171 – 0.540)

Procedural fairness and information sharing

0.052**

(0.003 - 0.125)

procedural fairness and Restraint from use of power

0.164***

(0.044 – 0.311)

procedural fairness and Joint problem solving

0.047*

(0.003 – 0.109)

Total Indirect Effects 0.254 0.460 0.322 0.262

The effects of Procedural Fairness Through:

Information sharing 0.073*

(0.004 – 0.176)

Restraints from the use of power

0.228***

(0.059 – 0.420)

Joint Problem solving 0.065 *

(0.005 – 0.148)

Total Indirect Effects 0.365

n = 156 ; ***99% confidence level; **95% confidence level; *90% confidence level