the effect of managers' enabling perceptions on costing system use, psychological empowerment,...

BEHAVIORAL RESEARCH IN ACCOUNTING American Accounting AssociationVol. 25, No. 1 DOI: 10.2308/bria-503332013pp. 89–114

The Effect of Managers’ Enabling Perceptionson Costing System Use, PsychologicalEmpowerment, and Task Performance

Habib Mahama

Australian National University

Mandy M. Cheng

The University of New South Wales

ABSTRACT: This study investigates whether and how managers’ enabling perceptions

of their costing systems affect task performance. We propose that managers who

perceive their costing system as more enabling will have higher levels of task

performance, and that this relationship occurs through the intensity with which the

costing system is used and the level of psychological empowerment experienced by the

managers. To test these propositions, we conduct a survey of middle-level managers

and analyze the responses using a PLS model. Our results generally support our

propositions. Specifically, we find a positive relationship between managers’ enabling

perceptions and the intensity with which the costing system is used. The intensity of use

is further associated with all four dimensions of psychological empowerment (meaning,

competence, self-determination, and impact). Finally, the intensity of use also has an

indirect impact on task performance via the competence dimension of psychological

empowerment. The present study extends prior research on costing systems, and adds

to our understanding of the role managers’ perceptions play in improving costing system

effectiveness.

Keywords: enabling perception; costing system; pyschological empowerment; task

performance.

Data Availability: Data available upon request.

INTRODUCTION

Costing systems are part of wider management information systems used in organizations to

improve performance. As information systems, costing systems have the potential to

deepen managers’ understanding of the organizational decision context, improve

managerial judgment and decision making, and enhance productivity. While in principle, these

The authors acknowledge the helpful comments from participants at the 2008 EIASM Conference, 2009 AAA AnnualMeeting, and 2009 AFAANZ Conference.

Steve G. Sutton, Associate Editor.

Published Online: October 2012

89

benefits are possible, in practice, organizations have difficulty realizing these benefits (Bhimani

1996; Innes et al. 2000; Anderson et al. 2002; Chenhall 2004). Consequently, management

accounting researchers have sought to understand how, and the extent to which, these systems are

successful in achieving these benefits for organizations.

Much of the prior research in this area has focused on the technical design aspects of these

systems (e.g., sophistication of the costing system, selection of cost drivers and cost pools) as a

variable for explaining the success of costing systems in improving the quality/relevance of cost

information for decision making and enhancing organizational performance (Christensen and

Demski 1997; Anderson and Young 1999; Bromwich and Hong 1999; Ittner et al. 2002). Other

researchers have criticized the over-emphasis on the technical design of costing systems, and have

highlighted the need for studies that examine the effect of behavioral implementation factors on

costing system success (Shields and Young 1989; Shields 1995; Foster and Swenson 1997;

McGowan and Klammer 1997; McGowan 1998; Anderson et al. 2002; Chenhall 2004). In this

study, we aim to extend prior research in this area by focusing on the impact of psychological/

behavioral factors on the post-implementation use of costing systems. Specifically, we aim to

provide evidence on how managers’ perceptions of a costing system as enabling (‘‘enabling

perceptions’’) influence their usage of the system, their psychological empowerment, and task

performance.

Adler and Borys (1996) develop the concept of ‘‘enabling’’ to describe an organizational

technology that helps workers to deal effectively with work contingencies, rather than one designed

to fool-proof work processes. Subsequently, accounting studies have shown that the principles

underlying an enabling system are relevant to understanding the design of performance

measurement, information system integration, and management control (e.g., Ahrens and Chapman

2004; Wouters and Wilderon 2008; Chapman and Kihn 2009). Our study extends the prior research

by focusing on managers’ enabling perceptions in relation to their costing systems.

The importance of focusing on managers’ perception derives mainly from psychology

literature that suggests that perception is an important factor affecting the attitudes and behavior of

organizational members. This literature argues that individuals actively perceive their work

environments, and that their reactions to those environments are shaped by their perceptions and

interpretations that go beyond the verifiable reality of those environments (Thomas and Velthouse

1990; Spreitzer 1996). More specifically, Spreitzer (1996) argues that rather than the ‘‘objective’’work environment determining behavior in organizations, individuals’ perceptions of those

environments influence behavior. As costing systems are part of the work environment of managers

and following from the above, we argue that the perceptions managers hold about the enabling

property of these systems will play significant roles in determining their behavioral reactions toward

those systems.

Consistent with the psychology literature, the information systems (IS) and accounting

literatures have also recognized the need to understand the behavioral and performance

consequences of the psychological properties associated with an information system. In the IS

literature, DeLone and McLean (2003) argue that evaluating the success of any information system

requires an examination of factors not only at the technical level (i.e., factors in relation to the

accuracy/efficiency of the system), but also factors at the semantic level, which concerns how the

meaning and the purpose of the information produced by the system is perceived by its users.1

Further, as users experience features of the system, it becomes important to also assess system

successes at the influence level; that is, how the system influences the way individual users conduct

1 Other well-established models in the IS literature, such as the Technology Acceptance Model (TAM), also arguethat users’ perceptions of an information system drive their behavioral responses, such as their willingness to usethe information system.

90 Mahama and Cheng

Behavioral Research In AccountingVolume 25, Number 1, 2013

their work. Similarly, in the accounting literature, managers’ behavioral responses to management

accounting practices and systems are significantly influenced by their perceptions, including

perceived fairness and justice (e.g., Lau and Moser 2008; Burney et al. 2009). Here, we argue that

managers’ enabling perceptions can explain the benefits they derive from their costing systems;

namely, improved task performance.2

Prior research also suggests that perception is linked to use (behavior) and feelings of

psychological empowerment (Spreitzer 1996; Seddon 1997). ‘‘Use’’ is a conduit through which

information systems generate organization and individual effects (Davis 1993; Straub et al. 1995;

Chenhall 2003); hence, we expect that the performance impact of managers’ enabling perceptions

will be realized through increasing the intensity with which the system is used. Psychological

empowerment has also been established by recent accounting research as a key factor that mediates

the relationship between management accounting practices and individual-level performance

(Drake et al. 2007; Hall 2008). Spreitzer (1996, 486) argues that ‘‘[f]or individuals to feel

empowered, they must perceive a role environment to be liberating rather than constraining.’’ This

suggests psychological empowerment is also an important variable through which managers’

perceptions of costing systems can affect their task performance. These potentially important

linkages between enabling perception, use, psychological empowerment, and task performance

form the basis of the proposed conceptual model and the hypothesis developed for the present

study.

We test our proposed hypothesis by conducting a cross-sectional survey of middle-level

managers in Australia. We hypothesize and show that the intensity with which a costing system is

used is positively associated with managers’ enabling perceptions. Following from Spreitzer

(1995), we model psychological empowerment as a four-dimensional construct (meaning,

competence, self-determination, and impact), and predict that the intensity of use is positively

associated with all four dimensions, and the results support this prediction. We also find that

managers’ enabling perceptions are indirectly related to three of the dimensions of psychological

empowerment (meaning, competence, and impact) through the intensity of use. In addition, we

hypothesize that psychological empowerment and the intensity of use are positively associated with

task performance. We find support for two dimensions of psychological empowerment (competenceand self-determination) and task performance. There is no direct relationship between intensity of

use and task performance; however, the intensity of use is indirectly related to task performance

through the competence dimension of psychological empowerment.

Our study makes a number of contributions to the literature. First, prior literature has found

mixed evidence on the benefits of sophisticated costing systems such as activity-based costing

(ABC) systems (Bhimani 1996; Innes et al. 2000; Chenhall 2004). Our study contributes to this

literature by highlighting the importance of examining the enabling property of a costing system as

perceived by the managers. Although past studies in the costing system research literature have

investigated the impact behavioral implementation factors have on costing system effectiveness

(e.g., McGowan and Klammer 1997; Chenhall 2004), our study is one of the first to show that the

behavioral issues associated with the usage of these systems are also significant factors to be

considered. From the practitioners’ point of view, the findings from the present study suggest that in

addition to the technical design, costing systems designers should consider user perceptions.

Second, we provide further support to the concept of enabling as an important accounting system

attribute, and show that enabling perception is an antecedent to important behavioral variables such

as psychological empowerment and costing system usage.

2 In this study, we use the term ‘‘task performance’’ to refer to individual managers’ performance with respect totheir work tasks. We view task performance as a subset of individual performance, with a focus specifically onhow well individuals perform on their work tasks.

The Effect of Managers’ Enabling Perceptions on Costing System Use 91


Third, Chenhall (2003) argues that the positive link between the usefulness of management

control systems, their subsequent use, and enhanced organizational outcomes is a ‘‘broad leap in

logic’’ that is not supported by compelling empirical evidence. Our study shows that while an

enabling perception of a costing system has a direct positive impact on its usage, the subsequent

link from use to task performance is indirect, via elements of psychological empowerment. The

result, thus, illustrates the importance of identifying intervening variables in explaining the benefits

of costing systems. Although our study does not extend to performance at the organizational level,

understanding the positive effects of costing system on task performance is the first step toward

improving organization-level outcomes. Finally, while prior studies have established that

psychological empowerment is important in designing management accounting systems (Drake

et al. 2007; Hall 2008; Marginson and Bui 2009), its role is still under-researched (Drake et al.

2007). We add to the literature by showing that psychological empowerment is indirectly

influenced by the extent to which managers perceive their costing systems as enabling.

The rest of the paper is organized as follows. First, we present the overall proposed model.

Next, the relationship between managers’ enabling perceptions and their behavioral responses

(namely, psychological empowerment and intensity of use) are discussed. We then explain the task

performance effect of managers’ intensive usage of the costing system. This is followed by an

outline of our research method, result analysis (including both the measurement model and the

structural model of our PLS analysis) and, finally, discussions and conclusions.

THEORY AND HYPOTHESIS DEVELOPMENT

Costing Systems

Costing systems are interrelated manual and computer parts used to gather, analyze, and

manage cost and activity data to provide information for internal users estimating the cost of cost

objects, better cost management, strategy development and management, enhanced product

planning decisions, and improved managerial judgment. Since the 1980s, there has been growing

interest in costing systems, and this interest is driven by changes in the corporate landscape,

including changes in manufacturing technology, global competition, and shorter product life cycle

(Al-Omiri and Drury 2007).

Despite this growing interest, there has been mixed evidence on the ability of costing systems

to deliver benefits. A review of recent literature in this area suggests that some researchers have

approached this issue by focusing on costing system design, exploring specific technical attributes

of these systems (e.g., system sophistication, cost behavioral classifications) and/or the interaction

of these technical attributes with the organization’s external environment and internal

characteristics to deliver positive results (e.g., Abernathy et al. 2001; Ittner et al. 2002; Pizzini

2006; Al-Omiri and Drury 2007). Others adopt a different approach and focus their attention on the

implementation process (e.g., Anderson and Young 1999; Anderson et al. 2002; Chenhall 2004),

exploring both behavioral factors (e.g., cognitive conflict) and technical attributes (e.g., the size of

the implementation team and the use of external consultants).

We seek to contribute to the existing literature by examining the psychological/behavioral

factors associated with costing systems use and the consequences of that for task performance. We

propose a model linking the costing system users’ enabling perceptions of their systems to their

experience of psychological empowerment and task performance via the intensity with which these

systems are used. DeLone and McLean (1992) argue that in examining systems use, it is important

to consider the nature and intensity of use. Following from this, we focus on the intensity with

which costing systems are used for cost management practices. The hypothesized relationships in

our conceptual model are shown in Figure 1.

92 Mahama and Cheng


Enabling Perceptions of Costing Systems and the Intensity of Use of These Systems

The concept of enabling formalization was initially introduced to the accounting literature by

Ahrens and Chapman (2004), and subsequent studies have applied it to the study of accounting

phenomena (Naranjo-Gil and Hartmann 2006; Wouters and Wilderon 2008; Chapman and Kihn

2009). The core aim of this concept is to provide a useful framework for understanding how

employees distinguish good (positive) administrative rules and systems from bad (negative) ones

(Adler and Borys 1996). Specifically, the notion of enabling formalization describes rules and

systems that are designed to facilitate how employees structure, refine, and conduct their work

processes with no necessary hierarchical implications. That is, they are rules and systems designed

to support rather than control the employee (Ahrens and Chapman 2004). In contrast, rules and

systems that are designed to force ‘‘reluctant compliance’’ and, thus, seek to police adherence to

pre-specified standards, are said to be less enabling and rather coercive (Adler and Borys 1996;

Ahrens and Chapman 2004).

Recently, Chapman and Kihn (2009) have shown that the positive effect of integrated IS such

as enterprise resource planning (ERP) systems on organizational outcomes can be explained by the

association between IS integration and the design characteristics of enabling management control.

In this study, we focus our attention on the perceptions managers hold about the enabling property

of costing systems, and the impact of such perceptions on managers’ behavioral responses to these

systems. Perception has long been held as an important factor affecting the attitudes and behavior of

organizational members (Agarwal and Prasad 1998; Pierce and O’Dea 2003; Jarrar et al. 2007).

Perception is an internal representation whose functional role is to ‘‘stand in’’ for features of the

percept (i.e., the events or objects being perceived) (Knill and Richards 1996; Warren 2006). More

FIGURE 1Theoretical Model



specifically, it is a process through which individuals acquire information about events and objects

in their environment, and by which individuals construct representations of their worlds. Such

representations are said to be sufficiently rich to provide the basis for behavior or conduct (Warren

2006). That is, perceptions about events and objects influence behavior. Generally, positive

perceptions are associated with more desirable behavior toward the percept, and negative

perceptions lead to less desirable reactions.

To develop positive or negative perceptions of a costing system, individual managers need a

conceptual base. Following from Shields (1995), we conceptualize costing systems as

administrative innovations. This then allows us to draw on the concept of enabling formalization

to understand the conceptual basis of individual managers’ perceptions of costing systems. Our

premise is that individual managers will perceive costing systems as more or less enabling of their

work processes. We argue that systems that are thought to support, rather than control, managers’

work processes will be perceived more positively than those that are less enabling. We further posit

that this variation in enabling perceptions influences managers’ behaviors, and as use is considered

to be a type of behavior (see Seddon 1997), we argue that a manager’s perceptions of how enabling

a costing system is will be significantly and positively related to the degree to which he or she will

use the costing system for cost management.3 Following prior literature, we use the term ‘‘intensity

of use’’ to describe the extent users draw on the system to support their work (e.g., Teng and

Calhoun 1996; Schoute 2009). More formally, we hypothesize the following:

H1: There is a positive direct relationship between individual managers’ enabling perceptions

of their costing systems and the intensity with which individual managers use a costing

system for cost management

Enabling Perceptions of Costing Systems, Intensity of Use, and Psychological Empowerment

Psychological empowerment refers to an individual’s intrinsic motivation toward, or

psychological reaction to, his or her work environment (Spreitzer 1995; Spreitzer et al. 1997;

Kraimer et al. 1999). It reflects an individual’s feelings about his or her ability to influence his or

her work roles. Spreitzer (1995) argues that an individual’s psychological empowerment is manifest

in a set of four cognitions: meaning, competence, self-determination, and impact. Meaning refers to

the value placed on work goals relative to an individual’s own standards. It reflects a fit between an

individual’s work role requirements and their personal beliefs, values, and behavior (Spreitzer

1996; Carless 2004). Competence relates to an individual’s belief that he or she has the capability to

accomplish specific activities skillfully. It reflects ‘‘an individual’s belief about mastery over

behavior’’ (Carless 2004, 407). Self-determination is similar to Bandura’s (1997) notion of self-

efficacy, and refers to an individual’s belief that he or she has the autonomy to initiate and regulate

their work behaviors. Impact denotes an individual’s beliefs that he or she can influence work

outcomes.

Psychology research suggests that an individual’s perception and interpretation of his or her

work environment is an important determinant of his or her level of psychological empowerment

(Thomas and Velthouse 1990; Spreitzer 1995, 1996; Burney et al. 2009). Spreitzer (1995) contends

that there is a positive relationship between an individual’s work context and psychological

empowerment, and that the direction of the influence in this relationship flows from the work

environment to empowerment. She notes, ‘‘[e]xcept in contexts like organizations undergoing

3 Consistent with Chenhall (2004), we view cost management practices as using cost information to make a rangeof decisions, including cost reductions and modeling, reengineering and improvement, budgeting, andperformance measurement.

94 Mahama and Cheng


metamorphic change or entrepreneurial start-up firms, where individuals can have considerable

leverage, the environment will generally tend to have a more powerful effect on individuals than the

converse’’ (Spreitzer 1996, 486). Support for this direction of influence can also be found in

Martinko and Gardner (1982), Ashforth (1989), and Carless (2004). Overall, the literature suggests

that the work environment influences psychological empowerment.

An important part of the work environment is information. Empirical research suggests that

managers’ access to information is associated with their feelings of empowerment (Drake et al.

2007; Spreitzer 1996 [cited in Hall 2008]). Costing systems are an integral part of the information

systems of organizations and, thus, an important part of the work environment. We contend that

managers’ perceptions of their costing systems as enabling will significantly and positively

influence their feeling of empowerment.

More specifically, we expect that managers’ enabling perceptions of their costing system will

influence each of the four cognitions of psychological empowerment. For instance, individuals are

said to feel a sense of meaning when an activity or event ‘‘counts’’ in their value systems (Hackman

and Oldham 1980; Carless 2004). A costing system that is perceived as enabling or supportive of

work roles will ‘‘count’’ as something meaningful to their task requirements and, thus, influence

their feeling of empowerment. Also, when individuals perceive a costing system to be enabling,

they are more likely to feel confident that it will support them to competently execute work tasks

and, thus, improve the competence dimension of their psychological empowerment (Carless 2004).

We also argue that in considering the enabling property of a costing system, individuals will

evaluate how the system will support them in the decision-making (choice) processes about how

and what task to undertake. A perception that the system will be supportive of the decision-making

processes will, thus, positively influence individuals’ belief in their self-determination (Bandura

1997). Finally, a costing system perceived to be enabling will invoke a feeling of support in an

individual’s quest to influence work outcomes and, thus, affect the impact dimension of

psychological empowerment. This leads to our second hypothesis:

H2: There is a positive direct relationship between individual managers’ enabling perceptions

of their costing systems and individual managers’ psychological empowerment.

Further, we expect that the intensity with which the costing system is used is associated with a

greater sense of psychological empowerment. Using costing systems for cost management involves

modeling the information to account for the relevant decision variables. Such modeling generally

involves classifying, categorizing, and structuring cost information in a manner that enables

managers to establish relationships between problems, decision variables, and task goals. Prior

research suggests that the structuring or modeling of information has both cognitive effects (allows

a complex set of information to be dealt with as one element) and motivational effects (improves

the meaningfulness of the information and related task) (van Merrienboer and Sweller 2005).

Models that emerge from the structuring of information also serve as useful sources for explaining

and justifying decisions and actions (Sweller 1994). Given that the use of costing systems for cost

management involves the structuring of information through cost modeling, we expect that such use

will have effects on psychological empowerment.

More specifically, we argue that the structuring of information that occurs when costing

systems are used for cost management will improve the meaningfulness of a manager’s work role

by allowing the manager to actively engage in the relevant work tasks. This expectation is based on

Paas et al.’s (2004) findings that the structuring of information helps to align (fit) decision variables

with one’s cognitive architecture, and such alignment improves the meaningfulness of work

processes. We, therefore, expect that when costing systems are used intensively for a variety of cost

management decisions, such as budgeting and performance measurement, it will increase

managers’ feelings that there is a fit between the requirements of the work role (to manage costs)



and their behaviors (using costing systems to support relevant cost management initiatives); this, in

turn, heightens the meaningfulness of their job (Spreitzer 1995).

We also expect the intensity with which costing systems are used in cost management will be

positively associated with competence. This is because the structuring/modeling of information in

cost management practices involves establishing problem categories and relating these categories to

possible solutions. Categorizing information helps people to determine how to deal with problems

and, thus, solve problems they would otherwise have difficulty solving (Sweller 1994). As cost

modeling in cost management processes involves categorizing, we expect that the intensity of using

costing systems for cost management will increase the confidence of a manager in his or her

capability to carry out specific tasks successfully. In this respect, the competence dimension of the

manager’s psychological empowerment is expected to be positively associated with the intensity

with which costing systems are used.

Similarly, a manager’s self-determination can be enhanced through an intensive use of a

costing system. By intensively using the costing system for cost management, individual managers

will be able to assess the outcomes of different behaviors or task strategies, and make choices

relating to their work process based on these assessments (Spreitzer et al. 1997). Also, using the

costing system to model the impact of task strategies can provide a manager with an internal

justification to make autonomous decisions, thereby improving the manager’s self-determination(Spreitzer et al. 1997). Furthermore, the intensity of costing systems use is expected to enable

individuals to model the effect of their work behaviors on organizational outcomes, and through

such modeling, the manager is able to provide justification for effects arising from his or her

actions, thus enhancing the impact dimension of his or her psychological empowerment. For

example, using costing systems for process improvement allows managers to see how their work

and their decisions affect organization outcomes. We, therefore, predict that high intensity of

costing systems use will be associated with high levels of psychological empowerment:

H3a: There is a positive direct relationship between the intensity with which individual

managers use a costing system for cost management and individual managers’

psychological empowerment.

In addition, as system usage allows users to experience the features of a system (DeLone and

McLean 1992; Seddon 1997), we propose that the enabling perception of a costing system will

indirectly flow through to psychological empowerment via intensity of use. That is, as a manager’s

enabling perception of a costing system increases the intensity with which the system is used, this

usage will increase the manager’s feeling of psychological empowerment. More formally, we

hypothesize the following:

H3b: There is a positive indirect relationship between individual managers’ enabling

perception of their costing systems and their psychological empowerment through the

intensity with which individual managers use a costing system for cost management.

The Performance Impacts of Costing System Use

Next, we consider the task performance impacts associated with the perception and use of

costing systems. Prior IS research suggests that system use is an important conduit through which

these systems generate both individual and organizational outcomes (Davis 1993; Straub et al.

1995; Seddon 1997). DeLone and McLean (1992, 2003), in particular, argue that without use, there

can be no observable consequences. In extending their model, Seddon (1997) also suggests that use

is not an indicator of information system success in itself, but an important variable that leads to

individual and organizational impacts. Similarly, accounting researchers argue that the success of

96 Mahama and Cheng


accounting information systems is associated with usage (McGowan and Klammer 1997;

McGowan 1998; Chenhall 2004). Although prior research has not directly examined the

relationship between the use of accounting information systems and task performance, based on

these prior findings on the positive relationships between system usage and individual-level

impacts, we predict that the intensity of costing systems use will be associated with task

performance:

H4a: There is a direct positive relationship between the intensity with which individual

managers use a costing system for cost management and their task performance.

Prior research also suggests that an individual’s level of psychological empowerment resulting

from costing system use will be associated with task performance. Existing research on

psychological empowerment indicates that because empowered managers see themselves as

competent and able to influence their work processes in meaningful ways, they are more likely to be

proactive in the execution of their task responsibilities (Spreitzer 1995; Spreitzer et al. 1997).

Spreitzer (1995) argues that psychological empowerment increases concentration, initiative, and

resilience, and these translate into effective task performance. In particular, each of the four

dimensions of psychological empowerment has been related to a number of conditions for effective

task performance.

Meaning is said to be associated with high commitment and the concentration of energy on task

(Kanter 1983; Spreitzer 1995; Liden et al. 2000). Competence is found to be related to greater effort

exertion, high goal expectations, and persistence in challenging situations (Ozer and Bandura 1990;

Gist and Mitchell 1992; Spreitzer 1995). Self-determination has been found to result in learning,

interest in activity, and resilience (Spreitzer 1995). Impact has also been found to be associated with

absence of withdrawal from difficult situations and high task performance (Ashforth 1989; Spreitzer

1995). Consistent with these studies, we predict that the individual manager’s psychological

empowerment will be associated with task performance:

H4b: There is a direct positive relationship between individual managers’ psychological

empowerment and their task performance.

Based on our arguments underlying H3a, that costing system usage increases psychological

empowerment (Spreitzer 1995; Spreitzer et al. 1997), and the literature supporting a relationship

between psychological empowerment and task performance, as discussed earlier, we also predict

that the intensity of use will be indirectly associated with task performance through psychological

empowerment. More formally:

H4c: There is an indirect positive relationship between the intensity with which individual

managers use a costing system for cost management and their task performance, through

psychological empowerment.

RESEARCH METHODS

Sample Selection and Data Collection

The sample for this study was composed of middle-level managers. Our choice of middle-level

managers was based on prior research findings that this level of managers is interesting

theoretically, as their work varies from relatively structured to unstructured and they have access to

more resources and information (Johnson and Frohman 1989; Spreitzer et al. 1997). Given that our

study focuses on the information generated from costing systems, we felt that a sample of middle-

level managers was most appropriate. However, due to the fact that the study focuses on the



intensity of use of costing systems, we excluded managers from accounting and finance

departments from our sample, since they are considered to be designers/preparers of cost

information.

Potential middle-level managers who were targeted for this study were drawn from a cross-

section of companies listed on the Australian Stock Exchange (ASX) and the Business ReviewWeekly (BRW) directory of the top 500 Australian private companies. Consistent with Hoque and

James’ (2000) findings that large companies are those that are likely to have adequate costing

systems, we ensured that the companies sampled in this study were sufficiently large.4 An initial

sample of 500 companies (400 listed and 100 private) was compiled. We then conducted Internet

searches to identify specific middle-level managers in those companies and the relevant postal

addresses. The Internet searches yielded a list of 474 managers out of the 500 companies initially

identified. These middle-level managers were across different functional areas, including

production managers, marketing managers, sales managers, branch/divisional managers, inventory

controllers, supply chain managers, general managers, contracts managers, commercial managers,

logistics managers, publications managers, and operations managers.

A self-administered survey instrument (including a reply paid envelop) was mailed to each of

the 474 managers in the sample. The managers were assured of anonymity and confidentiality, and

were also encouraged to complete and return the instrument within one week of receiving it. Of the

mailed survey instruments, 122 were returned undelivered because the address changed, the address

was incorrect, or the manager no longer worked for the company. In part, this is explained by the

target companies not updating staff information on their website. This left us with 352 potential

respondents. Reminder letters were sent to these 352 potential respondents four weeks after the

initial mail-out to encourage those who had not responded to do so and to thank those who had

already responded. Of the 352 managers, 74 (21 percent) responded. The 74 responses came from

across the range of middle-level managers in our target sample. Out of the 74 responses, three had

missing data and were, therefore, excluded.5 A useable sample of 71 (20.2 percent) was, therefore,

used in our analysis. These 71 respondents had a mean of 10.56 years (standard deviation¼ 7.702

years) working with their respective companies, and a mean of 2.20 years (standard deviation ¼1.879 years) working in their current roles. We compared the means of the first ten responses and

the last ten responses as a proxy for non-response bias, and there were no statistically significant

differences between the two groups across all measures (p-values range from 0.105 to 0.912, one-

tailed).6

Measurement of Variables

The constructs in this study were measured using multi-item scales (see Appendix A for the

measures used for each construct). With the exception of measures for the perception of costing

systems construct, measures for all the constructs were adapted from previous studies. Each

measure was anchored on a seven-point scale. The survey instrument resulting from these measures

was pilot tested on ten accounting academics, and they were generally satisfied with the wording

4 The Australian Securities and Investments Commission (ASIC) guidelines suggest that a corporation shouldhave at least 50 employees to be considered large. The respondents in our study all work for companies withemployee numbers greater than 50.

5 Excluding these three responses was necessary as they had data missing for a substantial number of the surveyquestions (hence, replacing these data using a statistical approach is not meaningful). Nonetheless, we repeatedour PLS analysis by replacing the missing data using the serial mean approach in SPSS. The inclusions of thereplacement data did not have any significant effect on our results.

6 In this study, we used the traditional ‘‘first-last’’ comparison to proxy for non-response bias because we are notable to identify the non-respondents and their characteristics.

98 Mahama and Cheng


and the clarity of the questions. To be sure that we adapted these measures in a way consistent with

the spirit of the original instrument, we performed preliminary factor analysis of the items before

including them in our PLS model. The measures for each construct and the results of the

preliminary factor are discussed below.

Enabling Perception

A six-item scale was developed from Adler and Borys (1996) and Ahrens and Chapman

(2004) to measure the extent to which managers perceived their organizational costing systems as

enabling (see Appendix A).7 Cronbach’s alpha for this scale was 0.81. A principal component

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

loadings ranging from 0.622 to 0.823. This factor explained 51.65 percent of the total variance.

Intensity of Use of Costing Systems for Cost Management

A four-item scale was adapted from Chenhall (2004) to measure the intensity with which

costing systems were used for cost management (see Appendix A). Cronbach’s alpha for this scale

was 0.76. A principal component analysis of the measure revealed a single factor with eigenvalues

exceeding 1 and factor loadings ranging from 0.741 to 0.776. This factor explained 58.28 percent of

the total variance.

Psychological Empowerment

The psychological empowerment construct has four dimensions: meaning, competence, self-

determination, and impact (Spreitzer 1995). Hulland (1999) argues that when a construct is multi-

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

model. Consistent with this, we used separate scales to measure each of the four dimensions of this

construct. Three item-scales, adapted from Spreitzer (1995), were used to measure each dimension.

Cronbach’s alphas for the four dimensions of psychological empowerment range from 0.76 to 0.95.

We conducted a principal components analysis of all the measures and they loaded strongly on the

various dimensions they were meant to represent, with eigenvalues exceeding 1. The four factor

solution accounted for 86.1 percent of the total variance with meaning (with factor loadings from

0.856 to 0.910) contributing 29.0 percent, competence (with factor loadings from 0.613 to 0.833)

accounting for 13.5 percent, self-determination (with factor loadings from 0.814 to 0.889)

contributing 20.5 percent, and impact (with factor loadings from 0.860 to 0.903) accounting for

23.1 percent.

Task Performance

We measured task performance using a six-item scale adapted from Kathuria and Davis (2001).

We used a subjective measure of task performance for two main reasons. First, prior research has

established that accurate estimates of ‘‘objective measures’’ are difficult to obtain in survey research

due to the confidential nature of the data and variance among firms with regard to accounting

procedures (Dess and Robinson 1984). Second, prior accounting research suggests that subjective

measures are not worse than objective measures in survey studies (Abernethy and Stoelwinder

1991). Cronbach’s alpha for this construct was 0.83. A principal component analysis of these

measures yielded a single factor solution with eigenvalues exceeding 1 and factor loadings ranging

from 0.538 to 0.805. This factor accounted for 55.55 percent of the total variance.

7 We modeled this as a single construct because we are interested in the overall perception of enabling, which is incontrast to Chapman and Kihn (2009), who were concerned with the design principles of the system.



Descriptive Statistics and Control Variables

In addition to the primary constructs of interest to this study, we collected information about

the industry groupings of the respondents’ companies, the relative size of their companies, and the

respondents’ experience (with the company and also in their current role). We included these

variables in our statistical model to partial out their effects on our dependent variables. For the

industry grouping factor, construction/property development, manufacturing, and wholesale, retail,

and distribution had the highest representation among the survey respondents. Table 1 shows the

industry groupings of the 71 respondents. To include the industry grouping variable in our

statistical model, we coded construction/property development as 2, and all other industry groups as

1. The data were split between construction/property development and all other industry groups

because construction represented one of the largest groups represented in our sample (with 12

respondents), and we wanted to be sure that the results were not driven by that industry. We

repeated this grouping by replacing the construction/property industry with the other industries with

more than ten respondents and ran separate models for each grouping, and the results were

unchanged.

We used number of employees as proxy for size. The respondents’ years of experience with the

company and their years of experience in their current role were also included as proxies for

experience. The statistical results show that none of these control variables were significantly

related to our dependent variables.8

RESULTS

We analyzed the data gathered using the partial least squares (PLS) approach to structural

equation modeling (SEM). PLS is a component-based modeling approach that seeks to maximize

TABLE 1

Industry Groupings

Industry Frequency Percentage

Construction/Property Development 12 16.9%

Engineering 2 2.8%

Financial Services 9 12.7%

Health Care 3 4.2%

Hospitality 3 4.2%

Logistics/Transportation 3 4.2%

Manufacturing 11 15.5%

Media 3 4.2%

Mining 7 9.9%

Utilities 3 4.2%

Wholesale, Retail, Distribution 12 16.9%

Other 3 4.2%

Total 71 100.0%

8 To improve the clarity of our model presentation, we did not include these control variables in our diagram inFigure 2. The control variables were, however, included in our statistical model, and their relationships with ourprimary variables of interest were not statistically significant.

100 Mahama and Cheng


the variance explained and to minimize error. It is able to examine theory (structural model) and

measures (measurement model) simultaneously. The benefit of using PLS is its ability to handle

small sample size, as it does not make any distributional assumptions about the data. Given our

useable sample of 71, we considered PLS to be the most suitable approach. Chin and Newsted

(1999) argue that the minimum sample size for PLS modeling should be ten times of the part of the

model that requires the largest multiple regression. In our model, the largest multiple regression has

six independent variables, which implies a minimum sample size of 60. Our sample size of 71 is,

therefore, adequate for PLS modeling.

Since PLS makes no distributional assumptions about the data being analyzed, fit indices

generally associated with covariance-based structural equation modeling are not appropriate (Chin

1998; Hulland 1999). Rather, the R2 is considered adequate in assessing the stability of the model.

Also, because of the distribution-free assumption of PLS, parametric-based approaches to

significant testing are not possible. Instead, bootstrapping resampling is used to test the significance

of factor loadings and path coefficients. In this study, we used PLS-Graph software (version 3.0) to

simultaneously estimate the measurement and structural models. The results are discussed below.

Discussion of the Measurement Model

The measurement model estimates the relationship between scale measures (manifest variables)

and the constructs they represent (latent variables). This involves the estimation and evaluation of

the reliability (individual item and composite reliabilities) and validity (convergent and

discriminant validities) of the measurement model. The individual item reliability, composite

reliability, convergent validity, and descriptive statistics of our final measurement model are

summarized in Table 2.

Individual item reliability is assessed by examining the factor loading of each scale item to the

construct to which it relates. While factor loadings of 0.70 and above are recommended in the PLS

literature, Hair et al. (2010) argue for sample size to be taken into account in the interpretation of the

significance of factor loadings. For our sample size of 71, minimum factor loading of 0.65 is

required to obtain a power level of 80 percent and 0.05 significance level. In our measurement

model, all items loaded significantly (p , 0.001, one-tailed) to the respective constructs except

CS2.9 CS2 (with loadings of�0.6102, p . 0.1, one-tailed) was, therefore, excluded from the final

measurement model. Nunnally (1978) suggests that composite reliability should be 0.7 or higher for

a construct to demonstrate adequate composite reliability. The composite reliability for all the

constructs in our model ranges from 0.848 to 0.970, indicating adequate composite reliability.

Convergent validity measures the extent to which scale items for each construct are correlated.

This is assessed using the average variance extracted (AVE). Adequate convergent validity is

demonstrated by AVE measure of 0.5 or more (Chin 1998; Hulland 1999). As reported in Table 2,

the AVEs for all the constructs in our model were more than 0.5, which provides evidence of

adequate convergent validity. Finally, we assessed discriminant validity by comparing the square

roots of the AVEs of the constructs to the correlation coefficient between constructs (Chin 1998;

Hulland 1999). Table 3 reports the correlation coefficients in the off-diagonal, and the square roots

of the AVEs in the diagonal. All the square roots of the AVEs are greater than the correlation

coefficients, and that indicates adequate discriminant validity. The above analyses and evaluations

indicate that our measurement model is satisfactorily reliable and valid.

9 CS2 asks the following question: ‘‘I feel that the costing system is designed with the aim of monitoring how Icomply with company procedures (R).’’ The failure of CS2 (which focuses on monitoring) to load significantlysupports our view that the costing system is an information system.



Common Method Bias

Given that we collected data through self-reported survey, there is the potential for the

responses to be affected by common method bias. While our research design did not have adequate

procedural remedies for minimizing the effects of this bias, we performed three statistical analyses

to assess whether common method bias was likely to be a serious concern. First, we performed

Harman’s single-factor test, in which measures representing the constructs in our model were

entered into an unrotated exploratory factor analysis (Podsakoff et al. 2003). Results from the factor

TABLE 2

Reliability and Convergent Validity (AVE)

Latent Variable Mean SD Loading t-statistics

Enabling Perception of Costing Systems (composite reliability ¼ 0.853; AVE ¼ 0.539)

CS1 4.718 1.416 0.7406 4.3096

CS3 3.113 1.508 0.6910 5.1383

CS4 4.789 1.548 0.8872 9.4090

CS5 3.697 1.443 0.6850 3.9546

CS6 4.296 1.616 0.6441 3.4929

Intensity of Use (composite reliability ¼ 0.848; AVE ¼ 0.583)

CMAN1 5.239 1.497 0.7548 8.7056

CMAN2 4.634 1.684 0.7664 12.1667

CMAN3 5.535 1.350 0.7542 7.9761

CMAN4 5.366 1.417 0.7774 7.9227

Meaning (composite reliability ¼ 0.961; AVE ¼ 0.890)

MEAN1 6.197 0.821 0.9051 24.9228

MEAN2 6.141 0.816 0.9542 56.4020

MEAN3 6.183 0.780 0.9701 87.1064

Competence (composite reliability ¼ 0.884; AVE ¼ 0.723)

COMP1 6.324 0.713 0.9178 27.3853

COMP2 6.183 0.743 0.9416 37.8444

COMP3 5.507 1.054 0.6623 6.7017

Self-Determination (composite reliability ¼ 0.918; AVE ¼ 0.789)

SELF1 5.803 1.023 0.8587 17.8500

SELF2 5.690 1.022 0.8908 25.7556

SELF3 5.704 1.020 0.9155 24.1572

Impact (composite reliability ¼ 0.970; AVE ¼ 0.916)

IMPACT1 6.028 0.985 0.9627 55.3168

IMPACT2 5.746 1.105 0.9352 22.6404

IMPACT3 5.915 1.105 0.9726 67.2285

Task Performance (composite reliability ¼ 0.879; AVE ¼ 0.549)

PERF1 5.451 0.824 0.7611 9.6736

PERF2 5.549 0.968 0.6233 5.4557

PERF3 5.563 0.857 0.8311 19.0270

PERF4 5.113 1.090 0.7340 8.2885

PERF5 5.380 1.100 0.7884 13.9832

PERF6 5.211 1.206 0.6914 7.2616

All item loadings are statistically significant (p , 0.001, one-tailed).



analysis yielded a seven-factor solution (corresponding to the seven constructs in our model), and

the most covariance explained by one factor was 27.85 percent, indicating that common method

bias is unlikely to be a serious concern.

Second, we followed the procedures outlined in Liang et al. (2007) to implement the single

unmeasured method factor design in our PLS model. This approach focuses on partialing out the

error variance at the indicator level in order to eliminate their effects on the structural model (Chin

et al. 2012). We created a common method factor from all the measures for our substantive (main)

constructs in our model and included in our PLS model (see Liang et al. [2007] for detail

procedure). To include the common method factor, we modeled each measure as a single-indicator

latent variable (first-order construct), and the substantive construct as a second-order construct of

their respective single-indicator variables. We then included the common method factor in the PLS

model with links to all the single-indicator variables in the model. The path coefficient between the

single-indicator variables and the constructs (substantive constructs and the common method factor)

are interpreted as the factor loadings. We assessed the results of the PLS test by (1) examining the

statistical significance of factor loadings on both the substantive constructs and the common method

factor, and (2) comparing the percentage variance of each single-indicator variable explained by its

substantive construct and by the method factor.

As shown in Table 4, loadings on the method factor are insignificant (except for two items),

and the percentage of the variances of the indictor variables explained by the substantive construct

(average variance ¼ 0.679) is substantially greater than the percentage variances explained by the

common method factor (average ¼ 0.013), thus providing evidence that common method bias is

unlikely to be a serious concern for this study (Liang et al. 2007). Also, the results of the PLS

structural model (when the common method factor is included) are similar to those of the base

model (see Appendix B).

Third, we implemented the marker variable method suggested by Chin et al. (2012). While

the single unmeasured method factor design focuses on partialing out the effect of common

method bias at the indicator level, Chin et al. (2012) call for controlling for this bias at the level of

the structural model. This approach requires measured indicators for the marker variables to be

included in the PLS model, and these indicators should not be correlated with the indicators

measuring the substantive constructs of the study except for the correlation caused by common

method bias. Our research instrument had some additional indicators that were not measuring the

substantive constructs of our study, so we assessed the correlation between these indicators and

TABLE 3

Discriminant Validity

EnablingPerception

Intensityof Use Meaning Competence

Self-Determi-

nation ImpactTask

Performance

Enabling Perception 0.734

Intensity of Use 0.506 0.764

Meaning 0.126 0.243 0.943

Competence 0.032 0.202 0.609 0.850

Self-Determination �0.023 0.143 0.288 0.398 0.888

Impact 0.106 0.365 0.544 0.427 0.469 0.957

Task Performance 0.117 0.135 0.399 0.512 0.353 0.240 0.741

Diagonal elements are square roots of AVE; off-diagonal elements are correlations between constructs.



those measuring our substantive constructs. Three of the indicators were minimally correlated

with the indicators of our substantive constructs; hence, we used these three indicators to form our

marker variable. We included the marker variable in our PLS model as an independent variable

predicting each of our dependent variables. We then compared the results of the model with the

marker variable (see Appendix C) with our base model to determine whether the path coefficients

that were significant in our base model will become insignificant after the inclusion of the marker

variables. All the paths that were significant in the base model remained after including the

marker variable, thus providing further evidence that common method bias is not a serious

concern for this study.

As the three analyses we performed suggest that common method bias is not likely to be a

serious concern, we proceed to discuss only the PLS structural model results for our base model.

TABLE 4

Common Method Bias Analysis

Construct Indicator

SubstantiveFactor Loading

(R1) R12

MethodFactor Loading

(R2) R22

Enabling Perception of Costing Systems CS1 0.646*** 0.417 0.170 0.029

CS3 0.768*** 0.590 �0.121 0.015

CS4 0.834*** 0.695 0.058 0.003

CS5 0.756*** 0.571 �0.021 0.000

CS6 0.688*** 0.473 �0.099 0.010

Intensity of Use CMAN1 0.763*** 0.581 0.005 0.000

CMAN2 0.768*** 0.591 0.012 0.000

CMAN3 0.719*** 0.517 0.054 0.003

CMAN4 0.803*** 0.644 �0.069 0.005

Meaning MEAN1 0.906*** 0.821 0.003 0.000

MEAN2 0.993*** 0.987 �0.051 0.003

MEAN3 0.931*** 0.867 0.047 0.002

Competence COMP1 0.814*** 0.662 0.132 0.017

COMP2 0.893*** 0.797 0.061 0.004

COMP3 0.875*** 0.766 �0.269 0.072

Self-Determination SELF1 0.849*** 0.721 0.030 0.001

SELF2 0.935*** 0.873 �0.082 0.007

SELF3 0.883*** 0.780 0.051 0.003

Impact IMPACT1 0.881*** 0.777 0.099** 0.010

IMPACT2 0.928*** 0.861 �0.112 0.013

IMPACT3 0.965*** 0.932 0.009 0.000

Task Performance PERF1 0.919*** 0.845 �0.207 0.043

PERF2 0.372*** 0.138 0.265** 0.070

PERF3 0.747*** 0.557 0.093 0.009

PERF4 0.859*** 0.738 �0.134 0.018

PERF5 0.731*** 0.534 0.094 0.009

PERF6 0.773*** 0.598 �0.052 0.003

Average 0.815 0.679 �0.001 0.013

**, *** p , 0.05 and p , 0.01, respectively.



Discussion of the Structural Model

We used the PLS structural model to test the hypothesized relationships between the

constructs in our theoretical model. Table 5 and Figure 2 provide summaries of results for the

structural model. In Table 5, direct relationships are shown in Panel A and indirect relationships

are shown in Panel B.

TABLE 5

PLS Results

Panel A: Path Coefficient, t-statistics (in parentheses), and R2 PLS Results

Latent Variable

Path to:

R2Intensity

of Use Meaning CompetenceSelf-

Determination ImpactTask

Performance

Enabling

Perception

0.506 0.004 �0.093 �0.128 �0.105 —

(3.8268)*** (0.0215) (0.5311) (0.6700) (0.6480)

Intensity

of Use

0.241 0.249 0.207 0.417 �0.051 0.26

(1.6471)* (1.7761)** (1.3306)* (4.0329)*** (0.3787)

Meaning 0.197 0.06

(1.2823)

Competence 0.363 0.05

(2.8757)***

Self-

Determination

0.228 0.03

(1.7287)**

Impact �0.121 0.14

(0.6974)

Task

Performance

0.34

Panel B: Indirect Effects and Bootstrap Confidence Intervals (in parentheses)a

Latent Variable Linkages

Path to:

Meaning CompetenceSelf-


Performance

Enabling

Perception

Intensity

of Use

0.122 0.126 0.105 0.212

(0.0027 �0.2846)*

(0.0066 �0.2703)*

ns (0.0621 �0.3813)**

Intensity

of Use

Competence 0.10

(0.0047 �0.2087)*

Self-

Determination

0.05

ns

*, **, *** p , 0.10, p , 0.05, and p , 0.01, respectively, one-tailed.n ¼ 71a We used the bootstrap confidence level as proposed in Hayes (2009) to determine the significance of indirect effects.



H1 predicted a positive relationship between managers’ enabling perceptions of their costing

systems and the intensity with which costing systems are used for cost management. The PLS

structural path coefficient for this was significant (p , 0.01) and in the hypothesized direction.

This suggests that managers who perceive their organizations’ costing systems to be enabling are

more likely to use the system more intensively in their cost management practices. The highly

significant result and the relatively large coefficient (0.506) are consistent with our expectation

that managers’ enabling perceptions have important impacts on their behaviors. Thus, H1 is

supported.

We also predicted that enabling perception has both a direct impact and an indirect impact (via

intensity of use) on managers’ psychological empowerment (H2 and H3b, respectively). The

structural paths between enabling perception and all four dimensions of psychological

empowerment were not significant, suggesting that there is no direct relationship between enabling

perception and psychological empowerment. Thus, H2 is not supported. However, we found that

enabling perception was significantly indirectly related to one dimension of psychological

empowerment (impact ¼ 0.212, p , 0.05), and marginally indirectly related to two dimensions

(meaning¼ 0.122, p , 0.10, and competence¼ 0.126, p , 0.10) through the intensity with which

these managers use their organizations’ costing systems for cost management (see Panel B, Table

5), providing support for H3b.

The intensity with which costing systems are used for cost management was hypothesized to be

positively related to managers’ psychological empowerment (H3a). The structural path coefficients

between intensity of use and the meaning (0.241) and competence (0.249) dimensions were

FIGURE 2PLS Structural Model with Path Coefficients

*,**,*** p , 0.10, p , 0.05, and p , 0.01, respectively, one-tailed.

n¼ 71.



significant at the 0.05 level. The path coefficient between intensity of use and self-determination(0.207) was only marginally significant at the 0.10 level, while the path leading from intensity of use

to impact (0.417) was significant at the 0.01 level. H3a is, therefore, supported. Taken together, the

insignificant result for H2 and the significant support for H3a and H3b are consistent with DeLone

and McLean’s (1992) argument that usage is an important conduit through which the effect of an

information system can be experienced by individuals. In our study, psychological empowerment

only increases when managers experience their costing systems through intensive usage.

Next, we examine the performance impact of the costing system. Our model predicted that

psychological empowerment is positively associated with task performance (H4b), and that the

intensity of use has an indirect effect on task performance (via psychological empowerment, H4c).

As shown in Table 5 (Panel A) and Figure 2, H4b was partially supported. The paths leading from

competence (0.363, p , 0.01) and self-determination (0.228, p , 0.05) to task performance were

significant. However, the paths from meaning and impact were not significant.

Drawing conclusions on insignificant paths is difficult, but prior findings by Hall (2008)

allow us to speculate on the reasons for the partial support we find for H4b. As noted earlier,

Spreitzer (1995) contends that an individual’s work context influences psychological

empowerment. Our results for the paths linking psychological empowerment to performance

and the results from Hall (2008) suggest that the differences in contexts of use and purposes for

accounting systems may explain which dimension of psychological empowerment will be salient

in explaining performance. While we find that paths leading from competence and self-determination are significantly related to task performance in the context of costing systems use,

Hall (2008) found only the meaning dimension to be significantly related to managerial

performance in the context where comprehensive performance measurement systems (CPMS) are

used. We speculate that in the context of costing systems, the emphasis is on using cost

information for actions and decision making (e.g., using cost information for budgeting); this

could be a possible explanation underlying the strength of the relationship between the

competence and self-determination dimensions of psychological empowerment and task

performance. In contrast, the significant emphasis on providing performance feedback in CPMS

and the stronger link between CPMS and strategy could be reasonable explanations of the

importance of the meaning dimension in explaining managerial performance in Hall (2008).10

Neither Hall (2008) nor our study find significant results for the impact dimension; again, we can

only speculate that the performance effects of the impact dimension may be less salient when it

comes to certain accounting information systems. These speculations are worth examining in an

empirical setting.

While there was no significant direct relationship between the intensity of use of costing

systems for cost management and task performance (H4a), we found a significant indirect

relationship through the competence dimension of psychological empowerment (see Panel B, Table

5). Our results, thus, provide support for H4c, which suggests that intensity of use does not impact

directly on task performance, but rather, it acts through the psychological empowerment

experienced by the managers. Also, competence, which relates to an individual’s beliefs about

his or her capabilities in performing an activity, appears to play a particularly important role in the

model. The empirical results provide partial support for our theoretical model. The empirics suggest

that enabling perceptions strengthen managers’ beliefs about their own abilities via increased usage

of the system and, in doing so, allow managers’ enabling perceptions to ultimately influence their

task performance.

10 We should also note that Hall’s (2008) model focuses on managerial performance rather than taskperformance.



DISCUSSION AND CONCLUSION

In this study, we aim to provide an understanding of how the enabling perceptions managers

hold about their costing systems affect the intensity of use of the system for cost management,

psychological empowerment, and task performance. Our focus is on the end users, rather than the

designers of costing systems. Although managers’ enabling perceptions are likely the result of

specific system design features,11 the direct examination of the role of managerial perception is

important because individuals’ behavior is largely based on how they perceive a system (Burney et

al. 2009). We found that the extent to which a costing system is viewed as enabling is positively

associated with the intensity with which the system is used by managers. The intensity of use, in

turn, is associated with all four dimensions of psychological empowerment experienced by the

managers and, finally, through an increased feeling of competence, indirectly related to task

performance.

Our findings have a number of theoretical implications. First, while prior literature has investigated

the technical factors associated with costing system design choices and the behavioral implications of

the implementation process, our study takes a different perspective and explores the psychological

properties of a costing system associated with its usage. We show that managers’ perceptions of a

costing system as enabling have a significant, but indirect, impact on task performance. In doing so, we

highlight that an understanding of costing system effectiveness can be achieved by considering its

characteristics as perceived by the user managers, in addition to its technical characteristics. Further,

these results also help clarify the ‘‘broad leaps of logic’’ (Chenhall 2003, 132) from positive perceptions

of an enabling costing system to positive task performance implications, via its usage. The implications

of this finding go beyond costing system design. The impact of enabling perception likely extends to

other accounting information systems, such as performance management systems, budgeting and

forecasting systems, and nonfinancial reporting systems.

Second, prior literature has demonstrated that both the intensity of use and psychological

empowerment are important considerations when implementing accounting information systems

(e.g., Chenhall 2004; Drake et al. 2007; Hall 2008). In the present study, we show that enabling

perception of a costing system is an antecedent to these variables. Our findings are consistent with

Delone and McLean’s (1992, 2003) model, which argues for the importance of meaning of an IS

system to the users (semantic level variables) when examining system successes, and how such

perceptions then flow through to impact on individuals’ task performance.

Third, in our model, competence stands out as a critical dimension of employee empowerment.

While enabling perception indirectly affects three out of the four empowerment dimensions via

intensity of use (meaning, competence, and impact), the competence dimension is the main

dimension through which the intensity of use affects task performance. Our result, therefore,

indicates the importance of studying the concept of employee empowerment as four distinctive, but

related, dimensions.

Our study also has practical implications. Our findings demonstrate that building managers’

enabling perceptions is important given its effects on costing system usage, psychological

empowerment, and task performance. Although the enabling perception relates to how a system is

used rather than the technical characteristic of a system (Ahrens and Chapman 2004), there are a

number of ways in which designers of costing systems can influence managers’ enabling

perceptions. Prior literature has suggested that designers of an accounting system can generate an

11 Although managers’ perception of a system as enabling is likely the result of the design principles behind thecosting system, the current study focuses on managers’ perceptions. Consistent with prior literature, we arguethat, ultimately, it is how the managers view their work environment that is critical to organizationalconsequences (Spreitzer 1996; Leigh et al. 1988).



enabling perception by ‘‘unlocking’’ the system; for example, through communicating the logic of

the costing system to the user managers, or incorporating functions that allow user managers to

update the formula, information, and measures contained in the costing system as the operation

environment changes (Ahrens and Chapman 2004). Designers of costing systems can also enhance

the enabling perception of a costing system by improving the internal transparency of the work

processes, allowing managers broader access and linkages to other departments’ systems, and

allowing users more flexibility with respect to customizing their reports from the costing systems

(Ahrens and Chapman 2004).

Our findings should be interpreted in the light of four limitations. First, while our response rate

and sample size are considered acceptable, our study would have benefited from a stronger response

from the intended survey participants. While the sample size is adequate for testing our hypotheses, it

limits the extent to which our results can be generalized to the larger population. Second, the research

design would have benefited from an explicit definition of what we meant by costing systems and to

determine how relevant these systems were to the roles performed by the respondents. While this can

be a potential concern, the additional comments provided as part of responses to our survey questions

provide us with confidence that the respondents’ understanding of costing systems is consistent with

what we intended, and that they encounter these systems in their specific roles. Third, the present

study focuses on examining the impact of managers’ enabling perceptions of the costing system,

rather than its technical aspects. However, an interaction may possibly exist between managers’

perceptions about the costing system and its technical design. For example, managers may be more

willing to use a costing system that is seen as enabling if it is technically sound (i.e., provides more

accurate and relevant information in a timely manner) than if the costing system is technically

deficient. Nonetheless, past literature suggests that, ultimately, the subjective perception of the users

of information systems, such as costing systems, may impact behavior (Spreitzer 1996; Burney et al.

2009). Finally, our paper would have benefited from using more than three item-measures for each of

the dimensions of psychological empowerment. While we do not consider this as a serious concern

(given that our ultimate dependent variable and almost half of the constructs in our model have a

sufficient number of items), we believe that future research will benefit from a sufficiently large

number of items to measure each of the dimensions of the construct.

Further research can extend our study in a number of ways. First, as discussed earlier, we have

focused our attention on managers’ perceptions of the costing system, rather than its technical

functions. Future research can explore the interactive nature of the technical, semantic, and

influence levels of costing system designs. For example, researchers can investigate whether the

effect of the enabling perception may be moderated by the sophistication of the costing system. In

addition, future research can also extend our proposed framework to examine other individual-level

and organizational-level impact variables.

REFERENCES

Abernethy, M. A., and J. U. Stoelwinder. 1991. Budget use, task uncertainty, system goal orientation and

subunit performance: A test of the ‘‘fit’’ hypothesis in not-for-profit hospitals. Accounting,Organizations and Society 16 (2): 105–120.

Abernethy, M. A., A. M. Lillis, P. Brownell, and P. A. Carter. 2001. Product diversity and costing system

design choice: Field study evidence. Management Accounting Research 12 (3): 261–279.

Adler, P. S., and B. Borys. 1996. Two types of bureaucracy: Enabling and coercive. Administrative ScienceQuarterly 41 (1): 61–89.

Agarwal, R., and J. Prasad. 1998. The antecedents and consequents of user perceptions in information

technology adoption. Decision Support Systems 22 (1): 15–29.



Ahrens, T., and C. S. Chapman. 2004. Accounting for flexibility and efficiency: A field study of management

control systems in a restaurant chain. Contemporary Accounting Research 21 (2): 275–301.

Al-Omiri, M., and C. Drury. 2007. A survey of factors influencing the choice of product costing systems in

U.K. organizations. Management Accounting Research 18 (4): 399–424.

Anderson, S. W., and S. M. Young. 1999. The impact of contextual and process factors on the evaluation of

activity-based costing systems. Accounting, Organizations and Society 24 (7): 525–559.

Anderson, S. W., J. W. Hesford, and S. M. Young. 2002. Factors influencing the performance of activity-

based costing teams: A field study of ABC model development time in the automobile industry.

Accounting, Organizations and Society 27 (3): 195–211.

Ashforth, B. E. 1989. The experience of powerlessness in organizations. Organizational Behavior andHuman Decision Processes 43 (2): 207–242.

Bandura, A. 1997. Self-Efficacy: The Exercise of Control. New York, NY: W. H. Freeman and Company.

Bhimani, A. (ed.) 1996. Management Accounting: European Perspectives. Oxford, U.K.: Oxford

University Press.

Bromwich, M., and C. Hong. 1999. Activity-based costing systems and incremental costs. ManagementAccounting Research 3: 159–168.

Burney, L. L., C. A. Henle, and S. A. Widener. 2009. A path model examining the relations among strategic

performance system characteristics, organizational justice, and extra- and in-role performance.

Accounting, Organizations and Society 34 (3-4): 305–321.

Carless, S. 2004. Does psychological empowerment mediate the relationship between psychological climate

and job satisfaction? Journal of Business and Psychology 18 (4): 405–425.

Chapman, C. S., and L. A. Kihn. 2009. Information system integration, enabling control and performance.

Accounting, Organizations and Society 34 (2): 151–169.

Chenhall, R. H. 2003. Management control systems design within its organizational context: Findings from

contingency-based research and directions for the future. Accounting, Organizations and Society 28

(2-3): 127–168.

Chenhall, R. H. 2004. The role of cognitive and affective conflict in early implementation of activity-based

cost management. Behavioral Research in Accounting 16: 19–44.

Chin, W. W. 1998. The partial least squares approach to structural equation modeling. In Modern Methodsfor Business Research, edited by Marcoulides, G. A., 295�336. Hillsdale, NJ: Lawrence Erlbaum

Associates.

Chin, W. W., and P. R. Newsted. 1999. Structural equation modeling analysis with small samples using

partial least squares. In Statistical Strategies for Small Sample Research, edited by Hoyle, R. H.,

307–341. Thousand Oaks, CA: Sage Publications.

Chin, W. W., J. S. Thatcher, and T. W. Ryan. 2012. Assessing common method bias: Problems with the

ULMC technique. MIS Quarterly 36 (3): 1003–1019.

Christensen, J., and J. S. Demski. 1997. Product costing in the presence of endogenous subcost functions.

Review of Accounting Studies 2 (1): 65–87.

Davis, F. D. 1993. User acceptance of information technology: System characteristics, user perceptions and

behavioral impacts. International Journal of Man-Machine Studies 38 (3): 475–487.

DeLone, W. H., and E. R. McLean. 1992. Information systems success: The quest for the dependent

variable. Information Systems Research 3 (1): 60–95.

DeLone, W. H., and E. R. McLean. 2003. The DeLone and McLean model of information systems success:

A ten-year update. Journal of Management Information Systems 19 (4): 9–30.

Dess, G. G., and R. B. Robinson, Jr. 1984. Measuring organizational performance in the absence of

objective measures: The case of the privately held firm and conglomerate business unit. StrategicManagement Journal 5 (3): 265–273.

Drake, A., J. Wong, and S. Salter. 2007. Empowerment, motivation, and performance: Examining the

impact of feedback and incentives on non-management employees. Behavioral Research inAccounting 19: 71–89.

Foster, G., and D. W. Swenson. 1997. Measuring the success of activity-based cost management and its

determinants. Journal of Management Accounting Research 9: 109–141.

Gist, M., and T. N. Mitchell. 1992. Self-efficacy: A theoretical analysis of its determinants and malleability.

Academy of Management Review 17 (2): 183–211.



Hackman, J. R., and G. R. Oldham. 1980. Work Redesign. Reading, MA: Addison-Wesley.

Hair, J. F., W. C. Black, B. J. Babin, and R. E. Anderson. 2010. Multivariate Data Analysis: A GlobalPerspective. 7th Edition. Upper Saddle River, NJ: Pearson.

Hall, M. 2008. The effect of comprehensive performance measurement systems on role clarity,

psychological empowerment and managerial performance. Accounting, Organizations and Society33 (2-3): 141–163.

Hansen, D. R., M. M. Mowen, and L. Guan. 2006. Cost Management: Accounting and Control. Mason,

OH: South-Western.

Hayes, A. F. 2009. Beyond Baron and Kenny: Statistical mediation analysis in the new millennium.

Communication Monographs 76 (4): 408–420.

Hoque, Z., and W. James. 2000. Linking balanced scorecard measures to size and market factors: Impact on

organizational performance. Journal of Management Accounting Research 12: 1–17.

Hulland, J. 1999. Use of partial least squares (PLS) in strategic management research: A review of four

recent studies. Strategic Management Journal 20 (2): 195–204.

Innes, J., F. Mitchell, and D. Sinclair. 2000. Activity-based costing in the U.K.’s largest companies: A

comparison of 1994 and 1999 survey results. Management Accounting Research 11 (3): 349–

362.

Ittner, C. D., W. N. Lanen, and D. F. Larcker. 2002. The association between activity-based costing and

manufacturing performance. Journal of Accounting Research 40 (3): 711–726.

Jarrar, N. S., M. Smith, and C. Dolley. 2007. Perceptions of preparers and users of accounting change: A

case study in an Australian university. Managerial Auditing Journal 22 (1): 80–94.

Johnson, L. W., and A. L. Frohman. 1989. Identifying and closing the gap in the middle of organizations.

Academy of Management Executive 3 (2): 107–114.

Kanter, R. M. 1983. The Change Masters. New York, NY: Simon and Schuster.

Kathuria, R., and E. B. Davis. 2001. Quality and work force management practices: The managerial

performance implication. Production and Operations Management 10 (4): 460–477.

Knill, D., and W. Richards. 1996. Perception as Bayesian Inference. New York, NY: Cambridge University

Press.

Kraimer, M. L., S. E. Seibert, and R. C. Liden. 1999. Psychological empowerment as a multidimensional

construct: A test of construct validity. Educational and Psychological Measurement 59: 127–142.

Lau, C. M., and A. Moser. 2008. Performance measures: The role of procedural fairness. BehavioralResearch in Accounting 20 (2): 55–71.

Leigh, J. H., G. H. Lucas, Jr., and R. W. Woodman. 1988. Effects of perceived organizational factors on

role stress-job attitude relationships. Journal of Management 14 (1): 41–58.

Liang, H., N. Saraf, Q. Hu, and Y. Xue. 2007. Assimilation of enterprise systems: The effect of institutional

pressures and the mediating role of top management. MIS Quarterly 31: 59–87.

Liden, R. C., S. J. Wayne, and R. T. Sparrowe. 2000. An examination of the mediating role of psychological

empowerment on the relations between the job, interpersonal relationships, and work outcomes.

Journal of Applied Psychology 85 (3): 407–416.

Marginson, D., and B. Bui. 2009. Examining the human cost of multiple role expectations. BehavioralResearch in Accounting 21 (1): 59–81.

Martinko, M. J., and W. L. Gardner. 1982. Learned helplessness: An alternative explanation of performance

deficits. Academy of Management Review 7 (2): 195–204.

McGowan, A. S. 1998. Perceived benefits of ABCM implementation. Accounting Horizons 12 (1): 31–50.

McGowan, A. S., and T. P. Klammer. 1997. Satisfaction with activity-based cost management

implementation. Journal of Management Accounting Research 9: 217–237.

Naranjo-Gil, D., and F. Hartman. 2006. How top management teams use management accounting systems

to implement strategy. Journal of Management Accounting Research 18: 21–53.

Nunnally, J. C. 1978. Psychometric Theory. New York, NY: McGraw-Hill.

Ozer, E. M., and A. Bandura. 1990. Mechanisms governing empowerment effects: A self-efficacy analysis.

Journal of Personality and Social Psychology 58 (3): 472–486.

Paas, F., A. Renkl, and J. Sweller. 2004. Cognitive load theory: Instructional implications of the interaction

between information structures and cognitive architecture. Instructional Science 32 (1-2): 1–8.



Pierce, B., and T. O’Dea. 2003. Management accounting information and the needs of managers:

Perceptions of managers and accountants compared. The British Accounting Review 35: 257–290.

Pizzini, M. 2006. The relation between cost-system design, managers’ evaluations of the relevance and

usefulness of cost data, and financial performance: An empirical study of U.S. hospitals. Accounting,Organizations and Society 31: 179–210.

Podsakoff, P. M., J. Y. Lee, and N. P. Podsakoff. 2003. Common method biases in behavioral research: A

critical review of the literature and recommended remedies. Journal of Applied Psychology 88 (5):

879–903.

Schoute, M. 2009. The relationship between cost system complexity, purposes of use, and cost system

effectiveness. The British Accounting Review 41: 208–226.

Seddon, P. B. 1997. A respecification and extension of the DeLone and McLean model of IS success.

Information Systems Research 8 (3): 240–253.

Shields, M. D. 1995. An empirical analysis of firms’ implementation experiences with activity-based

costing. Journal of Management Accounting Research 7: 148–166.

Shields, M. D., and S. M. Young. 1989. A behavioral model for implementing cost management systems.

Journal of Cost Management (Winter): 17–27.

Spreitzer, G. M. 1995. Psychological empowerment in the workplace: Dimensions, measurement, and

validation. The Academy of Management Journal 38 (5): 1442–1465.

Spreitzer, G. M. 1996. Social structural characteristics of psychological empowerment. Academy ofManagement Journal 39 (2): 483–504.

Spreitzer, G. M., M. A. Kizilos, and S. W. Nason. 1997. A dimensional analysis of the relationship between

psychological empowerment and effectiveness, satisfaction and strain. Journal of Management 23

(5): 679–704.

Straub, D., M. Limayem, and E. Karahanna-Evaristo. 1995. Measuring system usage: Implications for IS

theory testing. Management Science 41 (8): 1328–1342.

Sweller, J. 1994. Cognitive load theory, learning difficulty, and instructional design. Learning andInstruction 4: 295–312.

Teng, J. T., and K. J. Calhoun. 1996. Organizational computing as facilitator of operational and managerial

decision making: An exploratory study of managers’ perceptions. Decision Science 27 (4): 673–710.

Thomas, K. W., and B. A. Velthouse. 1990. Cognitive elements of empowerment: An ‘‘interpretive’’ model

of intrinsic motivation. Academy of Management Review 15 (4): 666–681.

Van Merrienboer, J. J. G., and J. Sweller. 2005. Cognitive load theory and complex learning: Recent

developments and future directions. Educational Psychology Review 17 (2): 147–177.

Warren, W. H. 2006. The dynamics of perception and action. Psychological Review 113 (2): 358–389.

Wouters, M., and C. Wilderon. 2008. Developing performance-measurement systems as enabling

formalization: A longitudinal field study of a logistics department. Accounting, Organizations andSociety 33: 488–516.

APPENDIX A

MEASURES FOR SUBSTANTIVE CONSTRUCTS

1. Enabling Perception of Costing Systems

The following scale items ranged from 1 (strongly disagree) to 7 (strongly agree):

CS1: I feel that the costing system is designed with the aim of enabling me to work more efficiently.

CS2: I feel that the costing system is designed with the aim of monitoring how I comply with

company procedures (R).

CS3: I feel that the costing system is designed with the aim of facilitating how I deal with

unanticipated work problems.

CS4: I feel that the costing system is designed with the aim of improving the visibility I have over

the work I am responsible for.



CS5: I feel that the costing system is designed with the aim of enhancing the flexibility with which I

perform my job.

CS6: I feel that the costing system is designed with the aim of allowing me to understand the

broader processes within my company.

2. Intensity of Use of Costing Systems for Cost Management

To what extent do you use your organization’s costing system to facilitate the following (the

following scale items ranged from 1 [lesser extent] to 7 [greater extent]):

CMAN1: Cost reduction and modeling

CMAN2: Reengineering and improvement

CMAN3: Budgeting

CMAN4: Performance measurement

3. Psychological Empowerment

The following scale items ranged from 1 (strongly disagree) to 7 (strongly agree):

3.1. Meaning

MEAN1: The work I do is very important to me.

MEAN2: My job activities are personally meaningful to me.

MEAN3: The work I do is meaningful to me.

3.2. Competence

COMP1: I am confident about my ability to do my job.

COMP2: I am self-assured about my capabilities to perform my work activities.

COMP3: I have mastered the skills necessary for my job.

3.3. Self-Determination

SELF1: I have significant autonomy in determining how I do my job.

SELF2: I can decide on my own how to go about doing my work.

SELF3: I have considerable independence and freedom in how I do my job.

3.4. Impact

IMPACT1: I have a large impact on what happens in my department.

IMPACT2: I have a great deal of control over what happens in my department.

IMPACT3: I have significant influence over what happens in my department.

4. Task Performance

How satisfied are you with your performance on the following (the following scale items

ranged from 1 [not satisfied] to 7 [very satisfied]):

PERF1: Accuracy of work performed

PERF2: Quantity of work performed

PERF3: Quality of work performed

PERF4: Operating efficiency



PERF5: Customer satisfaction

PERF6: Timeliness in meeting delivery schedules

APPENDIX B

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

Latent Variable

Path to:Path Coefficient and t-statistics (in parentheses)

R2Intensity of

Use Meaning CompetenceSelf-


Performance

Enabling

perception

0.480 0.0276 �0.108 �0.125 �0.122

(4.5364)*** (0.1979) (0.8235) (0.7468) (1.0065)

Intensity

of Use

0.256 0.251 0.203 0.419 0.044 0.23

(1.8553)** (1.8524)** (1.3748)* (4.0444)*** (0.3529)

Meaning 0.216 0.06

(1.2467)


(2.6003)***

Self-

Determination

0.232 0.03

(1.8128)**

Impact �0.127 0.14

(0.7800)

Task

Performance

0.32

*, **, *** p , 0.1, p , 0.05, and p , 0.01, respectively.

APPENDIX C

PLS Structural Model (with Marker Variable Included) Results

Latent Variable

Path to:Path Coefficient and t-statistics (in parentheses)

R2Intensity

of Use Meaning CompetenceSelf-


Performance

Enabling

Perception

0.429 0.017 �0.082 �0.144 �0.111 —

(3.0592)*** (0.0905) (0.4942) (0.7604) (0.7294)

Intensity

of Use

0.282 0.264 0.172 0.373 0.036 0.27

(1.9072)** (1.7665)** (1.3848)* (3.3803)*** (0.2768)

Meaning 0.259 0.06

(1.2670)


(3.0323)***

Self-

Determination

0.222 0.08

(1.6513)**

Impact �0.174 0.17

(0.9915)

Task

Performance

— 0.34

*, **, *** p , 0.1, p , 0.05, and p , 0.01, respectively.



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