Assignment: MBA Management accounting article summary

European Accounting Review, 2018 Vol. 27, No. 4, 747–770, https://doi.org/10.1080/09638180.2017.1375417

The Impact of Managers’ Participation in Costing System Design on Their Perceived Contributions to Process Improvement

SOPHIE HOOZÉE ∗ and QUANG-HUY NGO∗∗

∗Department of Accounting, Corporate Finance and Taxation, Ghent University, Ghent, Belgium; ∗∗Department of Accounting, Can Tho Technical Economic College, Can Tho, Vietnam

(Received: January 2016; accepted: August 2017)

Abstract The aim of this paper is to investigate the impact of managers’ participation in costing sys- tem design on their perceived contributions to process improvement. Drawing on the literature on business process management, participative decision-making and self-determination theory, we propose that partici- pation in costing system design fosters managers’ perceived contributions to process improvement through their autonomous motivation for cost management and their perceived usefulness of cost information. Ques- tionnaire data obtained from 170 Belgian managers were used to test the proposed model. The results suggest that participation in costing system design increases managers’ autonomous motivation for cost management and enhances their perceived usefulness of cost information. Managers’ perceived usefulness of cost information is, in turn, positively associated with their perceived contributions to process improve- ment. The effect of managers’ autonomous motivation for cost management on their perceived contributions to process improvement is, however, not significant. Taken together, our findings imply that contribu- tions to process improvement mainly emerge through informational mechanisms rather than motivational mechanisms triggered by the participation process.

1. Introduction

Firms often use costing systems to increase their financial performance by improving their business processes (e.g. Banker, Bardhan, & Chen, 2008). In particular, costing systems may reveal sources of inefficiency and ineffectiveness of business processes by providing detailed information about the consumption of resources by each of the firm’s activities (e.g. Innes & Mitchell, 1990; Turney, 1991). Prior research has demonstrated that participation in costing sys- tem design may help to identify such process improvements (e.g. Hoozée & Bruggeman, 2010). It is, however, unclear how participation can actually result in these beneficial outcomes.

The purpose of this study is to disentangle the mechanisms through which managers’ participa- tion in costing system design may foster their perceived contributions to process improvement. Understanding these mechanisms is important as they can provide insight into how firms can leverage the benefits offered by participation into process improvement. Although the relation

Correspondence Address: Sophie Hoozée, Department of Accounting, Corporate Finance and Taxation, Ghent Univer- sity, Sint-Pietersplein 7, 9000 Ghent, Belgium. Email: [email protected]

Paper accepted by Sally Widener.

© 2017 European Accounting Association

748 S. Hoozée and Q.-H. Ngo

between participation and desired outcomes may be influenced by many intervening variables, in line with the literature on participative decision-making (e.g. Latham, Winters, & Locke, 1994), we focus on motivational and informational factors. More specifically, building on self- determination theory and research on business process management, we predict the impact of participation in costing system design on perceived contributions to process improvement in terms of its role as an enabler of autonomous motivation for cost management and perceived usefulness of cost information.

Using survey data from 170 Belgian managers, we find that although managers’ autonomous motivation for cost management increases as a result of their participation in the costing system design process, this higher autonomous motivation does not stimulate their perceived contri- butions to process improvement. Regarding the informational path, we do find that perceived usefulness of cost information positively mediates the impact of managers’ participation in costing system design on their perceived contributions to process improvement.

Compared with prior research, this study provides three unique contributions. First, by unrav- eling the mechanisms that enable participation to result in process improvements, this study complements prior work on the potential of cost information to improve business processes (e.g. Hoozée & Bruggeman, 2010; Innes & Mitchell, 1990), as well as studies that have investi- gated the determinants of business process re-engineering success (e.g. Terziovski, Fitzpatrick, & O’Neill, 2003). Second, our study contributes to the literature on participative decision-making by highlighting the importance of informational mechanisms over motivational mechanisms in explaining why participation could lead to process improvements. This is in line with Latham et al. (1994), who argued that studies on participation, instead of focusing on motivational mechanisms, should be redirected to investigate informational mechanisms because the effi- cacy of participation as an organizational process lies not only in its potential to promote motivation or commitment, but also in its ability to facilitate information exchange and knowl- edge transfer. Third, by showing that a participative system design strategy could actually be used to enhance motivation, we clarify equivocal results of previous research on the link between budgetary participation and motivation (cf. Mia, 1989). In particular, according to Brownell and McInnes (1986), the inconsistent results in budgeting studies investigating the participation-motivation relationship may be due to differences in the approaches used to mea- sure motivation. We addressed their concern by using well-developed scales from studies on self-determination theory to measure autonomous motivation. As such, we also contribute to the growing body of accounting evidence on the effects of autonomous motivation, for example, regarding subordinates’ work effort induced by subjective performance evaluation (Kunz, 2015), managers’ creation of budget slack (De Baerdemaeker & Bruggeman, 2015) and the impact of different uses of performance metrics on employee job performance (Groen, Wouters, & Wilderom, 2017).

The remainder of this paper is structured as follows. The next section presents the theoretical background and hypotheses development. Section 3 describes the research method. Section 4 shows the results of this study. The last section concludes, discusses the limitations and offers suggestions for future research.

2. Literature Review and Hypotheses Development

Drawing on the literature on business process management, we first introduce the definition of a business process and the role of costing systems in process improvement. Second, using the literature on participative decision-making and self-determination theory, we identify the charac- teristics of participation in costing system design and explain the motivational and informational

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mechanisms through which managers’ participation may stimulate their perceived contributions to process improvement.

2.1. The Use of Costing Systems for Process Improvements

Davenport and Short (1990, p. 12) defined the concept of business processes as ‘a set of logically related tasks performed to achieve a defined business outcome’. Hammer and Champy (1993, p. 35) later emphasized the client-centred aspects of a business process: ‘a collection of activities that takes one or more kinds of input and creates an output that is of value to the customer’. In the context of the present study, we conceive of a business process as an umbrella term that combines various operational work processes, such as product/service delivery, sales order processing, budget preparation, new product/service development, etc.

In both manufacturing and service environments, business processes may be redesigned by breaking them down in activities or work processes in order to reveal sources of inefficiency and ineffectiveness (Davenport & Short, 1990). As such, business process redesign focuses on creating and delivering value by rethinking, restructuring and streamlining work processes (Talwar, 1993).

To detect inefficient and ineffective parts of work processes, information systems are required (e.g. Davenport & Short, 1990). In this respect, costing systems, as a particular example of infor- mation systems, serve four important purposes in that costing systems that (1) provide a high level of cost information detail, (2) have a high ability to classify costs according to their behav- ior, (3) frequently disseminate cost information throughout the organization and (4) calculate various types of variances, can be expected to produce more useful and relevant cost informa- tion for managerial decision-making (Pizzini, 2006). As such, costing systems are equipped to help users identify process improvements. For instance, a costing system such as activity-based costing may reveal opportunities for process improvement by providing detailed insights into the consumption of resources by each activity of a firm (e.g. Holton, 2007; Innes & Mitchell, 1990; Turney, 1991).

To foster users’ acceptance of an information system and to make sure that it meets users’ information requirements, Tarafdar, Tu, and Ragu-Nathan (2010) suggest a participative strategy. In a similar vein, the beneficial outcomes of user participation have been investigated in the con- text of costing systems (e.g. Bhimani & Pigott, 1992; Hoozée & Bruggeman, 2010; McGowan & Klammer, 1997).

2.2. Participation in Costing System Design

Research on participative decision-making assumes that the relationship between participa- tion and its desired outcomes is driven by two mechanisms: motivational and informational (cognitive) mechanisms (Locke & Schweiger, 1979). First, from a motivational point of view, participation enables greater ‘trust, greater control of the work, more ego involvement in the job, increased identification with the organization, more group support (if it is group participa- tion) and, most important, the setting of higher goals and/or increased goal acceptance’ (Locke, Schweiger, & Latham, 1986, p. 69). Performance can then be improved through lower resis- tance to change and higher acceptance of difficult targets (Locke & Schweiger, 1979). Second, from an informational perspective, participation is viewed as a conduit for upward information and knowledge exchange, which allows better communication and understanding of job require- ments as well as decision-making processes. Hence, informational factors are important for the enhancement of information flows between participants (Locke & Schweiger, 1979).

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Figure 1. Research model.

In line with this reasoning, we propose that the relationship between managers’ participation in costing system design and their perceived contributions to process improvement is driven by both motivational and informational effects (see Figure 1).

2.2.1. Motivational Effects Traditionally, motivation has been referred to as a concept varying in size rather than quality (Gagné & Deci, 2005). Motivation theorists, however, emphasize the importance of distinguish- ing between several types of motivation, because each type of motivation can lead to different outcomes (Ryan & Deci, 2000; Vansteenkiste, Lens, & Deci, 2006).

To provide insight into different motivation types, Deci and Ryan (1985) proposed self- determination theory, which distinguishes between autonomous and controlled motivation. Whereas autonomous motivation is characterized by underlying feelings of freedom and voli- tion, controlled motivation is characterized by an overarching feeling of pressure (Vansteenkiste, Sierens, Soenens, Luyckx, & Lens, 2009). Importantly, psychology research has repeatedly demonstrated that autonomous motivation is associated with positive outcomes, whereas con- trolled motivation is associated with negative outcomes (e.g. Ryan & Deci, 2000; Vansteenkiste et al., 2006). In addition, the beneficial outcomes of autonomous motivation are also supported by recent management accounting research on participation (De Baerdemaeker & Bruggeman, 2015; Groen et al., 2017; Wong-On-Wing, Guo, & Lui, 2010).1

To understand the difference between autonomous and controlled motivation, the distinction between intrinsic and extrinsic motivation is important. While intrinsic motivation refers to doing an activity for its own sake because it is interesting and enjoyable, extrinsic motivation refers to doing something in order to attain some separable outcome (Ryan & Deci, 2000). According to self-determination theory, extrinsic motivation may vary in the degree to which it is controlled or autonomous based on the degree of internalization (e.g. Gagné & Deci, 2005; Ryan & Deci, 2000). Internalization is defined as ‘people taking in values, attitudes, or regulatory structures, such that the external regulation of a behavior is transformed into an internal regulation and thus no longer requires the presence of an external contingency’ (Gagné & Deci, 2005, p. 334).

Controlled motivation consists of the two least autonomous forms of extrinsic motivation: external regulation and introjected regulation (e.g. Williams, Grow, Freedman, Ryan, & Deci, 1996). External regulation (being interpersonally controlled) is not internalized at all because

1Because in this study we focus on the beneficial outcomes of participation in costing system design, we only hypothesize its effect on autonomous motivation for cost management. We do not develop a hypothesis for controlled motivation, but will control for it in our empirical model instead (see Section 3.3 and Figure 2).

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the person’s behaviors are initiated and maintained by external contingencies such as rewards or punishments (Gagné & Deci, 2005; Ryan & Deci, 2000). This is the classic type of extrin- sic motivation and a prototype of controlled motivation (Gagné & Deci, 2005). Next, introjected regulation (being intrapersonally controlled) implies that people perform activities ‘to avoid guilt or anxiety, or attain ego enhancement such as pride’ (Ryan & Deci, 2000, p. 72). This type of motivation is also a form of controlled motivation because people feel pressured to do some- thing due to internal contingencies that link feelings of self-esteem and social acceptance to the enactment of specific behaviors (Assor, Roth, & Deci, 2004).

Autonomous motivation combines identified regulation and intrinsic motivation (e.g. Gagné et al., 2015).2 Although still extrinsic in nature, identified regulation is considered as autonomous motivation because it is volitional in that it results from identifying with the value of an activ- ity, such that regulation of the activity is accepted or owned as personally important (Ryan & Deci, 2000; Vansteenkiste et al., 2006). Identification differs from intrinsic motivation in that the activity is not done out of inherent satisfaction, but for the instrumental value it represents (Gagné et al., 2015). The last and most autonomous form of motivation is intrinsic motivation, which, as already mentioned, motivates people to be involved in an activity for its own sake. It is characterized by feelings of ‘enthusiasm, spontaneity, excitement, intense concentration, and joy’ (Roth, Assor, Kanat-Maymon, & Kaplan, 2007, p. 762).

Self-determination theory assumes that three basic psychological needs drive the motivational mechanisms that energize people’s behavior (Deci & Ryan, 2000). The satisfaction of these needs is an essential nutriment for individuals’ autonomous motivation (Vansteenkiste, Niemiec, & Soenens, 2010). The three needs are the needs for autonomy, relatedness and competence. The first one, the need for autonomy, represents individuals’ inherent desire to experience a sense of volition and psychological freedom (deCharms, 1968). Second, the need for relatedness, is an individual’s inherent propensity to feel connected to others, to be a member of a group, to love and care and be loved and cared for (Baumeister & Leary, 1995). Third, the need for competence, is defined as the desire to feel effective in one’s interactions with the social and physical environment (Deci, 1975).

Prior studies indicate that participation in decision-making processes may enable the satisfac- tion of the three basic psychological needs, which, in turn, fosters autonomous motivation. First, participation in decision-making can provide managers with a sense of willpower and choice (Deci, Connell, & Ryan, 1989), which satisfies their need for autonomy. Second, participation in decision-making enables employees to receive positive feedback (Deci, Koestner, & Ryan, 1999), which stimulates an atmosphere of caring for and being recognized by others and thereby triggers the sense of relatedness. Third, participation may enhance self-efficacy, which has been shown to be related to the feeling of competence (Van den Broeck, Vansteenkiste, De Witte, Soenens, & Lens, 2010).

In the context of budget participation, De Baerdemaeker and Bruggeman (2015) found that participation can engender autonomous budget motivation. In a similar vein, we argue that par- ticipation in costing system design may foster autonomous motivation for cost management. Through participation, managers are more likely to choose how to define the important compo- nents of the costing system (e.g. cost allocation bases, cost pools, frequency of reporting) used in their departments. Hence, the more managers are involved in the design of a costing system, the greater their perceived sense of autonomy. Participation in costing system design creates

2In line with recent psychology research (Gagné et al., 2015), we do not include integrated regulation, which is the most autonomous form of extrinsic motivation, as it has typically been very difficult to psychometrically distinguish integration from identification (Vallerand et al., 1992).

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opportunities for cost-related discussions among managers from different functions in an orga- nization. Positive interactions with colleagues in discussions about the factors influencing costs could trigger managers’ sense of relatedness because they feed the sense of group belongingness. Furthermore, when managers are invited to participate in a costing system design process, their feeling of competence increases thanks to the enhanced recognition and appreciation that they perceive. Hence, we expect that the satisfaction of the three basic psychological needs through participation in costing system design will encourage managers to identify with the value of cost management, such that they experience autonomous motivation for cost management. Accord- ingly, our first hypothesis proposes a positive association between managers’ participation in costing system design and their autonomous motivation for cost management.

H1: Managers’ participation in costing system design is positively associated with their autonomous motivation for cost management.

Although economists argue that external factors such as monetary incentives can reinforce employees’ effort and performance (e.g. Bonner & Sprinkle, 2002), psychologists have inves- tigated the negative impact of such controlled motivation on employees’ behavior (e.g. Deci, 1971). In particular, research has shown that contingent, tangible rewards and other extrinsic factors such as competition and evaluation can be detrimental to outcomes such as creativ- ity and cognitive flexibility in problem-solving (e.g. Amabile, Goldfarb, & Brackfleld, 1990). Autonomous motivation, in contrast, has consistently been demonstrated to facilitate positive work outcomes, such as performance, persistence, creativity and initiative (e.g. Gagné & Deci, 2005; Wong-On-Wing et al., 2010).

It should be noted that ability also relates to performance. However, given a certain level of ability, increased autonomous motivation should result in increased performance because (in contrast to controlled motivation) it positively relates to individuals’ optimal functioning (e.g. Gagné et al., 2015). In addition, despite the longstanding belief that ability and motivation inter- act to affect performance, a recent meta-analysis has demonstrated that their effects are additive and that both are similarly important to job performance (Van Iddekinge, Aguinis, Mackey, & DeOrtentiis, forthcoming).

Hence, we argue that managers having autonomous motivation for cost management under- stand the importance of cost management, which will stimulate them to put more effort in it. As a result, they are more inclined to suggest improvement actions. Hence, our second hypothesis predicts that when managers are more autonomously motivated for cost management, they are more likely to contribute to process improvement.

H2: Managers’ autonomous motivation for cost management is positively associated with their perceived contribu- tions to process improvement.

2.2.2. Informational Effects Although participation may foster managers’ motivation, there are also informational benefits resulting from participation. Informational benefits refer to the dissemination of task-relevant knowledge,3 which participation could facilitate. Information sharing has been shown to play a central role in the process of participation (Locke & Schweiger, 1979). When people participate in a process, the outcome is of higher quality thanks to information exchange and knowledge transfer (Latham et al., 1994). Because subordinates often hold more information about their jobs, they know more about how to perform their tasks effectively than their superiors do.

3It should be noted that the literature on participative decision-making typically also includes knowledge discovery in addition to knowledge dissemination (e.g. Latham et al., 1994). As we are studying improvement processes and not innovation, we focus on the latter only.

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Including users in the design process helps to ensure the success of the new system in terms of information quality because users are considered as experts in their work due to a better under- standing of their working environment (Beyer & Holtzblatt, 1995). Participation in the design of an information system thus allows users to customize the output information according to their working habits and, as such, fosters satisfaction as well as higher intensity of use (e.g. Tait & Vessey, 1988). Through participation, systems can also be designed at the appropriate level of detail (e.g. Tarafdar et al., 2010). Hence, because users’ information and knowledge as well as their desired system features are included through participation, it may foster system quality and acceptance (Ives & Olson, 1984).

As a result of common costs of joint processes, costing system design also requires input from managers from other functions. By including them in the participation process, more and better information is obtained (Moreland, Argote, & Krishnan, 1996). In particular, since each individ- ual has partial and biased information about current processes, group discussions may perform a corrective function that enables members of the group to collectively gain more access to pri- vate information (Stasser & Titus, 1985). Hence, through participation in costing system design, knowledge is exchanged, which results in improved understanding of costs and processes.

To summarize, managers’ participation in the costing system design process may enhance their perception of the usefulness of the information supplied by the costing system or the extent to which they rely on this information for decision-making (cf. Chenhall & Morris, 1986; Pizzini, 2006). In particular, we argue that participation engenders informational effects in that it provides managers with the opportunity to incorporate their own knowledge into the costing system and enables them to learn from what other managers contribute. This reasoning leads to our third hypothesis.

H3: Managers’ participation in costing system design is positively associated with their perceived usefulness of cost information.

Cost information may help managers to enhance process performance by reducing and eliminat- ing unnecessary resources. In addition, it may also provide insight into the causes of high costs, which may help to redesign the underlying processes. In particular, the literature on business pro- cess management indicates that process redesign begins with defining what the business process under consideration means for an organization and then selecting the most critical areas where it can be redesigned (Davenport & Stoddard, 1994). The improvement of these critical areas is referred to as the detection of areas of inefficiency and ineffectiveness. Inefficiency implies that a process generates too much wasteful resources even though it meets operational goals (Wastell, White, & Kawalek, 1994). Cost disaggregation may reveal redundant activities as it can help managers to monitor the performance of each activity within a process and identify value-added versus non value-added activities (e.g. Swenson, 1995; Turney, 1991). The second problem, inef- fectiveness of a process, implies that it fails to satisfy customer requirements. Typical symptoms are customer complaints, late or incomplete output and the need to repeat work (Wastell et al., 1994). In order to redesign a process, performance standards may be set. Variance analysis can then explain the actual performance of a process compared with its standard performance in terms of costs generated or resources consumed at a disaggregated level (Weber, Dodd, Wood, & Wolk, 1997). By revealing the most ineffective parts of a process, such disaggreated information may provide better insight into process performance (Carpinetti, Buosi, & Gerólamo, 2003). Hence, through the analysis of activities, users may identify performance problems by detecting sources of inefficiency and ineffectiveness at the activity level and, subsequently, develop strategies for improvement (Furey, 1993).

When managers perceive the cost information as more useful, they believe that using a cost- ing system could enhance the performance of their work processes by revealing unnecessary

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resources and providing insight into the sources of inefficiency and ineffectiveness. As a result, they will be more likely to suggest improvement actions. Accordingly, our fourth hypothesis proposes a positive association between managers’ perceived usefulness of cost information and their perceived contributions to process improvement.

H4: Managers’ perceived usefulness of cost information is positively associated with their perceived contributions to process improvement.

3. Research Method

3.1. Data Collection

The data used in our study were collected through an online survey. The survey instrument was pretested with a practitioner (consultant) and two academics. An invitation asking for partici- pation in this study was sent via email to 3000 Belgian managers responsible for departments of accounting and finance, manufacturing, HR, marketing, R&D, sales or logistics. The email addresses were obtained from a Belgian commercial mailing list provider holding approximately 300,000 email addresses. We targeted managers who work in companies that have more than 50 employees because these companies are more likely to have a formal costing system.

The procedure for sending the surveys consisted of two phases. In the first phase, 3000 invi- tations containing the link to access the survey were sent to respondents by email. In the second phase, we sent a second email to thank the respondents who had completed the survey and to remind the respondents who had not. In total, 354 emails failed to reach target respondents due to invalid email addresses, retirement or firm leaving so that the target sample of this study con- sists of 2646 managers. In total, 173 questionnaires were completed, yielding a response rate of 6.54%. Three respondents were deleted due to problematic answer patterns,4 resulting in a final sample of 170 observations. To investigate the possibility of non-response bias, an early/late respondents’ analysis was conducted, in which early and late respondents were, respectively defined as having sent back the initial or the replacement questionnaire. The results of the t-tests show a non-significant difference in means (all p > .05) for all measured items.

3.2. Sample Characteristics

Table 1 presents the respondents’ characteristics as well as the companies’ background. 78.82% of our respondents are male. The majority of the respondents (67.06%) graduated more than 20 years ago and obtained at least a master degree (70.00%). Most respondents are top-level managers (67.06%) and work at the department of accounting and finance (66.47%). We will control for these two sources of sample heterogeneity (i.e. LEVEL and DEPARTMENT) in our analyses. The number of years the respondents have been working in their organizations and current positions varies greatly. The companies in which they work operate in wide range of different sectors and mostly employ between 50 and 500 people (71.76%).

3.3. Measures

All survey items used to measure the constructs (see Appendix) were scored on seven-point Likert scales, unless stated otherwise. We first performed an exploratory factor analysis using SPSS to establish the unidimensionality of the constructs and examine the item loadings. More

4Two respondents chose the neutral option for all questions and one respondent chose a score of six for all answers. When we include these three respondents in our analyses, our results do not change.

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Table 1. Respondents’ characteristics and companies’ background

Panel A: Respondents’ characteristics Gender % DEPARTMENT %

Male 78.82 Accounting and finance 66.47 Female 20.59 Manufacturing 11.18 Not specified 0.59 HR 0.59

Education Marketing 1.76 Secondary education or less 1.76 R&D 5.29 Professional bachelor 15.88 Sales 2.35 Academic bachelor 11.18 Logistics 4.71 Master 51.18 Not specified 7.65 Postgraduate degree 16.47 Years in organization PhD 2.35 < 1 5.88 Not specified 1.18 From 2 to 5 22.35

Years since graduation From 6 to 10 18.24 < 1 0.00 From 11 to 15 15.29 From 2 to 5 1.76 From 16 to 20 10.00 From 6 to 10 7.65 From 21 to 25 10.59 From 11 to 15 10.59 From 26 to 30 10.00 From 16 to 20 12.35 ≥ 31 5.29 From 21 to 25 28.24 Not specified 2.35 From 26 to 30 22.35 Years in current position ≥ 31 16.47 < 1 6.47 Not specified 0.59 From 2 to 5 38.24

Professional level (LEVEL) From 6 to 10 21.18 Top-management level 67.06 From 11 to 15 12.94 Middle-management level 26.47 From 16 to 20 12.35 Lower-management level 5.29 From 21 to 25 3.53 Not specified 1.18 From 26 to 30 3.53

≥ 31 0.00 Not specified 1.76

Panel B: Companies’ background Sector % SIZE %

Processing industry 10.00 50–100 23.53 Construction 7.65 101–250 32.94 Metal 14.71 251–500 15.29 Wholesale and retail trade 12.35 501–1000 10.00 Hotel, restaurant, tourism 9.41 1001–2000 7.06 Chemical industry 10.00 2001–5000 5.29 Energy and water 3.53 5001–10,000 1.18 Agriculture and forestry 0.00 ≥ 10,001 3.53 Transportation and communication 15.29 Not specified 1.18 Banking and insurance 5.88 Health care or welfare services 5.29 Not specified 5.88

specifically, we conducted principal axis factoring (PAF) analyses using oblique rotation (Direct Oblimin) with Kaiser Normalization on all reflective constructs (e.g. Fabrigar, Wegener, Mac- Callum, & Strahan, 1999). The validity of formative constructs (i.e. AUT_MOTIVATION and CONT_MOTIVATION, see below) was assessed through principal components analysis (PCA) (e.g. Petter, Straub, & Rai, 2007). Extraction was based on Eigenvalues above 1.0.

Participation in costing system design (PARTICIPATION). To measure participation, we used an instrument from the budgeting literature (Milani, 1975) that has been used before (e.g. Chen- hall & Brownell, 1988; De Baerdemaeker & Bruggeman, 2015; Dunk, 1993; Leach-López, Stammerjohan, & McNair, 2007; Mia, 1989). We adapted the instrument to reflect the speci- ficities of costing system design. To introduce the meaning of participation in costing system design, we first specified the tasks that respondents may have been involved with: establishing

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cost pools/centres and defining the areas of responsibility; specifying cost categories; identifying product/service flows from the input stage to the output stage of a product or service; determin- ing the cost allocation methods (e.g. process costing, job costing, batch costing, service costing, contract costing, activity-based costing, etc.); providing frequency of reporting; identifying the cost of each activity/task providing products/services; choosing the proper allocation methods and identifying cost drivers; and analyzing factors influencing costs. Next, we revised the orig- inal instrument of budget participation by replacing ‘your involvement in the budget’ by ‘your involvement in designing the current costing system’. Respondents were asked to indicate their involvement in the design of the costing system for six items. The results of the PAF analysis show that PARTICIPATION is a unidimensional construct. All loadings are higher than the mini- mum of 0.40–0.45 recommended by Hair, Black, Babin, and Anderson (2014, p. 115) for sample sizes between 150 and 200.

Autonomous motivation for cost management (AUT_MOTIVATION). We used the multidi- mensional work motivation scale developed by Gagné et al. (2015) to measure the degree of autonomous motivation for cost management. To fit with the purpose of this study, the original question ‘Why do you or would you put efforts into your current job?’ was replaced by the altered question ‘Why do you or would you put efforts into cost management?’ In line with Gagné et al. (2010), we use six statements that indicate two types of autonomous motivation (intrinsic moti- vation and identified regulation). The results of the PCA on AUT_MOTIVATION reveal that, as expected, this formative construct is two-dimensional, with all item loadings exceeding 0.45. The first dimension consists of the first three statements and represents intrinsic motivation. The second dimension contains the last three statements and reflects identified regulation. To test the hypotheses, we later merge these two scales and use one construct for autonomous motivation by including the scores of all six items (e.g. Van den Broeck et al., 2010).

Perceived usefulness of cost information (USEFULNESS). Perceived usefulness is defined in the information systems literature as the degree to which an individual believes that using a par- ticular information system would enhance task performance (Davis, 1989). In costing research, perceived usefulness is defined as the manager’s belief about the importance of information sup- plied by the costing system or the extent to which this information could be used in making managerial decisions (Chenhall & Morris, 1986; Pizzini, 2006). We asked managers to specify the degree to which they believe cost information is useful for the improvement of their work processes through six statements. The results of the PAF analysis show that, after deleting the item USEFULNESS_01,5 USEFULNESS is a unidimensional construct, with all item loadings exceeding 0.45.

Perceived contributions to process improvement (IMPROVEMENT). This newly developed instrument measures managers’ perceptions about their contributions to the improvement of their work processes. The survey asked the respondents to rate their contributions for the following eight tasks: (1) reduction of costs of current processes providing products/services; (2) reduc- tion of process errors (e.g. stoppage, scrap, rework); (3) reduction of process lead times (e.g. queue, waiting time); (4) controlling work processes to ensure their correctness; (5) checking work processes to prevent defects in products/services; (6) redesigning and testing new work processes; (7) setting standards for improvement of work process and (8) continuously evalu- ating work processes to find opportunities for improvement. These tasks are critical to process improvement because prior studies have shown that process improvement can be achieved by eliminating waste (e.g. scrap, rework and other redundant activities), preventing defects (e.g. avoiding mistakes), setting new standards for improvement and continuously evaluating the pro- cess to improve (Bhatt, 2000). Examples of operational improvements in different departments

5We will discuss the conceptual impact of this deletion in Section 4.1.

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include optimizing delivery routes (Everaert, Bruggeman, Sarens, Anderson, & Levant, 2008), reducing picking errors (Hoozée & Bruggeman, 2010), improvements in the order-to-cash and purchase-to-pay processes (e.g. reducing manual efforts in invoice processing), enhancing data collection processes for budgeting and reporting, increasing the speed to market of new products, more efficient product registration processes, etc. Although several authors have criticized the use of self-rated measures for individuals’ contribution as well as performance, advocates of self- rated measures have argued that they are valid and tend to exhibit less bias than superior-rated measures (e.g. Dunk, 1993; Parker & Kyj, 2006). Moreover, self-rated measures of subordinate performance have been shown to be correlated with measures rated by superiors (e.g. Furnham & Stringfield, 1994; Venkatraman & Ramanujam, 1987). The results of the PAF analysis show that IMPROVEMENT is a unidimensional construct, with all item loadings exceeding 0.45.

Control variables. Due to the heterogeneity of our sample, we control for the department that our respondents work in and their professional level. More specifically, because the benefits of participation in system design may be dependent on functional expertise (Kruis & Widener, 2014), we created a dummy variable DEPARTMENT, which takes value 1 for respondents working at the department of accounting and finance (66.47% of the cases, see Table 1), zero otherwise. LEVEL is a dummy variable that takes value 1 when respondents are top-level man- agers (67.06% of the cases, see Table 1), zero otherwise. However, it should be noted that, as holding companies are very prominent in Belgium (Deloof & Jegers, 1999), a ‘top manager’ should mostly be interpreted as a senior executive of a subsidiary. Apart from the control vari- ables DEPARTMENT and LEVEL, we also added an additional mediating variable to control for the effect of controlled motivation. Again based on the multidimensional work motivation scale of Gagné et al. (2015), we use 10 statements to measure the degree of controlled motivation for cost management. The PCA used to assess the validity of this formative construct reveals that, after deleting two items (MOTIVATION_07 and MOTIVATION_08), CONT_MOTIVATION is unidimensional, with all item loadings exceeding 0.45.6

3.4. Assessment of Common Method Bias

The subjective measures used in this study were gathered from the same source in the same ques- tionnaire, which may create an issue of common method bias. We therefore executed Harman’s single-factor test (Podsakoff & Organ, 1986). This test assumes that if a substantial amount of common method variance is present, a factor analysis of all the data will result in a single fac- tor accounting for the majority of the covariance in the independent and dependent variables. More specifically, we performed a PAF analysis on the 26 items measuring our 4 main variables (USEFULNESS, PARTICIPATION, AUT_MOTIVATION, IMPROVEMENT). The results of Harman’s single-factor test revealed that no single factor accounts for the majority of the variance in the instruments,7 showing that this type bias was not a concern in this study.

4. Results

The research model (see Figure 1) was tested using partial least squares (PLS).8 PLS is a non-parametric component-based structural equation modelling (SEM) technique. In contrast to covariance-based SEM methods (e.g. AMOS and LISREL), PLS puts low demands on sample

6When we later imported these two items into the SmartPLS software, we again found support for their elimination, as their loadings were not significant (Diamantopoulos & Winklhofer, 2001). 7The total variance explained by one single factor is 22.69%. 8We used SmartPLS (version 3.2.4).

758 S. Hoozée and Q.-H. Ngo

size (Hair, Ringle, & Sarstedt, 2011). In line with Verbeeten and Speklé (2015), we chose PLS because we test a relatively complex model with only 170 observations. An additional argument for using PLS is our usage of formative constructs, which are more difficult to model and test when using covariance-based SEM methods.

We analyze our results in two stages. First, we examine the reliability and validity of the mea- surement model. Second, we assess the structural model by examining the relationships between the constructs. After our main analyses, we also present the results of some additional tests.

4.1. Measurement Model

First, we conducted an overall PAF analysis (using SPSS) on all items from the measure- ment model using Direct Oblimin rotation with Kaiser Normalization and extraction based on Eigenvalues above 1.0. One item (USEFULNESS_02) loaded onto an unintended construct. We decided to leave this item out of further analyses (just like USEFULNESS_01; see Section 3.3) as we have no theoretical arguments to support the inclusion of an additional variable in our model. Hence, it seems that our respondents perceived insights into wasted resources and oppor- tunities for cost reduction (i.e. USEFULNESS _01 and USEFULNESS_02) differently compared to insights into causes of high costs (e.g. quality problems, long throughput times, suboptimal standards), which are measured by the remaining items.9

After the deletion of two items (i.e. USEFULNESS_01 and USEFULNESS_02), the extracted factors corresponded with the number of intended constructs.10 All remaining items were imported into the SmartPLS software. Table 2 shows the item loadings for the multi-item constructs.

To assess convergent validity, we examined the average variance extracted (AVE). An AVE value of 0.50 and higher indicates a sufficient degree of convergent validity (Chin, 1998, p. 321; Hair et al., 2014, p. 605). Table 4 demonstrates that the AVE of all constructs is above the threshold of 0.50. Moreover, Table 3 shows that all items load on their respective construct with a lower bound of 0.460. Therefore, we conclude that convergent validity is established.

After establishing convergent validity, we assessed discriminant validity to ensure that all con- struct measures are empirically unique and represent phenomena of interest that other measures in the structural equation model do not capture (Hair et al., 2014). To determine discriminant validity, we first used the AVE values from Table 4 and found that the square root of the AVE for each latent variable is larger than any correlation among any pair of latent variables (see Table 5), as recommended by Chin (1998), Fornell and Larcker (1981) and Hair et al. (2014). In line with Chin’s (1998) suggestion, all items also load higher on their respective construct than on any other (see Table 3). These analyses confirm the discriminant validity of our constructs.

Next, we assessed the internal consistency reliability of the measurement model by calculating the composite reliability (CR) and Cronbach’s alpha for each latent variable. Table 4 demon- strates that the composite reliability and Cronbach’s alpha scores of all reflective constructs are above the threshold value of 0.70 (Hair, Hult, Ringle, & Sarstedt, 2017, p. 112).

9We conjecture that managers are focused on operational processes and, hence, use underlying process insights as opposed to cost information itself to contribute to process improvement. Thanks to these enhanced process insights, they are then able to reduce costs and waste indirectly. If we had surveyed (lower level) employees or workers, the results may have been different, as they are less able to change the processes but instead have to deal with waste and cost reduction directly. 10In the final PAF analysis, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.798, which is above the suggested rule-of-thumb threshold of 0.5 and indicates adequate sample size. The chi-square value for Bartlett’s test of sphericity was large (3207.25) and significant (p < .001) indicating sufficient correlations among the variables. Taken together, these two tests indicate that it is safe to proceed with and interpret the factor analysis (Hair et al., 2014, p. 103).

The Impact of Managers’ Participation in Costing System Design 759

Table 2. Item loadings

Original sample

Sample mean

Standard deviation t-statistics p-values

MOTIVATION_01 → AUT_MOTIVATION 0.538 0.457 0.229 2.352 .019 MOTIVATION_02 → AUT_MOTIVATION 0.641 0.541 0.206 3.114 .002 MOTIVATION_03 → AUT_MOTIVATION 0.681 0.572 0.184 3.693 .000 MOTIVATION_04 → AUT_MOTIVATION 0.860 0.711 0.184 4.665 .000 MOTIVATION_05 → AUT_MOTIVATION 0.788 0.656 0.231 3.417 .001 MOTIVATION_06 → AUT_MOTIVATION 0.460 0.383 0.249 1.847 .065 MOTIVATION_09 → CONT_MOTIVATION 0.843 0.508 0.289 2.914 .004 MOTIVATION_10 → CONT_MOTIVATION 0.771 0.475 0.271 2.851 .004 MOTIVATION_11 → CONT_MOTIVATION 0.613 0.381 0.269 2.275 .023 MOTIVATION_12 → CONT_MOTIVATION 0.570 0.361 0.263 2.170 .030 MOTIVATION_13 → CONT_MOTIVATION 0.766 0.476 0.285 2.687 .007 MOTIVATION_14 → CONT_MOTIVATION 0.577 0.370 0.235 2.456 .014 MOTIVATION_15 → CONT_MOTIVATION 0.676 0.415 0.271 2.497 .013 MOTIVATION_16 → CONT_MOTIVATION 0.596 0.386 0.247 2.410 .016 PARTICIPATION_01 ← PARTICIPATION 0.825 0.824 0.038 21.828 .000 PARTICIPATION_02 ← PARTICIPATION 0.645 0.651 0.070 9.175 .000 PARTICIPATION_03 ← PARTICIPATION 0.759 0.747 0.053 14.212 .000 PARTICIPATION_04 ← PARTICIPATION 0.875 0.873 0.029 29.899 .000 PARTICIPATION_05 ← PARTICIPATION 0.887 0.885 0.026 34.423 .000 PARTICIPATION_06 ← PARTICIPATION 0.836 0.833 0.031 27.307 .000 IMPROVEMENT_01 ← IMPROVEMENT 0.731 0.725 0.048 15.385 .000 IMPROVEMENT_02 ← IMPROVEMENT 0.735 0.734 0.050 14.634 .000 IMPROVEMENT_03 ← IMPROVEMENT 0.831 0.830 0.031 26.940 .000 IMPROVEMENT_04 ← IMPROVEMENT 0.629 0.623 0.075 8.436 .000 IMPROVEMENT_05 ← IMPROVEMENT 0.772 0.770 0.039 19.788 .000 IMPROVEMENT_06 ← IMPROVEMENT 0.739 0.739 0.047 15.731 .000 IMPROVEMENT_07 ← IMPROVEMENT 0.818 0.813 0.031 26.512 .000 IMPROVEMENT_08 ← IMPROVEMENT 0.773 0.771 0.033 23.431 .000 USEFULNESS_03 ← USEFULNESS 0.707 0.705 0.065 10.934 .000 USEFULNESS_04 ← USEFULNESS 0.801 0.795 0.060 13.327 .000 USEFULNESS_05 ← USEFULNESS 0.604 0.597 0.096 6.281 .000 USEFULNESS_06 ← USEFULNESS 0.797 0.790 0.051 15.571 .000

Notes: AUT_MOTIVATION, autonomous motivation for cost management; CONT_MOTIVATION, controlled moti- vation for cost management; PARTICIPATION, participation in costing system design; IMPROVEMENT, perceived contributions to process improvement; USEFULNESS, perceived usefulness of cost information. Details on the items used to construct the latent variables are provided in the Appendix.

Finally, we investigated multicollinearity by examining the variance inflation factor (VIF) scores between the latent variables. In line with the suggestion of Hair et al. (2014, p. 200), they are all below the threshold value of 10 (with an upper bound 1.289), confirming that the issue of multicollinearity is not present.

4.2. Structural Model

In the assessment of the structural model, the specified structural equations are estimated. The path coefficients indicate the strength and direction of the relationships among the latent vari- ables. We assessed statistical significance of parameter estimates using a bootstrap procedure with 5000 replacements, as suggested by Hair et al. (2011).11 In addition, in line with prior accounting research (e.g. Hartmann & Slapničar, 2009), we also examine the predictive valid- ity of the parameter estimates. Tenenhaus, Vinzi, Chatelin, and Lauro (2005) and Vandenbosch

11As recommended by Hair et al. (2017), we used the path weighting scheme as the structural path weighting method. We also chose to ignore sign changes in the resamples, as this is the most conservative estimation option.

760 S.H

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Table 3. Cross-loadings

AUT_MOTIVATION CONT_MOTIVATION DEPARTMENT IMPROVEMENT PARTICIPATION LEVEL USEFULNESS

DEPARTMENT 0.111 0.095 1.000 − 0.277 0.255 − 0.074 0.148 MOTIVATION_01 0.538 0.192 0.110 − 0.010 0.204 − 0.127 0.069 MOTIVATION_02 0.641 0.263 0.126 0.014 0.233 0.002 0.177 MOTIVATION_03 0.681 0.180 0.071 0.046 0.235 0.025 0.218 MOTIVATION_04 0.860 0.146 0.064 0.124 0.272 0.068 0.287 MOTIVATION_05 0.788 0.237 − 0.003 0.184 0.222 − 0.021 0.230 MOTIVATION_06 0.460 0.291 − 0.155 0.152 0.112 0.065 0.185 MOTIVATION_09 0.172 0.843 0.059 − 0.061 0.191 − 0.125 0.186 MOTIVATION_10 0.154 0.771 0.003 − 0.036 0.180 − 0.099 0.187 MOTIVATION_11 0.232 0.613 0.028 0.027 0.156 − 0.156 0.151 MOTIVATION_12 0.231 0.570 0.043 0.024 0.145 − 0.180 0.171 MOTIVATION_13 0.187 0.766 0.145 0.045 0.198 − 0.113 0.230 MOTIVATION_14 0.121 0.577 0.080 0.003 0.142 − 0.170 0.149 MOTIVATION_15 0.178 0.676 0.013 − 0.046 0.154 − 0.121 0.035 MOTIVATION_16 0.104 0.596 0.031 0.031 0.153 − 0.179 0.128 PARTICIPATION_01 0.248 0.208 0.313 0.031 0.825 0.194 0.184 PARTICIPATION_02 0.201 0.069 0.243 − 0.076 0.645 0.115 0.132 PARTICIPATION_03 0.288 0.189 0.110 0.119 0.759 0.034 0.255 PARTICIPATION_04 0.275 0.200 0.201 0.084 0.875 0.237 0.141 PARTICIPATION_05 0.287 0.199 0.171 0.170 0.887 0.187 0.234 PARTICIPATION_06 0.253 0.211 0.247 0.046 0.836 0.013 0.276 IMPROVEMENT_01 0.196 − 0.067 − 0.166 0.731 0.027 0.278 0.275 IMPROVEMENT_02 0.118 − 0.048 − 0.194 0.735 − 0.028 0.107 0.186 IMPROVEMENT_03 0.047 − 0.043 − 0.270 0.831 − 0.014 0.198 0.228 IMPROVEMENT_04 0.140 0.045 − 0.012 0.629 0.173 0.109 0.360 IMPROVEMENT_05 0.022 − 0.042 − 0.232 0.772 0.063 0.151 0.258 IMPROVEMENT_06 0.016 − 0.087 − 0.263 0.739 0.068 0.120 0.163 IMPROVEMENT_07 0.087 − 0.027 − 0.260 0.818 0.149 0.255 0.339 IMPROVEMENT_08 0.126 − 0.065 − 0.239 0.773 0.105 0.260 0.246 LEVEL − 0.002 − 0.075 − 0.074 0.256 0.155 1.000 0.027 USEFULNESS_03 0.249 0.252 0.131 0.241 0.238 − 0.044 0.707 USEFULNESS_04 0.201 − 0.003 0.018 0.351 0.144 0.058 0.801 USEFULNESS_05 0.131 0.023 0.183 0.181 0.136 0.050 0.604 USEFULNESS_06 0.248 0.222 0.140 0.207 0.241 0.022 0.797

Notes: AUT_MOTIVATION, autonomous motivation for cost management; CONT_MOTIVATION, controlled motivation for cost management; DEPARTMENT, accounting and finance unit (value 1) versus otherwise (value 0); IMPROVEMENT, perceived contributions to process improvement; PARTICIPATION, participation in costing system design; LEVEL, top-level managers (value 1) versus otherwise (value 0); USEFULNESS, perceived usefulness of cost information. Details on the items used to construct the latent variables are provided in the Appendix.

The Impact of Managers’ Participation in Costing System Design 761

Table 4. AVE, composite reliability and Cronbach’s alpha of reflective, multi-item constructs

AVE Composite reliability Cronbach’s alpha

IMPROVEMENT 0.571 0.914 0.892 PARTICIPATION 0.654 0.918 0.893 USEFULNESS 0.535 0.820 0.709

Table 5. Discriminant validity of constructs

1 2 3 4 5 6 7

1. AUT_MOTIVATION – 2. CONT_MOTIVATION 0.225** – 3. DEPARTMENT 0.111 0.095 1.000 4. IMPROVEMENT 0.125 − 0.056 − 0.277** 0.756 5. PARTICIPATION 0.323** 0.230** 0.255** 0.093 0.809 6. LEVEL − 0.002 − 0.075 − 0.074 0.256** 0.155* 1.000 7. USEFULNESS 0.288** 0.172* 0.148 0.344** 0.260** 0.027 0.731

Notes: the square root of the AVE is shown on the diagonal (not for formative constructs). Off-diagonal elements are the Pearson correlations among the variables. The superscripts ** and * denote significance at the 0.01 and 0.05 levels, respectively (two-tailed).

Table 6. R2 and Q2

Construct R2 Q2

AUT_MOTIVATION 0.105 0.033 CONT_MOTIVATION 0.053 0.008 IMPROVEMENT 0.290 0.143 USEFULNESS 0.068 0.031

(1996) argued that in order to provide sufficient evidence of model fit, it is necessary to examine the Stone-Geisser Q2-test because PLS models lack an index providing the goodness of fit statis- tics like in variance-covariance-based structural equation models. Q2 values larger than zero for a certain endogenous latent variable indicate the path model’s predictive relevance for this par- ticular construct (Hair et al., 2011, p. 147). Table 6 shows that the Q2 values of all endogenous variables are greater than zero, suggesting sufficient evidence of model fit. Table 6 also reports the R2 values.

Next, we examine the magnitude and strength of the paths, where each of our hypotheses corresponds to a specific structural model path (see Figure 2). To allow for the possibility of un-modelled mediators and the potential direct effect of participation, we also added the direct effect from managers’ participation in costing system design (PARTICIPATION) to their perceived contributions to process improvement (IMPROVEMENT). The results suggest that all but one hypothesized path are significant. More specifically, the path between managers’ participation in costing system design (PARTICIPATION) and their autonomous motivation for cost management (AUT_MOTIVATION) is significant (β = 0.323, p < .001), which sup- ports Hypothesis 1. However, the path between managers’ autonomous motivation for cost management (AUT_MOTIVATION) and their perceived contributions to process improvement (IMPROVEMENT) is not significant (β = 0.060, p = .550), such that Hypothesis 2 is not sup- ported. In line with Hypothesis 3, the path between managers’ participation in costing system design (PARTICIPATION) and their perceived usefulness of cost information (USEFULNESS) is significant (β = 0.260, p < .01). Finally, the path between managers’ perceived usefulness of cost information (USEFULNESS) and their perceived contributions to process improvement (IMPROVEMENT) is also significant (β = 0.373, p < .001) and therefore supports Hypothesis

762 S. Hoozée and Q.-H. Ngo

Figure 2. Results of the structural model with path coefficients (associated t-statistics are in parentheses). Notes: DEPARTMENT is a dummy variable that indicates whether it is an accounting and finance unit (value 1) versus otherwise (value 0); LEVEL is a dummy variable that takes value 1 when respondents are top-level managers, zero otherwise. The superscripts *** and ** denote significance at the 0.001 and 0.01 levels, respectively (two-tailed).

4. Hence, as expected, managers’ participation in costing system design increases their perceived usefulness of cost information as well as their autonomous motivation for cost management. However, whereas managers’ perceived usefulness of cost information is positively associated with their perceived contributions to process improvement, their autonomous motivation for cost management is not. It should be noted that these effects hold after controlling for the mediat- ing effect of managers’ controlled motivation for cost management (CONT_MOTIVATION) on their perceived contributions to process improvement (IMPROVEMENT), the direct effect of managers’ participation in costing system design (PARTICIPATION; β = 0.051, p = .563), as well as the effect of the department that they work in (DEPARTMENT; β = −0.327, p < .001) and their professional level (LEVEL; β = 0.207, p < .01).

Finally, we also specifically examined the indirect effect of managers’ participation in cost- ing system design (PARTICIPATION) on their perceived contributions to process improvement (IMPROVEMENT) through their perceived usefulness of cost information (USEFULNESS). Rather than running a simple mediation analysis for this particular mediator only, we considered all mediators simultaneously in one model and, as such, control for the other included media- tors (Preacher & Hayes, 2008). In particular, to assess the significance of the indirect effect, we manually calculated its standard error using the SmartPLS 3 output from the bootstrapping rou- tine (for the computation steps, see Hair et al., 2017, p. 238). We find that the indirect effect of managers’ participation in costing system design (PARTICIPATION) on their perceived contri- butions to process improvement (IMPROVEMENT) through their perceived usefulness of cost information (USEFULNESS) is significant (β = 0.097, p < .01).

4.3. Additional Analyses

To examine the robustness of our findings, we ran four additional analyses.12 First, to test whether our results hold for firms of different sizes, we added two additional control variables. SIZE_MEDIUM is a dummy variable that takes value 1 when the number of employees is

12Similar to the baseline model, we also assessed convergent validity, discriminant validity, internal consistency reliability and multicollinearity in all these additional analyses.

The Impact of Managers’ Participation in Costing System Design 763

between 101 and 500, SIZE_LARGE is a dummy variable that takes value 1 when the number of employees is larger than 500.13 The effect of these two size variables on managers’ perceived contributions to process improvement (IMPROVEMENT) is not significant and our other results remain consistent.

Second, to investigate whether the impact of managers’ participation in costing system design (PARTICIPATION) on their perceived usefulness of cost information (USEFULNESS) depends on the level of costing system complexity, we added the effect of costing system complexity (COMPLEXITY14) on perceived usefulness of cost information (USEFULNESS) as well as its interaction with participation in costing system design (PARTICIPATION) to our model. Nei- ther the main effect of costing system complexity (COMPLEXITY) on perceived usefulness of cost information (USEFULNESS), nor its interaction with participation in costing system design (PARTICIPATION) is significant. As in the previous additional analysis, our other results remain consistent.

Third, it may be argued that our variable PARTICIPATION might also measure costing sys- tem development. We therefore re-ran our model by splitting out those questions that would only work when there is actual costing system development going on (i.e. PARTICIPATION_02, PARTICIPATION_03 and PARTICIPATION_06). Our results do not change with this alternative operationalization. Hence, we conclude that the variable PARTICIPATION is measuring what it is expected to measure.

Finally, given that PLS has been heavily criticized by some influential methodologists because it suffers, for instance, from inconsistent and biased estimators (e.g. Rönkkö, McIntosh, Anton- akis, & Edwards, 2016), we test whether our results are robust to using an alternative statistical method. More specifically, we performed a regression-based mediation analysis based on average construct scores using Hayes’ (2013) PROCESS macro for SPSS. We find consistent evidence for Hypothesis 1 (β = 0.208, p < .001), Hypothesis 3 (β = 0.201, p < .001) and Hypothesis 4 (β = 0.377, p < .0001), whereas Hypothesis 2 is again not supported (β = 0.029, p = .714). As in our main analysis, these effects hold after controlling for the mediating effect of man- agers’ controlled motivation for cost management, the direct effect of managers’ participation in costing system design (PARTICIPATION; β = 0.029, p = .640), as well as the effect of the department that they work in (DEPARTMENT; β = −0.743, p < .0001) and their professional level (LEVEL; β = 0.449, p < .01). The indirect effect of managers’ participation in costing system design on their perceived contributions to process improvement through their perceived usefulness of cost information is also significant (BootLLCI = 0.028; BootULCI = 0.146). The only difference with our PLS results is that managers’ participation in costing system design is now also significantly associated with their controlled motivation for cost manage- ment (CONT_MOTIVATION; β = 0.188, p < .01). As we find consistent (and even stronger) support for our hypotheses using regression-based mediation analysis based on average construct scores instead of PLS, we conclude that our results are robust to using this alternative statistical method.

13This categorization is in line with, for instance, the study of Du, Deloof, and Jorissen (2013) who also collected Belgian data (to investigate the impact of headquarters-subsidiary interdependencies on performance evaluation and reward systems). 14In line with Drury and Tayles (2005), we used two questions to measure costing system complexity. More specifically, respondents were asked to indicate the number of cost pools used and the number of cost allocation bases used on two 8-point log2 N scales. The variable COMPLEX was then constructed by adding the two scores (Schoute, 2009).

764 S. Hoozée and Q.-H. Ngo

5. Conclusion, Limitations and Future Research

The purpose of this study was to unravel the mechanisms through which managers’ participa- tion in costing system design fosters their perceived contributions to process improvement. The results of our survey show that managers’ participation in costing system design is positively associated with both their autonomous motivation for cost management and their perceived usefulness of cost information. However, only managers’ perceived usefulness of cost infor- mation is significantly related to their perceived contributions to process improvement. Our data do not support the predicted effect for autonomous motivation. In particular, although participation in costing system design enhances managers’ autonomous motivation for cost management, this increase in autonomous motivation as such does not seem to stimulate per- ceived contributions to process improvement. Process improvement thus appears to be a matter of better information and knowledge sharing rather than a higher autonomous motivation for reducing costs.

Our findings are in line with prior studies that have shown informational mechanisms drive the relationship between participation and performance. Chenhall and Brownell (1988) found that budgetary participation provides information that can reduce role ambiguity, which, in turn, may stimulate efforts to improve performance. Similarily, Chalos and Poon (2000), Chong and Chong (2002) and Kren (1992) also demonstrated the informational role of budget participation, which can enable individuals to enhance their performance. Consistent with these studies, our results show that participation in costing system design allows managers to understand the usefulness of cost information and, as a result, they contribute more to process improvement.

As with any study, the results of our study are subject to some potential caveats. First, our response rate is rather low. Although an early/late respondent’s analysis did not reveal any issues, we cannot ascertain that non-response bias is absent even though our sample size is comparable to prior management accounting studies (Van der Stede, Young, & Chen, 2005).

Second, we used a self-rating scale to measure managers’ contributions to process improve- ment. However, our research question is not such that one might expect a large degree of impression management (Speklé & Widener, forthcoming). Moreover, (budgetary) participa- tion and job performance have been shown to be uncorrelated with social desirability (Parker & Kyj, 2006). Hence, we believe that social desirability is not a concern in this study. Future research may, however, test the robustness of our findings by using an objective measure of process improvement. In this respect, it may also be particularly insightful to add ability to the research model as it has been shown to be a better predictor of objective performance measures than motivation (Van Iddekinge et al., forthcoming).

Third, even though we controlled for the effect of managers’ hierarchical level and the depart- ment they are working in, due to the heterogeneity of our sample, our respondents’ costing system and work process knowledge may vary greatly. Although this might have increased the noisiness of our measures, we believe that all respondents had the knowledge necessary to answer the questions. In particular, we avoided academic jargon (Speklé & Widener, forth- coming; Wiersma, 2009), such as activity-based costing, and provided examples of operational work processes to ensure that our conception of a business process as an umbrella term would capture the most important work processes.

Fourth, we acknowledge that there may be an overlap in the arguments leading up to the first and third hypothesis (i.e. the motivational and the informational effect of participation). In particular, although self-determination theory is only about a feeling of competence instead of actual competence, because we measured this feeling after the participation process, it is possible that it has been influenced by the increased competence acquired during the participative learning process. However, as we do not find support for our second hypothesis (which predicted an association between autonomous motivation for cost management and perceived contributions

The Impact of Managers’ Participation in Costing System Design 765

to process improvement), our data seems to suggest that it is not the feeling of competence that stimulates contributions to process improvement, but the enhanced insights themselves.

Finally, regarding potential sampling and generalizability issues, given that our theory is about managers, we consider them to be appropriate respondents. Moreover, as they work in firms of different sizes operating in a wide range of industries, we believe our sample is sufficiently broad to be considered similar to the ‘universe of managers’ (Speklé and Widener, forthcoming). How- ever, based upon our study, generic statements about the effects of employee participation in general should not be made. In this respect, future research may examine (lower level) employ- ees and workers to further enhance our understanding of the relation between participation in costing system design and perceived contributions to process improvement, especially as this group might have a different view on the usefulness of cost information (as we conjecture based on the results of our factor analyses).

To conclude, we would like to offer two more avenues for future research. A first would be to examine the impact of participation in the design process of a costing system in different operating environments, such as static versus dynamic environments, since the usefulness of cost information for process improvement might differ in different environments. For instance, information exchange and knowledge transfer among employees may be more important for organizations operating in rapidly changing environments (Lawler, 1994). Second, despite the fact that we found managers’ perceived contributions to process improvement to be mainly driven by informational mechanisms, this does not imply that increased autonomous motivation for cost management should be ignored. In fact, it may be more relevant for organizations fol- lowing a low cost strategy. In these organizations, tight control is usually performed to enhance efficient use of resources, which is critical to process improvement (Menguc, Auh, & Shih, 2007). Hence, it is possible that in organizations following a low cost strategy, managers identify more with the importance of cost reduction and this increased autonomous motivation for cost man- agement may stimulate their perceived contributions to process improvement to a greater extent than in organizations following a differentiation strategy.

Acknowledgements Suggestions from and/or discussions with Josep Bisbe, Werner Bruggeman, Johan Dergård, Patricia Everaert, Mirjam Knockaert, Falconer Mitchell, Anja Van den Broeck, Heidi Vander Bauwhede and especially Anne-Marie Kruis were very much appreciated. We also would like to acknowledge the helpful comments we received from participants at the 8th Conference on Performance Management and Management Control (Nice, France, 30 September–2 October 2015), the 13th Annual Conference for Management Accounting Research (Vallendar, Germany, 10–11 March 2016), the 37th Congress of the AFC (Clermont-Ferrand, France, 19–20 May 2016), the 10th Conference on New Directions in Management Accounting (Brussels, Belgium, 14–16 December 2016) and workshops at Ghent University. Finally, we are greatly indebted to Sally Widener and two anonymous reviewers for their extremely constructive and thought-provoking feedback throughout the entire review process.

Funding This project has been funded with support from the European Commission. This manuscript reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

ORCID Sophie Hoozée http://orcid.org/0000-0002-7046-5853

References

Amabile, T. M., Goldfarb, P., & Brackfleld, S. C. (1990). Social influences on creativity: Evaluation, coaction, and surveillance. Creativity Research Journal, 3(1), 6–21.

766 S. Hoozée and Q.-H. Ngo

Assor, A., Roth, G., & Deci, E. L. (2004). The emotional costs of parents’ conditional regard: A self-determination theory analysis. Journal of Personality, 72(1), 47–88.

Banker, R. D., Bardhan, I. R., & Chen, T.-Y. (2008). The role of manufacturing practices in mediating the impact of activity-based costing on plant performance. Accounting, Organizations and Society, 33(1), 1–19.

Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117(3), 497–529.

Beyer, H. R., & Holtzblatt, K. (1995). Apprenticing with the customer. Communications of the ACM , 38(5), 45–52. Bhatt, G. D. (2000). An empirical examination of the effects of information systems integration on business process

improvement. International Journal of Operations and Production Management, 20(11), 1331–1359. Bhimani, A., & Pigott, D. (1992). Implementing ABC: A case study of organizational and behavioural consequences.

Management Accounting Research, 3(2), 119–132. Bonner, S. E., & Sprinkle, G. B. (2002). The effects of monetary incentives on effort and task performance: Theories,

evidence, and a framework for research. Accounting, Organizations and Society, 27(4–5), 303–345. Brownell, P., & McInnes, M. (1986). Budgetary participation, motivation, and managerial performance. The Accounting

Review, 61(4), 587–600. Carpinetti, L. C. R., Buosi, T., & Gerólamo, M. C. (2003). Quality management and improvement: A framework and a

business-process reference model. Business Process Management Journal, 9(4), 543–554. Chalos, P., & Poon, M. C. C. (2000). Participation and performance in capital budgeting teams. Behavioral Research in

Accounting, 12, 199–229. Chenhall, R. H., & Brownell, P. (1988). The effect of participative budgeting on job satisfaction and performance: Role

ambiguity as an intervening variable. Accounting, Organizations and Society, 13(3), 225–233. Chenhall, R. H., & Morris, D. (1986). The impact of structure, environment, and interdependence on the perceived

usefulness of management accounting systems. The Accounting Review, 61(1), 16–35. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.),

Modern methods for business research (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum Associates. Chong, V. K., & Chong, K. M. (2002). Budget goal commitment and informational effects of budget participation on

performance: A structural equation modeling approach. Behavioral Research in Accounting, 14, 65–86. Davenport, T. H., & Short, J. E. (1990). The new industrial engineering: Information technology and business process

redesign. Sloan Management Review, 31(4), 11–27. Davenport, T. H., & Stoddard, D. B. (1994). Reengineering: Business change of mythic proportions? MIS Quarterly,

18(2), 121–127. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS

Quarterly, 13(3), 319–340. De Baerdemaeker, J., & Bruggeman, W. (2015). The impact of participation in strategic planning on managers’ cre-

ation of budgetary slack: The mediating role of autonomous motivation and affective organisational commitment. Management Accounting Research, 29, 1–12.

deCharms, R. (1968). Personal causation: The internal affective determinants of behavior. New York, NY: Academic Press.

Deci, E. L. (1971). Effects of externally mediated rewards on intrinsic motivation. Journal of Personality and Social Psychology, 18(1), 105–115.

Deci, E. L. (1975). Intrinsic motivation. New York, NY: Plenum Press. Deci, E. L., Connell, J. P., & Ryan, R. M. (1989). Self-determination in a work organization. Journal of Applied

Psychology, 74(4), 580–590. Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic

rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627–668. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York, NY:

Plenum Press. Deci, E. L., & Ryan, R. M. (2000). The ‘what’ and ‘why’ of goal pursuits: Human needs and the self-determination of

behavior. Psychological Inquiry, 11(4), 227–268. Deloof, M., & Jegers, M. (1999). Trade credit, corporate groups, and the financing of Belgian firms. Journal of Business,

Finance and Accounting, 26(7–8), 945–966. Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale

development. Journal of Marketing Research, 38(2), 269–277. Drury, C., & Tayles, M. (2005). Explicating the design of overhead absorption procedures in UK organizations. The

British Accounting Review, 37(1), 47–84. Du, Y., Deloof, M., & Jorissen, A. (2013). Headquarters-subsidiary interdependencies and the design of performance

evaluation and reward systems in multinational enterprises. European Accounting Review, 22(2), 391–424. Dunk, A. S. (1993). The effect of budget emphasis and information asymmetry on the relation between budgetary

participation and slack. The Accounting Review, 68(2), 400–410.

The Impact of Managers’ Participation in Costing System Design 767

Everaert, P., Bruggeman, W., Sarens, G., Anderson, S. R., & Levant, Y. (2008). Cost modeling in logistics using time-driven ABC: Experiences from a wholesaler. International Journal of Physical Distribution and Logistics Management, 38(3), 172–191.

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Furey, T. R. (1993). A six-step guide to process reengineering. Planning Review, 21(2), 20–23. Furnham, A., & Stringfield, P. (1994). Congruence of self and subordinate ratings of managerial practices as a correlate

of superior evaluation. Journal of Occupational and Organizational Psychology, 67(1), 57–67. Gagné, M., & Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational Behavior,

26(4), 331–362. Gagné, M., Forest, J., Gilbert, M.-H., Aubé, C., Morin, E., & Malorni, A. (2010). The motivation at work scale: Validation

evidence in two languages. Educational and Psychological Measurement, 70(4), 628–646. Gagné, M., Forest, J., Vansteenkiste, M., Crevier-Braud, L., Van den Broeck, A., Aspeli, A. K., . . . Westbye, C. (2015).

The Multidimensional Work Motivation Scale: Validation evidence in seven languages and nine countries. European Journal of Work and Organizational Psychology, 24(2), 178–196.

Groen, B. A. C., Wouters, M. J. F., & Wilderom, C. P. M. (2017). Employee participation, performance metrics, and job performance: A survey study based on self-determination theory. Management Accounting Research, 36, 51–66.

Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Harlow: Pearson Education.

Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Los Angeles, CA: Sage.

Hair, J. F., Jr., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–151.

Hammer, M., & Champy, J. (1993). Reengineering the corporation: A manifesto for business revolution. New York, NY: HarperCollins.

Hartmann, F., & Slapničar, S. (2009). How formal performance evaluation affects trust between superior and subordinate managers. Accounting, Organizations and Society, 34(6–7), 722–737.

Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford Press.

Holton, M., 2007. Implementing ABC in a service-driven business – DHL Worldwide Express. In J. A. Smith (Ed.), Handbook of management accounting (4th ed., pp. 543–554). Oxford: CIMA and Elsevier.

Hoozée, S., & Bruggeman, W. (2010). Identifying operational improvements during the design process of a time-driven ABC system: The role of collective worker participation and leadership style. Management Accounting Research, 21(3), 185–198.

Innes, J., & Mitchell, F. (1990). Activity based costing: A review with case studies. London: CIMA. Ives, B., & Olson, M. H. (1984). User involvement and MIS success: A review of research. Management Science, 30(5),

586–603. Kren, L. (1992). Budgetary participation and managerial performance: The impact of information and environmental

volatility. The Accounting Review, 67(3), 511–526. Kruis, A.-M., & Widener, S. K. (2014). Managerial influence in performance measurement system design: A recipe for

failure? Behavioral Research in Accounting, 26(2), 1–34. Kunz, J. (2015). Objectivity and subjectivity in performance evaluation and autonomous motivation: An exploratory

study. Management Accounting Research, 27, 27–46. Latham, G. P., Winters, D. C., & Locke, E. A. (1994). Cognitive and motivational effects of participation: A mediator

study. Journal of Organizational Behavior, 15(1), 49–63. Lawler, E. E. III. (1994). Total quality management and employee involvement: Are they compatible? Academy of

Management Executive, 8(1), 68–76. Leach-López, M. A., Stammerjohan, W. W., & McNair, F. M. (2007). Differences in the role of job-relevant information

in the budget participation-performance relationship among U.S. and Mexican managers: A question of culture or communication. Journal of Management Accounting Research, 19, 105–136.

Locke, E. A., & Schweiger, D. M. (1979). Participation in decision-making: One more look. In B. M. Staw (Ed.), Research in organizational behavior: An annual series of analytical essays and critical reviews (Vol. 1, pp. 265– 339). Greenwich, CT: Jai Press.

Locke, E. A., Schweiger, D. M., & Latham, G. P. (1986). Participation in decision making: When should it be used? Organizational Dynamics, 14(3), 65–79.

768 S. Hoozée and Q.-H. Ngo

McGowan, A. S., & Klammer, T. P. (1997). Satisfaction with activity-based cost management implementation. Journal of Management Accounting Research, 9, 217–237.

Menguc, B., Auh, S., & Shih, E. (2007). Transformational leadership and market orientation: Implications for the implementation of competitive strategies and business unit performance. Journal of Business Research, 60(4), 314–321.

Mia, L. (1989). The impact of participation in budgeting and job difficulty on managerial performance and work motivation: A research note. Accounting, Organizations and Society, 14(4), 347–357.

Milani, K. (1975). The relationship of participation in budget-setting to industrial supervisor performance and attitudes: A field study. The Accounting Review, 50(2), 274–284.

Moreland, R. L., Argote, L., & Krishnan, R. (1996). Socially shared cognition at work: Transactive memory and group performance. In J. L. Nye & A. M. Brower (Eds.), What’s social about social cognition? Research on socially shared cognition in small groups (pp. 57–84). Thousand Oaks, CA: Sage.

Parker, R. J., & Kyj, L. (2006). Vertical information sharing in the budgeting process. Accounting, Organizations and Society, 31(1), 27–45.

Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 31(4), 623–656.

Pizzini, M. J. (2006). The relation between cost-system design, managers’ evaluations of the relevance and usefulness of cost data, and financial performance: An empirical study of US hospitals. Accounting, Organizations and Society, 31(2), 179–210.

Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531–544.

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.

Rönkkö, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016). Partial least squares path modeling: Time for some serious second thoughts. Journal of Operations Management, 47-48, 9–27.

Roth, G., Assor, A., Kanat-Maymon, Y., & Kaplan, H. (2007). Autonomous motivation for teaching: How self-determined teaching may lead to self-determined learning. Journal of Educational Psychology, 99(4), 761–774.

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.

Schoute, M. (2009). The relationship between cost system complexity, purposes of use, and cost system effectiveness. The British Accounting Review, 41(4), 208–226.

Speklé, R. F., & Widener, S. K. (forthcoming). Challenging issues in survey research: Discussion and suggestions. Journal of Management Accounting Research. Advance online publication. doi:10.2308/jmar-51860

Stasser, G., & Titus, W. (1985). Pooling of unshared information in group decision making: Biased information sampling during discussion. Journal of Personality and Social Psychology, 48(6), 1467–1478.

Swenson, D. (1995). The benefits of activity-based cost management to the manufacturing industry. Journal of Management Accounting Research, 7, 167–180.

Tait, P., & Vessey, I. (1988). The effect of user involvement on system success: A contingency approach. MIS Quarterly, 12(1), 91–108.

Talwar, R. (1993). Business re-engineering – a strategy-driven approach. Long Range Planning, 26(6), 22–40. Tarafdar, M., Tu, Q., & Ragu-Nathan, T. S. (2010). Impact of technostress on end-user satisfaction and performance.

Journal of Management Information Systems, 27(3), 303–334. Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data

Analysis, 48(1), 159–205. Terziovski, M., Fitzpatrick, P., & O’Neill, P. (2003). Successful predictors of business process reengineering (BPR) in

financial services. International Journal of Production Economics, 84(1), 35–50. Turney, P. B. B. (1991). How activity-based costing helps reduce cost. Journal of Cost Management, 4(4), 29–35. Vallerand, R. J., Pelletier, L. G., Blais, M. R., Brière, N. M., Senécal, C., & Vallières, E. F. (1992). The academic

motivation scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and Psychological Measurement, 52(4), 1003–1017.

Vandenbosch, M. B. (1996). Confirmatory compositional approaches to the development of product spaces. European Journal of Marketing, 30(3), 23–46.

Van den Broeck, A., Vansteenkiste, M., De Witte, H., Soenens, B., & Lens, W. (2010). Capturing autonomy, competence, and relatedness at work: Construction and initial validation of the Work-related Basic Need Satisfaction Scale. Journal of Occupational and Organizational Psychology, 83(4), 981–1002.

Van der Stede, W. A., Young, S. M., & Chen, C. X. (2005). Assessing the quality of evidence in empirical man- agement accounting research: The case of survey studies. Accounting, Organizations and Society, 30(7–8), 655–684.

The Impact of Managers’ Participation in Costing System Design 769

Van Iddekinge, C. H., Aguinis, H., Mackey, J. D., & DeOrtentiis, P. S. (forthcoming). A meta-analysis of the interactive, additive, and relative effects of cognitive ability and motivation on performance. Journal of Management. Advance online publication. doi:10.1177/0149206317702220

Vansteenkiste, M., Lens, W., & Deci, E. L. (2006). Intrinsic versus extrinsic goal contents in self-determination theory: Another look at the quality of academic motivation. Educational Psychologist, 41(1), 19–31.

Vansteenkiste, M., Niemiec, C. P., & Soenens, B. (2010). The development of the five mini-theories of self-determination theory: An historical overview, emerging trends, and future directions. In: T. C. Urdan & S. A. Karabenick (Eds.), The decade ahead: Theoretical perspectives on motivation and achievement (Advances in Motivation and Achievement, Vol. 16A, pp. 105–165). Bingley: Emerald Group.

Vansteenkiste, M., Sierens, E., Soenens, B., Luyckx, K., & Lens, W. (2009). Motivational profiles from a self- determination perspective: The quality of motivation matters. Journal of Educational Psychology, 101(3), 671–688.

Venkatraman, N., & Ramanujam, V. (1987). Measurement of business economic performance: An examination of method convergence. Journal of Management, 13(1), 109–122.

Verbeeten, F. H. M., & Speklé, R. F. (2015). Management control, results-oriented culture and public sector performance: Empirical evidence on new public management. Organization Studies, 36(7), 953–978.

Wastell, D. G., White, P., & Kawalek, P. (1994). A methodology for business process redesign: Experiences and issues. Journal of Strategic Information Systems, 3(1), 23–40.

Weber, M. M., Dodd, J. L., Wood, R. E., & Wolk, H. I. (1997). Process improvement through marketing variance analysis. Journal of Business and Industrial Marketing, 12(2), 103–117.

Wiersma, E. (2009). For which purposes do managers use Balanced Scorecards? An empirical study. Management Accounting Research, 20(4), 239–251.

Williams, G. C., Grow, V. M., Freedman, Z. R., Ryan, R. M., & Deci, E. L. (1996). Motivational predictors of weight loss and weight-loss maintenance. Journal of Personality and Social Psychology, 70(1), 115–126.

Wong-On-Wing, B., Guo, L., & Lui, G. (2010). Intrinsic and extrinsic motivation and participation in budgeting: Antecedents and consequences. Behavioral Research in Accounting, 22(2), 133–153.

Appendix. Construct items

Formative/ reflective

Items included

PARTICIPATION Reflective Please indicate to what extent you agree with each of the following statements

regarding your involvement in designing the current costing system. (1: strongly disagree; 4: neutral; 7: strongly agree)

01. I am involved in developing each element in the design of the costing system. X 02. When an element in the design of the costing system is revised, the reasons

provided by my supervisor are logical. X

03. I frequently discuss the elements in the design of the costing system with my supervisor.

X

04. I have a great deal of influence on the elements in the final design of the costing system.

X

05. My contribution to each element in the design of the costing system is very important.

X

06. My supervisor seeks my requests, opinions, or suggestions very frequently when an element in the design of the costing system is changed.

X

AUT_MOTIVATION Formative Why do you or would you put efforts into cost management? (1: not at all; 4:

moderately; 7: completely) 01. Because I have fun doing it X 02. Because what I do in my task is exciting X 03. Because the task I do is interesting X 04. Because I personally consider it important to put efforts in this task X 05. Because putting efforts in this task aligns with my personal values X 06. Because putting efforts in this task has personal significance to me X

(Continued).

770 S. Hoozée and Q.-H. Ngo

Appendix. Continued

Formative/ reflective

Items included

CONT_MOTIVATION Formative Why do you or would you put efforts into cost management? (1: not at all; 4:

moderately; 7: completely) 07. Because I have to prove to myself that I can 08. Because it makes me feel proud of myself 09. Because otherwise I will feel ashamed of myself X 10. Because otherwise I will feel bad about myself X 11. Because others will reward me financially only if I put enough effort in this

task (e.g. employer, supervisor, . . . ) X

12. Because others offer me greater job security if I put enough effort in this task (e.g. employer, supervisor, . . . )

X

13. Because I risk losing my job if I don’t put enough effort in this task X 14. To get others’ approval (e.g. direct superior, colleagues, family, clients, . . . ) X 15. Because others will respect me more (e.g. direct superior, colleagues, family,

clients, . . . ) X

16. To avoid being criticized by others (e.g. direct superior, colleagues, family, clients, . . . )

X

USEFULNESS Reflective Please indicate the degree to which you agree with the proposed statements. (1:

not at all; 4: moderately; 7: completely) 01. The cost information helps us to identify wasted resources. 02. The cost information helps us to identify opportunities for cost reduction. 03. The cost information helps us to control and improve the quality performance. X 04. The cost information helps us to easily update costs of a process when

adjustments in a process are made. X

05. The cost information helps us to identify which activities or processes can be shared across departments.

X

06. The cost information is a key factor to set standards for work process improvement.

X

IMPROVEMENT Reflective How do you rate your contribution to work process improvement in the areas

indicated below? (1: poor; 4: average; 7: good) 01. Reduction of costs of current processes providing products/services X 02. Reduction of process errors (e.g. stoppage, scrap, rework) X 03. Reduction of process lead times (e.g. queue, waiting time) X 04. Controlling work processes to ensure their correctness X 05. Checking work processes to prevent defects in products/services X 06. Redesigning and testing new work processes X 07. Setting standards for improvement of work processes X 08. Continuously evaluating work processes to find opportunities for improvement X

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