European Accounting Review, 2018Vol. 27, No. 4, 747–770, https://doi.org/10.1080/09638180.2017.1375417
The Impact of Managers’ Participation inCosting System Design on Their PerceivedContributions to Process Improvement
SOPHIE HOOZÉE ∗ and QUANG-HUY NGO∗∗
∗Department of Accounting, Corporate Finance and Taxation, Ghent University, Ghent, Belgium; ∗∗Department ofAccounting, 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 businessprocess management, participative decision-making and self-determination theory, we propose that partici-pation in costing system design fosters managers’ perceived contributions to process improvement throughtheir 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 resultssuggest that participation in costing system design increases managers’ autonomous motivation for costmanagement and enhances their perceived usefulness of cost information. Managers’ perceived usefulnessof 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 contributionsto process improvement is, however, not significant. Taken together, our findings imply that contribu-tions to process improvement mainly emerge through informational mechanisms rather than motivationalmechanisms triggered by the participation process.
1. Introduction
Firms often use costing systems to increase their financial performance by improving theirbusiness processes (e.g. Banker, Bardhan, & Chen, 2008). In particular, costing systems mayreveal sources of inefficiency and ineffectiveness of business processes by providing detailedinformation 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 canleverage 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: sophie.hoozee@ugent.be
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 ofparticipation in costing system design on perceived contributions to process improvement interms of its role as an enabler of autonomous motivation for cost management and perceivedusefulness of cost information.
Using survey data from 170 Belgian managers, we find that although managers’ autonomousmotivation for cost management increases as a result of their participation in the costing systemdesign process, this higher autonomous motivation does not stimulate their perceived contri-butions to process improvement. Regarding the informational path, we do find that perceivedusefulness of cost information positively mediates the impact of managers’ participation incosting 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 studycomplements 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-makingby highlighting the importance of informational mechanisms over motivational mechanisms inexplaining why participation could lead to process improvements. This is in line with Lathamet al. (1994), who argued that studies on participation, instead of focusing on motivationalmechanisms, should be redirected to investigate informational mechanisms because the effi-cacy of participation as an organizational process lies not only in its potential to promotemotivation 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 actuallybe used to enhance motivation, we clarify equivocal results of previous research on the linkbetween budgetary participation and motivation (cf. Mia, 1989). In particular, according toBrownell and McInnes (1986), the inconsistent results in budgeting studies investigating theparticipation-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 onself-determination theory to measure autonomous motivation. As such, we also contribute tothe 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 impactof 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 theoreticalbackground and hypotheses development. Section 3 describes the research method. Section 4shows the results of this study. The last section concludes, discusses the limitations and offerssuggestions for future research.
2. Literature Review and Hypotheses Development
Drawing on the literature on business process management, we first introduce the definition ofa business process and the role of costing systems in process improvement. Second, using theliterature 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 contributionsto 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 logicallyrelated 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 activitiesthat takes one or more kinds of input and creates an output that is of value to the customer’. In thecontext of the present study, we conceive of a business process as an umbrella term that combinesvarious 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 bybreaking them down in activities or work processes in order to reveal sources of inefficiencyand ineffectiveness (Davenport & Short, 1990). As such, business process redesign focuseson 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 highlevel 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) calculatevarious 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 tohelp users identify process improvements. For instance, a costing system such as activity-basedcosting may reveal opportunities for process improvement by providing detailed insights into theconsumption 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 thejob, 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 informationand knowledge exchange, which allows better communication and understanding of job require-ments as well as decision-making processes. Hence, informational factors are important for theenhancement 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’ participationin costing system design and their perceived contributions to process improvement is driven byboth motivational and informational effects (see Figure 1).
2.2.1. Motivational EffectsTraditionally, 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 differentoutcomes (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 repeatedlydemonstrated that autonomous motivation is associated with positive outcomes, whereas con-trolled motivation is associated with negative outcomes (e.g. Ryan & Deci, 2000; Vansteenkisteet al., 2006). In addition, the beneficial outcomes of autonomous motivation are also supportedby 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 distinctionbetween intrinsic and extrinsic motivation is important. While intrinsic motivation refers to doingan activity for its own sake because it is interesting and enjoyable, extrinsic motivation refers todoing something in order to attain some separable outcome (Ryan & Deci, 2000). According toself-determination theory, extrinsic motivation may vary in the degree to which it is controlledor 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 thusno 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 hypothesizeits effect on autonomous motivation for cost management. We do not develop a hypothesis for controlled motivation, butwill 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 rewardsor 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, introjectedregulation (being intrapersonally controlled) implies that people perform activities ‘to avoid guiltor anxiety, or attain ego enhancement such as pride’ (Ryan & Deci, 2000, p. 72). This type ofmotivation 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 theenactment of specific behaviors (Assor, Roth, & Deci, 2004).
Autonomous motivation combines identified regulation and intrinsic motivation (e.g. Gagnéet al., 2015).2 Although still extrinsic in nature, identified regulation is considered as autonomousmotivation 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 thatthe 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. Itis characterized by feelings of ‘enthusiasm, spontaneity, excitement, intense concentration, andjoy’ (Roth, Assor, Kanat-Maymon, & Kaplan, 2007, p. 762).
Self-determination theory assumes that three basic psychological needs drive the motivationalmechanisms that energize people’s behavior (Deci & Ryan, 2000). The satisfaction of theseneeds 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 asense 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 forcompetence, is defined as the desire to feel effective in one’s interactions with the social andphysical 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, participationin 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 therebytriggers the sense of relatedness. Third, participation may enhance self-efficacy, which has beenshown 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 thatparticipation 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) usedin 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 themost autonomous form of extrinsic motivation, as it has typically been very difficult to psychometrically distinguishintegration 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 costscould 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, theirfeeling of competence increases thanks to the enhanced recognition and appreciation that theyperceive. Hence, we expect that the satisfaction of the three basic psychological needs throughparticipation in costing system design will encourage managers to identify with the value of costmanagement, such that they experience autonomous motivation for cost management. Accord-ingly, our first hypothesis proposes a positive association between managers’ participation incosting system design and their autonomous motivation for cost management.
H1: Managers’ participation in costing system design is positively associated with their autonomous motivation forcost management.
Although economists argue that external factors such as monetary incentives can reinforceemployees’ 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 extrinsicfactors 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 positivework 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 levelof ability, increased autonomous motivation should result in increased performance because (incontrast 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 additiveand 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. Asa result, they are more inclined to suggest improvement actions. Hence, our second hypothesispredicts that when managers are more autonomously motivated for cost management, they aremore 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 EffectsAlthough participation may foster managers’ motivation, there are also informational benefitsresulting from participation. Informational benefits refer to the dissemination of task-relevantknowledge,3 which participation could facilitate. Information sharing has been shown to play acentral role in the process of participation (Locke & Schweiger, 1979). When people participatein a process, the outcome is of higher quality thanks to information exchange and knowledgetransfer (Latham et al., 1994). Because subordinates often hold more information about theirjobs, 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 inaddition to knowledge dissemination (e.g. Latham et al., 1994). As we are studying improvement processes and notinnovation, 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 ofinformation 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 ofan information system thus allows users to customize the output information according to theirworking 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 ofdetail (e.g. Tarafdar et al., 2010). Hence, because users’ information and knowledge as well astheir desired system features are included through participation, it may foster system quality andacceptance (Ives & Olson, 1984).
As a result of common costs of joint processes, costing system design also requires input frommanagers from other functions. By including them in the participation process, more and betterinformation is obtained (Moreland, Argote, & Krishnan, 1996). In particular, since each individ-ual has partial and biased information about current processes, group discussions may performa 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 theirperception of the usefulness of the information supplied by the costing system or the extent towhich 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 providesmanagers with the opportunity to incorporate their own knowledge into the costing system andenables them to learn from what other managers contribute. This reasoning leads to our thirdhypothesis.
H3: Managers’ participation in costing system design is positively associated with their perceived usefulness of costinformation.
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 processunder consideration means for an organization and then selecting the most critical areas whereit can be redesigned (Davenport & Stoddard, 1994). The improvement of these critical areas isreferred to as the detection of areas of inefficiency and ineffectiveness. Inefficiency implies that aprocess generates too much wasteful resources even though it meets operational goals (Wastell,White, & Kawalek, 1994). Cost disaggregation may reveal redundant activities as it can helpmanagers to monitor the performance of each activity within a process and identify value-addedversus 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 symptomsare 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 canthen explain the actual performance of a process compared with its standard performance in termsof 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 mayprovide better insight into process performance (Carpinetti, Buosi, & Gerólamo, 2003). Hence,through the analysis of activities, users may identify performance problems by detecting sourcesof inefficiency and ineffectiveness at the activity level and, subsequently, develop strategies forimprovement (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 hypothesisproposes a positive association between managers’ perceived usefulness of cost information andtheir perceived contributions to process improvement.
H4: Managers’ perceived usefulness of cost information is positively associated with their perceived contributionsto process improvement.
3. Research Method
3.1. Data Collection
The data used in our study were collected through an online survey. The survey instrument waspretested 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 departmentsof accounting and finance, manufacturing, HR, marketing, R&D, sales or logistics. The emailaddresses were obtained from a Belgian commercial mailing list provider holding approximately300,000 email addresses. We targeted managers who work in companies that have more than 50employees 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 secondphase, we sent a second email to thank the respondents who had completed the survey and toremind the respondents who had not. In total, 354 emails failed to reach target respondents dueto 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 of6.54%. Three respondents were deleted due to problematic answer patterns,4 resulting in a finalsample of 170 observations. To investigate the possibility of non-response bias, an early/laterespondents’ analysis was conducted, in which early and late respondents were, respectivelydefined as having sent back the initial or the replacement questionnaire. The results of the t-testsshow 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 than20 years ago and obtained at least a master degree (70.00%). Most respondents are top-levelmanagers (67.06%) and work at the department of accounting and finance (66.47%). We willcontrol for these two sources of sample heterogeneity (i.e. LEVEL and DEPARTMENT) in ouranalyses. The number of years the respondents have been working in their organizations andcurrent positions varies greatly. The companies in which they work operate in wide range ofdifferent 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-pointLikert scales, unless stated otherwise. We first performed an exploratory factor analysis usingSPSS 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’ characteristicsGender % DEPARTMENT %
Male 78.82 Accounting and finance 66.47Female 20.59 Manufacturing 11.18Not specified 0.59 HR 0.59
Education Marketing 1.76Secondary education or less 1.76 R&D 5.29Professional bachelor 15.88 Sales 2.35Academic bachelor 11.18 Logistics 4.71Master 51.18 Not specified 7.65Postgraduate degree 16.47 Years in organizationPhD 2.35 < 1 5.88Not 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 15.29From 2 to 5 1.76 From 16 to 20 10.00From 6 to 10 7.65 From 21 to 25 10.59From 11 to 15 10.59 From 26 to 30 10.00From 16 to 20 12.35 ≥ 31 5.29From 21 to 25 28.24 Not specified 2.35From 26 to 30 22.35 Years in current position≥ 31 16.47 < 1 6.47Not specified 0.59 From 2 to 5 38.24
Professional level (LEVEL) From 6 to 10 21.18Top-management level 67.06 From 11 to 15 12.94Middle-management level 26.47 From 16 to 20 12.35Lower-management level 5.29 From 21 to 25 3.53Not specified 1.18 From 26 to 30 3.53
≥ 31 0.00Not specified 1.76
Panel B: Companies’ backgroundSector % SIZE %
Processing industry 10.00 50–100 23.53Construction 7.65 101–250 32.94Metal 14.71 251–500 15.29Wholesale and retail trade 12.35 501–1000 10.00Hotel, restaurant, tourism 9.41 1001–2000 7.06Chemical industry 10.00 2001–5000 5.29Energy and water 3.53 5001–10,000 1.18Agriculture and forestry 0.00 ≥ 10,001 3.53Transportation and communication 15.29 Not specified 1.18Banking and insurance 5.88Health care or welfare services 5.29Not specified 5.88
specifically, we conducted principal axis factoring (PAF) analyses using oblique rotation (DirectOblimin) with Kaiser Normalization on all reflective constructs (e.g. Fabrigar, Wegener, Mac-Callum, & Strahan, 1999). The validity of formative constructs (i.e. AUT_MOTIVATION andCONT_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 usedan 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 systemdesign, 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; identifyingproduct/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 thecost of each activity/task providing products/services; choosing the proper allocation methodsand 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 ‘yourinvolvement in designing the current costing system’. Respondents were asked to indicate theirinvolvement in the design of the costing system for six items. The results of the PAF analysisshow 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 samplesizes 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 ofautonomous motivation for cost management. To fit with the purpose of this study, the originalquestion ‘Why do you or would you put efforts into your current job?’ was replaced by the alteredquestion ‘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. Thesecond dimension contains the last three statements and reflects identified regulation. To test thehypotheses, we later merge these two scales and use one construct for autonomous motivationby 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 inthe 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 makingmanagerial decisions (Chenhall & Morris, 1986; Pizzini, 2006). We asked managers to specifythe degree to which they believe cost information is useful for the improvement of their workprocesses through six statements. The results of the PAF analysis show that, after deleting theitem USEFULNESS_01,5 USEFULNESS is a unidimensional construct, with all item loadingsexceeding 0.45.
Perceived contributions to process improvement (IMPROVEMENT). This newly developedinstrument measures managers’ perceptions about their contributions to the improvement of theirwork processes. The survey asked the respondents to rate their contributions for the followingeight 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) checkingwork processes to prevent defects in products/services; (6) redesigning and testing new workprocesses; (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 processimprovement because prior studies have shown that process improvement can be achieved byeliminating 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 andpurchase-to-pay processes (e.g. reducing manual efforts in invoice processing), enhancing datacollection processes for budgeting and reporting, increasing the speed to market of new products,more efficient product registration processes, etc. Although several authors have criticized theuse 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-ratedmeasures (e.g. Dunk, 1993; Parker & Kyj, 2006). Moreover, self-rated measures of subordinateperformance 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 showthat 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 thatour respondents work in and their professional level. More specifically, because the benefits ofparticipation in system design may be dependent on functional expertise (Kruis & Widener,2014), we created a dummy variable DEPARTMENT, which takes value 1 for respondentsworking at the department of accounting and finance (66.47% of the cases, see Table 1), zerootherwise. 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, asholding 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 forthe effect of controlled motivation. Again based on the multidimensional work motivation scaleof Gagné et al. (2015), we use 10 statements to measure the degree of controlled motivation forcost 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 isunidimensional, 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’ssingle-factor test (Podsakoff & Organ, 1986). This test assumes that if a substantial amount ofcommon 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 ofHarman’s single-factor test revealed that no single factor accounts for the majority of the variancein 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 anon-parametric component-based structural equation modelling (SEM) technique. In contrastto 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, astheir 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).
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size (Hair, Ringle, & Sarstedt, 2011). In line with Verbeeten and Speklé (2015), we chose PLSbecause we test a relatively complex model with only 170 observations. An additional argumentfor using PLS is our usage of formative constructs, which are more difficult to model and testwhen 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 betweenthe 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 onEigenvalues above 1.0. One item (USEFULNESS_02) loaded onto an unintended construct. Wedecided 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 ourmodel. 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 comparedto insights into causes of high costs (e.g. quality problems, long throughput times, suboptimalstandards), which are measured by the remaining items.9
After the deletion of two items (i.e. USEFULNESS_01 and USEFULNESS_02), the extractedfactors corresponded with the number of intended constructs.10 All remaining items wereimported into the SmartPLS software. Table 2 shows the item loadings for the multi-itemconstructs.
To assess convergent validity, we examined the average variance extracted (AVE). An AVEvalue 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 thethreshold of 0.50. Moreover, Table 3 shows that all items load on their respective construct witha 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 measuresin the structural equation model do not capture (Hair et al., 2014). To determine discriminantvalidity, we first used the AVE values from Table 4 and found that the square root of the AVE foreach 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 withChin’s (1998) suggestion, all items also load higher on their respective construct than on anyother (see Table 3). These analyses confirm the discriminant validity of our constructs.
Next, we assessed the internal consistency reliability of the measurement model by calculatingthe 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 areabove 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 asopposed 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, theresults may have been different, as they are less able to change the processes but instead have to deal with waste and costreduction directly.10In the final PAF analysis, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.798, which is abovethe suggested rule-of-thumb threshold of 0.5 and indicates adequate sample size. The chi-square value for Bartlett’s testof sphericity was large (3207.25) and significant (p < .001) indicating sufficient correlations among the variables. Takentogether, these two tests indicate that it is safe to proceed with and interpret the factor analysis (Hair et al., 2014, p. 103).
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Table 2. Item loadings
Originalsample
Samplemean
Standarddeviation t-statistics p-values
MOTIVATION_01 → AUT_MOTIVATION 0.538 0.457 0.229 2.352 .019MOTIVATION_02 → AUT_MOTIVATION 0.641 0.541 0.206 3.114 .002MOTIVATION_03 → AUT_MOTIVATION 0.681 0.572 0.184 3.693 .000MOTIVATION_04 → AUT_MOTIVATION 0.860 0.711 0.184 4.665 .000MOTIVATION_05 → AUT_MOTIVATION 0.788 0.656 0.231 3.417 .001MOTIVATION_06 → AUT_MOTIVATION 0.460 0.383 0.249 1.847 .065MOTIVATION_09 → CONT_MOTIVATION 0.843 0.508 0.289 2.914 .004MOTIVATION_10 → CONT_MOTIVATION 0.771 0.475 0.271 2.851 .004MOTIVATION_11 → CONT_MOTIVATION 0.613 0.381 0.269 2.275 .023MOTIVATION_12 → CONT_MOTIVATION 0.570 0.361 0.263 2.170 .030MOTIVATION_13 → CONT_MOTIVATION 0.766 0.476 0.285 2.687 .007MOTIVATION_14 → CONT_MOTIVATION 0.577 0.370 0.235 2.456 .014MOTIVATION_15 → CONT_MOTIVATION 0.676 0.415 0.271 2.497 .013MOTIVATION_16 → CONT_MOTIVATION 0.596 0.386 0.247 2.410 .016PARTICIPATION_01 ← PARTICIPATION 0.825 0.824 0.038 21.828 .000PARTICIPATION_02 ← PARTICIPATION 0.645 0.651 0.070 9.175 .000PARTICIPATION_03 ← PARTICIPATION 0.759 0.747 0.053 14.212 .000PARTICIPATION_04 ← PARTICIPATION 0.875 0.873 0.029 29.899 .000PARTICIPATION_05 ← PARTICIPATION 0.887 0.885 0.026 34.423 .000PARTICIPATION_06 ← PARTICIPATION 0.836 0.833 0.031 27.307 .000IMPROVEMENT_01 ← IMPROVEMENT 0.731 0.725 0.048 15.385 .000IMPROVEMENT_02 ← IMPROVEMENT 0.735 0.734 0.050 14.634 .000IMPROVEMENT_03 ← IMPROVEMENT 0.831 0.830 0.031 26.940 .000IMPROVEMENT_04 ← IMPROVEMENT 0.629 0.623 0.075 8.436 .000IMPROVEMENT_05 ← IMPROVEMENT 0.772 0.770 0.039 19.788 .000IMPROVEMENT_06 ← IMPROVEMENT 0.739 0.739 0.047 15.731 .000IMPROVEMENT_07 ← IMPROVEMENT 0.818 0.813 0.031 26.512 .000IMPROVEMENT_08 ← IMPROVEMENT 0.773 0.771 0.033 23.431 .000USEFULNESS_03 ← USEFULNESS 0.707 0.705 0.065 10.934 .000USEFULNESS_04 ← USEFULNESS 0.801 0.795 0.060 13.327 .000USEFULNESS_05 ← USEFULNESS 0.604 0.597 0.096 6.281 .000USEFULNESS_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, perceivedcontributions to process improvement; USEFULNESS, perceived usefulness of cost information. Details on the itemsused 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), theyare all below the threshold value of 10 (with an upper bound 1.289), confirming that the issue ofmulticollinearity is not present.
4.2. Structural Model
In the assessment of the structural model, the specified structural equations are estimated. Thepath coefficients indicate the strength and direction of the relationships among the latent vari-ables. We assessed statistical significance of parameter estimates using a bootstrap procedurewith 5000 replacements, as suggested by Hair et al. (2011).11 In addition, in line with prioraccounting 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.
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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 0.148MOTIVATION_01 0.538 0.192 0.110 − 0.010 0.204 − 0.127 0.069MOTIVATION_02 0.641 0.263 0.126 0.014 0.233 0.002 0.177MOTIVATION_03 0.681 0.180 0.071 0.046 0.235 0.025 0.218MOTIVATION_04 0.860 0.146 0.064 0.124 0.272 0.068 0.287MOTIVATION_05 0.788 0.237 − 0.003 0.184 0.222 − 0.021 0.230MOTIVATION_06 0.460 0.291 − 0.155 0.152 0.112 0.065 0.185MOTIVATION_09 0.172 0.843 0.059 − 0.061 0.191 − 0.125 0.186MOTIVATION_10 0.154 0.771 0.003 − 0.036 0.180 − 0.099 0.187MOTIVATION_11 0.232 0.613 0.028 0.027 0.156 − 0.156 0.151MOTIVATION_12 0.231 0.570 0.043 0.024 0.145 − 0.180 0.171MOTIVATION_13 0.187 0.766 0.145 0.045 0.198 − 0.113 0.230MOTIVATION_14 0.121 0.577 0.080 0.003 0.142 − 0.170 0.149MOTIVATION_15 0.178 0.676 0.013 − 0.046 0.154 − 0.121 0.035MOTIVATION_16 0.104 0.596 0.031 0.031 0.153 − 0.179 0.128PARTICIPATION_01 0.248 0.208 0.313 0.031 0.825 0.194 0.184PARTICIPATION_02 0.201 0.069 0.243 − 0.076 0.645 0.115 0.132PARTICIPATION_03 0.288 0.189 0.110 0.119 0.759 0.034 0.255PARTICIPATION_04 0.275 0.200 0.201 0.084 0.875 0.237 0.141PARTICIPATION_05 0.287 0.199 0.171 0.170 0.887 0.187 0.234PARTICIPATION_06 0.253 0.211 0.247 0.046 0.836 0.013 0.276IMPROVEMENT_01 0.196 − 0.067 − 0.166 0.731 0.027 0.278 0.275IMPROVEMENT_02 0.118 − 0.048 − 0.194 0.735 − 0.028 0.107 0.186IMPROVEMENT_03 0.047 − 0.043 − 0.270 0.831 − 0.014 0.198 0.228IMPROVEMENT_04 0.140 0.045 − 0.012 0.629 0.173 0.109 0.360IMPROVEMENT_05 0.022 − 0.042 − 0.232 0.772 0.063 0.151 0.258IMPROVEMENT_06 0.016 − 0.087 − 0.263 0.739 0.068 0.120 0.163IMPROVEMENT_07 0.087 − 0.027 − 0.260 0.818 0.149 0.255 0.339IMPROVEMENT_08 0.126 − 0.065 − 0.239 0.773 0.105 0.260 0.246LEVEL − 0.002 − 0.075 − 0.074 0.256 0.155 1.000 0.027USEFULNESS_03 0.249 0.252 0.131 0.241 0.238 − 0.044 0.707USEFULNESS_04 0.201 − 0.003 0.018 0.351 0.144 0.058 0.801USEFULNESS_05 0.131 0.023 0.183 0.181 0.136 0.050 0.604USEFULNESS_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 andfinance 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 variablesare provided in the Appendix.
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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 0.892PARTICIPATION 0.654 0.918 0.893USEFULNESS 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 1.0004. IMPROVEMENT 0.125 − 0.056 − 0.277** 0.7565. PARTICIPATION 0.323** 0.230** 0.255** 0.093 0.8096. LEVEL − 0.002 − 0.075 − 0.074 0.256** 0.155* 1.0007. 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 arethe 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 0.033CONT_MOTIVATION 0.053 0.008IMPROVEMENT 0.290 0.143USEFULNESS 0.068 0.031
(1996) argued that in order to provide sufficient evidence of model fit, it is necessary to examinethe 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 fora 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 endogenousvariables are greater than zero, suggesting sufficient evidence of model fit. Table 6 also reportsthe R2 values.
Next, we examine the magnitude and strength of the paths, where each of our hypothesescorresponds to a specific structural model path (see Figure 2). To allow for the possibilityof un-modelled mediators and the potential direct effect of participation, we also added thedirect effect from managers’ participation in costing system design (PARTICIPATION) to theirperceived contributions to process improvement (IMPROVEMENT). The results suggest thatall but one hypothesized path are significant. More specifically, the path between managers’participation in costing system design (PARTICIPATION) and their autonomous motivationfor cost management (AUT_MOTIVATION) is significant (β = 0.323, p < .001), which sup-ports Hypothesis 1. However, the path between managers’ autonomous motivation for costmanagement (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 systemdesign (PARTICIPATION) and their perceived usefulness of cost information (USEFULNESS)is significant (β = 0.260, p < .01). Finally, the path between managers’ perceived usefulnessof cost information (USEFULNESS) and their perceived contributions to process improvement(IMPROVEMENT) is also significant (β = 0.373, p < .001) and therefore supports Hypothesis
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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) versusotherwise (value 0); LEVEL is a dummy variable that takes value 1 when respondents are top-level managers, zerootherwise. 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 perceivedusefulness of cost information as well as their autonomous motivation for cost management.However, whereas managers’ perceived usefulness of cost information is positively associatedwith their perceived contributions to process improvement, their autonomous motivation for costmanagement 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) ontheir perceived contributions to process improvement (IMPROVEMENT), the direct effect ofmanagers’ participation in costing system design (PARTICIPATION; β = 0.051, p = .563), aswell 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 consideredall 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, wemanually 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 ofmanagers’ participation in costing system design (PARTICIPATION) on their perceived contri-butions to process improvement (IMPROVEMENT) through their perceived usefulness of costinformation (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 testwhether 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 consistencyreliability and multicollinearity in all these additional analyses.
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between 101 and 500, SIZE_LARGE is a dummy variable that takes value 1 when the numberof employees is larger than 500.13 The effect of these two size variables on managers’ perceivedcontributions to process improvement (IMPROVEMENT) is not significant and our other resultsremain consistent.
Second, to investigate whether the impact of managers’ participation in costing system design(PARTICIPATION) on their perceived usefulness of cost information (USEFULNESS) dependson 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 itsinteraction with participation in costing system design (PARTICIPATION) to our model. Nei-ther the main effect of costing system complexity (COMPLEXITY) on perceived usefulness ofcost information (USEFULNESS), nor its interaction with participation in costing system design(PARTICIPATION) is significant. As in the previous additional analysis, our other results remainconsistent.
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 wouldonly 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 alternativeoperationalization. Hence, we conclude that the variable PARTICIPATION is measuring what itis expected to measure.
Finally, given that PLS has been heavily criticized by some influential methodologists becauseit 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 statisticalmethod. More specifically, we performed a regression-based mediation analysis based on averageconstruct scores using Hayes’ (2013) PROCESS macro for SPSS. We find consistent evidencefor Hypothesis 1 (β = 0.208, p < .001), Hypothesis 3 (β = 0.201, p < .001) and Hypothesis4 (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’ participationin costing system design (PARTICIPATION; β = 0.029, p = .640), as well as the effect of thedepartment that they work in (DEPARTMENT; β = −0.743, p < .0001) and their professionallevel (LEVEL; β = 0.449, p < .01). The indirect effect of managers’ participation in costingsystem design on their perceived contributions to process improvement through their perceivedusefulness 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 systemdesign 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 constructscores instead of PLS, we conclude that our results are robust to using this alternative statisticalmethod.
13This categorization is in line with, for instance, the study of Du, Deloof, and Jorissen (2013) who also collectedBelgian data (to investigate the impact of headquarters-subsidiary interdependencies on performance evaluation andreward 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 two8-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. Theresults of our survey show that managers’ participation in costing system design is positivelyassociated with both their autonomous motivation for cost management and their perceivedusefulness of cost information. However, only managers’ perceived usefulness of cost infor-mation is significantly related to their perceived contributions to process improvement. Ourdata do not support the predicted effect for autonomous motivation. In particular, althoughparticipation in costing system design enhances managers’ autonomous motivation for costmanagement, 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 matterof better information and knowledge sharing rather than a higher autonomous motivation forreducing costs.
Our findings are in line with prior studies that have shown informational mechanisms drive therelationship between participation and performance. Chenhall and Brownell (1988) found thatbudgetary participation provides information that can reduce role ambiguity, which, in turn, maystimulate efforts to improve performance. Similarily, Chalos and Poon (2000), Chong and Chong(2002) and Kren (1992) also demonstrated the informational role of budget participation, whichcan enable individuals to enhance their performance. Consistent with these studies, our resultsshow that participation in costing system design allows managers to understand the usefulnessof 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, ourresponse 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 comparableto 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 ofimpression 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. Futureresearch may, however, test the robustness of our findings by using an objective measure ofprocess improvement. In this respect, it may also be particularly insightful to add ability to theresearch model as it has been shown to be a better predictor of objective performance measuresthan 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’ costingsystem and work process knowledge may vary greatly. Although this might have increasedthe noisiness of our measures, we believe that all respondents had the knowledge necessaryto answer the questions. In particular, we avoided academic jargon (Speklé & Widener, forth-coming; Wiersma, 2009), such as activity-based costing, and provided examples of operationalwork processes to ensure that our conception of a business process as an umbrella term wouldcapture the most important work processes.
Fourth, we acknowledge that there may be an overlap in the arguments leading up to thefirst and third hypothesis (i.e. the motivational and the informational effect of participation). Inparticular, although self-determination theory is only about a feeling of competence instead ofactual competence, because we measured this feeling after the participation process, it is possiblethat it has been influenced by the increased competence acquired during the participative learningprocess. However, as we do not find support for our second hypothesis (which predicted anassociation 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 thatstimulates contributions to process improvement, but the enhanced insights themselves.
Finally, regarding potential sampling and generalizability issues, given that our theory is aboutmanagers, we consider them to be appropriate respondents. Moreover, as they work in firms ofdifferent sizes operating in a wide range of industries, we believe our sample is sufficiently broadto 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 ingeneral 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 incosting system design and perceived contributions to process improvement, especially as thisgroup might have a different view on the usefulness of cost information (as we conjecture basedon the results of our factor analyses).
To conclude, we would like to offer two more avenues for future research. A first would beto examine the impact of participation in the design process of a costing system in differentoperating environments, such as static versus dynamic environments, since the usefulness ofcost information for process improvement might differ in different environments. For instance,information exchange and knowledge transfer among employees may be more important fororganizations operating in rapidly changing environments (Lawler, 1994). Second, despite thefact that we found managers’ perceived contributions to process improvement to be mainlydriven by informational mechanisms, this does not imply that increased autonomous motivationfor 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 enhanceefficient 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 morewith 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 extentthan in organizations following a differentiation strategy.
AcknowledgementsSuggestions from and/or discussions with Josep Bisbe, Werner Bruggeman, Johan Dergård, Patricia Everaert, MirjamKnockaert, Falconer Mitchell, Anja Van den Broeck, Heidi Vander Bauwhede and especially Anne-Marie Kruis werevery much appreciated. We also would like to acknowledge the helpful comments we received from participants atthe 8th Conference on Performance Management and Management Control (Nice, France, 30 September–2 October2015), 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 inManagement Accounting (Brussels, Belgium, 14–16 December 2016) and workshops at Ghent University. Finally, we aregreatly indebted to Sally Widener and two anonymous reviewers for their extremely constructive and thought-provokingfeedback throughout the entire review process.
FundingThis project has been funded with support from the European Commission. This manuscript reflects the views only ofthe authors, and the Commission cannot be held responsible for any use which may be made of the information containedtherein.
ORCIDSophie Hoozée http://orcid.org/0000-0002-7046-5853
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Appendix. Construct items
Formative/reflective
Itemsincluded
PARTICIPATION ReflectivePlease 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. X02. 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 mysupervisor.
X
04. I have a great deal of influence on the elements in the final design of thecosting system.
X
05. My contribution to each element in the design of the costing system is veryimportant.
X
06. My supervisor seeks my requests, opinions, or suggestions very frequentlywhen an element in the design of the costing system is changed.
X
AUT_MOTIVATION FormativeWhy 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 X02. Because what I do in my task is exciting X03. Because the task I do is interesting X04. Because I personally consider it important to put efforts in this task X05. Because putting efforts in this task aligns with my personal values X06. Because putting efforts in this task has personal significance to me X
(Continued).
770 S. Hoozée and Q.-H. Ngo
Appendix. Continued
Formative/reflective
Itemsincluded
CONT_MOTIVATION FormativeWhy 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 can08. Because it makes me feel proud of myself09. Because otherwise I will feel ashamed of myself X10. Because otherwise I will feel bad about myself X11. 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 X14. To get others’ approval (e.g. direct superior, colleagues, family, clients, . . . ) X15. 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 ReflectivePlease 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. X04. 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 beshared across departments.
X
06. The cost information is a key factor to set standards for work processimprovement.
X
IMPROVEMENT ReflectiveHow 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 X02. Reduction of process errors (e.g. stoppage, scrap, rework) X03. Reduction of process lead times (e.g. queue, waiting time) X04. Controlling work processes to ensure their correctness X05. Checking work processes to prevent defects in products/services X06. Redesigning and testing new work processes X07. Setting standards for improvement of work processes X08. Continuously evaluating work processes to find opportunities for improvement X
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