Melco Management Accounting Research
Discussion Paper Series
No.MDP2018-001
How Do Management Accounting Capabilities Affect Organizational
Performance?

May 2018

Kazunori Fukushima*
Associate Professor, Chuo University, Faculty of Commerce
742-1 Higashinakano, Hachioji-shi, Tokyo, 192-0393 Japane-mail: k-fuku@tamacc.chuo-u.ac.jp
Takehiro Metoki
Associate Professor, Musashi University
Faculty of Economics Department of Management*Corresponding author

The Melco Foundation
Nagoya, Japan
Discussion Paper Series of the Melco Management Accounting Research, in order to promote
the study of management accounting, has published this unfinished paper onto the Web. Pleaseobtain the permission of the author when citing this paper.

How Do the Management Accounting Capabilities Impact on
Organizational Performance?

Kazunori Fukushima
Chuo University

Takehiro Metoki
Musashi University

Abstract
This study explores the relationship between contemporary performancemeasurement systems (CPMS) use and organizational performanceorganizational in term of organizational (i.e., management accounting)capabilities. Data collected from our mail-based questionnaires reveal thatabsorptive capacity and experiential learning capabilities in CPMS use,
one of the organizational capabilities of management accounting, play
important roles in organizational performance improvement. Our results
also show that absorptive capacity and experiential learning capabilities
have opposite effects that are conditioned by the specific situation.

Keywords
Management Accounting Capabilities, Absorptive Capacity, Experiential Learning
Capabilities, Contemporary Performance Measurement System, Organizational
Performance, Exploratory Study

11 Introduction
How does the use of contemporary performance measurement systems (CPMS)
impact organizational performance? The CPMS is often used interchangeably with othersystems, such as the balanced scorecard (Kaplan and Norton 1996), strategic
performance measurement systems (Chenhall, 2005; Ittner et al., 2003), andcomprehensive performance measurement systems (Hall, 2008). Many studies haveinvestigated the relationship between CPMS use and organizational performance(Banker et al., 2000; Davis and Albright, 2004; Ittner and Larker, 1998; Ittner et al.,
2003; Said et al., 2003); however, none has been able to identify a specific relationshipbetween these two variables (Ittner and Larcker, 2009). Several studies have tried to
explain this relationship by hypothesizing that CPMS use affects organizational
processes such as organizational behavior or organizational capabilities andperformance (Grafton et al., 2010; Henri, 2006; Widener, 2007). If such a relationshipexists, however, clearly determining the exact nature of the relationship between CPMS
use and organizational performance will be difficult (Franco-Santos et al., 2012).
Furthermore, it remains to be explained why the use of similar kinds of CPMS andorganizational processes can affect organizational performance differently.
Incorporating a perspective on the organizational capabilities to use management
accounting systems may help resolve these problems. For example, it has been revealed
that organizational capabilities related to target costing may improve results and prevent
negative effects (Yook, 2003; Yoshida, 2003). Other studies have discussed themoderating impact of strategic orientation (Chanhall and Langfield-Smith, 1998; Ittner
et al., 2003; Perera et al., 1997; Van der Stede et al., 2006), organizational structure andcompetition (Lee and Yang, 2010), and environmental uncertainty (Hoque, 2004), while
Franco-Santos et al. (2013) have suggested that other factors should be investigated. It
is expected that the extent of these organizational capabilities influences theeffectiveness of management accounting system use and organizational performance.
Therefore, by hypothesizing that organizational capabilities related to management
accounting (management accounting capabilities) moderate the effect of CPMS onorganizational performance, we may reveal how CPMS use could improveorganizational performance. Then, this study may be able to contribute to extant bodyliteratures that has sought to explore what the effects of CPMS on organizational
performance by introducing moderating effects.
This study examines the relationship between CPMS use, management
accounting capabilities to use such systems effectively, and organizational performance2

by employing empirical evidence based on data obtained from responses to mail-basedquestionnaires.

We focus on the CPMS, which is strongly associated withorganizational performance, as well as absorptive capacity and experiential learning
capabilities, among the organizational capabilities required to utilize management
accounting systems effectively.
The remainder of this paper is structured as follows. Section 2 reviews the literature andpresents the study’s research questions. The study’s research methodologies, researchsetting, and variable measurement are explained in Section 3. Finally, Section 4
provides the results of the analysis and discusses them.

2 Literature
2.1 Management accounting capabilities
Few studies attempt to closely investigate the organizational capabilities to use
management accounting systems effectively or reveal how these capabilities affects
management accounting use (Yook, 2003; Yoshida, 2003). However, some studies dosuggest the existence of such organizational capabilities (Kaplan and Norton, 1996;
Tani et al., 1994). For example, Kaplan and Norton (1996) reveal that, while newly
introduced balanced scorecards do not necessarily begin to function immediately,organizations could successfully adopt this system gradually, through trial anderror. Tani et al. (1994) examine data collected by a mail-based survey on the use oftarget costing and show that the long-term use of target costing could enableorganizations to use this system for multiple purposes rather than for only what was
originally planned. These studies suggest that, while using management accounting
systems, organizations could store knowledge on how to use them effectively. Yoshida(2001a, 2001b, 2003) explicitly reveals the existence of the organizational capabilities
to use management accounting systems effectively, naming the organizational capacity
related to target costing “target costing capabilities” and examining its effects on theoutcomes and adverse results of target costing. The studies reveal that accumulatingknowledge related to target costing enhanced target costing capabilities and prevented
burnout, an adverse effect of target costing. Yook (2003) shows that higher target
costing capabilities improves target costing outcomes. All these studies suggest that theorganizational capacity to utilize management accounting systems effectively has apositive effect on organizational performance.
What are the organizational capabilities that enable organizations to use
management accounting systems more effectively? Management information systems
3

that include management accounting systems must produce sustainable competitive
advantage and fit the organization’s management objectives (Mata et al., 1995). To
make the best use of its knowledge, an organization must continuously acquire
knowledge from both external and internal sources (Kogut and Zander, 1992; Teece etal., 1997).
2.2 Absorptive capacity
Absorptive capacity may be conceptually key to the acquisition and effective useof knowledge gleaned from outside the organization (Zahra and George, 2002). The
absorptive capacity of an organization is its capabilities to recognize and incorporate thevalue of new knowledge and information taken from outside the organization and toadapt them to existing knowledge and organizational routines (Cohen and Levinthal,
1990; Zahra and George, 2002). The extent of the organization’s absorptive capacity
affects its performance (Lane et al., 2006; Szulanski, 1996). It has also been found thatthe extent of absorptive capacity plays an important role in the introduction and use ofmanagement accounting systems (Fayrad et al., 2012; Libby and Waterhouse, 1996;
Williams and Seaman, 2001). For example, Libby and Waterhouse (1996) and Williams
and Seaman (2001) suggest that absorptive capabilities facilitate the introduction of new
management accounting systems.
In addition, Fayard et al. (2012) suggest that using inter-organizational costmanagement and information sharing with external organizations such as suppliers
encourage organizations to use inter-organizational cost management by increasing thedepth, frequency, ease of communication, and absorptive capacity in their explorationand evaluation of knowledge.
Thus, absorptive capacity encourages organizations to introduce and employ
sophisticated management accounting systems. Some studies examine the moderating
effect of absorptive capacity (Fernhaber and Patel, 2012; Kauppi et al., 2013). Kauppi etal. (2013) reveal that a purchasing function’s absorptive capacity moderates the effect ofelectronic purchasing tolls on category-level purchasing performance, while Fernhaberand Patel (2012) reveal that having high absorptive capacity and building a complex
portfolio of products can be beneficial and that having low absorptive capacity can beharmful. Therefore, absorptive capacity is one of the organizational capabilities thatenable better management accounting systems performance. We thus pose the following
research question:

4

RQ1: What effect does absorptive capacity in CPMS use have on the relationshipbetween CPMS use and organizational performance?
2.3 Experiential learning capabilities
Experiential learning may be conceptually key to acquiring and using knowledge
gleaned from inside the organization (Kolb, 1984; Yeung et al., 1999). Experiential
learning is the process of learning through experience in order to acquire new ideas(Kolb 1984). Experiential learning occurs not only among individuals but also amongorganizations, which must reflect upon their organizational experience in order toacquire new ideas (Argote et al., 2001; Edmondson, 2002; Kayes et al., 2005). Somestudies indicate that experiential learning exerts a positive influence on theimplementation of management accounting systems (Kaplan and Norton, 1996; Tani etal., 1994). For example, Kaplan and Norton (1996) argue that establishing targets andtolerances for a balanced scorecard requires a trial-and-error process. Tani et al. (1994)
found that, when first implemented, target costing was meant to reduce costs but was
ultimately used to achieve other objectives, including the timely introduction of new
products and the satisfaction of consumer needs. These studies suggest thatexperimental learning about the use of management accounting helps firms realize thedesired results of management accounting. We thus pose the following researchquestion:
RQ2: What effect does experiential learning capabilities in CPMS use have on therelationship between CPMS use and organizational performance?

3 Methodology
3.1 Research methods and data
We conducted a mail-based survey of 1,524 Japanese manufacturing firms listed
in stock exchanges around Japan, targeting individuals responsible for businessperformance. We posted the questionnaires in July 2016 with a submission deadlineof August 2016. To increase the response rate, we sent a follow-up chaser
in September 2016. A total of 236 questionnaires were collected, for a response rate of
15.5%. We used data from 220 individuals, and 16 questionnaires with missing were
excluded (Table 1).
Two tests were conducted to assess whether the data suffered from any response
bias. First, a comparative analysis (t-test) between the responding and non-responding
5

firms indicated no significant differences in size (in terms of sales and number of
employees). Second, a chi-square goodness-of-fit test between the sample andresponding firms indicated no significant difference in industry distributions. These
results suggest that the empirical data display no response bias.
Table 1. Demographic data
PanelA:
Industry
Industry classification
classification

Sent

Received

3050

Foods

134

17

12.7%

3100

Textiles and apparels

55

6

3150

Pulp and paper

26

4

3200

Chemicals

215

3250

Pharmaceutical

3300

Oil and coal products

3350

Panel B: Size
Number of employees
0 – 25034

10.9%

250 – 500

36

15.4%

500 – 1,000

36

30

14.0%

1,000 – 2,500

41

65

4

6.2%

2,500 – 5,000

27

13

1

7.7%

5,000 –

46

Rubber products

19

4

21.1%

3400

Glass and ceramics

61

8

13.1%

3450

products
Iron
and steel

48

8

16.7%

3500

Nonferrous metals

35

5

14.3%

3550

Metal products

91

14

15.4%

3600

Machinery

234

46

19.7%

3650

Electric appliances

266

36

13.5%

3700

Transportation

99

15

15.2%

3750

equipment
Precision
instruments

51

5

9.8%

3800

Other products

112

17

15.2%

Table 2. Questionnaire items and descriptive statics
Items in questionnaire

Range

Mean

S. D.

Theoretical Actual
PMS_C1 Setting financial performance target

1–5

3–5

4.66

0.50

PMS_C2 Setting measureable non-financial performance target

1–5

1–5

3.48

0.96

PMS_C3 Setting non-financial performance target, which is difficult to measure

1–5

1–5

3.33

0.95

PMS_C4 Evaluating financial performance

1–5

3–5

4.58

0.57

PMS_C5 Evaluating measureable non-financial performance

1–5

1–5

3.43

0.95

PMS_C6 Evaluating non-financial performance, which is difficult to measure

1–5

1–5

3.31

0.93

PMS_C7 Performance goals for realizing a strategy

1–5

1–5

4.17

0.84

PMS_C8 Relationship between the performance measures

1–5

1–5

3.80

0.91

PMS_C9 Action plan for achieving performance target

1–5

2–5

4.12

0.79

PMS_U1 Presentation of the cause of the problem

1–5

1–5

3.81

0.86

PMS_U2 Provision of the materials required for decision-making

1–5

1–5

3.83

0.78

PMS_U3 Support for decision-making

1–5

1–5

3.74

0.80

PMS_U4 Provision of the materials required for verifying the results of decision- making

1–5

1–5

3.69

0.76

PMS_U5 Understanding the results of business activities

1–5

2–5

4.43

0.65

PMS_U6 Understanding the progress of achieving the goals

1–5

2–5

4.48

0.63

PMS_U7 Relationship between the performance measures

1–5

2–5

4.47

0.68

PMS_U8 Action plan for achieving performance target

1–5

1–5

3.86

0.87

PMS_U9 Link between the performance and monetary rewards of general manager

1–5

1–5

3.79

0.84

PMS_U10 Link between the performance and monetary rewards of manager

1–5

1–5

3.51

0.80

6

PMS_U11 Link between the performance and monetary rewards of subsection chief

1–5

1–5

3.09

0.90

PMS_U12 Link between the performance and promotion of general manager

1–5

1–5

3.66

0.79

PMS_U13 Link between the performance and promotion of manager

1–5

1–5

3.45

0.76

PMS_U14 Link between the performance and promotion of subsection chief

1–5

1–5

3.16

0.86

AC_1

Acquiring knowledge about PMS from the president or director

1–5

1–5

3.61

0.98

AC_2

Acquiring knowledge about PMS from consultant

1–5

1–5

2.67

1.06

AC_3

Acquiring knowledge about PMS from off-site exchanges

1–5

1–5

2.40

0.98

AC_4

Recording and accumulation of knowledge about PMS

1–5

1–5

3.33

0.90

AC_5

Determining the usefulness of knowledge about PMS quickly

1–5

1–5

3.06

0.80

AC_6

Discussing about improvement plan of PMS based on acquired knowledge

1–5

1–5

3.47

0.87

AC_7

Reflection of improvement plan of PMS discussed

1–5

1–5

3.50

0.84

ELC_1

Review types of performance indicators

1–5

1–5

3.62

0.88

ELC_2

Review performance target tightness

1–5

2–5

3.77

0.82

ELC_3

Review relationship between performance goals and business strategy

1–5

2–5

3.88

0.80

ELC_4

Review relationship between the performance measures

1–5

1–5

3.36

0.84

ELC_5

Review utilization of PMS

1–5

1–5

3.36

0.78

ELC_6

Refine types of performance indicators

1–5

1–5

3.81

0.82

ELC_7

Refine performance target tightness

1–5

2–5

3.96

0.79

ELC_8

Refine relationship between performance goals and business strategy

1–5

2–5

3.82

0.75

ELC_9

Refine relationship between the performance measures

1–5

1–5

3.43

0.78

ELC_10Refine utilization of PMS

1–5

1–5

3.42

0.77

Table 3. Factor analysis of variables
Panel ACPMS components

FPM

NFPM

SPM

PMS_C6 Evaluating non-financial performance, which is difficult to measure

0.853-0.073-0.031

PMS_C3 Setting non-financial performance target, which is difficult to measure

0.819-0.005-0.047

PMS_C5 Evaluating measureable non-financial performance

0.767

0.041

0.056

PMS_C2 Setting measureable non-financial performance target

0.708

0.089

0.066

PMS_C1 Setting financial performance target-0.0031.024-0.052

PMS_C4 Evaluating financial performance target

0.021

0.804

0.125

PMS_C8 Relationship between the performance measures

0.003-0.0800.809

PMS_C9 Action plan for achieving performance target

0.006

0.066

0.719

PMS_C7 Performance goals for realizing a strategy

0.059

0.185

0.532

28.3%

20.7%

16.8%

PfP

PE

DM

PMS_U13 Link between the performance and promotion of manager

0.805-0.114

0.087

PMS_U10 Link between the performance and monetary rewards of manager

0.803

0.269

-0.150PMS_U14 Link between the performance and promotion of subsection chief

0.733-0.2480.144

PMS_U11 Link between the performance and monetary rewards of subsection chief

0.731-0.005-0.001

PMS_U12 Link between the performance and promotion of general manager

0.599-0.001

0.133

PMS_U9 Link between the performance and monetary rewards of general manager

0.522

0.361

-0.044

PMS_U5 Evaluating measureable non-financial performance

0.002

0.872

0.027

PMS_U7 Relationship between the performance measures-0.034

0.824

0.132

PMS_U6 Understanding the progress of achieving the goals

0.027

0.818

0.050

PMS_U8 Action plan for achieving performance target

0.039

0.487

0.198

Variance explained

Panel B

CPMS purpose

7

PMS_U4 Provision of the materials required for verifying the results of decision- making-0.061

0.041

0.789

PMS_U2 Provision of the materials required for decision-making

0.053

0.047

0.781

PMS_U3 Support for decision-making

0.036

0.066

0.750

PMS_U1 Presentation of the cause of the problem

0.074

0.138

0.537

22.0%

19.7%

17.6%

Variance explained

Panel C

Absorptive capacity

Utilizing

Acquiring

AC_6

Discussing about improvement plan of PMS based on acquired knowledge

0.917-0.051

AC_7

Reflection of improvement plan of PMS discussed

0.896-0.034

AC_5

Determining the usefulness of knowledge about PMS quickly

0.696

0.134

AC_4

Recording and accumulation of knowledge about PMS

0.594

0.131

AC_2

Acquiring knowledge about PMS from consultant

0.028

0.693

AC_3

Acquiring knowledge about PMS from off-site exchanges-0.025

0.644

AC_1

Acquiring knowledge about PMS from the president or director
Variance explained

0.070

0.394

36.4%

16.7%

Panel D

Experiential learning capabilities

ELCAPA

ELC_3

Review relationship between performance goals and business strategy

0.804

ELC_8

Refine relationship between performance goals and business strategy

0.799

ELC_4

Review relationship between the performance measures

0.786

ELC_6

Refine types of performance indicators

0.786

ELC_7

Refine performance target tightness

0.785

ELC_10Refine utilization of PMS

0.775

ELC_2

Review performance target tightness

0.771

ELC_5

Review utilization of PMS

0.761

ELC_1

Review types of performance indicators

0.754

ELC_6

Refine types of performance indicators

0.754

Variance explained

60.1%

3.2 Variables measurement and setting
3.2.1 CPMS
Among the new concepts concerning performance management systems that havebeen introduced in recent years is CPMS, which is also known as “strategic
performance

management

systems”(or“strategic

performancemeasurement

systems”). Although these modern performance management systems are defined invarious ways, they are similar to CPMS in their components—as they use both financial
and non-financial indicators of performance and performance measures linked to
strategy and show explicit and implicit cause-and-effect relationships among
measures—and in their purpose, which is to evaluate organizational performance inorder to inform decision making (Franco-Santos et al., 2012).
We measured the CPMS components using nine questions (see Table 2,
PMS_C1–PMS_C9) and measured CPMS purpose using 14 questions (see Table 2,
8

PMS_U1–PMS_U14) suggested by Franco-Santos et al. (2013). An exploratory factor
analysis on CPMS components resulted in three factors within constructs above 0.4 (seeTable 3, Panel A). The first factor relates to the setting and evaluation of non-financial
measures presented in a non-financial performance measurement system (α = 0.88). The
second factor relates to the setting and evaluation of financial measures presented in afinancial performance measurement system (α = 0.88). The third factor relates toperformance measures linked to strategy and the showing of explicit and implicit
cause-and-effect relationships among the measures presented in strategic performancemeasurement systems (α = 0.70).
Another exploratory factor analysis using polychoric correlation matrix on CPMS
purpose resulted in three factors within constructs above 0.4 (see Table 3, Panel B). The
first factor relates to performance-influenced monetary rewards and promotion in
pay-for-performance use (α = 0.88). The second factor relates to the evaluation oforganizational performance in performance evaluation use (α = 0.84). The third factor
relates to the checking or analysis of decision making in order to inform managerial
decision making (α = 0.85). As in the other scales, the high reliability estimate allowed
for the calculation of a mean that could serve as the composite score for these scales.

3.2.2 Absorptive capacity
We investigated the organizational features of effective CPMS use in key
business sectors. As we could not use the measurement items employed in existing
management accounting studies on the effective use of CPMS, we developed sevenquestions (see Table 2, AC_1–AC_7) based on Jansen et al. (2005) and Lichtenthaler(2009). The results of exploratory factor analysis extracted two factors showing thecharacteristics of the absorptive capacity (see Table 3, Panel C). The first factor
concerns the transforming acquired knowledge and exploitation it to effective use of
CPMS (α = 0.88). The second factor concerns the acquiring knowledge existing outside
the organization (α = 0.62). Like the other scales, the high reliability estimate allowed
for the calculation of a mean that could serve as the composite score for these scales.
Because, the absorptive capacity of an organization is its capabilities to not only
recognize and incorporate the value of new knowledge taken from outside theorganization and but also adapt it to existing knowledge and organizational routines(Cohen and Levinthal, 1990; Zahra and George, 2002), we calculate the product of thetwo factors.

9

3.2.3 Experiential learning capabilities
We measured experiential learning capabilities using 10 questions (see Table 2,
ELC_1–ELC_10) that asked respondents to assess the extent to which theirorganizations not only reviewed performance measurement activities and businessstrategies but also refined them. These questions were developed on the basis of past
research on experiential learning (Huber, 1991; Kolb, 1984). The results of the factor
analysis revealed one major factor that shows the characteristic of experiential learning(see Table 3, Panel D), which was named "experiential learning capabilities" (α = 0.91).
For the operation of the variables, we scored the average value of the 10 questions.
3.2.4 Organizational performance
How best to measure organizational performance has been the subject of debate.
Groot and Selto (2013) argue that a discussion based on subjective measures of
performance has merit because it is difficult to describe the relationship between the useof performance management systems and objective performance. However, otherstudies suggest that examining the impact of management accounting and control
systems such as CPMS on organizational performance requires objective performancemeasures (Smith 2011). In this study, organizational performance is measured throughobjective instruments using return on sales (ROS) and return on equity (ROE).

4 Findings
4.1 Method of analysis
We explore the research questions on how the absorptive capacity andexperiential learning capabilities related to CPMS affect the relationship between
CPMS and organizational performance. We test the relations using OLS (ordinary leastsquares) regression through the following models:
PERFi = β0 + β1FPMi + β2NFPMi + β3SPMi + β4PEi + β5DMi + β6PfPi + β7ACAPi +β8FPMi * ACAPi + β9NFPMi * ACAPi + β10SPMi * ACAPi + β11PEi * ACAPi+ β12DMi * ACAPi + β13PfPi * ACAPi + β14-16 (CONTROLSi) + εi
(1)
PERFi = β0 + β1FPMi + β2NFPMi + β3SPMi + β4PEi + β5DMi + β6PfPi + β7ELCAPAi +β8FPMi * ELCAPAi + β9NFPMi * ELCAPAi + β10SPMi * ELCAPAi + β11PEi *ELCAPAi + β12DMi * ELCAPAi + β13PfPi * ELCAPAi + β14-16 (CONTROLSi)
+ εi

(2)

10

PERFi = β0 + β1FPMi + β2NFPMi + β3SPMi + β4PEi + β5DMi + β6PfPi + β7ACAPi +β8ELCAPAi + β9FPMi * ACAPi + β10NFPMi * ACAPi + β11SPMi * ACAPi +β12PEi * ACAPi + β13DMi * ACAPi + β14PfPi * ACAPi + β15FPMi * ELCAPAi+ β16NFPMi * ELCAPAi + β17SPMi * ELCAPAi + β18PEi * ELCAPAi +β19DMi * ELCAPAi + β20PfPi * ELCAPAi + β21-23 (CONTROLSi) + εi(3)where PERF represents firm performance; FPM, NFPM, SPM, PE, DM, and PfP
represent the emphasis placed on financial performance measurement systems,
non-financial performance measurement systems, strategic performance measurement
systems, performance evaluation use, decision making use, and pay-for-performance
use respectively; ACAP represents absorptive capacity; ELCAPA represents
experiential learning capabilities; and * ACAP and * ELCAPA represent the interaction
effects of CPMS components/purposes and absorptive capacity and experiential
learning capabilities.
4.2 Result
A correlation matrix is presented in Figure 3, and Figure 4 presents the regression
results. Model 1 represents the baseline regression that includes the individual CPMS
components and purposes and the firm control variables. Models 2 and 3 introduce theinteraction terms of absorptive capacity (Model 2), as shown in equation (1), andexperiential learning capabilities (Model 3), as shown in equation (2). Model 4 presents
the full model, as shown in equation (3). Prior to the construction of the interaction andinteraction terms, the independent variables are mean-centered to reduce the potential
effects of multicollinearity (Cohen et al. 2003). The maximum VIF across the models is
5.43, which is well below the general threshold of 10 (Hair et al., 2006).
First, Model 3a shows that the interaction of NFPM * ELCAPA is positive andsignificant for ROS (β = 0.134, p < 0.10); on the other hand, Model 3b shows that theinteraction of PE * ELCAPA is negative and significant for ROE (β = -0.236, p < 0.05).
These models show that the interaction effects of experiential learning capabilities and
CPMS components and purposes are uncertain. When the interaction terms ofabsorptive capacity are introduced, models 4a and 4b show similar interaction effects:
the interaction effect of DM * ELCAPA is positive and significant for ROS (β = 0.319,
p < 0.01) and for ROE (β = 0.242, p < 0.05), while the interaction effect of PE *ELCAPA is negative and significant for ROS (β = -0.127, p < 0.05) and for ROE (β =-0.415, p < 0.01).
11

Second, Model 2a shows that introducing the interaction terms of absorptive
capacity and CPMS components and purposes has no significant interaction effects.
Models 4a and 4b, introducing the interaction terms of experiential learning capabilities,
show that the interaction effect of PE * ACAP is positive and significant for ROS (β =0.319, p < 0.05) and for ROE (β = 0.254, p < 0.10), while the interaction effect of DM *ACAP is negative and significant for ROS (β = -0.262, p < 0.05) and for ROE (β =-0.248, p < 0.10).
Table 4. Correlation matrix of the variables in the study
Mean

S.D. Actual Range(1)(1) FPM

4.63

0.510

3.00 – 5.00

1(2) NFPM

3.39

0.819

**
1.00 – 5.00 0.235

0.670

**

(3) SPM

4.04

(2)

(3)

(5)

(7)

4.33

0.565

**
2.00 – 5.00 0.547 0.239 0.592(5) DM

3.79

0.649

**
**
**
**
2.00 – 5.00 0.299 0.263 0.579 0.529

**

**

**

**
1.00 – 5.00 0.170 0.220 0.299 0.230 0.294(7) ACAP

**
**
**
**
**
**
10.16 4.000 1.80 – 25.00 0.209 0.296 0.444 0.384 0.503 0.344

(12)

**

**

**

**

1

3.64

0.640

**
1.80 – 5.00 0.407 0.371 0.584 0.605 0.621 0.332 0.556(9) ENVHOST

2.90

0.841

2.00 – 5.00

(10) ENVDYN

(11)

1

(8) ELCAPA**

1

0.049 0.072 0.148* 0.154* 0.162* 0.133* 0.241** 0.151*
**

(10)

1

**

3.49

**

(9)

1

(6) PfP

0.618

(8)

1

(4) PE

*

(6)

1

**
2.00 – 5.00 0.388 0.349
**

(4)

*

**

**

**

*

**

**

1

**
1.00 – 5.00 0.221 0.164 0.338 0.298 0.302 0.170 0.310 0.345 0.279

1

3.60

0.639

(11) IND_ROS

0.07

0.016 -0.02 – 0.11 0.057 -0.005 -0.056 -0.059 0.003 0.095 -0.058 -0.017 0.003 0.026(12) IND_ROE

0.05

**
0.013 -0.03 – 0.10 0.049 -0.038 -0.065 -0.043 -0.005 0.078 -0.054 -0.050 0.058 0.020 0.896

12

1
1

Table 5. Results of OLS regression
ROS (Return on Sales)
Model 1a
FPM
NFPM
SPM
PE
DM
PfP

Model 2aModel 3aModel 4b

0.088

0.051

0.070

0.095

0.086

0.098

0.094

(0.608)(0.815)(1.178)(1.011)(1.154)(1.083)

0.031

0.045

-0.029-0.029-0.049-0.011-0.081-0.072(0.429)(0.595)(-0.389)(-0.362)(-0.679)(-0.143)(-1.081)(-0.893)-0.106-0.108-0.112-0.079-0.117-0.124-0.135-0.090(-1.139)(-1.105)(-1.195)(-0.823)(-1.246)(-1.266)(-1.430)(-0.922)

0.165

0.168

0.056

0.026

0.131

0.124

-0.003-0.027(1.756*)(1.719*)(0.551)(0.259)(1.379)(1.266)(-0.027)(-0.262)-0.092-0.097-0.143-0.117

0.019

0.029

-0.035-0.004(-1.081)(-1.068)(-1.551)(-1.264)(0.218)(0.319)(-0.380)(-0.041)

0.135

0.135

0.094

0.089

0.098

0.102

0.052

0.067

(1.920*)(1.841*)(1.312)(1.220)(1.377)(1.391)(0.733)(0.914)

SPM * ACAPPE * ACAPDM * ACAPPfP * ACAP

-0.008-0.088-0.060-0.159(-0.089)(-0.940)(-0.673)(-1.676*)

0.041

0.054

-0.008-0.082(0.436)(0.446)(-0.085)(-0.674)

0.042-0.0300.069-0.009(0.455)(-0.303)(0.742)(-0.090)-0.136-0.167-0.065-0.046(-1.236)(-1.364)(-0.595)(-0.374)

0.082

0.319

-0.0440.254(0.705)**(-0.376)(1.743*)(2.215 )-0.046-0.262-0.097-0.248(-0.452)(-2.105**)(-0.946)(-1.965*)

0.080

0.226

0.091

0.143

(0.861)(1.784*)(0.985)(1.116)

ELCAPAFPM * ELCAPANFPM * ELCAPASPM * ELCAPAPE * ELCAPADM * ELCAPA

0.197

0.225

0.190

0.236

(1.999**)(2.172**)(1.922*)(2.246**)-0.050-0.079

0.040

0.084

(-0.536)(-0.630)(0.430)(0.657)

0.134

0.117

0.110

0.101

*

(1.733 )(1.393)(1.421)(1.198)-0.0350.054-0.115-0.063(-0.341)(0.469)(-1.135)(-0.533)-0.127-0.326-0.236-0.415(-1.071)(-2.183**)(-1.988**)(-2.745***)

0.156

0.319

0.097

0.242

(1.058)(2.069**)*(1.712 )PfP * ELCAPA

R2Model 3b(1.041)

NFPM * ACAP

IND_PERF

Model 2b0.066

FPM * ACAPENVDYN

Model 1b(0.824)ACAP

ENVHOSTROE (Return on Equity)
Model 4a***(2.776 )-0.054-0.1960.032-0.048(-0.652)(-1.658*)(0.387)(-0.398)-0.134-0.157-0.142-0.167-0.043-0.045-0.066-0.059(-1.931*)(-2.169**)(-2.042**)(-2.372**)(-0.615)(-0.620)(-0.946)(-0.829)-0.074-0.086-0.062-0.042-0.066-0.079-0.067-0.043(-1.012)(-1.113)(-0.826)(-0.558)(-0.899)(-1.028)(-0.893)(-0.557)

0.139

0.134

0.164

0.172

0.171

0.165

0.197

0.195

(2.092*)(1.972*)(2.427**)(2.563**)(2.548**)(2.438**)(2.898***)(2.863***)

0.093

0.108

0.139

0.195

0.074

0.100

0.137

0.176

13

Adj. R2
F

0.054

0.038

0.071

**

*

**

2.389

1.539

2.050

0.100
2.063

***

0.035
1.877

*

0.029

0.069

0.079

1.411

**

1.818**

2.018

4.3 Discussion
The results of this study contribute to the growing number of contradictory
findings on CPMS’ effects on organizational performance. This study finds that theeffects of individual CPMS components and purposes have no significant impact onorganizational performance. When the effects of absorptive capacity and experiential
learning capabilities in CPMS use are introduced, the results show positive and negative
influences on organizational performance. The effects of absorptive capacity areopposite to those of experiential learning capabilities, indicating that the effects ofabsorptive capacity and experiential learning capabilities will depend on which
components and purposes are being emphasized in the effort to improve organizational
performance.

6 Conclusion
In this study, we investigated the relationship between CPMS use, theorganizational capabilities to use such systems effectively, and organizational
performance employing empirical evidence based on data obtained through mail-basedquestionnaires. This study offers important implications, as it highlights the importanceof absorptive capacity and experiential learning capabilities related to CPMS
components and purposes. The results of our analysis suggest that an organization withhigh absorptive capacity and experiential learning capabilities related to CPMS may
attain higher performance. It is important that organizations continuously accumulate
the knowledge required to use information systems, to ensure that these systems,
including management accounting systems, perform as expected (Kogut and Zander,
1992; Mata et al., 1995). Therefore, improving absorptive capacity and experiential
learning capabilities related to CPMS is important for organizations, since businesses
that strive to acquire and use internal and external knowledge could enhance theirorganizational performance.
This study has several limitations. First, regarding the interaction of absorptive
capacity and experiential learning capabilities, this study shows that these capabilities
have contradictory effects. The relationship between these capabilities should be further
investigated. Second, this study captured organizational performance in terms ofobjective organizational performance. Other performance issues should be studied, suchas the effects of performance management systems in terms of organizational
14performance objectives and subjective measures of performance objective achievement(Groot and Selto, 2013).
Acknowledgement
This work was supported by JSPS KAKENHI Grant No.15K17175.

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