Extent of ABC Use and Its Consequences*
ADAM S. MAIGA, Florida International University
FRED A. JACOBS, Auburn University, Montgomery and
Luleå University of Technology
Abstract
This study uses structural equation modeling to investigate the association between extent
of ABC use and quality, cost, and cycle-time improvements; the relations among quality,
cost, and cycle-time improvements; and the association of quality, cost, and cycle-time
improvements with profitability at the manufacturing plant level. Overall, the results of the
structural analyses support the theoretical model, indicating that (a) extent of ABC use has a
significant positive association on cost improvement, quality improvement, and cycle-time
improvement; (b) quality improvement is significantly associated with both cost
improvement and profitability; (c) cost improvement is significantly associated with
profitability; (d) quality improvement is significantly associated with cycle-time
improvement; and (e) cycle-time improvement is significantly associated with both cost
improvement and profitability. However, the direct association between extent of ABC use
and profitability is not significant. Rather, the association is through cost improvement,
quality improvement, and cycle-time improvement acting as intervening variables.
Keywords Activity-based costing; Operational performance; Profitability
JEL Descriptors C12, C31, L25, L60, M41
The full text of this papeer begins on p. xxx of this issue.
1. Introduction
Activity-based costing (ABC) systems have been suggested as a management
innovation that can lead to increased competitiveness and enhanced profitability by
firms (Cooper and Kaplan 1992). Because the costs associated with replacing a traditional costing system with ABC, including time commitments by employees and
management as well as direct investments in technology and process interruptions,
are not insignificant, researchers and business managers are naturally interested in
whether this innovation produces the positive financial results suggested (Cooper
and Kaplan 1992). Knowledge of the linkage between ABC and firm performance,
as well as the organizational circumstances under which ABC can provide performance enhancements to companies, are essential inputs to the investment and
operational decisions that companies must make before approving this important
*
Accepted by Steven Salterio. The authors would like to thank Steve Salterio (associate editor),
two anonymous reviewers, Professors Edward E. Rigdon, Lynn Hannan, Ernie Larkins, Nicholas
Marudas, and participants in workshops where this paper has been presented. However, we
remain responsible for all errors and misstatements.
Contemporary Accounting Research Vol. 25 No. 2 (Summer 2008) pp. 1–39 © CAAA
doi:10.1506/car.25.2.9
2
Contemporary Accounting Research
resource allocation decision. Many companies have adopted ABC, and researchers
have examined important issues related to the financial impact of this organizational innovation.
Research on the link between ABC and its impact on profitability is still
evolving and many questions remain. Advocates argue that ABC provides a means
of enhancing profitability (Plowman 2001; Cooper and Kaplan 1991; Carolfi 1996)
and that it provides companies with a powerful tool for improving financial performance (Dodd, Lavelle, and Margolis 2002). Findings by Rafig and Garg 2002 suggest
that there is a strong relationship between ABC implementation and profitability;
however, many other studies find no relationship between ABC use and superior
firm performance (Bromwich and Bhimani 1989; Gordon and Silvester 1999;
Innes and Mitchell 1995; Ittner, Lanen, and Larcker 2002). Therefore, given the
important investments in ABC and the related need to increase global competitiveness in the U.S. economy, more research is needed to assist businesses in understanding the mechanisms, if any, that contribute to profit enhancements from ABC
implementation, so that economically optimal investments may be made in the
economy by individual businesses.
Many reasons have been posited for the inconclusive research results on the
linkage between ABC and firm performance. For example, Chenhall and LangfieldSmith (1998) suggest the potential for intervening effects of organizational variables
and call for further research that considers the role of additional relevant firmspecific variables. Similarly, Kennedy and Affleck-Graves (2001) suggest that
ABC may not, per se, add value, but may merely be correlated with other variables
that are true value drivers.1 Therefore, the current research question, related to the
above conjecture, is to investigate whether the effect of the extent of ABC use has
a significant direct impact on plant profitability and/or whether plant operational
performance measures (i.e., quality, cost, and cycle time) act as intervening variables in the relationship between extent of ABC use and profitability.
From a related theoretical and strategic perspective, additional debate among
scholars of business strategy and marketing is occurring about whether organizations can achieve improvements in both cost and quality simultaneously (Porter
1980, 1985) and whether quality can have both revenue-enhancing and cost-reducing
effects (Rust, Zahorik, and Keiningham 1995). For example, Porter (1980, 1985)
has taken the position that organizations seldom are able to achieve cost and quality
improvements simultaneously, while other scholars have argued that organizations
may pursue both strategies simultaneously (Hall 1983; Murray 1988; Hill 1988;
Cronshaw, Davis, and Kay 1994; White 1986). From the marketing literature, Rust
et al. (1995) comment on the “duality” debate defined as whether quality improvements can both have a positive revenue impact and act to reduce costs. The current
research contributes to both streams of literature.
This study is, therefore, timely and important for several reasons. First, empirical findings on the direct improvement of ABC on business profitability have been
mixed (e.g., Innes, Mitchell, and Sinclair 2000; Foster and Swenson 1997; Malmi
1997; McGowan and Klammer 1997; Anderson 1995; Shields 1995; [AU: Shields
is 1997 in References; please correct here or there] Morrow and Connolly 1994;
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Extent of ABC Use and Its Consequences
3
Cooper and Zmud 1990). Second, the debate on organizations’ ability to pursue
both quality and cost improvements provides an important conceptual foundation
for investigating the extent to which extent of ABC use is associated with
improved quality and cost. Third, the study is important for academics interested in
the association between ABC and profitability to examine intervening variables
that affect this relationship. Fourth, this study can begin an exploratory contribution to the dialogue regarding whether quality can have a positive, direct effect on
profitability and on reduction of costs. Finally, an important contribution of this
study is that managers, too, require a more complete understanding of the conditions under which extent of ABC use should be expected to pay off. To our knowledge, no prior study has empirically tested the ABC–profitability link within the
context of plant operational performance as measured by quality, cost, and cycle
time.
To address the research issue, this study applies a more sophisticated approach
to theory testing and development than that used in prior research, which enables
simultaneous estimation of the relationships among the variables in this study.
More specifically, collecting survey data from a cross-section of U.S. manufacturing plants, the present study uses structural equation modeling (SEM) to assess (a)
the relations between extent of ABC use and quality improvement, cycle-time
improvement, and cost improvement and (b) the relations among quality improvement, cycle-time improvement, and cost improvement and their association with
profitability.2 Further, this study investigates whether extent of ABC use produces
a direct improvement in plant profitability or whether this relationship is established through plant operational performance (i.e., quality, cost, and cycle-time
improvement), as conjectured by Kennedy and Affleck-Graves 2001.
Overall, the results of this study indicate support for the hypotheses developed. The extent of ABC use is significantly and positively associated with quality,
cost, and cycle-time improvements and quality improvement has a significant positive impact on cost improvement, cycle-time improvement, and profitability.
Cycle time is significantly and positively associated with cost improvement and
both are associated with enhanced profitability. Further analysis shows that the
direct relationship between extent of ABC use and manufacturing plant profitability
is not significant; rather, plant operational performance measures act as intervening
variables in the relationship between extent of ABC use and profitability. Therefore,
this research makes an important contribution to the literature on the relationship
between ABC and profitability, to the debate about whether there is a strategic
trade-off between investments in quality and cost improvement (Porter 1980,
1985), and to the discourse on whether quality improvements can have positive
impact on profits through both revenue enhancements and cost reductions (Rust
et al. 1995).
This paper is organized as follows. The next section provides the theoretical
justification of the relationships examined in the study and the hypotheses. Section 3
presents a discussion of the research methods that lead to the empirical results in
section 4. Section 5 concludes with a summary of findings, explanation of limitations, and suggestions for future research.
CAR Vol. 25 No. 2 (Summer 2008)
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2. Literature review and hypotheses development
Extent of ABC use and quality improvement
ABC can serve as a useful information system to support effective decision-making
processes related to quality initiatives (Gupta and Galloway 2003). The information provided by ABC can help identify the drivers of quality problems (Armitage
and Russell 1993; Carolfi 1996) by highlighting the quality-related non-valueadded activities, which can therefore facilitate quality improvement (Cooper,
Kaplan, Maisel, Morrissey, and Oehm 1992; Ittner 1999; Ittner et al. 2002). Jorgenson and Enkerlin (1992) describe how ABC information helped Hewlett-Packard
product teams simulate and improve quality early in the product-design phase. Ittner
et al. (2002) found that ABC use is positively related to higher quality levels.
The preceding argument is captured in the following hypothesis:
HYPOTHESIS 1. Extent of ABC use has a significant positive improvement in
quality.
Extent of ABC use and cost improvement
Prior studies suggest that the information provided by ABC allows managers to
reduce costs by designing products and processes that consume fewer activity
resources, increasing the efficiency of existing activities, and eliminating activities
that do not add value to customers (Ittner et al. 2002; Gunasekaran and Sarhadi
1998). Furthermore, Cooper and Kaplan (1998) argue that when an entity implements ABC, its entire operation is scrutinized in great detail, its performance is
analyzed, and its employees are encouraged to suggest improvements. The following hypothesis captures the cost reduction benefits of extent of ABC use:
HYPOTHESIS 2. Extent of ABC use has a significant positive improvement in
cost.
Extent of ABC use and cycle-time improvement
Under ABC, non-value-added activities (e.g., counting, checking, and moving) are
highlighted (Gerring 1999; Ittner et al. 2002). As a result, ABC can assist in identifying activities that cost non-value-added time by providing information needed to
minimize delays (Kaplan 1992; Borthick and Roth 1995). Ittner et al. (2002) found
that ABC use is positively related to decrease in cycle time. Consequently, the
following hypothesis is proposed:
HYPOTHESIS 3. Extent of ABC use has a significant positive improvement in
cycle time.
Quality, cost, and profitability
Quality theorists and practitioners generally support the idea that quality improvements result in cost savings that outweigh the money spent on the quality efforts
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Extent of ABC Use and Its Consequences
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(Slaughter, Harter, and Krishnan 1998). Quality outputs may mean lower per unit
costs because of economies of scale (Kroll, Wright, and Heins 1999). This reasoning is in line with the learning curve theory that suggests that costs should actually
decline more rapidly with the experience of producing high-quality products (Fine
1986). Fine (1983) found that costs declined more rapidly for plants that produced
high-quality products than for plants that produced low-quality ones. Also, following the findings of previous empirical work (Noble 1995; Ferdows and De Meyer
1990), it can be assumed that the improvements on quality serve as a base for cost
improvements because processes become more stable and reliable, and less time
and cost are required for rework. In line with the learning curve theory and with
Ferdows and De Meyer 1990, the conceptual model proposed in this paper suggests that, ceteris paribus, quality improvements will lead to cost improvements.3
Therefore, the following hypothesis is tested:
HYPOTHESIS 4. Ceteris paribus, quality improvement has a significant positive
improvement in cost.
A quality improvement strategy broadly captures a firm's attempts to differentiate itself from its rivals using a variety of marketing and marketing-related activities
(Hambrick 1983). Hill (1988) argues that firms may pursue differentiation to build
market share under circumstances where the cost savings realized from economies
of scale outweigh the initial investment cost of pursuing a differentiation strategy.
The key to making quality improvement successful is an ability to charge above
market prices, which is possible because of the customer's perception that the
product is special in some way (Berman, Wicks, Kotha, and Jones 1999). [AU: Jones
included as per References — ok?] This ability to command a premium price
could, in turn, lead to greater profitability (Kotha and Vadlamani 1995; Porter
1980). In addition, quality improvements could lead to greater demand in the market, which would enhance profitability even if the per unit prices were held constant. This implies that, ceteris paribus, improvements in quality have a significant
improvement in profitability.4 Thus:
HYPOTHESIS 5. Ceteris paribus, quality improvement has a significant positive
improvement in profitability.
Cost-efficiency measures assess the degree to which costs per unit of output
are low (Berman et al. 1999). According to Porter 1980, this strategy of cost efficiency entails that the firm be constantly improving its ability to produce at costs
lower than the competition by emphasizing efficient-scale facilities, vigorously
pursuing cost reductions along the value chain driven by experience, tight cost and
overhead control, and cost minimization (Spanos, Zaralis, and Lioukas 2004; Wu,
Lin, and Chen 2007). This strategy of cost efficiency can also provide above-average
returns because it allows the firm to lower prices to match those of competitors and
still earn profits (Hambrick 1983; Henderson and Henderson 1979; Miller and
Friesen 1986; Porter 1980, 1985). To the extent that a firm succeeds in driving
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Contemporary Accounting Research
down costs per unit of output, thereby increasing gross margins, firm profitability
should, ceteris paribus, increase (Miller 1987; Porter 1980). Hence, cost improvement is expected to transfer businesses’ savings directly to the bottom line (Rust et
al. 2002).5 Therefore:
HYPOTHESIS 6. Ceteris paribus, cost improvement has a significant positive
improvement in profitability.
In summary, what is suggested in the discussion above relating to Hypothesis 4,
Hypothesis 5, and Hypothesis 6 is that while quality may be directly and positively
associated with profitability (McGuire, Schneeweis, and Branch 1990; Powell
1995), its association may also be indirect through cost improvement (Rust et al.
2002) because quality firms that enjoy lower costs may lessen the pressure that
may otherwise be intensely exerted by rivals, customers, suppliers, and firms in
substitute industries (Anderson, Fornell, and Lehmann 1994; Bettis 1983; Fornell
and Wernerfelt 1987).
Quality improvement and cycle-time improvement
Quality improvement can be attained by reducing defects and rework (Harter,
Krishnan, and Slaughter 2000). The rationale for this argument is that the time
spent on preventing and uncovering defects early in the product development stage
can be more than recovered by avoiding rework to correct defects detected at the
later stages of product development (Jones 1997). Empirical research supports this
rationale. For example, in a study of information technology firms, Harter et al.
(2000) find that higher-quality products exhibit significant reductions in cycle
time. Given that quality and cycle time are known to be complementary — that is,
improvements in quality directly relate to improved cycle time (Crosby 1979;
Deming 1986; Nandakumar, Datar, and Akella 1993) — we develop the following
hypothesis:
HYPOTHESIS 7. Quality improvement has a significant positive improvement
in cycle time.
Cycle-time improvement and cost improvement
Achieving cost efficiency is increasingly important in globally competitive markets
(Young and Selto 1993). Decreasing cycle time eventually means decreasing
non-value-added time and inventory, thereby lowering product cost (e.g., reduced
overhead). Even if these savings occur through reductions in overall capacity, if
existing product volume remains constant, unit product costs will decrease (Campbell 1995). We therefore conjecture that cycle-time improvement will exhibit a
positive association with cost improvement.6 This leads to the following hypothesis:
HYPOTHESIS 8. Ceteris paribus, cycle-time improvement has a significant
positive improvement in cost.
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Extent of ABC Use and Its Consequences
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Cycle time and profitability
Manufacturers are under pressure to get products to market quickly and stay ahead
of competitors (Milligan 1999). Product development research, related to time to
market, posits that firms compete on new-product-development cycle time as lost
profits accrue when new products are delayed in development and shipped late
(Clark 1989). That is, for firms that compete by being first to market with new
products, being able to develop products faster than competitors supports the
organization’s strategy by enabling quicker response to changing technologies and
customer demands. Firms that succeed in developing and marketing their products
faster than competitors can obtain first-mover advantages (Carmel 1995; Gupta,
Brockhoff, and Weisenfeld 1992; Kuzmarski 1992), which can allow them to garner
dominant market share (Langerak and Hultink 2005). According to Stalk and Hout
1990, “generally, if a time-based competitor can establish a response three or four
times faster than its competitors, it will grow at least three times faster than the
market and will be at least twice as profitable as the typical industry competitor.”
The above argument suggests that the focus on cycle time, ceteris paribus,
translates into bottom-line profits.7 Therefore:
HYPOTHESIS 9. Ceteris paribus, cycle-time improvement has a significant
positive improvement in profitability.
In summary, in this study we explore the relationships among extent of ABC
use, quality improvement, cost improvement, cycle-time improvement, and manufacturing plant profitability. Figure 1 presents the basic conceptual model, which
uses extent of ABC use as the exogenous construct and quality improvement,
cost improvement, cycle-time improvement, and profitability as the endogenous
constructs.
[CATCH — Figure 1 near here]
3. Research design and methods
Variable measures
The constructs are developed on the basis of theory and on items proposed and validated in prior studies.8 From these efforts, several items are generated to measure
the different aspects of the constructs. We use a seven-point Likert scale to increase
the sensitivity of the measurement instrument and because we believe that this
scale represents an appropriate measurement instrument for the assumptions of
factor analysis used in the analysis of research findings. In addition, the use of a
seven-point scale is believed to be appropriate because it is the most common scale
in U.S. research (Wolak, Kalafatis, and Harris 1998). Next, the questionnaire was
evaluated by academics at two universities with expertise in accounting, manufacturing management, and marketing. The constructs and their indicators are discussed
in detail in the next section.
The questions used in the survey instrument were substantially borrowed from
the literature; thus, we did not pretest the instrument as required under Dillman’s
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Contemporary Accounting Research
1978 total design method. This practice is sometime referred to as the modified
Dillman approach and is a survey method common in other accounting research
(e.g., Cagwin and Bouwman 2002; Innes and Mitchell 1995; Ittner et al. 2002;
Krumwiede 1998). Nevertheless, the use of variables incorrectly assumed to be
validated in prior studies could lead to incorrect inferences, as discussed later in
this paper.
Extent of ABC use
One would expect the benefits received from an innovation, such as ABC, to
depend on the extent to which it becomes incorporated into organizational subsystems (Cagwin and Bouwman 2002). Therefore, the construct of extent of ABC
use in this study contains the breadth of use. Following Swenson 1995 and Cagwin
and Bouwman 2002, we measure this construct by asking respondents to indicate
the extent to which the following functions routinely use the ABC information for
decision making: design engineering, manufacturing engineering, and product
management. We also ask the extent to which ABC is used plant-wide.
Plant performance measures
This study uses four latent variables to assess plant performance.9 These measures
are based on prior literature relating to improvement in quality, cost, cycle time,
and profitability. The first variable, quality improvement, is based on responses
about three aspects of product quality: finished product first-pass quality yield in
percentage terms, scrap cost as a percentage of sales, and rework cost as a percentage of sales (Ittner et al. 2002). We measure the second variable, cost improvement,
using four categories of cost borrowed from the literature (e.g., Ittner et al. 2002):
materials cost, labor cost, overhead cost, and nonmanufacturing cost. We use
responses in the following four areas to measure cycle-time improvement: new
product introduction time — the ability to minimize the time to make improvements to existing products or to introduce completely new products (Safizadeh,
Ritzman, Sharma, and Wood 1996; Vickery, Droge, Yeomank, and Markland
1995); manufacturing lead time — the ability to minimize the time from when the
order was released to the shop floor to the time of its completion (Handfield and
Pannesi 1995); delivery reliability/dependability — the ability to deliver consistently
on the promised due date (Handheld 1995; Roth and Miller 1990); and customer
responsiveness — the ability to respond in a timely manner to the needs and wants
of customers, including potential customers (Tunc and Gupta 1993; Ward, McCreery, Ritzman, and Sharma 1995). Four measures capture improvement in plant
profitability: market share; return on sales (ROS) — net income before corporate
expenses divided by sales; turnover on assets (TOA) — sales divided by total
assets; and return on assets (ROA). Although interdependent, ROA and ROS reflect
different determinants of a business success or failure (Kinney and Wempe 2002).
Atkinson, Banker, Kaplan, and Young (2001) describe asset turnover as a measure
of productivity — the ability to generate sales with a given level of investment —
and ROS as a measure of efficiency — the ability to control costs at a given level of
CAR Vol. 25 No. 2 (Summer 2008)
Extent of ABC Use and Its Consequences
9
sales activity. All of these performance measures relate to improvement over the
previous three years.
Sample
To address the hypotheses, we surveyed a cross-section of U.S. manufacturing
plants. The plants and firms were identified from a literature review that included
the Wall Street Journal, Journal of Cost Management, Management Accounting,
Harvard Business Review, and various industrial engineering journals and books.
The review covered the years 1990–2003. We selected a plant if it was mentioned
in the literature as having adopted ABC. We selected a firm as a whole in a similar
manner.
From the literature review, the following steps were taken to collect information [AU: “information” OK?] from our target sample:
1. In this first step, the literature was reviewed to identify firms that have adopted
ABC; if the literature identified a specific plant of a firm as an ABC adopter,
we included that plant in our sample. This selection method led to 149 plants
and 57 firms.
2. In the second step, if a firm was mentioned in the literature above as an ABC
adopter, a random sample was taken by selecting every third plant from the
directory listing for each firm (without a priori knowledge of whether the plant
selected was an ABC or a non-ABC adopter or if the plant had abandoned
ABC). [AU: is this “the first mailing”? i.e., contact with firm?] For this sample selection process, we first used the 1997 Industry Week Annual Census of
Manufacturers from which Ittner et al. 2002 drew their sample. To update the
managers’ addresses, we used the 2002 Dun and Bradstreet Million Dollar
database. However, the difference with Ittner et al. 2002 is that we were interested only in ABC implementers.
After submitting the questionnaire to two accounting professors, two management professors, and two marketing professors for assessment of its validity, the
following specific steps were taken: [AU: the following steps imply contact with
firms but there’s no explicit mention of them; include?] (a) sending a second
mailing, (b) promising confidentiality of responses, (c) including deadline dates
for reply, (d) including personalized cover letters, (e) including a postage-paid,
self-addressed envelope for reply, and (f) promising to send a summary of results
on request.
As in prior studies (e.g., Ittner and McDuffie 1995; Swanson 2003), questionnaires were sent to plant managers as contact persons.10 A cover letter specified
that we were interested in plants that are profit centers and that have been using
ABC for at least the past three years.11 Initially, 2,506 questionnaires were mailed;
within the first three weeks, 686 questionnaires were returned. A second mailing
followed, and 227 new responses were received. This process resulted in 913
responses. Of these, 78 were from plants that had abandoned ABC and 144 were
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Contemporary Accounting Research
incomplete, leaving 691 usable responses, for a 27.57 percent usable response rate
(see Table 1).
[CATCH — Table 1 near here]
Nonresponse bias is always a concern in survey research. To investigate the
likelihood of nonresponse bias in the data, we test for statistical differences in
the responses between the early and late waves of survey respondents, with the last
wave of surveys received considered representative of nonrespondents (Armstrong
and Overton 1977). The mean scores of the early and late responses are compared
using t-tests. The t-tests yield no statistically significant differences among the survey items, suggesting that nonresponse bias is not a problem in this study.
Given the reflective nature of the measures used in this study, the analysis of
the data was done using structural equation modeling (SEM). Reflective measures
contain items that tap into or reflect a pre-existing level on the underlying construct
of interest. Reflective indicator items should be correlated with each other, and
reflective scales should have high internal reliability, because an individual’s
responses to all the items are influenced by the underlying construct (Diamantopoulos and Winklhofer 2001; Edwards and Bagozzi 2000; Jarvis, MacKenzie, and
Podsakoff 2003). Following previous studies, these measures are treated as reflective
indicators of existing latent constructs. In evaluating the reflective measurement
model, we consider path loadings to be acceptable at 0.70 or higher (Limayem,
Khalifa, and Chin 2004).
4. Results
In this section, we first present the descriptive statistics. Then we examine the
research model depicted in Figure 1 using SEM. Numerous researchers have proposed a two-stage model-building process when applying SEM (Joreskog and
Sorbom 1993; Hair, Anderson, Tatham, and Black 1995; Maruyana 1998), in
which measurement models are tested before testing the structural model. The
measurement models specify how hypothetical constructs are measured in terms of
observed variables (Pijpers, Bemelmans, Heemstra and van Montfort 2001; Tan
2001), while the structural model depicts the hypothesized relationships between
latent constructs. Hence, we examine the measurement model first, then the structural model.
Descriptive statistics
The descriptive statistics in Table 2, panel A provide the profile of the responding
companies, showing that they constitute a broad spectrum of manufacturers as
defined by the two-digit Standard Industrial Classification (SIC) code. The sample
composition has the largest representation in electronic and electrical equipment
(16.35 percent), followed by paper and allied products (14.04 percent), chemical
and allied products (13.31 percent), food and kindred products (12.01 percent),
and apparel and other textile products (9.26 percent). Table 2, panel A also shows
the percentage of targeted sample based on SIC code. Additional information on
respondents’ characteristics is provided in Table 2, panel B. Answers to the quesCAR Vol. 25 No. 2 (Summer 2008)
Extent of ABC Use and Its Consequences
11
tion regarding number of years with the manufacturing plant showed that the
respondents have a mean of 10.56 years in their current position. To the question
regarding number of years in management, respondents indicated a mean of 17.31
years. It appears from their positions and tenure that the respondents are knowledgeable and experienced, have access to information on which to base reliable
perceptions, and are otherwise well qualified to provide the information required.
The results also show that the mean [AU: “mean” per table; change from
“average” ok?] number of employees is 947. Next, we calculate their minimum,
maximum, mean, and standard deviation for each of the latent variables used in the
study. Table 3 presents the summary statistics for all the constructs used in this study.
[CATCH — Tables 2 and 3 near here]
The pair-wise correlations in Table 4 show significant correlations between
extent of ABC use and quality improvement ( r 0.209), cost improvement
(r 0.245), and cycle-time improvement (r 0.211). Also, significant positive
correlations are demonstrated between quality improvement and cycle-time
improvement (r 0.226), quality improvement and cost improvement (r 0.486),
and cycle-time improvement and cost improvement (r 0.379). Finally, quality
improvement, cost improvement, and cycle-time improvement are significantly correlated with profitability (r 0.532, 0.539, and 0.674, respectively).
[CATCH — Table 4 near here]
Assessment of the measurement models
Given the reflective nature of the measures used in this study, the data were analyzed using SEM. The SEM procedure allows the researcher to specify both the
relationships among the conceptual factors of interest and the measures underlying
each construct. First, a confirmatory factor analysis (CFA) is conducted to examine
the reliability and validity of the proposed constructs. In addition, two important
dimensions of construct validity are assessed: convergent and discriminant validity.
In the CFA, convergent validity is examined by reviewing the t-tests for the factor
loadings. Hatcher (1994) [AU: please provide page no. for quote] notes that “if
all factor loadings for the indicators measuring the same construct are statistically
significant, this is viewed as evidence supporting the convergent validity of those
indicators”. Table 5 lists the results of the measurement model CFA. It is important
to note that all the indicators are statistically significant (i.e., t-values greater than
the 1.96 threshold) for large sample at p 0.05 level and have strong loadings
( 0.70) on their respective latent factors.
Multiple measures have been used to assess model fit. The rule of thumb is
that 2/df should be less than 3.00 (Wheaton, Muthen, Alwin, and Summers 1977),
while the goodness-of-fit index (GFI), comparative fit index (CFI), and normed fit
index (NFI) should be greater than 0.90 (Bentler and Bonnett 1980), and the residual mean square approximation (RMSEA) should be less than 0.10 (Kline 1998;
Steiger 1990). As shown in Table 5, the final results of CFA indicate that the measurement model fits the data reasonably well. The fit indices are 2/ df 2.751,
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Contemporary Accounting Research
GFI 0.931, CFI 0.943, NFI 0.938, and RMSEA 0.074.12 The fit loadings,
along with t-values, provide evidence of convergent validity.
[CATCH — Table 5 near here]
Next, we assess the discriminant and convergent validity. The average variance extracted (AVE) determines the average variance shared between constructs
and its measures and the variance shared between the constructs, which are the
square correlations between the constructs. To demonstrate the discriminant validity of the constructs, the AVE for each construct should be greater than the square
correlations between the constructs and all other constructs (Fornell and Larcker
1981). Table 6 shows that the AVE (on diagonal) is greater than the square correlation
matrix (off diagonal) of the constructs. Fornell and Larcker (1981) also suggest
that the AVE can be used to evaluate convergent validity. A test of convergent validity shows that the AVE exceeds 0.50 for all constructs (Fornell and Larcker 1981;
Chin 1998). Hence, the findings in this study demonstrate high discriminant and
convergent validity.
Reliability estimation is left for last because reliability is almost irrelevant
(Torkzadeh, Koufteros, and Doll 2005) unless there are invalid constructs. To
assess the reliability of responses across the measures, we calculated the internal
reliability of each construct using two methods: Cronbach's alpha and composite
reliability (Djurkovic, McCormack, and Casimir 2006). According to Nunnally’s
1978 0.70 criteria for Cronbach’s alpha, all of the scales had satisfactory internal
reliability (see Table 6). Composite reliability is similar to Cronbach’s alpha and
needs to exceed 0.70 to indicate satisfactory internal consistency (Gefen, Straub,
and Bourdeau 2000). The composite reliabilities for the constructs were satisfactory and exceeded the 0.70 acceptable threshold (also shown in Table 6).13
[CATCH — Table 6 near here]
The questionnaire groups questions according to the underlying constructs
being tested to aid reader understanding. However, control for common method
biases was accomplished by the design of the study’s procedures (procedural remedies), including (a) assuring respondents of anonymity, (b) careful construction of
the variable constructs (Podsakoff, MacKenzie, Lee, and Podsakoff 2003), and
(c) not requiring responses that were self-incriminating or sensitive. In addition,
upon collection of the survey data, the statistical method CFA was used to examine
whether common method bias represented a serious problem. A single-factor
model in which all the model items were assumed to load on one factor was compared with a five-factor model in which the construct items were assumed to load
on the scale or factor they represented. It has been suggested that in conducting a
CFA to test for the possibility of a common method bias that the best evaluation
measure is the relative noncentrality index (Gerbing and Anderson 1993). [AU: this
appears as 1988 in References; please correct or add 1993 reference] The
results of a single-factor model CFA produced a relative noncentrality index of
0.352, while the relative noncentrality index for the five-factor model was 0.817.
Other evaluation measures (see the assessment of the measurement model above)
CAR Vol. 25 No. 2 (Summer 2008)
Extent of ABC Use and Its Consequences
13
also strongly indicated that the fit for the five-factor model was superior to the singlefactor model. These results suggest that the common method bias is not a problem
in the study.
Assessment of the structural model and hypotheses
Before testing the hypotheses, it is important to assess the measures of fit. The overall fit statistics in Table 7 indicate that the model has adequate fit ( 2/df 2.866,
GFI 0.922, CFI 0.939, NFI 0.927, RMSEA 0.083). This is followed by
the assessment of the hypotheses.
[CATCH — Table 7 near here]
We examine the standardized parameter estimates for our model by using
the significance of individual path coefficients to evaluate the hypotheses. Consistent with theoretical expectations, the standardized parameter estimates indicate
that extent of ABC use is significantly associated with cost improvement (path coefficient 0.171, p 0.036), quality improvement (path coefficient 0.205,
p 0.000), and cycle-time improvement (path coefficient 0.190, p 0.000).
Similarly, the effects of quality improvement on both cost improvement (path coefficient 0.282, p 0.000) and profitability (path coefficient 0.223, p 0.001)
are significantly positive. Also, cost improvement is significantly associated with
profitability (path coefficient 0.690, p 0.000), and quality improvement is
significantly associated with cycle-time improvement (path coefficient 0.337,
p 0.000). Finally, cycle-time improvement is significantly associated with both
cost improvement (path coefficient 0.188, p 0.000) and profitability (path
coefficient 0.191, p 0.004). Thus, Hypotheses 1 through 9 are supported.
Table 7 and Figure 2 show the estimates and significance of the hypothesized paths
for the conceptual model.
Next, we assess the square multiple correlations (R 2), shown in Table 7, which
indicate that the model explains a low amount of variance in cost improvement
(0.174), cycle-time improvement (0.175), and quality improvement (0.142), and a
high amount of variance in profitability (0.743).
The conceptual framework (see Figure 1) suggests that extent of ABC use
affects profitability through quality, cost, and cycle-time improvements that act as
intervening variables. To explore this conjecture, we followed recommendations
suggested by Bollen 1989 and others (e.g., Hayduk 1987; Joreskog and Sorbom
1993; Medsker, Williams, and Holahan 1994; Quintana and Maxwell 1999).
To test whether extent of ABC use has a significant positive direct association
with profitability or whether this relationship is achieved through intervening variables (i.e., cost, quality and cycle-time improvements), we first assess the following
conditions: (a) the independent variable, extent of ABC use, must be related to the
intervening variables, (b) the intervening variables must be related to the dependent variable (i.e., profitability), and (c) the independent variable must have no
effect on the dependent variable when the intervening variables are held constant
(Baron and Kenny 1986; Prussia and Kinicki 1996).
CAR Vol. 25 No. 2 (Summer 2008)
14
Contemporary Accounting Research
For the first condition, the structural model results show that extent of ABC use
has significant positive improvement in cost, quality, and cycle time (see Table 7
and Figure 2). The second condition is also satisfied as the results show that cost,
quality, and cycle time have significant positive improvement in profitability (see
Table 7 and Figure 2).
[CATCH — Figure 2 near here]
To evaluate the last condition, between-model comparisons were undertaken
using the 2 difference test recommended by Bollen 1989 and others (e.g., Hayduk
1987; Joreskog and Sorbom 1993; Medsker et al. 1994), along with differences in
the fit indices (Gerbing and Anderson 1992; Medsker et al. 1994; Tanaka 1993).
More specifically, we test the appropriateness of the research model (nested
model) by comparing it to the full model, which includes the direct effect of extent
of ABC use on profitability. A 2 difference test between these two models ( 2 difference 1.279, df 1) indicates no significant difference between them. Thus
the nested model is accepted as the better representation (Joreskog and Sorbom,
(1993). Equally important, the lack of significance of the direct paths in the full
model ( 0.05, p 0.624) provides statistical support for the basic premise of
this research: that is, extent of ABC use does not have a direct association with
profitability. Rather, extent of ABC use is associated with profitability through
specified aspects of operational performance measures (quality improvement,
cycle-time improvement, and cost improvement) that act as intervening variables.14
[AU: original notes 14 and 15 combined; CAR does not allow immediately
successive notes]
However, the survey approach is not without potential problems. In collecting
data through mail questionnaires, a tradeoff is made with respect to efficiency
(e.g., lower cost, time, and staff requirements) versus accuracy (e.g., lower degree
of objectivity in the data). Surveys measure beliefs, which may not always coincide with actions (Graham, Harvey, and Rajgopal 2005). Surveys lack variable
manipulation (Krumwiede 1998); therefore, "cause" cannot be inferred from this
study. In addition, the survey method, as presented in this study, does limit the use
of open-ended questions and face-to-face data gathering and the richness such data
provides. Another potential problem with survey data is that questions, no matter
how carefully crafted, either might not be properly understood or might not elicit
the appropriate information. It is also possible that the survey items may not survive an interpretability test with a set of likely respondents or survive intact after a
review using Dillman’s 1978 complete methods. Therefore, the analyses we perform and conclusions we reach must be interpreted with caution.
As stated above, we did not pretest our variables as required under Dillman’s
1978 total design method. Rather, since our variables were used and validated in
prior studies (see Appendix), we used a modified Dillman approach, a survey
method that is common in other accounting research, perhaps due to a presumption
that published research has gone through a rigorous review process and any quality
controls would have been required as a condition of publication (e.g., Cagwin and
Bouwman 2002; Innes and Mitchell 1995; Ittner et al. 2002; Krumwiede 1998).
CAR Vol. 25 No. 2 (Summer 2008)
Extent of ABC Use and Its Consequences
15
However, to the extent that researchers borrow survey questions from published
papers that have not gone through the rigor of pretesting, including interpretability
testing, as suggested by Dillman 1978, Forza 2002, and others, this lack of pretest
is unfortunate and could potentially lead to incorrect inferences from the data
gathered. Given that there is some positive probability that even untested survey
instruments can provide data of suitable quality to lead to accurate inferences and
conclusions, researchers may consider the cost and the benefit of engaging in new
research programs that remedy old errors by using the most recognized survey
techniques to examine questions that have been examined in earlier publications.
An important consideration is that if new research, with adequately tested survey
instruments, is used and old questions revisited, will it be publishable in top journals if the findings are qualitatively similar to those of the older research, which
used less-than-perfect instruments? Notwithstanding the answer to this question,
the questions addressed are so important that it is desirable to at least begin using
the best methods and techniques available and known to the researchers.
5. Conclusions, limitations, and extensions
With a sample of 691 manufacturing plants, this study uses structural equation
modeling to examine the relationships among extent of ABC use, cost improvement,
quality improvement, cycle-time improvement, and profitability. Results indicate
that extent of ABC use is significantly associated with quality improvement, cycletime improvement, and cost improvement. Quality improvement is significantly
associated with cycle-time improvement, cost improvement, and profitability.
Cycle-time improvement is significantly associated with both cost improvement and
profitability. Also, cost improvement is significantly associated with profitability.
Extent of ABC use is not directly associated with plant profitability; rather the
relationship is through operational performance measures (quality improvement,
cycle-time improvement, and cost improvement) that act as intervening variables
between extent of ABC use and profitability.
Hence, this study contributes to the literature by improving our understanding
of how ABC improves quality, cost, and cycle time, which, in turn, improve plant
profitability. The importance of improving quality while improving cost and cycle
time also becomes critical to managers interested in retaining customers and enticing
them to buy products from their facilities. Thus, this study provides strong evidence
to suggest that ABC implementation efforts by managers generate an increased
tendency toward improved profitability through operational performance (improved
quality, cycle time, and cost). Consequently, management’s ability to utilize both
ABC and operational measures to manage their interplay is vital to firm financial
success. The framework presented in this paper provides managers with ideas
about how ABC can be successfully implemented. Specifically, within this
framework, managers need to utilize ABC information to make decisions about
operational performance measures that are expected to be positively associated
with firm profitability.
Our findings are consistent with prior studies that suggest that, to be successful, companies must be capable of manufacturing products of high quality and/or
CAR Vol. 25 No. 2 (Summer 2008)
16
Contemporary Accounting Research
low cost (Drury 1990; Kaplan 1983) and delivering them on time to meet customer
demands (Banker, Porter, and Schroeder 1993). The findings are also consistent
with prior theoretical research, which emphasizes the role of intervening variables
in explaining the relationship between ABC and firm profitability (Chenhall and
Langfield-Smith 1998; Kennedy and Affleck-Graves 2001; Shields, Deng, and Kato
2000).
The results are of particular interest to practicing and academic accountants
because they are often the primary proponents and designers of ABC systems. The
results are also of particular interest to both manufacturing and marketing managers
who are interested in preserving their quality image in order to influence repeat
purchase activities, while delivering their products on time and controlling their
costs. The importance of meeting customers’ product quality expectations thus
becomes critical to marketing managers interested in retaining and enticing customers to buy products from them. Hence, the results of this study should enhance
practitioners’ confidence in operational performance as a facilitator of the link
between ABC and profitability. We believe that our study model helps to build
intuition about the mechanisms driving these relationships. The model should help
to inform the development of more detailed models and help guide future empirical work with different sampling and industries.
As with any study of this type, our results are subject to a number of limitations. First, the correlation between ABC and profitability is not significant, nor is
a direct effect. As in Ittner et al. 2002, this may be attributed to the endogenous
choice of ABC. Further studies may investigate the issue of endogeneity, for example, by estimating the probability that the plant makes extensive use of ABC as a
function of four sets of variables that are not captured in this study.15 These
include product mix and volume, manufacturing practices (discrete and hybrid),
new product introductions, and advanced manufacturing practices (Ittner, Lanen,
and Larcker 2002). Second, all plants in the study have implemented ABC for
three or more years; however, we do not know the exact length of implementation
to assess the time lag effects of extent of ABC use. Further studies can assess the
model variable relationships using and comparing subsamples based on length of
ABC adoption. Third, given the difficulty in collecting “harder numbers”, perhaps
not based on respondent perceptions, we have relied on a survey to collect a sample
size large enough for structural equation modeling to test the hypotheses. Further
investigation is warranted using field study methods to corroborate our findings.
Fourth, although our research focuses on manufacturing plants, the nature and
strength of the findings suggest that we can extend some of their implications to
the service industry as well. Fifth, our study supports both the direct and indirect
(through cost improvements) association between quality and profitability. However, it is worth noting that the return on quality framework (Rust et al. 1995)
shows that firms investing in quality improvements contribute to the bottom line
through two routes. The first pertains to downstream, revenue-enhancing outcomes
of increased customer satisfaction (revenue emphasis), while the second pertains to
operational efficiencies and improvements that decrease costs (cost emphasis)
(Rust et al. 1995). Therefore, further study is necessary to investigate whether
CAR Vol. 25 No. 2 (Summer 2008)
Extent of ABC Use and Its Consequences
17
firms can achieve profitability through both customer satisfaction and cost reduction. Finally, this study uses perceptual measures for both exogenous and endogenous variables. In summary, the challenge for further research is to provide
insights that are relevant and useful for practitioners, to allow management
accounting research to have more of an impact on practice.
Despite these limitations, the results of this study have implications for both
theory and practice. From a theoretical or research perspective, we are again
reminded by this study that organizations are composed of complex sets of interrelationships, making it challenging to evaluate the impact of any single management
innovation and suggesting that the path analytical model is well suited to studies
seeking to learn more about the relationships of variables in complex business
environments. Thus, the conjecture by Kennedy and Affleck-Graves 2001 that
ABC may enhance profitability only indirectly through its impact on other variables
that ultimately add value is supported in this research and implies opportunity for
additional research examining more of the rich relationships found in business
organizations. Management learns from this research that in order to achieve
greater returns from their investment in ABC, they must also use ABC to improve
both internal quality drivers and cycle time while using fewer resources to achieve
both of these objectives. Thus, if managers believe that their organization’s strategic emphasis must be either on quality or cost improvement, but not both, then
they may behave suboptimally. In addition, managers who believe that using ABC
to drive new quality enhancement initiatives can result in both revenue expansion
and cost reduction, the “dual emphasis” according to Rust et al. 1995, can find
support in this research. Therefore, the findings of this research make important
contributions to the literature on the ABC – profitability link, to the debate about
the belief that organizations must focus on either quality or cost improvement to be
successful (Porter 1980, 1985) and that attempts to achieve both will leave an organization “stuck in the middle” (Mittal, Sayrak, Tadikamalla, and Anderson 2005),
and to the discourse on whether quality improvements can have a positive impact
on profits through both revenue enhancements and cost reductions (Rust et al.
1995). The good news for managers from this research is that the extent of ABC
use and its financial consequences are to a large degree under their control. However,
rather than providing final solutions to a complex puzzle, this research provides
important signals for a way forward for researchers and managers to make
improvements in both research and practice.
CAR Vol. 25 No. 2 (Summer 2008)
18
Contemporary Accounting Research
Appendix
In this project, we are investigating the relationships between extent of ABC use,
quality improvement, cost improvement, cycle-time improvement, and profitability.
The focus is on manufacturing plants that are profit centers with at least three years
of ABC adoption.
Please answer the questionnaire (or pass it to the most appropriate person
within the plant and return it to the address mentioned in the cover letter.
The answers in this questionnaire will be treated in the strictest confidence and
no information gained from this survey will be identified with any particular person
or manufacturing plant.
Part I.
Different overhead cost allocation methods are used in various business settings. Of the following three overhead cost allocation methods, please indicate
the method used at your plant (Krumwiede 1998).
A. Individual plant-wide overhead rate: allocates all indirect manufacturing
costs via a single overhead cost rate (e.g., 200% of direct labor, etc.).
B. Departmental or multiple plant-wide overhead rates: allocates all indirect
manufacturing costs using either different rates by department or multiple plant-wide
rates (e.g., or 66.2 percent).
C. Activity- or Process-based Costing Method (“ABC”): assigns indirect
costs to individual activity or process (rather than departmental) costs pools, then
traces costs to users activities (e.g., products, customers, etc.) based on more
than one cost driver.
Question:
Which of the above overhead cost allocation methods (A, B or C) is used at your
plant? Please tick against A, B or C below.
A _________
B _________
C _________
If you’ve ticked “C” above, please fill out the rest of the questionnaire.
Otherwise, please stop and return the questionnaire.
Part II.
Below, we seek to assess the extent to which ABC is used at your plant. For
this purpose, using the survey scale below, please provide the extent to which
the following functions routinely use the ABC information for decision-making
at your plant (Swenson 1995, Cagwin and Bouwman 2002):
1. Design engineering
Extremely
low use
1
Very
low use
2
Below
average use
3
CAR Vol. 25 No. 2 (Summer 2008)
Average
use
4
Above
average use
5
Very
high use
6
Extremely
high use
7
Extent of ABC Use and Its Consequences
19
2. Manufacturing engineering
Extremely
low use
1
Very
low use
2
Below
average use
3
Average
use
4
Above
average use
5
Very
high use
6
Extremely
high use
7
Very
low use
2
Below
average use
3
Average
use
4
Above
average use
5
Very
high use
6
Extremely
high use
7
Very
low use
2
Below
average use
3
Average
use
4
Above
average use
5
Very
high use
6
Extremely
high use
7
3. Product management
Extremely
low use
1
4. Plant-wide
Extremely
low use
1
Please use the seven-point scale below to indicate the extent to which your
plant has experienced improvement in product cost (direct materials, direct
labor, and overhead) and non-manufacturing costs over the last three years
(Ittner et al. 2002).
1. Direct materials costs
Extremely
Below
Above
Extremely
low
Very low
average
Average
average
Very high
high
improvement improvement improvement improvement improvement improvement improvement
1
2
3
4
5
6
7
2. Direct labor costs
Extremely
Below
Above
Extremely
low
Very low
average
Average
average
Very high
high
improvement improvement improvement improvement improvement improvement improvement
1
2
3
4
5
6
7
3. Overhead costs
Above
Extremely
Extremely
Below
average
Very high
high
Very low
average
Average
low
improvement improvement improvement improvement improvement improvement improvement
5
6
7
1
2
3
4
4. Non-manufacturing costs
Extremely
Extremely
Below
Above
high
Average
average
Very high
low
Very low
average
improvement improvement improvement improvement improvement improvement improvement
7
1
2
3
4
5
6
CAR Vol. 25 No. 2 (Summer 2008)
20
Contemporary Accounting Research
Please use the seven-point scale below to indicate the extent to which your
plant has experienced improvement in quality over the last three years (Ittner
et al. 2002):
1. Finished product first pass quality yield in percentage terms
Extremely
Below
Above
Extremely
low
Very low
average
Average
average
Very high
high
improvement improvement improvement improvement improvement improvement improvement
1
2
3
4
5
6
7
2. Scrap cost as a percentage of sales
Extremely
Below
Above
Extremely
low
Very low
average
Average
average
Very high
high
improvement improvement improvement improvement improvement improvement improvement
1
2
3
4
5
6
7
3. Rework cost as a percentage of sales
Above
Extremely
Extremely
Below
Very high
high
average
Very low
average
Average
low
improvement improvement improvement improvement improvement improvement improvement
5
6
7
1
2
3
4
Please use the seven-point scale below to indicate the extent to which your
plant has experienced improvement in cycle time over the last three years:
1. New product introduction time (the ability to minimize the time to make product
improvements / variations to existing products or to introduce completely new
products) (Safizadeh et al. 1996, Vickery et al. 1995):
Above
Extremely
Below
Extremely
high
average
Very high
average
Average
low
Very low
improvement improvement improvement improvement improvement improvement improvement
7
5
6
3
4
1
2
2. Manufacturing lead time (the ability to minimize the time from when the order
was released to the shop floor to the time of its completion) (Handfield and Pannesi
1995):
Below
Above
Extremely
Extremely
low
Very low
average
Average
average
Very high
high
improvement improvement improvement improvement improvement improvement improvement
1
2
3
4
5
6
7
3. Delivery reliability / dependability (the ability to deliver consistently on the
promised due date) (Handheld 1995, Roth and Miller 1990):
Extremely
Below
Above
Extremely
low
Very low
average
Average
average
Very high
high
improvement improvement improvement improvement improvement improvement improvement
1
2
3
4
5
6
7
CAR Vol. 25 No. 2 (Summer 2008)
Extent of ABC Use and Its Consequences
21
4. Customer responsiveness (the ability to respond in a timely manner to the needs
and wants of the plant’s customers including potential customers (Tunc and Gupta
1993, Ward et al. 1995):
Extremely
Below
Above
Extremely
low
Very low
average
Average
average
Very high
high
improvement improvement improvement improvement improvement improvement improvement
1
2
3
4
5
6
7
Please use the seven-point scale below to indicate the extent to which your
plant has experienced improvement in profitability over the last three years
(Kinney and Wempe 2002, Atkinson et al. 2001):
1. Market share (of products produced at your plant)
Extremely
Below
Above
Extremely
Very low
average
Average
average
Very high
high
low
improvement improvement improvement improvement improvement improvement improvement
1
2
3
4
5
6
7
2. Return on sales (net income before corporate expenses divided by sales)
Above
Extremely
Extremely
Below
high
Very high
Very low
average
Average
average
low
improvement improvement improvement improvement improvement improvement improvement
5
6
7
1
2
3
4
3. Turnover on assets (sales divided by total assets)
Extremely
Below
Above
Extremely
high
average
Very high
average
Average
low
Very low
improvement improvement improvement improvement improvement improvement improvement
7
5
6
3
4
1
2
4. Return on assets (net income before corporate taxes divided by total assets)
Extremely
Below
Above
Extremely
low
Very low
average
Average
average
Very high
high
improvement improvement improvement improvement improvement improvement improvement
1
2
3
4
5
6
7
Part III.
Please answer the following:
1. What is your plant two-digit SIC code? ____________
2. Number of years at this position? ___________
3. Number of years in management? __________
4. What is the number of employees at your plant? ________
CAR Vol. 25 No. 2 (Summer 2008)
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Contemporary Accounting Research
Endnotes
1. Since its inception, ABC has been mainly used for product costing (Börjesson 1994).
However, its real power lies in its ability to identify cost-reduction opportunities
(Cooper and Kaplan 1998; Shank and Govindarajan 1992). The second-generation
ABC model has been claimed to contain two dimensions: the cost view and the process
view (Cagwin and Bouwman 2002; Mévellec 1990; Lorino 1991; Turney 1991, p. 81).
[AU: Turney page no. necessary? it consorts oddly with other references]
2. Shields (1997) reviews management accounting research and calls for a greater use of
new research methods, such as structural equation modeling. Smith and LangfieldSmith (2002) review articles in management accounting using structural equation
modeling and suggest that there are many potential benefits of its greater use.
3. A “ceteris paribus” environment implies that the net effect of improving quality is to
reduce certain, but potentially not all, costs in the value chain. Thus, “ceteris paribus”
is used to limit the impact to the variable relationship examined. We show empirically
that quality improvements drive costs down in addition to impacting cycle time and
profitability directly.
4. A “ceteris paribus” environment implies that an improvement in quality can have a
“revenue effect” as defined by Rust et al. 1995, which could include higher absolute or
relative prices or stable prices but increases in market share with resulting increases in
total margin. The impact of quality enhancements on price elasticity of demand will
determine the specific nature of the revenue effect. Thus, the “ceteris paribus”
limitation is used to limit the impact to the variable relationship examined and exclude
the impact of improvements in quality impacting costs and cycle time.
5. SEM is an iterative, maximum likelihood process with all coefficients estimated
simultaneously in each iteration. Thus, cycle time and quality are held constant by
being included as predictors of profitability. Hypothesis 6 may be criticized as obvious
or even a tautology; however, a tautological relationship would have something
predicting itself and would result in an R 2 closer to 1. [AU: source of the following
quotation? please provide author, year, and page no., and add to the References if
necessary] “The presence of cost improvement in the equation gives meaning to the
finding that quality improvement and cycle-time improvement make incremental
contributions to profitability. So even if Hypothesis 6 is obvious, Cost improvement
must be part of this equation — otherwise, the model would be misspecified.”
6. A “ceteris paribus” environment implies that we are limiting the impact of the
relationship to the variable(s) examined. It is possible that improvements in costs due
to ABC and cycle time, could lead to decreases in costs but not higher profits due to the
other variables impacting total costs even more.
7. A “ceteris paribus” environment implies that we are limiting the impact of the
relationship to the variable(s) examined. It is possible that improvements in cycle time
could lead to increases in profitability independently of the effects of other variables,
but those increases could be offset by the negative contribution of other variables on
profitability.
8. See the appendix for an abbreviated copy of the research questionnaire used to measure
the variables.
CAR Vol. 25 No. 2 (Summer 2008)
Extent of ABC Use and Its Consequences
23
9. Prior studies have demonstrated statistically significant correlations between
perceptual and corresponding objective measures of performance (Dess and Robinson
1984; Vickery, Droge, and Markland 1997; Ward, Leong, and Boyer 1994; Wall,
Michie, Patterson, Wood, Sheehan, Clegg, and West 2004), indicating that perceptual
ratings of performance can be considered reliable indicators.
10. Managers were asked to fill out the questionnaire or to forward it to the most
appropriate person within the plant.
11. It is expected that three years allows enough time for ABC to be imbedded in the
organizations (Krumwiede 1998).
12. While we meet liberal fit measures, it is important to note that more conservative fit
indices are 0.95 and RMSEA is 0.05).
13. However, a more conservative measure is 0.80 as used in the marketing literature.
14. We also ran a one-stage multiple regression model to assess the direct effect of extent
of ABC use on profitability. Results indicate no significant effect. When these
relationships are further analyzed within the context of total quality management
(TQM) and level of information technology (IT) to determine whether TQM and IT are
complementary to ABC use, results show that, overall, manufacturing plants with
TQM/high IT perform better than those with non-TQM/low IT.
15. Omitted variables will only cause endogeneity if they are correlated with both ABC
and performance.
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Extent of ABC Use and Its Consequences
31
Figure 1 Conceptual model
Quality
improvement
Extent of
ABC use
Cost
improvement
Financial
performance
Cycle-time
improvement
CAR Vol. 25 No. 2 (Summer 2008)
32
Contemporary Accounting Research
Figure 2 Structural path coefficients of the overall model
Quality
improvement
0.223,
p = 0.001
0.205,
p = 0.000
0.171,
p = 0.036
0.337, p = 0.000
Extent of
ABC use
0.282,
p = 0.000
0.190,
p = 0.000
Cost
improvement
0.188,
p = 0.000
0.191,
p = 0.004
Cycle-time
improvement
CAR Vol. 25 No. 2 (Summer 2008)
Profitability
0.690,
p = 0.000
Extent of ABC Use and Its Consequences
33
TABLE 1
Summary of sample
Panel A: Sample collection procedures
Step 1. Sample of manufacturing plants collected from review of the
literature review (journals, periodicals, etc.)
Step 2. Sample of plants randomly selected from 57 firms (with
multiple plants) that have adopted ABC (from the literature
review):
Less sample already collected in Step 1 that is included
in step 2 sample
Total sample collected
149
2,403
(46)
2,506
Panel B: Responses received
First wave of responses received
686
Second wave of responses received
227
Total sample received
913
Less: Plants that have abandoned ABC
Incomplete responses
78
144
Usable responses
691
CAR Vol. 25 No. 2 (Summer 2008)
34
Contemporary Accounting Research
TABLE 2
Respondents’ characteristics
Panel A: Industry classification
Food and kindred products
Textile mill products
Apparel and other textile products
Paper and allied products
Chemicals and allied products
Rubber and plastics products
Primary metal industries
Fabricated metal products
Industrial machinery and equipment
Electronic, electrical equipment
Transportation equipment
Instruments and related products
SIC
Number
of
plants
in
sample
% of
sample
(n 691)
% of
targeted
population
(n 2,506)
20
22
23
26
28
30
33
34
35
36
37
38
83
13
64
97
92
35
19
42
35
113
43
55
12.01
1.88
9.26
14.04
13.31
5.07
2.75
6.08
5.07
16.35
6.22
7.96
9.537
7.023
9.936
12.530
13.009
5.467
3.472
3.392
14.645
7.143
6.605
7.542
691
100
100
Total
Panel B: Other characteristics of respondents
Length at present position (years)
Length in management (years)
Number of employees
Minimum
Maximum
Mean
s.d.
6
11
169
18
21
1,346
10.56
17.31
947
4.73
5.76
309
[AU: 100.00?]
CAR Vol. 25 No. 2 (Summer 2008)
Extent of ABC Use and Its Consequences
35
TABLE 3
Measurement items
Minimum
Maximum
Mean
s.d.
Extent of ABC use
Design manufacturing
Manufacturing engineering
Product management
Plantwide
1
1
1
1
7
7
7
6
4.496
5.245
5.849
4.041
1.611
1.662
1.279
0.683
Cost improvement
Materials cost
Labor cost
Overhead cost
Nonmanufacturing cost
1
1
1
1
7
7
7
7
5.310
5.143
5.819
5.738
1.016
0.969
0.836
1.197
Quality improvement
Finished product first pass quality
yield in percentage terms
Scrap cost as a percentage of sales
Rework cost as a percentage of sales
1
1
1
7
7
7
5.390
6.051
6.413
0.550
1.704
1.256
Cycle-time improvement
New product introduction time
Manufacturing lead time
Delivery reliability/dependability
Customer responsiveness
1
1
1
1
6
7
7
7
4.015
6.396
6.641
6.720
0.399
1.684
1.658
1.159
Profitability
Market share
Return on sales (ROS)
Turnover on assets (TOA)
Return on assets (ROA)
1
1
1
1
7
7
6
7
5.839
5.080
4.560
5.977
0.392
0.437
1.309
1.560
CAR Vol. 25 No. 2 (Summer 2008)
CAR Vol. 25 No. 2 (Summer 2008)
Extent of ABC use
Quality improvement
Cost improvement
Cycle-time improvement
Profitability
p 0.001.
†
[AU: please explain boldface?]
p 0.05.
*
Notes:
1.
2.
3.
4.
5.
TABLE 4
Correlation matrix
1
0.209*
0.245*
0.211*
0.165
1
Extent of
ABC use
1
0.486†
0.226*
0.532†
2
Quality
improvement
1
0.379†
0.539†
3
Cost
improvement
1
0.674†
4
Cycle-time
improvement
1
5
Profitability
36
Contemporary Accounting Research
Extent of ABC Use and Its Consequences
37
TABLE 5
Standardized loadings and estimates (t-values)
Standardized
loadings
t-value
Extent of ABC use
Design manufacturing
Manufacturing engineering
Product management
Plantwide
0.866
0.882
0.917
0.748
—*
12.879
13.631
10.482
Cost improvement
Materials cost
Labor cost
Overhead cost
Nonmanufacturing cost
0.863
0.843
0.834
0.879
—*
11.362
11.104
11.839
Quality improvement
Finished product first pass quality yield in
percentage terms
Scrap cost as a percentage of sales
Rework as a percentage of sales
0.831
0.710
0.869
—*
8.293
11.715
Cycle-time improvement
New product introduction time
Manufacturing lead time
Delivery reliability/dependability
Customer responsiveness
0.737
0.755
0.719
0.713
—*
9.474
7.938
7.573
Profitability
Market share
Return on sales (ROS)
Turnover on assets (TOA)
Return on assets (ROA)
0.737
0.762
0.730
0.711
—*
8.482
7.371
6.836
Notes:
*
Indicates a parameter is fixed at 1.0 in the original solution.
Fit indices: 2/df 2.751, GFI 0.931, CFI 0.943, NFI 0.938, RMSEA 0.074.
[AU: add note indicating p-levels?]
CAR Vol. 25 No. 2 (Summer 2008)
CAR Vol. 25 No. 2 (Summer 2008)
Extent of ABC use
Quality improvement
Cost improvement
Cycle-time improvement
Profitability
0.731
0.041
0.058
0.045
0.027
1
Extent of
ABC use
0.730
0.236
0.051
0.283
2
Quality
improvement
0.650
0.142
0.288
3
Cost
improvement
0.534
0.452
4
Cycle-time
improvement
0.540
5
Profitability
0.786
0.815
0.719
0.908
0.731
Cronbach’s
alpha
0.793
0.841
0.753
0.923
0.762
Composite
reliability
The boldfaced numbers on the diagonal are the average variance extracted (AVE). Off-diagonal elements are the square correlations between
constructs. For discriminant validity, diagonal elements should be larger than off-diagonal elements (Fornell and Larcker 1981). Also, the AVE
exceeds 0.50 for all constructs (Fornell and Larcker 1981; Chin 1998; Rivard and Huff 1988), indicating convergent validity.
Notes:
1.
2.
3.
4.
5.
TABLE 6
Discriminant validity analyses
38
Contemporary Accounting Research
Extent of ABC Use and Its Consequences
39
TABLE 7
Overall standardized path coefficients and significance
Standardized
path
Significance
coefficient
(p-value)
Hypotheses tests
Hypothesis 1: Extent of ABC use → Cost improvement
Hypothesis 2: Extent of ABC use → Quality improvement
Hypothesis 3: Extent of ABC use → Cycle-time
improvement
Hypothesis 4: Quality improvement → Cost improvement
Hypothesis 5: Quality improvement → Profitability
Hypothesis 6: Cost improvement → Profitability
Hypothesis 7: Quality improvement → Cycle-time
improvement
Hypothesis 8: Cycle-time improvement → Cost improvement
Hypothesis 9: Cycle-time improvement → Profitability
0.171
0.205
0.036
0.000
0.190
0.282
0.223
0.690
0.000
0.000
0.001
0.000
0.337
0.188
0.191
0.000
0.000
0.004
Notes:
Fit indices: 2/df 2.866, GFI 0.922, CFI 0.939, NFI 0.927, RMSEA 0.083.
Explained variances:
R 2 for quality improvement
0.142
R 2 for
cycle-time improvement 0.175
R 2 for
cost improvement
0.174
R 2 for
profitability
0.743
CAR Vol. 25 No. 2 (Summer 2008)