The context-specific benefit of use of activity-based costing with supply chain management and technology integration.

Cagwin, Douglass ; Ortiz, Dennis

ABSTRACT

This paper examines whether a context-specific benefit is obtained from the use of Activity-based Costing (ABC) with the business initiatives Supply Chain Management (SCM), Technology Integration (TI) Context-specific benefit is operationalized by a composite measure of financial performance, Return on Assets (ROA) Top executives of 305 firms operating in the motor carrier industry furnished information regarding use of the initiatives. Dependent variable information is obtained from financial statement data filed with the U.S. government. Multiple regression analysis is used to identify the improvement in ROA associated with 1) use of each initiative and 2) concurrent use of two initiatives.

A direct effect for use of SCM and TI is confirmed. Context-specific benefits obtained from concurrent use of ABC with SCM and TI are identified. It is likely that ABC functions as an enabler of other improvement initiatives, providing the information necessary to optimize the effectiveness of SCM and TI. The positive findings regarding ABC are of particular interest to practicing and academic accountants because they are often the primary proponents and administrators of ABC and most previous evidence of ABC efficacy has been theoretical or anecdotal.

INTRODUCTION

The focus on cost, quality and time has generated many management changes with significant accounting implications (Smith, 1998). These changes increasingly include the implementation of strategic business initiatives such as Supply Chain Management (SCM), Technology Integration (TI), and Activity-Based Costing (ABC). (1) Profit-maximizing firms would not implement strategic business initiatives if they did not expect a net financial benefit from their use. However, there has been little empirical evidence that demonstrates that SCM, TI, or ABC improves financial performance in any industry.

In addition, researchers have often suggested that ABC and other strategic business initiatives complement and enhance each other, rather than being individually necessary and sufficient conditions for improvement (e. g. Cagwin and Bouwman, 2002; Shields et. al,. 2000; Anderson, 1995; Swenson, 1998). There has been little empirical investigation of context-specific benefits obtained from using ABC to enhance the benefits of other initiatives or of a context-specific benefit obtained from concurrent use of SCM and TI.

The purpose of this study is to investigate the improvement in financial performance associated with the single and concurrent use of the strategic business initiatives SCM, TI, and ABC. Data is obtained through a cross-sectional mail survey of 305 motor carrier industry top executives and from a database containing financial statement information reported to the U.S. government. Multiple regression analysis is used to investigate the association between use of initiatives and improvement in financial performance (proxied by ROA) and to identify positive context-specific effects from the use of SCM with TI and of ABC with SCM and also with TI.

This research adds to the limited body of empirical strategic business initiative research in four ways. The first contribution is to provide empirical evidence that the benefits claimed by initiative advocates are net benefits. Second, the existence of context-specific benefit from concurrent use of ABC with SCM and with TI is confirmed. Third, the study focuses on the motor carrier industry, an important member of the service sector, which has become the dominant sector of the economy. Researchers have often postulated, but not tested, the efficacy of initiatives in a service setting. Finally, limitations of previous research (i.e., the lack of control for simultaneous use of multiple initiatives and prior level of performance) are addressed.

The remainder of the paper is organized as follows: Section II defines and describes strategic business initiatives, situates this study in the context of past research and provides hypothesis development. Section III describes sample selection and the survey instrument. Section IV describes the methodology used, including variable selection and specification. Results are presented in Section V, and a Summary and Discussion in Section VI.

BACKGROUND

A strategic business initiative is an innovative business technique, strategy or technology that is purported to increase business success. All initiatives broadly advocate change through continuous improvement, but each accomplishes continuous improvement somewhat differently. In recent years, strategic business initiatives such as SCM. TI, and ABC have been subjects of intense interest for practicing accountants, consultants, and academicians, motivating their selection for the current study. Each initiative is discussed below.

Technology Integration (TI)

TI takes place when the technology applied to a business process becomes indistinguishable from the process itself (Haag, 2005). Examples include using technology (bar coding, electronic data interchange) for fast order entry, automatic pricing and discounting, printing pick tickets for a specific route or delivery, and purchase orders (Sheth & Sisodia, 1995). Fleisch (2004) discusses opportunities and risks involved in such highly integrated information systems. Specific goals in using technology include reducing the cost of personnel and operations, as well as changes in the organizational structure. For sustained competitive advantage, companies need an IT platform that uniquely blends core marketing competencies with seamless technology. Over time IT becomes less of a driving force and more of a requisite infrastructure. This leads to the development of technology-based core competencies that are not readily duplicated by others (Sheth & Sisodia, 1995).

Supply Chain Management (SCM)

Russell and Taylor (2003) define a supply chain as the "facilities functions, and activities involved in producing and delivering a product or service from suppliers ... to customers ... " SCM is an advanced management discipline that uses structured planning techniques to optimize the performance of supply chains to increase value to the shareholders of the trading partners that comprise the supply chain. SCM has the potential to improve financial performance in three key areas: (1) revenue growth (e.g. through improved forecasting results), (2) profitability (e.g., through reduced costs), and (3) capacity utilization (Timme & Williams-Timme, 2000). For all its potential benefits, however, Dani et al. (2004), in their discussion of opportunistic behavior and gamesmanship in the supply chain context, point out that SCM is not a panacea.

Activity-Based Costing (ABC)

The arguments in support of Activity-Based Costing (ABC) are generally based on the superiority of information that can be generated in comparison with that generated by traditional cost management systems. According to the theory of information economics, better information leads to better decision-making, and better decision-making enhances firm value. For example, in their discussion of optimal factory design La Trobe-Bateman and Wild (2003) include ABC in their model of product manufacturability because it provides improved information quality. However, several reservations have been expressed regarding the efficacy of ABC (Innes & Sinclair, 2000), particularly that it is not suited for all business environments.

Association between Initiative Use and Improvement in Financial Performance

The theories of diffusion of innovations (Kwon & Zmud, 1987), transaction cost economics (Roberts & Sylvester, 1996), and information technology (Dixon, 1996) suggest that organizations adopt an innovation to obtain benefits that directly or indirectly impact financial performance measures. There have been numerous claims and counterclaims, rarely supported by objective and rigorous empirical evidence, regarding whether programs have yielded net financial gains. Evidence of the benefits of these systems is largely restricted to theoretical models and anecdotal information obtained from case studies that depend on anecdotal information related by practitioners. (2)

Hard empirical evidence of the benefits from innovation has been slow in coming. Consultants may have been active, and successful, in 'selling' the benefits of change, but accounting academics and the academic publications industry must shoulder the blame for not bringing relevant research findings to the attention of practitioners in a timely manner (Smith, 2000). Results from the limited empirical research examining the link between SCM, TI, and ABC and financial performance are mixed (Wouters et. al., 1999). With the exception of Giunipero et al. (2001), who used correlation analysis to find partial evidence of an association between use of Quick Response (SCM as applied to retailing) and financial performance, we are aware of no studies that have identified an association between improvement in net financial performance and use of SCM or TI.

Recently, researchers have been successful in detecting a link between use of ABC and improvement in financial performance in specific business environments. Kennedy and Afleck-Graves (2001) were successful in linking the implementation of ABC with a net improvement in financial performance in manufacturers. However, Ittner et al. (2002), and Cagwin and Bouwman (2002) found that ABC's contribution was an indirect, rather than direct effect on improvement in financial performance.

HYPOTHESIS DEVELOPMENT

Firms adopt initiatives in attempts to gain or maintain cost and market advantages (Kinney & Wempe, 1998). These advantages should in turn lead to improvement (or to maintenance of favorable values) in composite financial indicators, in the face of competitive pressures. The first hypothesis is in three parts and is consistent with hypotheses contained in prior research, suggesting that initiatives individually contribute toward an improvement in financial performance.

 H1: There is a positive association between use of a) SCM, b) TI,
 c) ABC and improvement in financial performance relative to the
 improvement in financial performance of non-users.

Context-specific Benefits Obtained from Concurrent Use of Initiatives

There may be context-specific benefits (positive or negative) leading to various optimal combinations of factor inputs, e.g., initiatives and management systems (Capon et al., 1988). If firms are rationally maximizing value they would choose initiative combinations that they believe lead to this objective.

There has been considerable academic and practitioner interest in investigating possible context-specific benefits of initiatives with management information systems and management techniques.

Context-specific Benefits Obtained from Concurrent Use of TI with SCM

Information technology by itself will do very little to lower the cost of receiving, inventory control, shipping or transportation. Reducing costs requires process and management improvements to take advantage of what technology can do (Dawe, 1994). The challenge in SCM today is matching material or service flow with the flow of information associated with it (Andel, 1998). For example, when materials show up at the receiving dock, how long does it take to get the information associated with that delivery into the system that houses customer order management? Many researchers have argued the efficacy of combining SCM and TI in specific circumstances (e.g., Larson and Lush, 1990 in retailing; Lewis, 2000 at McCormick & Co.; Lin et al., 2000 at IBM; and Palaniswamy and Frank, 2002 at Oracle), leading to the second hypothesis:

 H2: The financial performance of firms that use SCM with TI has
 improved more than the sum of the improvements directly associated
 with each initiative.

Use of ABC with SCM

The development of the supply chain concept poses a significant challenge for the cost accounting system. Porter (1985) and Drucker (1995) argue a firm must look beyond its internal actions to reduce costs and explore the linkages between suppliers' value chains and a firm's value chain to identify opportunities for competitive advantage. According to Pohlen and La Londe (1994), success of these efforts will largely depend on the ability of the firm's cost accounting system to trace costs to specific products, customers, supply channels, or logistics activities. Partridge and Perren (1994), Johnson and Kaplan (1987), Roth and Borthick (1991), and Pohlen and La Londe (1994) argue that with their inherent cost distortions, traditional cost systems are ill equipped to provide relevant information for analysis of supply chain costs; and that ABC, with its focus on activity analysis, is a natural choice for supply chain analysis. Noncontributing time can be identified and eliminated using information in the supply chain to avoid redundancies, compress the supply chain cycle, and synchronize lead times and capacities in the supply chain (Borthick & Roth, 1993; Partridge & Perren, 1994).

Use of ABC with TI

According to Drucker (1995), advances in information technology and the declining costs of computerized information have facilitated the development and maintenance of ABC and put ABC data at the fingertips of all management levels. Researchers and practitioners have often noted the natural relationship between ABC and TI. For example, Reeve (1995) suggests that an integrated ABC system presupposes a relatively high level of IT sophistication with extensive and flexible information stratification and real-time activity driver information; and Cooper (1988) and Koltai et al. (2000) suggest that ABC becomes more beneficial as the costs of measurement are reduced. That this connection is appreciated by the practitioner community is evidenced by a survey of software users where two-thirds said the full value of ABC would not be realized until improvements are made in data collection systems (Geishecker, 1996).

Context-specific Benefits Obtained from Concurrent Use of ABC with SCM or TI

Theory and anecdotal reports support the proposition that the improved costing information and intensive analysis of business activities provided by ABC lead to improved decision-making, and therefore should be associated with improved performance. However, Ittner et al. (2002) and Shields et al. (2000) argued that ABC has an indirect effect on financial performance by enhancing improvements contributed by other process improvement initiatives. Krumwiede (1998) provided additional weight to this argument by reporting that all fifteen "best practice" firms had linked ABC to another improvement initiative.

Although, as Shaw (1998) notes, ABC is now recognized as a fundamental business methodology for enabling business improvement, no empirical research has specifically targeted the combination of ABC with either SCM or TI and their combined association with improvement in financial performance, leading to the following hypothesis:

 H3: The financial performance of firms that use ABC with SCM or TI
 has improved more than the sum of the improvements directly
 associated with each initiative.

SAMPLE SELECTION AND SURVEY INSTRUMENT

Selection of Industry

Most research regarding strategic business initiatives has focused on the manufacturing segment of the economy. However, the major changes that manufacturing companies have experienced in recent years have also occurred in virtually all types of service organizations (Atkinson et al., 1995). Consequently, strategic business initiatives can be applied in all types of organizations (Rotch, 1990; Tanju & Helmi, 1991; Jarrar & Aspinwall, 1999). Since non-manufacturing activities represent the majority of the North American economy, there clearly is opportunity for research to focus on non-manufacturing settings, including transportation (Shields, 1997).

This study focuses on a single service industry: the motor carrier industry (SIC 4213). Restricting to a single industry reduces noise, thereby increasing statistical power, and consequently provides a higher likelihood of identifying valid relationships. The motor carrier industry is selected because of 1) the importance of the motor carrier industry to the nation's economy, 2) interest of the members of the industry in use of business initiatives that can potentially improve their competitive positions (3), and 3) the availability of detailed financial statement data for members of the industry.

Although it can be argued that the focus on a single industry tends to make results less generalizable than a study that crosses industries, the findings of this study have a wide appeal. The motor carrier industry generates about five percent of the gross domestic product and hauls approximately 55 percent of all domestic freight volume. It has an impact on virtually every organization in every industry and governmental agency in the U.S. economy. Furthermore, transportation is a major component of business logistics and is usually the single largest cost element in the logistics function. Companies not only contract with for-hire carriers but very often maintain private fleets of long-haul vehicles.

Survey Instrument and Procedures

The independent variable data used in this study (other than LEVEL) are extracted from an instrument that was used to collect data intended for use both in this study and also for other in-depth analyses of the trucking industry. The instrument is based on a thorough review of prescriptive, conceptual, practitioner, and empirical motor carrier industry literature. Content validity is addressed by asking representatives of the trucking industry, industry experts, and a group of faculty experienced in management innovation and survey research to review the instrument for clarity and meaning. Modifications were made as appropriate.

Most of the questions are close-ended and ask the respondent to rate or assess the item on a seven-point Likert balanced scale, anchored by 1 = "Almost Always Avoid" and 7 = "Almost Always Use." Some items ask for specific numerical information (e.g., "Truckload percent of total freight revenue").

Procedures prescribed by Dillman (1999) are followed to maximize response rates. Specific steps taken to strengthen this study include 1) pre-calling to obtain name of the CEO and to verify the mailing address, 2) sending a preliminary letter and brief summary of the project, 3) pre-calling to ask if the CEO had any questions, 4) including a personalized cover letter, 5) promising to send a summary of results and a Technical Report, 6) promising confidentiality, 7) including a stamped, self-addressed envelope for reply, 8) mailing a reminder letter at three weeks past initial mailing, and 9) mailing a reminder post card after seven weeks.

Population and Sample

The initial population for this study consisted of the 2,002 firms that reported to the Interstate Commerce Commission and were included in the 1998 TTS Blue Book of Trucking Companies. In order to focus on companies of sufficient size to have an established set of practices for conducting business, the population is limited to those companies that have at least thirty employees or $5 million in gross revenues. This constraint reduced the population by 383. From the remaining 1,619 companies, 1,100 were randomly selected for inclusion in the study. Of these, six were eliminated because they are Canadian companies, two were unable to be contacted by telephone or letter, nine had gone out of business, and 14 withdrew or refused to cooperate upon initial contact. The remaining 1,069 firms comprise the final sample. A total of 332 responses were received, a response rate of 31.1 percent. (4) Because of their larger size, the 332 sample firms represent 16.5 percent of the firms in the TTS database but contribute 23.1 percent (equity) to 41 percent (ton-miles) of the aggregated totals.

Financial data is available for 305 of the responses for both 1999 and 1998. Sample selection and response are summarized in Table 1.

The median industry, company, and position experience of the respondents is 25, 17, and nine years, respectively and 96 percent are of the rank of controller or officer (70 percent are President, Owner, or CEO). The extensive experience and high rank of the respondents lend considerable credibility to the survey responses.

METHODOLOGY

The impact of strategic business initiatives on a firm's improvement in financial performance is examined using the following model:

[DELTA](PERFORMANCE = f (Initiative Use, Initiative Use Interactions, Control Variables)

where [DELTA]PERFORMANCE is the change in ROA, measured for year t+1 minus year t. The Initiative Use variables are the set of binary measures of use of SCM, TI and ABC and are used to identify simple effects (H1). Interaction terms are created for concurrent use of SCM with TI (H2) and of ABC with SCM and with TI (H3). The dependent variables, variables of interest and control variables are discussed below.

Change in Return on Assets ([DELTA]ROA)

ROA, defined as after-tax net income scaled by total assets is generally accepted as a composite financial performance variable in empirical research. Many researchers, e.g., Ittner et al. (2002), Cagwin and Bouwman (2002); Kennedy and Affleck-Graves (2001) have used ROA as a dependent variable in their studies of ABC and financial performance. Furthermore, previous research shows a high correlation between ROA and other profitability measures (Prescott et al., 1986). For these reasons ROA is selected as the primary dependent variable.

Testing improvement in financial performance poses significant measurement problems. As Roberts and Silvester (1996) observe, numerous complications arise, including: (1) Modeling a company's "expected" profitability against which to compare realized profitability achieved after use of an initiative, (2) Controlling for concurrent changes in the organization, and (3) Controlling the breadth of implementation and integration of initiatives throughout the firm.

In general, comparison of "expected profitability" requires either specification of control variables which describe the industry in which the firm operates or the use of "industry mean-adjusted" measures. In the current study, expected profitability is addressed through restricting the study to a single industry, by using a fixed period of time (the change from 1998 to 1999) which provides control for macroeconomic and industry-specific factors that affect all firms equally, and by controlling for differences in the three segments of the industry. These restrictions allow comparison of the profitability of initiative users against that expected without use, proxied by the performance of equivalent non-users.

Concurrent changes in the organization are addressed through identifying and controlling for use of other initiatives and for prior performance. Control for use of other initiatives separates the effects of individual initiatives and allows comparison of users of an individual initiative to non-users of that initiative. Controlling for the moderating effects of length and breadth of implementation is addressed by inclusion of variables measuring extent of use derived from survey responses.

Archival dependent variable information is obtained from the TTS database. The TTS Blue Book of Trucking Companies is published by Transportation Technical Services, Inc., New York (TTS, 1998). The majority of Blue Book data is extracted from annual reports (Form M) that carriers file with the Interstate Commerce Commission. Form M requires use of standardized accounts defined in the Uniform System of Accounts for Motor Carriers of Property published by the American Trucking Associations, Inc.

Variables of Interest (ABC, SCM, and TI)

The simple variables of interest measure use of the initiatives SCM, TI, and ABC. These initiatives are established initiatives of significant interest to the motor carrier industry. ABC is of particular interest to the accounting profession.

Cross-sectional survey data are collected regarding the extent of use (diffusion) of initiatives at the survey date (mid-1999). The variables of interest are developed from 7-point Likert balanced scale (Dillman, 1999) responses to survey items introduced as "How much do you avoid or use the following competitive tactics to realize your competitive strategies?" Possible responses are: (1) "Almost Always Avoid," (2) "Mostly Avoid," (3) "Sometimes Avoid," (4) "Neither Avoid or Use," (5) "Sometimes Use," (6) "Mostly Use," and (7) "Almost Always Use."

ABC is measured by a single survey item. SCM is an additive measure comprised of three survey items: "Alliances with Competitors," "Partnership with Suppliers, " and "Inter-modal." TI is comprised of "Electronic Data Interchange," "Satellite Tracking Systems," "On-board Computers."

In addition, respondents furnished the year that they began use of each initiative. Responding firms are classified as significant users if their response averaged at least 5.5 to the questions regarding ABC, SCM or TI, and, because strategic initiatives are inherently multi-year projects, their year of beginning use was not 1999. As in Ittner et al. (2002), to avoid measurement problems with companies that are just beginning to implement initiatives, or that have not achieved full commitment to the systems, binary variables (SCM, TI and ABC) differentiate significant users from the remainder of the sample. These variables are the variables of interest for testing Hypothesis 1, which tests for a positive simple effect from use of the individual initiatives.

Control Variables

The implications of three control variables- two variables denoting type of company, TL and LTL (with specialized carrier the default), and beginning mean-adjusted LEVEL of performance are discussed in the following section.

Type of Company (TL, LTL)

The motor carrier industry is not entirely homogenous, but can be partitioned into three segments. One important distinction is between less-than-truckload (LTL) and truckload (TL) carriers. LTL carriers provide service to shippers who tender shipments lower than the minimum truckload quantities (i.e., 500 to 15,000 pounds). Consequently, the LTL carrier must consolidate the numerous smaller shipments into truckload quantities for inter-city movement and break down full truckloads at the destination city for delivery in smaller quantities. In contrast, the truckload carrier picks up a truckload and delivers the same truckload at destination.

Carriers may also be classified by the type of commodity they haul, general or specialized commodities. Specialized equipment carriers are carriers of goods requiring special handling (e.g., liquefied gases, frozen products, automobiles, or household goods). A specialized carrier is not permitted to transport other specialized commodities, or general commodities.

Industry type has been demonstrated as important in previous work (e.g., Capon et al. 1988) explaining cross-sectional variation in financial performance. The characteristics of the three types of service offered by carriers (TL, LTL, and specialized) in effect reflect three mini-industries. The impact of industry type is appropriately addressed through use of control variables. Because firms often offer more than one type of service, participating in more than a single mini-industry, self-reported continuous variables measuring the percentage of total freight revenues attributable to each classification (TL and LTL, with specialized carrier the default) are created. These variables provide control for differences in competitive environments, accounting practices, and other classification specific attributes that may impact performance. It is expected that LTL will be negatively signed because that segment of the industry has been under-performing the other segments during this decade.

Level of Performance (LEVEL)

As Balakrishnan et al. (1996) noted in their discussion of JIT, a firm's pre-adoption operating efficiency will influence its ROA response to the increased efficiency of initiative use. Because it appears that there are continuing pressures that tend to pull the performance of firms towards the average (5) (Bernard, 1994), higher performing companies may implement business initiatives to retain their comparative advantage, rather than to show improvement. In addition, firms are generally unable to sustain extremely poor performance for an extended period of time. They must either improve their performance towards the mean, or go out of business and thus would be not included in a cross-sectional study. These conditions may effectively create a "collar" around the performance of a sample firm, a ceiling limiting the improvement of the top performers and a floor limiting the deterioration of the already poor performers, resulting in a phenomenon with the statistical characteristics of mean reversion.

Significance of the variable of interest could result from lack of control for the effects of this "collar." If below average performers tend to implement initiatives more than successful firms, an upward change in performance may be due to the pressures noted above that tend to pull the performance of firms towards the average rather than efficacy of the initiatives. To control for the effects of mean reversion, beginning of test period (t) mean-adjusted level of performance (ROA) is included as an independent variable. It is expected that the sign of the regression coefficient associated with this variable will be negative (i.e., performance will be drawn toward the mean).

Regression Model

Testing of the three hypotheses is accomplished through estimation of the following OLS multiple regression:

[DELTA]ROA = [alpha] + [[beta].sub.1]SCM + [[beta].sub.2]TI + [[beta].sub.3]ABC + [[beta].sub.4]TL + [[beta].sub.5]LTL + [[beta].sub.6]ROA + [[beta].sub.7][SCM.sup.*]TI + [[beta].sub.8][SCM.sup.*]ABC + [[beta].sub.9][TI.sup.*]ABC + [epsilon]

The expected signs of the coefficients are: [[beta].sub.1] through [[beta].sub.3], and [[beta].sub.7] through [[beta].sub.9] > 0, and [[beta].sub.6], < 0; [[beta].sub.4] is not predicted.

RESULTS

Descriptive Statistics

Statistics relating to the use of SCM, TI and ABC are reported in panel A of Table 2. Over thirty percent (23 percent) of the respondents indicated that their firm "mostly" or "almost always" use ABC (TI), with 59 (19.3%) making heavy use of both. As might be expected given the recency of its widespread acceptance as a viable management strategy, fewer respondents (18.4%) use SCM heavily. However, in contrast to Morton (1997) who states that ABC has not been readily accepted by those in SCM, almost 2/3 of the SCM users also use ABC. There appears to be an adequate balance of users and non-users (control firms) to provide the contrast necessary to obtain adequate statistical testing power.

Use of ABC, SCM, and IT had an average response of 5.5 to 7 with the implementation date completed and before 1998. Firms are partitioned into High and Low performance at the median.

Descriptive statistics relating to the dependent and control variables used in statistical testing are presented in Table 3. The median (mean) change in ROA is a slightly negative (0.6) percent (positive 0.1 percent) from 1998 to 1999, reflecting the recent decline in profitability of the industry. The median level of performance for 1998 was a 3.7 percent ROA. Because the sample includes somewhat larger and less TL oriented firms than the industry population, this performance could indicate reduced profitability for the LTL segment of the industry.

The correlation matrix of the simple effect and control variables is shown in Table 4. As expected, use of initiatives is moderately positively correlated, with individual correlations ranging from 0.21 for ABC with SCM to 0.30 for SCM with TI. Initiative users also are more likely to be less-than-truckload (LTL) companies than truckload carriers (TL). Consistent with these pair-wise correlations, regressions of individual variables on the remaining independent variables show that the initiative variables have a moderate multivariate relationship with significance levels in the " = 0.10 range. All three initiatives are also moderately correlated with LTL. In no cases does the [R.sup.2] exceed 0.18 for these regressions. The extent of these correlations does not suggest that correlation among variables is a serious econometric issue.

There are statistically significant negative correlations between prior level of performance and SCM (-0.20) and ABC (-0.12), an indication of possible endogeneity. Lower performing firms tend to use SCM and ABC more often than high performers. A regression of initiative use on year t level of ROA (not presented) confirms that heavy users of initiatives tended to be slightly below mean in level of performance. If the form of the LEVEL variable does not adequately model mean-reversion (e.g., due to non-linearity), then performance improvement from lower performing SCM or ABC users cannot be specifically attributed to the initiative. This potential problem is addressed by performing an alternate test where the sample is partitioned into two groups based on prior performance, as shown in panel B of Table 2. As discussed later in the paper, consistent results for both groups indicate that results for the lower performing firms are not biased by improper modeling of mean reversion.

In Table 4, the use of ABC, TI, and SCM had a response of 5.5 to 7 with implementation date completed and before 1998; LTL and LTL equal the percentage of truckload and less-than-truckload carriage; and LEVEL equals the industry-adjusted level of the prior year's ROA.

Tests of Association are between Initiative Use and Financial Performance.

Results of the formal hypothesis tests are reported in Table 5 (6). The model is highly significant with an F-statistic of 24.71 and an R-square of .5501. SCM and TI have positive simple effects at the [alpha] = 0.05 level. Hypothesis 1 is confirmed for SCM (H1a) and TI (H1b). Although ABC (H1c) is positively signed, it does not attain statistical significance at conventional levels (p<0.157).

The interaction of ABC with TI is positively significant at [alpha] = 0.05, while that of ABC with SCM is significant at [alpha] = 0.059. Significance of a positively signed interaction term confirms that there is a positive effect created from concurrent use of the two tested initiatives (i.e., there is an association with improvement in financial performance over and above that of the sum of the effects of the initiatives used in isolation). Therefore, it appears there is a positive context-specific benefit created from concurrent use of these pairs of initiatives. Hypothesis 3 is therefore confirmed.

However, although the coefficient is positive, there is no statistical evidence that concurrent use of SCM and TI creates a positive context-specific benefit. Hypothesis 2 is not confirmed.

In Table 5, the use of ABC, TI, and SCM had an average response of 5.5 to 7 with implementation date completed and before 1998; LTL and LTL equal the percentage of truckload and less-than-truckload carriage; and LEVEL equals the industry-adjusted level of the prior year's ROA. Bold indicates significant at the [alpha] = 0.05 level; Italicized indicates significant at the [alpha] = 0.10 level. Tests on the coefficients are one-tailed for variables with an expected sign, and two tailed for remaining variables.

Results are consistent when each initiative's set of three variables (one simple and two interaction terms) is dropped from the model. In all cases, the adjusted [R.sup.2] decreases. Inclusion of each initiative adds to the explanatory power of the model. Also, the SCM-ABC and TI-ABC combinations individually contribute a positive adjusted [R.sup.2]. SCM-TI does not add to explanatory power.

Of the control variables, LTL is negatively signed and significant at [alpha] = 0.10, and LEVEL is negatively signed and highly significant at 0.001. The negative significance of LTL confirms that, as discussed in the motor carrier and transportation literatures, LTL and large companies did not perform as well as specialized carriers or TL companies during this period. The negative significance of LEVEL confirms the mean-reversion of earnings in the motor carrier industry. (7)

SUMMARY & DISCUSSION

This study investigates the use of SCM, TI and ABC in the motor carrier industry and the association of those initiatives with improvement in financial performance. Knowledge of the efficacy and context-specific benefit of business initiatives is of significant interest to three communities: 1) the practitioner community (including accountants, managerial decision-makers, potential project leaders, professional associations, and consultants) using, promoting, instructing in the use of, or contemplating the implementation of SCM, TI, or ABC, 2) researchers interested in the theoretical and empirical literature regarding these initiatives, and 3) educators who communicate the commonly believed benefits and instruct in their use.

Archival financial information obtained for 305 motor carriers is used to regress 1-year change in financial performance against initiative use. The first finding is that, consistent with the literature and after control for previous level of performance and for use of other initiatives, use of SCM and TI are significantly associated with ROA improvement. The second finding is that, although there was not a statistically significant simple effect obtained from use of ABC, there is empirical evidence that, consistent with the management accounting, SCM, and TI literatures, context-specific benefits are obtained from concurrent use of ABC with SCM and TI. These results are robust to the partitioning the sample into high and low performing groups. It is likely that ABC functions as an enabler of other improvement initiatives, providing the information necessary to optimize the effectiveness of SCM and TI. The positive findings regarding ABC are of particular interest to practicing and academic accountants because they are often the primary proponents and administrators of ABC and previous evidence of ABC efficacy has been theoretical or anecdotal.

However, more research is needed to explain how this effect occurs. It is possible that improvement in performance results more from the introspection and internal and external communication that occurs whenever the initiative is implemented rather than results achieved from its mechanical application. Research that investigates the conditions under which improvement occurs and that identifies the components of financial performance that are impacted by initiative use would be of benefit.

A third significant finding of this study is that there is a pronounced mean reversion of earnings, at least in the motor carrier industry. Since deregulation in the 1970s, the industry has become highly competitive, largely because of 1) low entry costs in the TL and specialized carrier segments, and 2) increased competition with other modes of transport. Overall the industry lacks the capital investment requirements, proprietary processes, technology, and territory and patent protection typical of many other industries. Therefore, trucking firms are not able to maintain their competitive position over extended periods of time without continuing improvements in efficiency and service (Coyle et al., 1994). To maintain their position, the best performing firms must implement solutions to counter the "collar" effect that pulls their performance towards the mean. Although cause cannot be directly inferred from this study, there is evidence that the use of initiatives can help to offset this effect, thereby facilitating top performers in maintaining their relative position.

As with all studies, there are several important limitations to the analyses. It is assumed that respondents know the extent of initiative use and have responded honestly. Although respondents were generally top executives who should be knowledgeable about major initiatives, the possibility exists that the responses do not represent actual company practices. Secondly, although this study is restricted to a single industry, level of use may not capture the effectiveness of an individual firm's implementation of an initiative. As argued by previous researchers (e.g., Cooper 1988, Cooper and Kaplan 1991), firm-specific factors such as complexity and diversity and information technology may limit or enhance this effectiveness. Further research testing the arguments of prior researchers would be of value.

Restriction to a single industry yields significant advantages in empirical testing. Although the motor carrier industry affects virtually all firms, there is no assurance that results are generalizable to firms in other industries. Research investigating other industries would complement the findings of this study.

This study does not control for varying risk among sample firms. It is possible that high risk firms would have a higher (lower) expected change in ROA change than low (high) risk firms. Moreover, high (low) risk firms might be more likely to adopt ABC or other measures than other firms.

Finally, significant interaction terms precludes interpretation of the individual coefficients of the initiative variables, and prevents the determination of the individual economic effect of TI, SCM, and ABC. A study that utilizes a different methodology but maintaining control for concurrent use, possibly through matched sample control groups, would be welcome.

NOTE

Data Availability: Dependent and control variable data are available from Transportation Technical Services, Inc. Initiative use data was obtained under promise of confidentiality.

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Dennis Ortiz, The University of Texas at Brownsville

END NOTES

(1) The terms activity-based costing (ABC) and activity-based management (ABM) are sometimes used interchangeably. Strictly speaking, ABC refers only to the actual techniques for determining the costs of activities and outputs that those activities produce. Some researchers and practitioners prefer to use the term activity-based management (ABM) when they describe how the activity information is used to support operating decisions. As in Swenson (1995) and Krumwiede (1998), this study defines ABC very broadly to include activity-based costing and activity-based management

(2) For examples, Barnes (1991), Brimson (1991), Bruns and Kaplan (1987), and Harris (1990).

(3) As a confirmation, of 332 total responses, 80 firms use TI, 97 use ABC, and 60 use SCM.

(4) The median response time was fifteen days. Non-response bias is tested by comparing the median responses of the early responders to those of late responders for statistical difference in responses. The tests reveal slightly more significant differences (p<.05) than would be expected by chance. Later respondents tend to be older, to have more industry experience and to be associated with smaller companies. These firms also exhibit a slightly higher use of TI.

It is not surprising that the non-response bias tests reveal some differences. For example, a possible explanation for the slower responses by older and more experienced respondents representing smaller companies is that the range of their responsibilities precludes a fast response.

(5) Previous research (DeBondt and Thaler 1987; Penman 1991; Penman 1992; Lieber et al. 1983) has documented the mean reversion of earnings. ARIMA (p,d,q) models with mean-reverting characteristics have been shown to be descriptive of annual earnings series (Halsey 1996; see Finger, 1994 and Foster, 1986 for a discussion). Halsey (1996) successfully tested a model of earnings consisting of three components: 1) an underlying trend to capture the permanent component of earnings, 2) a transitory component to reflect irregular shocks, and 3) a mean-reverting component. It is contended that use of initiatives provides a positive adjustment to the trend component.

(6) Regression diagnostics reveal no serious problems with multicollinearity. For a model without initiative interaction terms the condition index is 6, with no variance inflation factors above 2, well within the guidelines established by Belsley (1980). Addition of interaction terms increased the condition index to 24, still within acceptable limits. However, the addition of interaction terms tends to bias against finding simple effects and prevents interpretation of the individual initiative coefficients.

White's (1980) heteroskedasticity adjusted t-statistics are reported. Analysis of the Durbin-Watson statistics indicates no misspecification of variables.

Influential data points, generally outliers with extreme values of the dependent variable, are identified through analysis of the R-student residuals. Outliers are expected because extreme observations of ratios (e.g., ROA) occur frequently relative to typical level variables. Influential data points are addressed through an iterative process whereby a regression is run, the observation with the largest r-student residual (exceeding '3') is identified, investigated and eliminated, and the regression re-run. This process results in the elimination of eight observations (2.6 percent), well within normal limits. As discussed later in the paper, sensitivity testing is performed whereby the values of the dependent variables are transformed to eliminate the need for eliminating observations. Results are robust to these specifications.

(7) Several sensitivity tests are performed including alternative modeling of prior level of performance, and a search for missing variables i.e., controlling for level of equity and firm size (revenues, log of revenues, total assets and log of total assets). In addition, several alternative specifications of the dependent variable and variables of interest were tested including logarithmic transformation of ROA and winsorizing rather than deleting of outliers. Also, return on equity (ROE) and percentage change in income were substituted for ROA. Finally, the original 7-point likert responses and a three-point (heavy, light and non-users) specification of initiative use were substituted for binary measures. Results are generally robust to these specifications of the model.

Table 1: Summary of Sample

Initial Population 2,002
Less: Firms with Less than Thirty 383
 Employees or $5 million in Revenues
Population of Interest 1,619
Random Selection 1,100
Less: Canadian Companies 6
 Undeliverable 2
 Out of Business 9
 Withdrew or Refused to Cooperate 14 31
 Upon Initial Contact
Net Responses Possible 1,069
Responses Received 332
Response Rate 31.1%
Less: Data from 1999 Unavailable 27
Final Sample 305

Table 2: Descriptive Statistics

PANEL A
Characteristics of Responding Firms
Use of Initiatives
(n=305)

 # Responses # Responses

 Nonusers Users

 # % # %
Initiative
 Activity-Based Costing (ABC) 211 69.2 94 30.8
 Technology Integration (TI) 233 76.4 72 23.7
 Supply Chain Management (SCM) 249 81.6 56 18.4
Interactions
 TI*SCM 36 11.8
 TI*ABC 59 19.3
 SCM*ABC 41 13.4

Table 2: Descriptive Statistics

PANEL B
Sample Partitioned into Low and High Performing Firms
Based on LEVEL of Prior ROA

 # Nonusers # Users

 LEVEL LEVEL

 Low High Low High
Initiative
 Activity-Based Costing (ABC) 102 109 51 43
 Technology Integration (TI) 113 120 40 32
 Supply Chain Management (SCM) 127 122 26 30
Interactions
 TI*SCM 21 15
 TI*ABC 31 28
 SCM*ABC 20 21

Table 3: Descriptive Statistics

Panel A
Characteristics of Tested Firms

 Mean Median Std. Dev.

Financial Performance
 1999 ROA 0.040 0.036 0.096
 1999 Net Income (000s) 487.7 419.0 3210.0
Type (%)
 TL 48.360 35.0 44.207
 LTL 15.016 0.0 31.499
 Specialized 36.524 0.0 35.907
Size (000s)
 Revenue 53,171 29,087 107,992
 Assets 27,627 12,415 86,486

Panel B
Dependent and Control Variables

 Mean Median Std. Dev.

Performance
 ROA 0.001 (0.006) 0.101
 % INC (% Change in Income) 0.087 0.046 0.088
 Level (ROA), (t) before mean adjustment 0.039 0.037 0.098
Type (%)
 Truckload (TL) 48.360 35.0 44.207
 Less-than-Truckload (LTL) 15.016 0.0 31.499

Table 4: Spearman Correlation Matrix of the Independent Variables
(N = 305)

 ABC TI SCM TL LTL ROA

LEVEL
Activity-Based Costing (ABC) 1
Technology Integration (TI) .26 1
Supply Chain Management (SCM) .21 .30 1
Truckload % (TL) -.09 -10 -.15 1
Less-than-Truckload % (LTL) .20 .15 .17 -.12 1
LEVEL of year t (ROA) -.12 -.08 -.20 .01 -.02 1

Table 5: Regression of 1-Year Change in ROA on Initiatives including
Interactions of SCM with TI and ABC with SCM and TI
[DELTA]ROA = [alpha] + [[beta].sub.1]SCM + [[beta].sub.2]TI +
[[beta].sub.3]ABC + [[beta].sub.4]TL + [[beta].sub.5]LTL +
[[beta].sub.6]ROA + [[beta].sub.7]SCM*TI + [[beta].sub.8]SCM*ABC +
[[beta].sub.9]TI*ABC + [member of]

 F 24.71
 P-Value 0.001
 [R.sup.2] .5501
 Adjusted [R.sup.2] .5212

 Expected
 Sign Coefficient -Stat p-value

Intercept -0.005 2.078 0.026
Initiative Simple Effect
 Activity-Based Costing
 (ABC) + 0.004 1.004 0.157
 Technology Integration
 (TI) + 0.007 1.594 0.049
 Supply Chain Management
 (SCM) + 0.009 1.787 0.015
Interactions
 TI*SCM + 0.005 0.906 0.183
 TI*ABC + 0.014 2.147 0.020
 SCM*ABC + 0.009 1.694 0.059
Control Variables
 Truckload (TL) ? -0.000 -0.720 0.458
 Less-than-Truckload (LTL) - -0.000 -1.502 0.098
 LEVEL - -0.107 4.201 0.001