Communicating and controlling strategy: An empirical study of the effectiveness of the balanced scorecard.
Data indicate that this specific BSC, as designed and implemented, is an effective device for controlling corporate strategy. Results also indicate disagreement and tension between top and middle management regarding the appropriateness of specific aspects of the BSC as a communication, control, and evaluation mechanism. Specific results include evidence of causal relations between effective management control, motivation, strategic alignment, and beneficial effects of the BSC. These beneficial effects include changes in processes and improvements in both the BSC and customer-oriented services. In contrast, ineffective communication and management control cause poor motivation and conflict over the use of the BSC as an evaluation device.
Data Availability: Use of all data collected for this study is regulated by a strict nondisclosure agreement, which requires the researchers to protect the company's identity and its proprietary information.
The professional and academic strategy literatures claim that many organizations have found traditional performance measures (e.g., ex post costs, profits, and return on investment) to be insufficient guides for decision making in today's rapidly changing, hyper-competitive environment. Sole reliance on current, financial measures of performance does not arguably reflect the importance of current resource decisions for future financial performance (e.g., Dearden 1969). Though some firms recognized the importance of nonfinancial measures of performance many years ago (e.g., General Electric in the 1950s), growing international competition and the rise of the TQM movement have widened the appeal of nonfinancial performance measures. Since the 1980s, authors have filled the professional and academic literature with recommendations to rely more on nonfinancial measures for both managing and evaluating organizations (e.g., Johnson and Kaplan 1987; Berliner and Brimson 1988; Nanni et al. 1988; Dixon et al. 1990; Rappaport 1999).
In addition to normative arguments, empirical research can help to establish the roles and effectiveness of nonfinancial performance measurement. A number of studies have sought to link specific nonfinancial measures to financial performance (e.g., Banker et al. 2000; Behn and Riley 1999; Foster and Gupta 1999; Ittner and Larcker 1998a). (1) Evidence in the human resources literature shows that systems of nonfinancial measures, not individual measures themselves, appear to be more reliable determinants of firm performance. (e.g., Becker and Huselid 1998; Huselid 1995; Huselid et al. 1997). The objective of this study is to examine the process and impact of managing an organization with nonfinancial performance measures, specifically in the context of the balanced scorecard (BSC), which is a comprehensive system of performance measurement.
The BSC, popularized by Kaplan and Norton (1992, 1993, 1996a, 1996b, 1996c) and adopted widely around the world, has been offered as a supenor combination of nonfinancial and financial measures of performance. (2) Because the BSC explicitly focuses on links among business decisions and outcomes, it is intended to guide strategy development, implementation, and communication. Furthermore, a properly constructed BSC could provide reliable feedback for management control and performance evaluation.
Atkinson et al. (1997) regard the BSC as one of the most significant developments in management accounting, deserving intense research attention. Silk (1998) estimated that 60 percent of the U.S. Fortune 500 companies have implemented or are experimenting with a BSC. Given its high profile, surprisingly little academic research has focused on either the claims or outcomes of the BSC (Ittner and Larcker 1998b). A natural question is: does the BSC's content, format, implementation, or use have discernable effects on business decisions and outcomes that could not be attained with existing measures, alone or in combination? In the first study of its kind, Lipe and Salterio (2000) identify decision effects associated with the format of the BSC. The arrangement of performance measures into four related categories appears to convey decision-relevant information to subjects performing a laboratory evaluation task. Most other current BSC studies, however, are relatively uncritical descriptions of BSC adoptions.
Kaplan and Norton (1996b) argue that the BSC is not primarily an evaluation method, but is a strategic planning and communication device to (1) provide strategic guidance to divisional managers and (2) describe links among lagging and leading measures of financial and nonfinancial performance. The BSC purports to describe the steps necessary to reach financial success; for example, invest in specific types of knowledge to improve processes. If the links are valid reflections of a company's administrative and productive processes and economic opportunities, then the BSC embodies and can communicate the company's operational strategy. Furthermore, effectively communicating these links throughout the organization can be crucial to implementing that strategy successfully (Tucker et al 1996; West and Meyer 1997). Organizations also might use nonfinancial measures as the basis of performance evaluation. Alternatively, they might improve performance by using the BSC as a guide to financial success and by also judici ally using financial performance measures for evaluation purposes (e.g., Rappaport 1999).
The present study investigates the communication and management-control attributes and the effectiveness of a large, successful, international company's BSC model. The study includes archival and qualitative data from interviews with the BSC's designers, managers, and users to (1) assess the perceived attributes of the BSC as both a strategic communication and control device and (2) find evidence of the BSC's decision impacts. The current study does not test whether the company's BSC is a statistically valid model of the company's activities and performance. This feature of the BSC will be tested in subsequent research (Malina 2001).
The company introduced the BSC to advance its strategy. The scorecard has greatly affected the outlook and actions of users, both beneficially and adversely. When elements of the BSC are well designed and effectively communicated (according to criteria described in the study), the BSC appears to motivate and influence lower-level managers to conform their actions to company strategy. Furthermore, managers believe that these changes result in improved sub-unit performance. However, there also is consistent evidence that flaws in the BSC design and shortcomings in strategic communication have adversely affected relations between some top and middle managers. The tension exists because the BSC design exacerbates strong differences between their views of future opportunities. Shortcomings in communication generate mistrust and unwillingness to change. While the specific flaws and shortcomings may be unique to the studied company, these findings appear to reflect generally on issues of BSC design and uses.
The second section of this paper develops a research question from a review of the communication literature regarding characteristics of effective communication of strategy. The third section develops a second research question through an overview of attributes of management control devices that effectively control strategy. The fourth section describes the research site and the company's BSC. The fifth section describes procedures used to obtain and analyze the archival and qualitative interview data. This section also presents a theoretical model to describe BSC effectiveness. The sixth section addresses the research questions and derives an empirical model of BSC effectiveness. The final section summarizes conclusions and offers recommendations for future research.
THE BSC AND COMMUNICATION OF STRATEGY
Kaplan and Norton (1996c) state that, "by articulating the outcomes the organization desires as well as the drivers of those outcomes [by using the BSC], senior executives can channel the energies, the abilities, and the specific knowledge held by people throughout the organization towards achieving the business's long-term goals." Thus, Kaplan and Norton (1996c) assert that not only does the BSC embody or help create organizational strategy and knowledge, but also the BSC itself effectively communicates strategy and knowledge. Merchant (1989) argues that communication failure is an important cause of poor organizational performance. Because no organization's knowledge or strategy exists apart from or succeeds without its key human actors, the ability to effectively communicate may be itself a source of competitive advantage (Tucker et al. 1996; Daft and Lewin 1993; Grant 1991; Schulze 1992; Amit and Shoemaker 1990). If the BSC does articulate organizational knowledge and strategy in a superior manner, then i t may be a source of competitive advantage, at least until all competitors use it equally well. The organizational communication literature, however, identifies a complex set of characteristics that affect the quality or effectiveness of communication in organizations.
Based on a review of the literature, an organizational communication device or system may be characterized by the attributes of its (1) processes and messages, (2) support of organizational culture, and (3) creation and exchange of knowledge. Brief reviews of these communication characteristics follow.
Communication Processes and Messages
Individuals use and rely on communication if its processes and messages are perceived as understandable and trustworthy. Other characteristics of effective organizational communication processes are routineness, predictability, reliability, and completeness (Barker and Camarata 1998; Goodman 1998; Tuckeret al. 1996). Communication also is more effective if it uses concise messages and clearly defined terms (Goodman 1998). Furthermore, an effective communication system precludes suppression of truth or misstatement of performance. There should be no ambiguity regarding the differences between truthfulness and "looking good" or integrity with winning. The effective communication system and its users will be intolerant of "spin, deniability, and truth by assertion" (Goodman 1998). Therefore, organizational communication will be effective if processes and messages are valid representations of performance.
Support of Culture, Values, and Beliefs
The traditional view of effective organizational communication is that it supports organizational culture and individual interest by reinforcing desired patterns of behavior, shared values, and beliefs. Effective communication demonstrates that the organization does what it says and that individual or group rewards are predicated on their actions (Tucker et al. 1996; Goodman 1998). Communication by leaders that consistently articulates shared goals, values, and beliefs (Tucker et al. 1996; Goodman 1998) is also effective in reinforcing culture and directing behavior. Furthermore, effective communication must encourage behavior consistent with organizational goals, values, and beliefs (Goodman 1998).
Proponents of the BSC (e.g., Kaplan and Norton 2000) argue that it also can be an instrument of cultural and strategic change. Consistent with Kotter's (1995) observations of change processes, the BSC may facilitate change by effectively creating and communicating a credible vision of and method for achieving change.
Creation and Exchange of Knowledge
Knowledge, which may be objective or tacit, is the basis of strategy formulation and implementation. (3) Therefore, an effective communication system supports an organization's strategy by nurturing both objective and tacit knowledge. The effective communication system exchanges objective (observable) knowledge among key individuals so that all are aware of the organization's current status. Organizations create objective knowledge from the development and integration of new knowledge by individual specialists. Objective knowledge usually derives from the refining and sharing of individuals' tacit knowledge, which is understood but not yet articulated or usable by the organization. Therefore, an effective communication system encourages and enables the sharing of individuals' experiences and collects those shared experiences. This may be best accomplished by intense and frequent sharing, and by dialogue rather than one-directional reporting. Perhaps importantly for the effectiveness of the BSC, de Haas and K leingeld (1999) argue further that participation in the design of performance measurement systems is an important determinant of effective communication of strategy.
In summary, effective organizational communication devices should possess the observable attributes of:
* Valid messages--reliable, understandable, trustworthy
* Support of organizational culture--existing or changing
* Knowledge-sharing--including dialogue and participation
The organizational communication literature predicts that a BSC, which has these attributes, will create strategic alignment, positive motivation, and positive organizational outcomes. The first research question is:
RQ1: Is the BSC an (in)effective communication device, creating strategic (non)alignment, (in)effective motivation, and (negative) positive organizational outcomes?
THE BSC AND MANAGEMENT CONTROL OF STRATEGY
A common criticism of managing organizations based on financial measures of performance is that these measures induce managers to make myopic, short-run decisions. Financial measures tend to focus on the current impacts of decisions without a clear link between short-run actions and long-run strategy (recent criticisms include McKenzie and Schilling , Luft and Shields ). Furthermore, traditional financial measures of performance can work against knowledge-based strategies by treating the enhancement of resources such as human capital, which may be critical to implementing strategy, as current expenses (e.g., Johnson 1992). Dixon et al. (1990) argue that traditional financial measures, by expensing costs of many improvements, also work against strategies based on quality, flexibility, and minimization of manufacturing time. For many lower-level employees, most financial performance measures are too aggregated and too far removed from their actions to provide useful guidance or feedback on their de cisions. They might need measures that more directly and accurately relate to outcomes that they can influence (McKenzie and Schilling 1998). A number of studies have found evidence that traditional, financial measures of performance are most useful in conditions of relative certainty and low complexity--not the conditions faced by many organizations today (e.g., Gordon and Naranyan 1984; Govindarajan 1984; Govindarajan and Gupta 1985; Abernethy and Brownell 1997).
Lynch and Cross (1995) argue that performance measures should motivate behavior leading to continuous improvement in key areas of competition, such as customer satisfaction, flexibility, and productivity. That is, they should reflect cause and effect between operational behavior and strategic outcomes (Keegan et al. 1989; Ittner and Larcker 1998a). (4) Furthermore, as an organization identifies new strategic objectives, it also may realize a need for new performance measures that encourage and monitor new actions (Dixon et al. 1990). Thus, organizations sensibly and perhaps optimally may use a diverse set of performance measures to reflect the diversity of management decisions and efforts (e.g., Holmstrom 1979; Banker and Datar 1989; Feltham and Xie 1994; Ittner and Larcker 1998b). Empirical support for these propositions is limited but growing. (5)
The Management-Control Case for the Balanced Scorecard
Kaplan and Norton (1996b) have arranged multiple performance measures into the Balanced Scorecard, which is a logical expression of most models of Western business management. (6) Indeed, the BSC may have spread widely throughout the world on the strength of its intuition and internal logic. Kaplan and Norton (1996b) claim that the BSC offers two significant improvements over traditional financial or even nonfinancial measures of performance.
First, the BSC identifies four related areas of activity that may be critical to nearly all organizations and all levels within organizations:
* Investing in learning and growth capabilities
* Improving efficiency of internal processes
* Providing customer value
* Increasing financial success
Following the logic of the BSC and ignoring cost-benefit considerations, most organizations could use measures in all four areas to encourage and monitor actions appropriate to organizational strategy. In its most basic use, a properly configured BSC could provide a comprehensive picture of the state of the organization, much as an automobile's dashboard shows fuel level, oil pressure, coolant temperature, engine RPM, and speed. Thus, the BSC might promote positive organizational outcomes such as improvements in all four areas of organizational activity, which include administrative activities and the BSC itself. Assessing this first level of effectiveness is the objective of this research.
Furthermore, the BSC seeks to link these measures into a model that accurately reflects cause-and-effect relations among categories and individual measures. Using the automobile analogy, the BSC simulates a change in a car's performance (e.g., speed) given a planned increase in fuel consumption and engine RPM (and perhaps other factors). Such a model might support operational decisions, make predictions of outcomes given decisions and environmental conditions, and provide reliable feedback for learning and performance evaluation. (7)
The Role of the BSC for Strategy Implementation and Performance Measurement
Proponents of the BSC stress its alignment of critical measures with strategy and links of the measures to valued outcomes. In addition, the management control literature identifies other characteristics of control systems that may be critical to the successful implementation of strategy and should apply to the BSC. (8) To be effective, BSC measures should be accurate, objective, and verifiable. Otherwise, measures will not reflect performance and may be manipulated, or managers could in good faith achieve good measured performance but cause the organization harm. If managers can achieve good measured performance by cheating, then the system quickly will lose credibility and desired motivational effect. Furthermore, the set of BSC measures should completely describe the organization's critical performance variables, but should be limited in number to keep the measurement system cognitively and administratively simple. An exhaustive set of performance measures may accurately reflect the complexity of the orga nization's tasks, but too many measures may be distracting, confusing, and costly to administer. However, Lipe and Salterio (2000) did not find evidence of information overload from multiple measures in their experimental study of the BSC.
Positive motivational impact induces managers to exert effort to achieve organizational goals. While informative but not controllable performance measures may be important, positive motivation requires that at least some of the BSC measures should reflect managers' actions. For example, relative performance evaluation (e.g., across similar business units), which can identify "influenceable" but not completely controllable outcomes, may be an important component of the BSC (e.g., Antle and Demski 1988), but it may not be sufficient by itself Extensive goal-setting literature confirms that performance should be keyed to challenging but attainable targets (e.g., Locke and Latham 1990). Without such explicit BSC targets, performance likely would be lower than could be reasonably achieved. Finally to build goal commitment, the BSC should be linked with prompt and well-understood rewards and penalties. Rewards that are delayed, uncertain, or ambiguous may be ineffective motivational devices.
Therefore, even though an organization's BSC reflects its critical performance variables and links to valued outcomes, it may fail as an effective management control device if it lacks other attributes. For example, Ittner et al. (2000) found that subjectivity in a bank's BSC led to both its having little beneficial impact and the bank's reversion to short-term financial measures of performance. To summarize, an effective management control device, which is capable of promoting desired organizational outcomes, should have the following, observable management control attributes to, first, attain strategic alignment:
* A comprehensive but parsimonious set of measures of critical performance variables, linked with strategy;
* Critical performance measures causally linked to valued organizational outcomes; and
* Effective--accurate, objective, and verifiable--performance measures, which appears to be related to effective communication.
Second, to further promote positive motivation, an effective management control device should have attributes of:
* Performance measures that reflect managers' controllable actions and/or influenceable actions, e.g., measured by absolute and/or relative performance;
* Performance targets or appropriate benchmarks that are challenging but attainable; and
* Performance measures that are related to meaningful rewards.
Management control theory predicts that, if the BSC has these attributes, then it is likely that the BSC will promote strategic alignment and positive motivation and outcomes. Therefore, the second research question, which parallels the first, is:
RQ2: Is the BSC an (in)effective management control device, creating strategic (non)alignment, (in)effective motivation, and (negative) positive organizational outcomes?
Subsequent discussions elaborate the details of a model that reflects the two research questions. This model, based on the literature review, shows that the BSC's management control and communication characteristics generate outcomes by creating strategic alignment and motivation (or not). This study also describes efforts to collect data on an implemented BSC's management control and organizational communication attributes, as well as evidence on the BSC's effects on strategic alignment, motivation, and organizational outcomes. It is bold to judge the effectiveness of the BSC against evidence from a single, non-experimental BSC implementation. However, a thorough examination of a critical case can be instructive and generalizable to theory (i.e., analytical generalization, Yin [1994, 30-- 32]), which in this case is that the BSC can be an effective strategy communication and management control device.
RESEARCH SITE AND BSC CHARACTERISTICS
Overview of the Research Site
The research site is a U.S. Fortune 500 company with more than 25,000 employees and $6 billion sales of durable products and post-sale services. The company is regarded as a long-term, well-managed company. It is succeeding in highly competitive domestic and foreign markets, characterized by competition among relatively few, very large, international companies. The company recently adopted a customer- and quality-driven strategy to improve its competitiveness, and consequently perceived a need to expand its management controls and performance management beyond traditional, financial measures. The company began changing its performance measurement systems with a BSC that focuses on a very important part of the company. One and a half years before the start of this study, the company began its implementation of a Distributor-BSC (DBSC) for its 31 North American distributorships, which are responsible for a large share of the company's sales. The company has sufficient resources to assign BSC responsibilities t o key staff that are championing its continued development and implementation. These staff members have had formal BSC training and are not using the services of outside consultants. The DBSC was developed centrally and imposed on the distribution channel, with little initial input from distributors themselves.
The company's distributors in North America have primary responsibility for retail sales and service of company products. Distributorships are organized by geographical area and may not sell other companies' competing products. Although they are independently owned, individuals with employment experience in the company currently lead 30 of the 31 distributorships. Distributors operate under renewable three-year contracts with the company, which are based on realized and expected future performance. (9)
The authors gained access to this company because of a family relationship between one of the authors and executives of the company. (10) In this sense the field study is serendipitous, but the site is attractive on a priori, objective grounds, and would have been a top candidate in a purposive sampling approach. (11)
To summarize, the company has a long history of effective management control, extensive resources, and a commitment to communicate its strategy to its distributors. Furthermore, early in the investigation researchers perceived considerable tension and possible resistance to change among parties affected by the DBSC, which, as Ahrens and Dent (1998) counsel, usually makes for an engaging study. Thus, the company and its DBSC project are ideal for field study research on the balanced scorecard.
Overview of the DBSC
Purpose of the DBSC
In line with its new customer-driven strategy, the company recently changed its distribution strategy from one of operational efficiency to managing long-term customer relations. Until the DBSC, the company had evaluated formal distributor performance solely on financial performance and market share. Company documents and literature show that staff personnel designed the DBSC, top-down without input from distributors, to communicate the company's new retail distribution strategy to its distributors. Company documents state the purposes of the DBSC are to:
* Highlight areas within distributorships that need improvement to enhance customer relations; and
* Provide an objective set of criteria, consistent with the company's new strategic initiatives, to guide and measure total distributor performance.
These purposes fall well within the scope of the use of the BSC as envisioned by Kaplan and Norton. However, administrators who developed and use the DBSC describe two additional objectives, which have far-reaching implications for managing the company's distribution system:
* DBSC performance will be used as the starting point for the three-year contract review process; and
* The DBSC is used for comparing and ranking distributorships and may be used for performance-based compensation.
Because the DBSC includes many previously unevaluated areas of performance, it represents a dramatic change in communication, interactions, and formal relations between the company and its distributors. In particular, using the DBSC for distributor contract renewal and compensation added significant economic incentives and created uncertainty regarding the impacts of DBSC performance.
Structure of the DBSC
The DBSC contains measures of performance in each of the four BSC perspectives plus another for corporate citizenship, which the company felt was lacking in Kaplan and Norton's (1996b) specification of the BSC. (12) Additionally, the company has arranged its DBSC measures in categories that reflect its own priorities and culture. Though distributors prepare some DBSC measures in "real time," the company staff compiles, analyzes, and disseminates the DBSC quarterly to top management and to distributors. An internal document (usual BSC categories shown in brackets) describes the DBSC as:
comprised of measures that are categorized into groups, which are aligned with [the company's strategic] objectives: Competitive Advantage [customer value and internal processes], Profitability and Growth [internal processes and financial success], Corporate Citizenship, and Investments in Human Capital [learning and growth]. A fifth category has been added to include other measures important to distributor performance [internal processes]. Each of the categories includes specific measures with specific criteria for acceptability. The results for the measures within each category will be weighted to determine an overall score for each category and an overall score for the distributorship.
A summary of the measures and the weights currently used in the DBSC are in Table 1. For comparability with the literature, we have arranged these measures into the usual BSC categories, but we also note where the company has placed them in its own categories.
Both distributors and DBSC administrators understood immediately that the DBSC's relative weights reflect the company's view of the most important areas of performance. (13) Distributors' knowledge of "why" came later, if at all, as will be seen. Additionally and with experience, the company revised the weights to reflect learning about measures' impacts, reliability, or possible manipulation, particularly of some of the softer measures, as Flamholtz (1979) predicts. One of the principal designers of the DBSC stated:
Changes in weights are a function of two things: 1) how important we think the things are; 2) how credible the numbers we get are....How do we measure outstanding people at the distributor? It's important, but how substantive a measure can we come up with for it? Hardness of the number definitely affects the weights. If we place a heavier weighting on something you don't have confidence in, is that better than now? [11: 71-83] (14)
One of the key managers of the distribution channel also explained:
Now market share really is the driver and means more than the other things do. We did move more of the weighting there. It's more important to reflect the feeling of the management team. The distributors say, tell me how you're ranking me, and I'll do it even if I don't like it. [12:143-149]
The company's first version of the DBSC placed a total of 20 percent weighting on Investments in human capital (learning and growth area), but after a year that weight had been reduced to only 4 percent, primarily because management felt the numbers were unreliable. Likewise, the first scorecard placed a 10 percent weighting on Corporate citizenship (internal processes and customer value areas), later reduced to 4 percent. The company redistributed the original weightings mostly to the traditional market share measure (an outcome of building customer relationships), which grew in importance from 12 percent to 28 percent, to reflect the paramount importance of building long-term customer relationships that result in market share. The company also has added weight to quickly diagnosing and solving customer problems (internal processes area), which grew from 2 percent to 10 percent in importance, to reflect the company's belief about an essential element for building customer relationships. As discussed later, the weightings and changes in weighting affected distributors' perceptions about both the "balance" in the DBSC and the truly important measures of importance.
Management did not consider distributors to be partners in the process of developing the DBSC, which reflected the company's traditional, top-down approach to management. A more open, participative approach to the development and use of the DBSC (one attribute of effective communication) could have had an impact on distributors' acceptance of the DBSC's and their subsequent performance. Furthermore, the company did not explicitly design the DBSC to be a "strategy map," in Kaplan and Norton's (2000) terminology, (15) but let the measures and weights "speak for themselves" as key performance indicators. The top-down and ambiguous nature of the communication may have impeded the immediacy and effectiveness of the DBSC message. As will be demonstrated later, distributors had strong feelings on this, which can explain adverse impacts of the DBSC.
Figure 1 shows a quarterly DBSC, as reported to management for several representative distributors. This scorecard, which is based on numerical measures, is notable for several reasons. First, each distributor's quantified and internally benchmarked performance measure is labeled and colored "red" (R) for "fails to meet criteria for acceptability," (16) "yellow" (Y) for "meets criteria for acceptability," or "green" (G) for "exceeds criteria for acceptability." The total score in the last column is computed by multiplying each measure's numerical score by the appropriate weights. Second, each distributor obtains its own report and its relative, numerical ranking (e.g., 7th out of 31). Furthermore, names of distributors that achieve "green" ratings are posted on the company's intranet for all to see. (17)
This study investigates its research questions with qualitative, interview data obtained from individuals directly involved with the company's DBSC. Thus, the evidence is perceptual in nature and, while it ideally reflects the "reality" of the impact of the DBSC, it also may reflect individuals' and researchers' biases in ways that are not easily detectable. The study's research method attempted to mitigate the effects of these unknown biases. The research method is described below. (18)
Because the DBSC represents a dramatic change in distribution strategy from operational efficiency to managing customer relationships by the company's distributors, we sought and obtained direct commentary from two DBSC designers, three managers who use it to evaluate distributors, and nine of the 31 distributors. Because the research is interested in all facets of the DBSC, the scope of the distributor sample is limited to those who consistently reported complete or nearly complete data. At the time of the study, these distributors had the full six quarters' experience with the DBSC. While added experience may continue to refine perceptions, the sampled distributors represent the most experienced distributors available at the time of the study. This selection may bias the analysis if more experienced distributors who also report more complete data have systematically different perceptions than other distributors. Another source of bias may be scorecard performance, which could influence perceptions of the D BSC; the sample included nine distributors who reflected overall red, yellow, and green ratings. Of the distributors reporting complete data, only one "green" distributor was available and three "red," so the sample was filled out with five "yellow" distributors. At the time of the interviews, overall there were two green, 19 yellow, and ten red distributors. The sample of distributors also reflected geographic dispersion- three western, two midwestern, two southern, one northeastern U.S., and one Canadian. Mter analyzing the interviews, we feel confident that we have obtained a full range of distributor responses. As the interviews proceeded, responses became repetitive. While additional "green" distributor interviews would have been desirable, we feel they would be unlikely to contribute additional insights. (19)
The researchers obtained archival data (background and policy documents and quarterly DBSC scores) from managers who administer the DBSC. All interview data were obtained via telephone in mid-1999 after sponsoring managers informed designers, other managers, and all 31 distributors that the researchers were conducting this study and may call them for input about the DBSC. Interviews lasted from 45 minutes to 75 minutes, depending entirely on how much an interviewee had to say. The study used a semi-structured interview format and assured respondents of anonymity. (20)
To avoid responses that could be artifacts of the interview process itself, the researchers deliberately did not ask leading questions regarding management control or communication attributes of the DBSC or questions directly related to the study's research questions. While the study's use of management control and organizational communication theories represents a deductive approach to research and does guide later analysis and model building, we were not confident that we had identified all relevant factors related to the effectiveness of the DBSC. At this stage, we preferred to gather data more freely and let the respondents' natural, undirected commentary support, deny, or extend the theories. (21) An important benefit of this approach is that respondents may identify factors that affect the effectiveness of the DBSC other than those anticipated by the study's theory.
The researchers asked each distributor the following open questions:
1. In your own words, what is the distributor-balanced scorecard?
2. What do you think the objective of the balanced scorecard is?
3. What are the nine measures, which distributors report, really measuring?
4. What are the measures that are filled out by the company really measuring?
5. How do the measures that distributors report relate to the company's measures? (Follow-up: Do changes in distributor performance cause changes in the company's measures?)
6. Do the measures (distributors' and the company's) help you in any way? (Follow-up: How?)
7. Are there any benefits from the balanced scorecard itself? (Follow-up: Apart from the individual measures?)
8. Do you have any (other) recommendations for improving the balanced scorecard?
The researchers asked essentially the same questions of administrators of the DBSC, but their interviews tended to be more open and wide-ranging. To keep within the time available, the researchers usually did not ask the administrators questions about specific DBSC measures (questions 3 and 4). Thus, distributor and administrator interviews are not directly comparable on all questions. Because the administrator interviews are less focused on the DBSC measures, this study uses them for background information. Unless otherwise indicated, the analyses that follow refer to the distributor interviews only.
The interviews were conducted via conference calls conducted over a three-week period, with one researcher asking initial and follow-up questions and the second researcher taking notes and capturing the commentary on a laptop computer. After each interview, the two researchers conferred immediately to complete abbreviated comments that might be difficult to decipher later. Interview files were copied intact and archived in several locations.
Coding Interview Data
Two alternative coding procedures are (1) completely free-coding, unconstrained by prior theory or (2) strict use of codes based on theoretical constructs. Both approaches have their adherents. However, it is unusual for accounting researchers to enter the field free from preconceptions or prior theory. Miles and Huberman (1994) argue that, when theory guides inquiry, it is efficient and realistic to begin with a conceptual framework, and add "free" codes as the data suggest. The result is a hybrid approach that acknowledges theoretical guidance (or bias) and permits empirical flexibility (or theory revision). The research used the management control and organizational communication literatures surveyed earlier as a coding and analysis guide, but modified the framework as the researchers delved into the data. Thus, the study contains elements of both theory building and testing.
The computerized analysis method applies codes that reflect theoretical or empirical constructs to the qualitative data--a sophisticated way to annotate and generalize interview transcripts. The researchers predetermined codes for the interview data to reflect the interview questions--questions 1, 2, 4-8 and twelve distributor-supplied measure questions for question 3. (22) The researchers also created codes that reflect expected management control factors (e.g., Causality among measures), attributes of organizational communication (e.g., Supports company culture), and impacts of the DBSC (e.g., Measure causes change). As discussed earlier, some codes reflect additional concepts, revealed in the coding process (e.g., Weight of each measure in determining overall DBSC scores). These codes were then applied to the interview data as illustrated in Figure 2. (23) The study did not use the software specifically to search for or count specific words or phrases. Choice of vocabulary is arbitrary, and words or phrase s may not carry meaning outside of their spoken context (Miles and Huberman 1994). Analysis, therefore, required reading, understanding, and coding blocks of text in the context of each interview. This is the most subjective stage of the analysis, but in addition to using both an interview protocol and a coding scheme the researchers took other steps to increase the objectivity of the coding. Appendix B details these steps of the analysis.
The final list of codes, with frequencies by interview, is in Table 2. Observe that for ease of later exposition, the study collects related individual codes into large-pattern codes or "supercodes." These supercodes reflect ex ante theoretical constructs (e.g., Effective communication, Effective management control, Positive outcomes) and are analogous to statistical factor models. The frequencies of the codes are an indication of relative importance of each of these concepts, but frequency does not reflect intensity of feelings, nor does it reflect relations among concepts. These attributes of the data may be discovered through additional analyses, which are described next. One or a few talkative respondents did not dominate the coded comments, though one distributor's interview was briefer than the rest.
Relations among Codes
Theoretically Supported Model
Figure 3 is a model of relations among employees' perceptions of the management control and organizational communication attributes of the DBSC that is based on the prior literature review and codes applied during analysis. The arrows ([right arrow]) between the boxes reflect expectations about causal relations. The research expected that both Effective management control and Effective communication in the design and use of the DBSC would cause Strategy alignment, Effective motivation, and, ultimately, Positive outcomes. In contrast, the research expected that "ineffective" factors could cause "negative" outcomes (in this case, only Conflict/tension was observed qualitatively and coded). (24)
Observing Relations among Codes
The relational-query capabilities of qualitative software, such as Atlas.ti, permit extensive exploration of associations and possible causal hypotheses using coded interviewees' perceptions of the DBSC. Assessing the degree of relation among codes requires analysis of proximity and context of hypothesized relations (as in Figure 3). This is analogous to building a correlation table using a set of statistical measures, where the frequency and nature of qualitative associations are building blocks of causality. The study assessed causality by testing for multiple qualitative attributes of causal relations. Appendix B details the steps used to operationalize this approach to analyzing and establishing evidence of causality within the study's research questions.
It is clear that distributors are aware of and understand the company's diagnostic objective for the overall DBSC. Representative comments that explain their awareness of the new measures and their links include:
A lot of businesses tend to run with financial and market share measures, but those are pretty crude handles. We have to get underneath with measures like quality and cycle time, and softer things like employee development. That's where the leverage of the business is. The others are results of what you've done. [3: 154-158]
I think they are all linked. It's hard to be a good manager in one area and not another. [9: 118-119]
The first objective of this study is to find if the DBSC is perceived to possess the attributes of effective organizational communication and management control devices. The second objective is to determine whether these attributes can be causally related to goal alignment, motivation, and reported process or decision changes.
As detailed in Appendix B, where the research found specific, consistent, frequent patterns of association, the researchers looked for further evidence of causality, based on coherence, (25) which is closely related to face validity. This credible "story" of coherent causality is what distinguishes between findings of causality or mere association. Table 3 and Figure 4 summarize the results of an exhaustive audit of the coherence of specific, consistent, and frequent associations. These exhibits contain only those associations found to meet sufficient causality criteria (complete data are in Appendix B).
Overview of Data-Supported Model
Empirically associated quotations in the interview data, which are reflected by links in Figure 4, support the research questions in interesting ways. (26) Further analysis of all the paired codes in Table 3 and Figure 4 reveals answers to the study's research questions and leads to recommendations for improving the effectiveness of the DBSC.
Trimming the ex ante relations in Figure 3, reflected in Figure 4, has implications for understanding how the BSC may cause management control and communication of strategy. On the "effective" side of Figure 4, Effective management control appears to cause Aligned with strategy and Effective motivation, which in turn appears to cause Positive outcomes (e.g., perception of Improvement). These are consistent, strong associations between specific factors that tell a coherent story, which the research interprets as evidence of causality. There is, however, no consistent evidence of a direct link between Effective management control and Positive outcomes. In this model, Effective management control affects Positive outcomes through Aligned with strategy and Effective motivation. These data provide support for the "effective" form of the management control RQ2.
Surprisingly, there are no consistent links between perceptions of Effective communication and any other DBSC model factor, which provides no support for the "effective" form of the communication RQ1. In this case, the effective communication aspects of the BSC appear to be redundant to effective management control.
On the "ineffective" side of the model, Ineffective management control, Ineffective communication, and Ineffective motivation are associated or appear to be causally related. Furthermore, they appear to cause Conflict/Tension, which provides support for "ineffective" forms of both RQl and RQ2. This indicates that poorly designed and implemented features of the BSC can do harm to the communication and control of strategy. Several causal links involving Ineffective motivation and other factors were unexpected and will be discussed later. The research now addresses each of the causal and associative links in the context of the two research questions, referring to links in Figure 4.
Research Question 1: Is the BSC an (in)effective communication device, creating strategic (non)alignment, (in)effective motivation, and (negative) positive organizational outcomes?
Effective Communication [right arrow] Strategy Alignment/Effective Motivation [right arrow] Positive Outcomes
Unexpectedly, the study found no consistent evidence of specific relations between the attributes of Effective communication and other DBSC-model factors. Overall this study does not support the "effective" form of RQ1 that Effective communication is either associated with or causes Strategic alignment, Effective motivation, or Positive outcomes. (27)
Ineffective Communication [right arrow] Strategy Non-Alignment/Ineffective Motivation [right arrow] Conflict/Tension
Ineffective communication appeared to be largely independent of other "ineffective" DBSC factors. However, though the study found little evidence of the impact of Effective communication, there was abundant evidence that the DBSC administrators' frequent use of One-way reporting is a direct cause of Conflict/tension (16 causal links).
Unfortunately, the Conflict/tension appeared to be unproductive (i.e., no consistent links to Positive outcomes). This may contribute to a climate of distrust and alienation that reduces the company's and its distributors' effectiveness. The company imposed DBSC measures and benchmarks without seeking input, and then used the DBSC as a diagnostic control and an evaluation measure. Distributors felt ignored and trivialized because of their non-involvement. However, they have little recourse because of the frequency of One-way reporting, which was a common complaint. For example:
No response [to my complaints], so we stand by our measure [of safety]. I've gotten no response to my concerns, and I'm "PO'd" at them on this subject. Any distributor who is green is a liar. No realistic way in hell that that can happen. The nature of the work we do, we just can't do this....Do they have any idea what the distributor environment is? They don't care enough to reconcile issues, but the factor itself is important. [6: 116-123]
Partly as anticipated, the data provide support for the "ineffective" form of RQ1; Ineffective communication, specifically One-way reporting, has largely negative consequences for acceptance, perceptions, and reported uses of the DBSC.
Research Question 2: Is the BSC an (in)effective management control device, creating strategic (non)alignment, (in)effective motivation, and (negative) positive organizational outcomes?
Effective Management Control [right arrow] Strategy Alignment [right arrow] Positive Outcomes
As expected, BSC characteristics of Effective management control (Effective measurement, Comprehensive performance, and Weight) appear to be causally linked with Strategy alignment (nine causal links with Key factors, five with BSC important for business, and nine with Traditional market share, respectively). Distributors perceive that having reliable data leads to the ability to take actions that affect the new customer-relationship strategy (e.g., 17 causal links with Measure causes change), which might not have been feasible before the DBSC. For example, in the use of customer satisfaction measures:
We [now] give the work to an outside service. They call a couple of customers every day. We get input on a list of questions. If the customer ends up not having a good experience, [now] we can get that info that day and call the customer ourselves. [8: 119-122]
Because the DBSC measures Comprehensive performance, including the key financial and nonfinancial measures, it is a reflection of overall success of managing critical factors (BSC important for business) . Thus, managers have a better feel for how they are managing the overall business for both current and future results.
The BSC is trying to give us a broader business set of measures of success than the more traditional financial and market share. It wraps a set of things together that make sense for managing the business. [3: 5-7]
One of the company's key strategic goals is to increase its traditional market share. The relatively heavy Weight placed on this DBSC measure forces distributors (sometimes reluctantly) to align their goals with improving the traditional market segment. They value their relationship with the company, and the DBSC tells them what they must do to be a successful distributor, though they interpret this to mean they should pursue improvements in traditional market share to the exclusion of other growth opportunities.
If they care only about one-third of their business, then that's good. It's worth 28 points on the BSC. I'm red and yellow there so there's no hope to be green from all the other measures.... They are measuring only (traditional) market penetration.... Balanced scorecard is certainly a misnomer. [2: 122-126]
By including Key factors, the DBSC causes distributors to diagnose problems and change their processes and actions (Measure causes change) in significant ways. This leads to numerous recommendations to Modify measures or BSC (11 causal links)--an example of potential interactive use of the DBSC (e.g., Simons 2000, Chapter 10). Measuring the percentage of customers' problems diagnosed within one hour, for example, also caused most distributors to refocus their parts and service resources to building customer relationships, consistent with the new strategy, rather than fully utilizing capacity--an example of diagnostic use of the DBSC (Simons 2000). (28)
This [measure] differentiates our businesses from our competition. It requires a complete change of "culture" within the shop. Now we have to manage the service event instead of just scheduling work. [1: 102-104]
In the past, distributors had favored large, complex service jobs that were relatively easy to schedule and that could be counted on to occupy technicians and service space for blocks of time. Customers who had simple service requirements were placed in the service queue in order of arrival, with no preferential treatment, with the result that many began to take their simple jobs elsewhere and with the risk that they would be lost as permanent customers. Distributors observed that:
[One-hour diagnosis] requires a change in measurement and is creating a new mindset within the service organization.... We can't schedule it; we have to provide the capacity and the process. [1: 233-239]
[One-hour diagnosis] tends to make us triage like a hospital and do the quick jobs first. [2: 58-59]
I wasn't an advocate at the start, but now I am. It tells us how quickly we figure out what's wrong so we can make an intelligent statement to the customer, and so they can say "go ahead" or not. We have been able to flow more jobs through our shop by getting the quick, easy stuff through the shop. It lets us turn jobs quicker and avoids embarrassing situations.... It's helping us, though it's not easy to change the mentality, but it's good. [6: 56-63]
Although there is no evidence of a direct link from Effective management control to Positive outcomes, the data otherwise provide extensive support for the "effective" form of RQ2. Effective management control using the DBSC appears to indirectly cause Positive outcomes through Strategic alignment.
Effective Management Control [right arrow] Effective Motivation [right arrow] Positive Outcomes
The DBSC's motivational impacts were obvious overall and with respect to specific factors. Incentives included both improved distributor business performance and successful contract renewals. The management control design of the DBSC reflects the Causality of the DBSC description of the business, which causes Meaningful rewards (8 causal links). Distributors believe that improving nonfinancial DBSC measures will result in improved customer relationships and significant financial rewards.
[Service utilization is] the most important number in the whole business. [5: 102]
I gave the formula to our guys that, if we bill our technicians out (on average) one more hour a day, we would put over $X million to our [annual] bottom line. That's the kind of magnitude were talking about. [4: 79-82] (29)
The DBSC is successful as a motivational tool when it reflects relations between Strategic alignment and distributor performance. For example, setting Appropriate benchmark targets for motivation goes hand-in-hand with management control of Key factors (14 association links). Distributors do not object to tough, but attainable goals.
Good measurement. Don't have a problem with that hurdle. Huge issue and can't stress it enough. We have about [xxx] labor hours. I can have one [accident] per year to be green. That's a tight hurdle. It's probably a little tight right now. [4: 88-91]
Furthermore, setting attainable but tough DBSC goals (Appropriate benchmarks) motivates distributors to change their decisions and processes (Measure causes change).
80 percent of work is in four-hour range in our service shop. Great number because the mentality in our shops had been that we want that big overhaul, the long, lengthy jobs. But then, service efficiency suffers. We didn't turn many jobs and lost a lot of hours because there is a good chance of losing hours [on a large job]....Give the company credit for the four-hour target. They thought about it; it's probably the industry norm. Focusing on this number has changed some of the culture or at least the thought process in the shop. We changed to the little jobs and we can get the big jobs later. So our management has awakened to the fact that they can manage their shops better using the one-hour diagnostic time and four-hour jobs to make their shops more efficient. [4: 47-59]
Relative performance evaluation allows each distributor to know his relative standing and what others are doing, and thereby motivates distributors and gives them a tool for Improvement (seven causal links).
[Gathering] the information and sharing it back to us, saying other distributors are X. I can look at it and see how I am doing. Why am I different? I can use it as a lever to try to improve. [7: 123-125]
The data provide consistent evidence of causation and support the contention that perceptions of this BSC's effective management control characteristics lead to effective motivation, strategic alignment, and then positive outcomes, in support of the second research question.
Ineffective Management Control [right arrow] Ineffective Motivation [right arrow] Conflict/Tension
This study found no consistent or frequent links between any of the elements of Strategy non-alignment and other DBSC-model factors. However, Key factors that are poorly represented in the DBSC are associated with numerous examples of other shortcomings. Notably, Inaccurate/subjective measures of Key factors (24 associations) contribute to perceptions of Inappropriate benchmarks (eight causal links), which appear to cause widespread Conflict/tension (nine causal links). This nexus of factors appears to be responsible for much of the Conflict/tension caused by the use of the DBSC (17 out of 33 causal links), which reflected a lack of local autonomy and participation in determining measures and targets. For example:
[The measure is] a bunch of "hooey" as far as keeping score, but for us running our business it's an important measure. What we do internally is what's important, not if we get a "star" on our shoe. This is one area if the company wants to improve, we need to be a lot more consistent and define that criterion much more closely. We routinely measured ourselves before the company did this. We gauge ourselves monthly on this one. We ignore the BSC measure for our purposes, and use our own. [1: 128-135]
[Safety is a] hot button. The BSC uses a totally ludicrous measure, but the concept is great. I have written four memos on this subject. I ran two plants before this. I have 100 technicians, and if those 100 have more than one accident in a year, I'm in the red. Ridiculous! [6: 109-111] (30)
The data provide consistent evidence of causal paths connecting Ineffective management control, Ineffective motivation, and Conflict/tension, which support the "in-effective" form of RQ2.
Strategy Alignment--Ineffective Management Control or Ineffective Communication or Ineffective Motivation--Positive Outcomes
The study found several unexpected causal relations and associations. Upon reflection, however, it is not surprising that complaints about Inappropriate benchmarks are causes of recommendations to Modify measures or BSC. Clearly, distributors, who have economic stakes in both their business' success and contract renewal, want relevant measures and attainable goals for DBSC Key factors. For example:
Is the x% [benchmark percentage of technician hours used for training] appropriate? Hard to say. Probably now, that would be a low number given [that]...the company will completely obsolete its own product line soon. The need for training is much greater today than it has been in the past. Some companies will use training dollars [rather than percentage of training hours]. They are at like 5% [of revenues], which is much higher than us. This raises a question in our minds. Do we do enough? We are concerned if we are reinvesting enough in our employees. [1: 183-190]
Additionally, the most numerous, consistent evidence (62 associations shown in dotted lines on the right side of Figure 4) shows that Key factors are associated with Inaccurate/subjective measures, Not understandable messages, Inappropriate benchmarks, and Costly to measure. Distributors are frustrated when they perceive ineffective implementation of DBSC factors that they believe are key to their business success and contract renewal. Typical comments, for example involving the DBSC measure of training for salaried employees, include:
[Training of salaried employees is] as critical [as for technicians] but harder to measure. We have to use some guessing, because they are not paid hourly. Also, what's training? Clearly going to a class during the workday, but what about going after work? What percent of the total salaried hours is that? [5:155-160]
For salaried people, it's harder. We have to look at expense reports, and it's a horrendous process. When you bring this data collection problem to the company, they say we can't do that either. They don't even do it, and they aren't sure of the credibility of their number. From feedback from other distributors, they are just taking a stab at it. We actually compile the numbers, but others are getting green scores for just a guess. We're yellow or red, and it's a real number. The cost of the time isn't worth it. But, it's the right idea and the right thing to do. [1: 175-183]
Distributors' frustrations were obvious when they realized that the DBSC was attempting to measure and communicate important success factors, but that it was doing so ineffectively.
We don't grow much, so we need to find ways to expand. That's all they pushed here [in contract renewal]. At another distributor, all they pushed on was customer satisfaction. Some areas if we [both] know we're doing a bad job and were red, they don't seem to care....Great tool, but I'm not sure we are using it the way it should be used. [8: 175-181]
This is something we all should pay more attention to. We haven't done as well as we should have, but the goal means nothing to me because I'm so far away from it. [5: 122-125]
While the study did not anticipate these (and other similar) associations, their discovery provides ample additional evidence of opportunities to improve the control and communication of strategy with the DBSC. The study found relatively few instances of associations between Conflict/Tension and Positive Outcomes, which may reflect both the relative newness of the DBSC and the top-down, one-way dialogue prevalent in the company.
Summary of Results
The BSC is an innovative strategy communication and management control development. However, as with all innovations, establishing its validity takes time, objective evidence, and careful analysis. There is always the danger that promotional "hype" will promise more than a technique can deliver, which could lead to disappointment, skepticism, and failure to recognize significant benefits, even if they are not as grand as advertised. Kaplan and Norton (1996, 2000) bill the BSC as a complete, reliable strategic guide. It perhaps will prove to be just that. However, there is limited objective evidence presented in support of this proposition. For example, Ittner et al. (2000) do not find compelling evidence that a large bank's BSC promoted increased strategic awareness. More empirical evidence will be useful, because most of the BSC literature is either normative prescription or uncritical reports of BSC "successes." We believe this study provides a significant contribution to the literature, of interest to both academics and managers.
The present study uses a method of analysis that moves management accounting field research in the direction of more generalizability and internal validity than is apparent in most descriptive field research in the area. While this qualitative approach can never achieve the external validity of statistical analysis of archival data, perhaps it can aid researchers (and their critics) who seek to increase the objectivity and reliability of field-study analysis.
Our findings are that, in at least one corporate setting, the BSC does present significant opportunities to develop, communicate, and implement strategy--just as Kaplan and Norton aver. We find evidence that managers respond positively to BSC measures by reorganizing their resources and activities, in some cases dramatically, to improve their performance on those measures. More significantly, they believe that improving their BSC performance is improving their business efficiency and profitability. Managers react favorably to the BSC and heed its messages when:
* BSC elements are measured effectively, aligned with strategy, and reliable guides for changes, modifications, and improvements;
* The BSC is a comprehensive measure of performance that reflects the needs of effective management;
* The BSC factors are seen to be causally linked to each other and tied to meaningful rewards;
* BSC benchmarks are appropriate for evaluation and useful for guiding changes; and
* Relative BSC performance is a guide for improvement.
However, problems of designing and implementing the BSC may be no different from those associated with any major change in performance-measurement systems. The following factors were found to negatively affect perceptions of the BSC and cause significant conflict and tension between the company and its distributors.
* Measures are inaccurate or subjective;
* Communication about the BSC is one-way (i.e., top-down and not participative); and
* Benchmarks are inappropriate but used for evaluation.
Though some of these adverse findings are associated with recommendations for improvement, most are found to be causes of unproductive conflict and tension or a general atmosphere of ineffectiveness. For example, the study found many examples of key factors that were ineffectively implemented in the company's BSC. Left uncorrected, these negatives could result in deteriorated relations, increasing "imbalance" in the BSC as focus shifts to more objective, short-term financial measures (Ittner et al. 2000), and forfeiting the communication and management control benefits of the BSC. For example, we can speculate that we did not observe sufficient relations between Positive outcomes and Conflict/Tension because there is little dialogue (or dialectical process) between the company and its distributors--we found only six associations. In this case, it appears that conflict simmers and rarely results in a positive outcome.
On the brighter side, the previous bullet points represent value-added and non-value-added BSC activities. To successfully design, implement, and use the BSC, organizations should enhance the former, positive factors and eliminate or correct the latter, negative factors. It may be worth noting that the total number of consistent links on the "ineffective" side of the model in Figure 4 far outweighs those on the "effective" side (154 to 59). Thus, the predominance of negative perceptions reflects many opportunities to improve both communication and control of strategy. It seems likely that this ineffectiveness could be resolved and the negative outcomes of unnecessary conflict and tension could be avoided at relatively low cost (though it may require significant changes in attitudes). Possible solutions could be as simple as improved dialogue between the company and its distributors regarding important but ineffectively measured or poorly understood DBSC factors (e.g., Lindquist 1995).
Limitations and Future Research
Even though many of this company's managers and distributors apparently use the DBSC as a valid representation of their business, we recognize that their reported perceptions may not be valid representations of their actions. To our knowledge, however, there has been no rigorous, statistical test of the claim that the BSC is, in fact, a causal model, which is the focus of our ongoing research.
Preliminary analysis of the statistical properties of the host company's DBSC confirms many expected causal relations and in particular shows the importance of modeling time lags between changes in investments in internal processes, customer value, and financial performance. Consistent with distributors' beliefs, we have found that "upstream" changes may not result in tangible financial improvements for over a year.
You will see very little change from quarter to quarter. Last quarter only one measure changed. [9: 121--124]
I expect a three- to five-year lag to see a significant impact of market penetration investments. I'm spending a gazillion dollars on it, but returns will be in about five years. We'll see some short-term returns soon, but the big returns are five years down the road. [2: 148--151]
My gut feeling is that it took two to three years to reorganize and retrain, and four or five years later it started to pay off. I expect a quicker response now from improving the fill rate and one-hour diagnosis. [6: 205--207]
I would think about half a year to a year for the parts fill rate. Do well, and your reputation becomes known and you'll see some effect in the financials. It's a matter of customer awareness that we're doing something different here that will bring repeat business. [3: 130--133]
People are very sensitive. They let us know if we are not living up to expectations. Some of our customers are looking elsewhere to get parts because of stocking problems. Customers will react in a six-month window. [1: 217--223]
Practical difficulties that are encountered in any statistical test of a BSC include:
* Changes in BSC measures and links as systems evolve to meet changing conditions;
* Changes in organizations, markets, and personnel that may affect BSC structure and links;
* Long lead times before effects are seen in lagging measures of performance;
* No effects or negative results that may be attributed to "bad design" or "bad implementation" rather than to the concept of the BSC as a causal model; and
* Desirable effects or positive results that may be caused by other, related (but omitted) factors, but are attributed to the BSC.
Making progress on controlling these factors offers opportunities for significant contributions to our understanding of strategy communication, performance measurement, and evaluation characteristics of the BSC.
Since the data collection for this project in mid-1999, the DBSC has undergone significant changes. The company has added new measures and deleted some of the original ones, adjusted weightings, and reconfigured categories. The company did not change benchmark targets of the retained measures. The most notable changes came into effect at the end of 1999 when DBSC managers trimmed it from 30 to 20 measures. One major adjustment was continuing de-emphasis of the Learning and Growth category--it is now eliminated from the DBSC, but the measures continue to be compiled on an annual basis. This important area of performance was a casualty of unreliable measurement--but perhaps presents an opportunity for improving the DBSC. Another change is enhancement of measures of new market share, largely at the request of distributors facing significant growth opportunities in the new markets. In this case, the company acceded to the wishes of distributors although the new market share measures were perceived to be much less reliable than the traditional market share.
Company managers regard the DBSC project as an evolving process. Since the time the interviews were conducted, the percent of distributors rated "green" has risen from 2 (6 percent) to 16 (52 percent), while the percent of distributors rated "red" has fallen from 10 (32 percent) to only 1 (3 percent). The average overall BSC score has risen from 67 points to 74 points (out of 100). In addition, and importantly, distributors have realized, on average, modest but observable improvements in financial performance over the 12 quarters for which we have data. For example and as shown below, the DBSC financial measure, distributor PBIT/Sales, has improved by 6.4 percent (average over all distributors) comparing the first four quarters of the scorecard process to the last four quarters.
Eighteen of the 31 distributors experienced increases in PBIT/sales (average of 49.2 percent); 14 of them experienced declines (average of -19.1 percent). The largest decline in PBIT/sales was -53.3 percent, and the largest increase was +216.2 percent. From the first four to the last four quarters available for this study, distributors' DBSC and PBIT/ sales performance was distributed as follows:
PBIT/sales PBIT/sales increased (+) decreased (-) Total Distributors whose DBSC score increased (+) 12 5 17 Distributors whose DBSC score decreased (-) 6 8 14 Total 18 13 31 Average percentage change in PBIT/Sales: First four quarters to last four quarters 49.2% (19.1%) 6.4%
The actual distribution is marginally significantly different from a uniform distribution (p = 0.054), with 20 of the 31 distributors on the + +/- - diagonal, as one might expect. Both the company and its distributors expect "upstream" improvements to take several years to flow through financial results, so the data available for this study might not be sufficient to fully capture the effect of the DBSC. The changes in the DBSC and increases in scorecard and financial performance have encouraged the company to continue managing with the DBSC. Perhaps greater attention to the root causes of unproductive conflict surrounding the DBSC will result in higher distributor acceptance, use, and performance.
We gratefully acknowledge helpful comments from Luke George, David Guenther, Marlys Lipe, Peter Luckett, Bill Maguire, Axel Schulz, Phil Shone, Naomi Soderstrom, and Wim Van Der Stede, workshop participants at the University of Colorado, University of Melbourne, AAANZ 2000 and AAA 2000, and, in particular, the two anonymous reviewers who gave consistently insightful and constructive comments. This research was supported by a Hart Doctoral Fellowship from the University of Colorado at Boulder and by data generously provided by the anonymous host company.
(1.) The growing body of research that has investigated empirical links between nonfinancial and financial measures of performance in a variety of firms and industries also includes Amir and Lev (1996), Banker et al. (1993), Banker et al. (1995), Banker et al. (1996), Banker et al. (2000), Barth and McNichols (1994), Behn and Riley (1999), Foster and Gupta (1990, 1999), Ghosh and Lusch (2000), Hughes (2000), Ittner and Larcker (1997, 1998a), and Perera et al. (1997). These studies often find significant relations between nonfinancial measures and measures of financial performance, though studies of the performance effects of including nonfinancial measures in compensation plans are less consistent. Given extensive theoretical and growing empirical support, it is not surprising that many organizations report that they are turning to forward-looking, nonfinancial information to both guide decisions and evaluate current performance (Ittner and Larcker 1998b).
(2.) A similar approach to combining multiple measures of performance, the tableau de bard, has been used by some French companies for many years (Epstein and Manzoni 1997).
(3.) Objective knowledge is observable and expressible in normal language--production processes and outcomes, for example. Tacit knowledge, however, is known and understood but not easily expressed in language--an individual's experiences or insights, for example. This paragraph draws heavily from Tucker et al. (1996).
(4.) Consideration of time lags may be important to describing these cause-and-effect relations (e.g., Banker et al. 2000; Norrekilt 2000).
(5.) For example, Banker et al. (2000) provide empirical support from extensive time-series data within a service firm for relations between leading nonfinancial measures and lagging financial performance. Furthermore, they use an event-study method to find beneficial performance effects from including nonfinancial measures in management performance evaluations.
(6.) Proponents of economic-value added, or EVA[R], also claim improvements over traditional financial measures of performance, but it, too, is a summary financial measure, albeit one that corrects for alleged financial reporting errors. EVA[R] does not incorporate complementary, nonfinancial measures of performance.
(7.) While the first claim for the value of multiple measures of performance would generate little controversy beyond considerations of costs and benefits, the second claim is a bold and rigorous hypothesis. A literal and potentially testable description of the BSC is that it describes contemporaneous, leading, or lagging relations among performance measures. For example, improvements in learning and growth such as increased training should be reflected in predictable improvements in internal processes, such as reduced cycle time (e.g., Luft and Shields 1999). Likewise, improvements in internal processes would result predictably in improved customer value (e.g., satisfaction and market share). Finally, improvements in customer value would lead to predictable increases in financial success (e.g., profits). creating such a comprehensive and coherent model is an ambitious objective that is akin to simulating the business model of the organization itself. Accomplishing such an empirical result may not establish c ausality among BSC elements because (1) some proposed measures may not be independent, (2) causes of profitability may not be generalizable beyond the context of a specific firm (Norreklit 2000), and (3) factors omitted from the model may be correlated with both causes and effects.
(8.) Unless otherwise cited, this section draws from summaries in Simons (2000, Chapter 11) and Merchant (1989, Chapter 2).
(9.) Though this appears to be a novel application of the BSC, crossing the normal boundaries of the firm, it seems reasonable to expect that the BSC could be used to control and communicate strategy with "business partners" as well as normal employees. This use of the BSC may become particularly important as firms increasingly outsource more parts of the value chain.
(10.)Baxter and Chua (1998) provide a thoughtful essay on the practical difficulties of conducting field research, the first of which is gaining access to field sites. The method of access also may be a source of bias and a threat to internal and external validity of the field research (e.g., Atkinson and Shaffir 1998). Though several employees that we contacted knew or knew of the author's relative, we had no direct contact with that relative or his/her direct reports as part of this research effort. It is undeniable, however, that this relationship improved our access, but also may have moderated what some individuals revealed to us. we are not able to determine the expected direction or magnitude of any "sponsorship" bias, but since we heard considerable, apparently unrestrained criticism of company practices, we do not feel that any realized bias to suppress criticism was significant.
(11.) Miles and Huberman (1994) observe that random sampling usually is an inefficient approach to qualitative research, particularly when the research is theory-driven.
(12.) For purposes of this paper, we consider corporate citizenship as a dimension of the Bsc's customer value.
(13.) Weights may reflect the strength of causal links in a statistically fitted BSC, but the statistical analysis had not been completed at the time of this study. This is an area of current research. Thus, weights reflected management's degree of belief about the importance and quality of each measure, as explained later.
(14.) Numbers in brackets reflect interview number and lines of text referenced, e.g., [11:71-83] = interview 11, lines 71-83.
(15.) The DBSC pre-dated this specific terminology and technique, but the concept of communicating the "story of success" did exist.
(16.) company has established quantitative thresholds for each color rating of each measure of the DBSC. We are not at liberty to disclose these thresholds.
(17.) In Figure 1 we have obscured names of the representative distributorships and have ordered them randomly. This figure does show actual performance ratings for specific distributorships.
(18.) Lillis (1999) notes that "papers reporting the results of (qualitative) research studies disclose little detail regarding attributes of study design, analytical processes and methods actually used by researchers." Because the method of this paper is relatively new to the accounting literature, we devote considerable space to this topic. We acknowledge that computer-aided methods of qualitative research are controversial within their fields of origin, anthropology, and sociology. The primary source of controversy appears to be relative emphasis on positive method promoted by computer methods vs. insightful analysis allegedly sacrificed to the rigidity of the method (e.g., Coffey et al. 1996; Lee and Fielding 1996), but this type of controversy is familiar in most sub-fields of accounting, too. See also Trochim (2000).
(19.) The sampling scheme might be biased because of the relative undersampling of "green" distributors. The relative tenor of comments, however, does not appear to be related to the overall DBSC rating. There are no statistically significant ([alpha] = 0.05) correlations among DBSC scores or "effective" or "ineffective" comments. It does not appear that the sampling scheme is a source of bias in subject responses.
(20.) Distributors claimed that they always told the company what they thought, without restraint, though many were not sure they were heard. This is consistent with the study's characterization of the management style as top-down.
(21.) One alternative approach, which is more constrained and possibly more objective, is to conduct a written or telephone survey to elicit scaled responses to theoretically derived questions regarding specific characteristics of management control or organizational communication (e.g., Dillman 1978). This approach requires a more fully developed theory of BSC effectiveness than we believe was available and may restrict the range of data collected. The findings of this study could be used to develop a mail or telephone survey for gathering cross-sectional data.
(22.) We began the first interview with nine distributor-supplied measures, but quickly found it more descriptive to split several into separate measures. The set of codes used and their definitions are in Appendix A.
(23.) Figure 2 also contains examples of code associations, which are detailed in Appendix B.
(24.) Conflict and tension can be beneficial when they serve as catalysts for improved communication and resolution, but conflict that is left to simmer can worsen relations (e.g., Watson and Baumler 1975).
(25.) It was less feasible in this study to assess plausibility, temporality, or behavioral gradient, to analogize, or to experiment with levels of factors. See Appendix B for discussion of the characteristics of causal relations.
(26.) Numbers on the links in Figure 4 are counts of the verified, paired and linked quotations, corresponding to Table 3.
(27.) Nearly all distributors talked naturally about performance in terms of color ratings and rankings. If nothing else, then both color ratings and rankings from this DBSC have changed the company's language and reinforce its already competitive culture. For example:
I would be really reluctant to post this on the bulletin board. I don't want customers or technicians to see red. [1: 154-156]
If you're red you're an idiot. [3: 172]
By seeing it all, you can just call someone up and say, "How did you get green in service utilization?" [4: 188]
We were yellow, now we're bright red. [9: 59]
We are competitive, so it matters what rank you are. ...I want to be number one. [5: 18]
(28.) Most expressed uses of the DBSC (by both managers and distributors) appeared to be as diagnostic rather than interactive controls, also consistent with the company's top-down culture.
(29.) This quotation also is an example of a highly salient sub-code link (meaningful reward [right arrow] improvement) that did not meet our frequency threshold of at least 10 associations, illustrating one of the possible costs of striving to increase the objectivity of the analysis.
(30.) Distributors expressed strong feelings about several measures, which they felt were measured poorly, but accounted for relatively little weight in the overall DBSC score (e.g., safety had a 2 percent weight). This probably reflects general dissatisfaction with both the visibility of their poor performance on those measures and the top-down process of developing the DBSC.
(31.) Several weeks were sufficient for this team of researchers to all but forget the details of the original coding. Because the transcripts were relatively long and the coding was complex, there seems little chance that recoding was based on recall of original coding.
(32.) We are aware of research in other fields that uses as few as seven to ten codes and reports higher coding reliability. Though this may not be the only determinant of coding reliability, we did not reduce our coding scheme drastically just to increase this statistic. Qualitative methods sacrifice some objectivity to gain increased relevance, but by being as objective as possible, computer-aided qualitative researchers at a minimum disclose their potential biases and create an auditable database.
(33.) The choice of "one" line is discretionary and conservative. Upon further investigation, we found that codes within one line of each other, with one exception--which was discarded--were part of the same continuous thought and were evidence of causality; whereas many codes as much as five lines apart were coincidentally proximate--and thus not included as evidence of causality. For ease of coding, transcripts were saved in approximately 60-character lines.
(34.) These attributes are similar to Einhorn and Horgarth's (1986) spatial and temporal cues of causality.
(35.) Twenty-seven supercode "hits" is the mean number of supercode hits (15) plus one standard deviation (12).
Abernethy, M., and P. Brownell. 1997. Management control systems in research and development organizations: The role of accounting, behavior and personnel controls. Accounting, Organizations and Society 22: 233-248.
Ahrens, T., and J. F. Dent. 1998. Accounting and organizations: Realizing the richness of field research. Journal of Management Accounting Research 10: 1-39.
Amir, E., and B. Lev. 1996. Value-relevance of non-financial information: The wireless communications industry. Journal of Accounting and Economics 22 (1-3): 3-30.
Amit, R., and P. Shoemaker. 1990. Strategic assets and organizational rent. Strategic Management Journal 14: 33-46.
Antle, R., and J. S. Demski. 1988. The controllability principle in responsibility accounting. The Accounting Review 63 (4): 700-718.
Atkinson, A. A., R. Balakrishnan, P. Booth, J. M. Cote, T. Groot, T. Malmi, H. Roberts, E. Uliana, and A. Wu. 1997. New directions in management accounting research. Journal of Management Accounting Research 9: 70-108.
-----, and W. Shaffir. 1998. Standards for field research in management accounting. Journal of Management Accounting Research 10: 41-68.
Banker, R. D., and S. Datar. 1989. Sensitivity, precision and linear aggregation of signals for performance evaluation. Journal of Accounting Research 27: 21-39.
-----, H. Chang, and S. K. Majumdar. 1993. Analyzing the underlying dimensions of firm profitability. Managerial and Decision Economics 14 (1): 25-36.
-----, G. Potter, and R. G. Schroeder. 1995. An empirical analysis of manufacturing overhead cost drivers. Journal of Accounting and Economics 19 (1): 115-137.
-----, S. Lee, and G. Potter. 1996. A field study of the impact of a performance-based incentive plan. Journal of Accounting and Economics 21 (2): 195-226.
-----, G. Potter, and D. Srinivasan. 2000. An empirical investigation of an incentive plan that includes nonfinancial performance measures. The Accounting Review 75: 65-92.
Barker, R. T., and M. R. Camarata. 1998. The role of communication in creating and maintaining a learning organization: Preconditions, indicators and disciplines. Journal of Business Communication 35 (4): 443-467.
Barth, M. E., and M. F. McNichols. 1994. Estimation and market valuation of environmental liabilities relating to superfund sites. Journal of Accounting Research 32 (Supplement): 177-219.
Baxter, J. A., and W. F. Chua. 1998. Doing field research: Practice and meta-theory in counterpoint. Journal of Management Accounting Research 10: 69-87.
Becker, B., and M. Huselid. 1998. High performance work systems and firm performance: A synthesis of research and managerial implications. Research in Personnel and Human Resources Management 16: 53-101.
Behn, B. K., and R. A. Riley. 1999. Using non-financial information to predict financial performance: The case of the US airline industry. Journal of Accounting, Auditing & Finance 14 (1): 29-56.
Berliner, C., and J. A. Brimson, eds. 1988. Cost Management for Today's Advanced Manufacturing: The CAM-I Conceptual Design. Boston, MA: Harvard Business School Press.
Coffey, A., B. Holbrook, and P. Atkinson. 1996. Qualitative data analysis: Technologies and representations. Sociological Research Online. Available at: http:// www.socresonline.org.uk/socresonline/1/1/4.html.
Daft, R. L., and A. Y. Lewin. 1993. Where are the theories for the "new" organizational forms? An editorial essay. Organization Science 4: i-vi.
Dearden, J. 1969. The case against ROI control. Harvard Business Review (May-June): 124-135.
de Hass, M., and A. Kleingeld. 1999. Multilevel design of performance measurement systems: Enhancing strategic dialogue throughout the organization. Management Accounting Research 10: 233-261.
Dillman, D. 1978. Mail and Telephone Surveys: The Total Design Method. New York, NY: John Wiley & Sons.
Dixon, J. R., A. J. Nanni, and T. E. Vollman. 1990. The New Performance Challenge: Measuring Manufacturing for World Class Competition. Homewood, IL: Dow Jones-Irwin.
Einhorn, H. J., and R. M. Hogarth. 1986. Judging probable cause. Psychological Bulletin 99: 3-19.
Epstein, M. J., and J. Manzoni. 1997. The balanced scorecard and tableau de bord: Translating strategy into action. Management Accounting 79: 28-36.
Feltham, G., and J. Xie. 1994. Performance measure congruity and diversity in multi-task principal/agent relations. The Accounting Review 69: 429-453.
Flamholtz, E. G. 1979. The process of measurement in managerial accounting: A psychotechnical systems perspective. Accounting, Organizations and Society 5: 31-42.
Foster, G., and M. Gupta. 1990. Manufacturing overhead cost driver analysis. Journal of Accounting and Economics 12 (1-3): 309-337.
-----, and -----. 1999. The customer profitability implication of customer satisfaction. Working paper, Stanford University and Washington University.
Ghosh, D., and R. F. Lusch. 2000. Outcome effect, controllability and performance evaluation of managers: Some field evidence from multi-outlet businesses. Accounting, Organizations and Society 25: 411-425.
Goodman, M. B. 1998. Corporate Communications for Executives. Albany, NY: SUNY Press.
Gordon, L., and V. Naranyan. 1984. Management accounting systems, perceived environmental uncertainty and organization structure: An empirical investigation. Accounting, Organizations and Society: 33-47.
Govindarajan, V. 1984. Appropriateness of accounting data in performance evaluation: An empirical examination of environmental uncertainty as an intervening variable. Accounting, Organizations and Society: 125-135.
-----, and A. Gupta. 1985. Linking control systems to business unit strategy: Impact on performance. Accounting, Organizations and Society 10: 51-66.
Grant, R. 1991. The resource-based theory of competitive advantage. California Management Review 33: 114-135.
Holmstrom, B. 1979. Moral hazard and observability. Bell Journal of Economics 10: 74-91.
Huselid, M. 1995. The impact of human resource management practices on turnover, productivity, and corporate financial performance. Academy of Management Journal 38: 635-672.
Hughes, K. E., II, 2000. The value relevance of nonfinancial measures of air pollution in the electric utility industry. The Accounting Review 75 (2): 209-228.
-----, S. Jackson, and R. Schuler. 1997. Technical and strategic human resource management effectiveness as determinants of firm performance. Academy of Management Journal 40: 171-188.
Ittner, C. D., and D. F. Larcker. 1997. Quality strategy, strategic control systems, and organizational performance. Accounting, Organizations and Society 22 (3/4): 293-314.
-----, and -----. 1998a. Are non-financial measures leading indicators of financial performance? An analysis of customer satisfaction. Journal of Accounting Research 26 (Supplement): 1-34.
-----, and -----. 1998b. Innovations in performance measurement: Trends and research implications. Journal of Management Accounting Research 10: 205-238.
-----, -----, and M. W. Meyer. 2000. The use of subjectivity in multi-criteria bonus plans. Working paper, University of Pennsylvania.
Johnson, H. T., and R. S. Kaplan. 1987. Relevance Lost: The Rise and Fall of Management Accounting. Boston, MA: Harvard Business School Press.
-----. 1992. Relevance Regained: From Top-Down Control to Bottom-Up Empowerment. New York, NY: The Free Press.
Kaplan, R. S., and D. P. Norton. 1992. The balanced scorecard--Measures that drive performance. Harvard Business Review (January-February): 71-79.
-----, and -----. 1993. Putting the balanced scorecard to work. Harvard Business Review (September-October): 143-142.
-----, and -----. 1996a. Using the balanced scorecard as a strategic management system. Harvard Business Review (January-February): 75-85.
_____, and _____. 1996b. The Balanced Scorecard. Boston, MA: Harvard Business School Press.
_____, and _____. 1996c. Linking the balanced scorecard to strategy. California Management Review (Fall): 53-79.
_____, and _____. 2000. The Strategy-Focused Organization. Boston, MA: Harvard Business School Press.
Keegan, D. P., R. G. Eiler, and C. R. Jones. 1989. Are your performance measures obsolete? Management Accounting: 45-50.
Kotter, J. P. 1995. Why transformation efforts fail. Harvard Business Review (March-April):61.
Lee, R., and N. Fielding. 1996. Qualitative data analysis: Representations of a technology. A comment on Coffey, Holbrook, and Atkinson. Sociological Research Online. Available at: http://www.socresonline.org.uk/socresonline/1/4/lf.html.
Lillis, A. M. 1999. A framework for the analysis of interview data from multiple field research sites Accounting & Finance 39: 79-105.
Lindquist, T. 1995. Fairness as an antecedent to participative budgeting: Examining the effects of distributive justice, procedural justice and referent cognitions on satisfaction and performance. Management Accounting Research 7: 122-147.
Lipe, M. G., and S. Salterio. 2000. The balanced scorecard: Judgmental effects of information organization and diversity. The Accounting Review 75 (3): 283-298.
Locke, E. A., and G. P. Latham. 1990. A Theory of Goal Setting and Task Performance. Englewood Cliffs, NJ: Prentice Hall.
Luft, J., and M. D. Shields. 1999. Accounting classification of expenditures on intangibles: Cognitive causes of managerial myopia. Working paper, Michigan State University.
Lynch, R. L., and K. F. Cross. 1995. Measure Up! Yardsticks for Continuous Improvement. Cambridge, MA: Blackwell Business.
Malina, M. A. 2001. Management control and the balanced scorecard: An empirical test of causal relations. Working paper, University of Melbourne.
McKenzie, F. C., and M. D. Schilling. 1998. Avoiding performance measurement traps: Ensuring effective incentive designs and implementation. Compensation and Benefits Review 30 (4): 57-65.
Merchant, K. A. 1989. Rewarding Results: Motivating Profit Center Managers. Boston, MA: Harvard Business School Press.
Miles, M. B., and A. M. Huberman. 1994. Qualitative Data Analysis. Thousand Oaks, CA: Sage Publications.
Nanni, A. J., J. G. Miller, and T. E. Vollman. 1988. What shall we account for? Management Accounting 69 (7): 42-48.
Norreklit, H. 2000. The balance on the balanced scorecard--A critical analysis of some of its assumptions. Management Accounting Research 11: 65-88.
Perera, S., G. Harrison, and M. Poole. 1997. Customer-focused manufacturing strategy and the use of operations-based non-financial performance measures: A research note. Accounting, Organizations and Society 22 (6): 557-572.
Rappaport, A. 1999. New thinking on how to link executive pay to performance. Harvard Business Review (March-April): 91-101.
Schulze, W. 1992. The two schools of thought in resource-based theory: Definitions and implications for research. Paper presented at the annual meeting of the Academy of Management, Las Vegas, Nevada.
Silk, S. 1998. Automating the balanced scorecard. Management Accounting 79 (11): 38-42.
Simons, R. 2000. Performance Measurement & Control Systems for Implementing Strategy. Upper Saddle River, NJ: Prentice Hall.
Trochim, W. M. 1999. The Research Methods Knowledge Base. First edition. Atomic Dog Publishing, Cincinnati, OH.
_____. 2000. The Research Methods Knowledge Base. Second edition. Available at: http://trochim.human.cornell.edu/kb/index.htm.
Tucker M. L., G. D. Meyer, and J. W. Westerman. 1996. Organizational communication: Development of internal strategic competitive advantage. Journal of Business Communication 33 (1): 51-69.
Watson, D. J., and J. V. Baumler. 1975. Transfer pricing: A behavioral context. The Accounting Review 50: 466-74.
West, G. P., and G. D. Meyer. 1997. Communicated knowledge as a learning foundation. The International Journal of Organizational Analysis 5 (1): 25-58.
Yin, R. K. 1994. Case Study Research: Design and Methods. Second edition. Applied Social Research Methods Series, Volume 5. Thousand Oaks, CA: Sage Publications.
[Figure 3 omitted]
[Figure 4 omitted]
TABLE 1 DBSC Measures and Approximate Weights Traditional BSC Distributor BSC Measures Categories (Company Category) Weights Learning and growth Employee skill inventory and personal development plans (HC) 1 Industry involvement (HC) 1 Training (HC) 2 Efficient internal Customer orders, first-time fill rate (CA) 3 processes Customer service, problems diagnosed in 1 hour (CA) 5 Customer service, problems solved in 6 hours (CA) 5 Management excellence awards (CA) 3 Adoption of best practices (CA) 1 Inventory turnover (PG) 4 Days' sales outstanding (PG) 2 Service hours utilization (PG) 2 Safety (CC) 2 Warranties (Other) 8 Building condition (Other) 3 Miscellaneous (Other) 3 Customer value Customer satisfaction (CA) 4 Traditional market share #1 (easily tracked) (CA) 28 New market share #2 (no measure yet available) (CA) 6 Environmental assessment and remediation (CC) 2 Financial success PBIT, % of sales (PG) 4 Cash flow from operations, % of sales (PG) 2 Sales growth (PG) 9 Traditional BSC Categories Learning and growth 4% Efficient internal processes 41% Customer value 40% Financial success 15% 100% Company BSC categories: HC = investments in human capital; CA = competitive advantage; PG = profitability and growth; and CC = corporate citizenship. TABLE 2 Interview Codes and Frequencies by Interview Distributo Interview 1 2 3 Overall BSC rating at time of Y Y R interview Overall BSC score at time of 72 67 62 interview sc-Interview protocol (a) 17 18 17 ?benefits of BSC 1 1 1 ?best practices 1 1 1 ?company's measures 1 1 1 ?definition of BSC 1 1 1 ?diagnose-1-hour 1 1 1 ?first-pass fill rate 1 1 1 ?industry involvement 1 2 1 ?objective/purpose of BSC 1 1 1 ?other recommendations 1 1 1 ?personnel reviews 1 1 1 ?relations among measures 1 1 1 ?safety 1 1 1 ?service utilization 1 1 1 ?solve-4-hour 1 1 1 ?training-salary 1 1 1 ?training-technician 1 1 1 ?useful measure 1 1 1 sc-Strategy alignment (a) 23 20 20 Key factors 12 11 13 BSC important for business 3 3 4 Imbalanced market share 3 4 1 Support company strategy 5 2 2 Strategy non-alignment 2 2 1 sc-Effective communication (a) 2 1 6 Routine 1 0 3 Support company culture 1 0 0 Trustworthy 0 0 0 Two-way dialogue 0 0 3 Understandable 0 1 0 sc-Ineffective communication (a) 8 10 10 Not routine 0 0 1 Not support company culture 0 0 0 Not trustworthy 0 3 1 Not understandable 4 4 6 One-way reporting 4 3 2 Most recent overall BSC rating Y Y R Most recent overall BSC score 72 67 62 sc-Effective management control (a) 12 13 12 Causality among measures 3 1 5 Comprehensive performance 4 1 2 Effective measurement 4 4 3 Time lag 1 1 1 Weight 0 6 2 sc-Ineffective mgt control (a) 11 7 7 Inaccurate/subjective measure 9 6 5 Limited scope of measure 2 1 2 sc-Effective motivation (a) 4 11 15 Absolute performance 0 0 0 Appropriate benchmark 0 2 4 Meaningful reward/penalty 0 3 1 Motivate distributors 1 1 8 Obective performance 0 2 0 Relative performance 3 3 2 sc-Ineffective motivation (a) 11 5 3 Costly to measure 2 3 2 Inappropriate benchmark 6 1 0 Ltd controllability by distrib. 3 1 0 Subjective performance 0 0 1 sc-Positive outcomes (a) 12 5 14 Improvement 3 2 3 Measure causes change 5 2 6 Modify measure or BSC 4 1 5 Conflict/tension 6 2 2 Distributo Interview 4 5 6 Overall BSC rating at time of Y Y G interview Overall BSC score at time of 74 67 84 interview sc-Interview protocol (a) 17 17 17 ?benefits of BSC 1 1 1 ?best practices 1 1 1 ?company's measures 1 1 1 ?definition of BSC 1 1 1 ?diagnose-1-hour 1 1 1 ?first-pass fill rate 1 1 1 ?industry involvement 1 1 0 ?objective/purpose of BSC 1 1 1 ?other recommendations 1 1 1 ?personnel reviews 1 1 2 ?relations among measures 1 1 1 ?safety 1 1 1 ?service utilization 1 1 1 ?solve-4-hour 1 1 1 ?training-salary 1 1 1 ?training-technician 1 1 1 ?useful measure 1 1 1 sc-Strategy alignment (a) 12 22 20 Key factors 10 13 16 BSC important for business 2 4 3 Imbalanced market share 0 3 0 Support company strategy 0 2 1 Strategy non-alignment 2 0 0 sc-Effective communication (a) 6 4 6 Routine 4 2 1 Support company culture 0 1 1 Trustworthy 0 0 0 Two-way dialogue 2 1 3 Understandable 0 0 1 sc-Ineffective communication (a) 6 10 13 Not routine 1 0 1 Not support company culture 0 0 0 Not trustworthy 1 4 1 Not understandable 3 4 1 One-way reporting 1 2 10 Most recent overall BSC rating Y Y G Most recent overall BSC score 74 67 84 sc-Effective management control (a) 9 18 9 Causality among measures 4 6 4 Comprehensive performance 1 3 1 Effective measurement 3 4 2 Time lag 1 0 1 Weight 0 5 1 sc-Ineffective mgt control (a) 4 7 4 Inaccurate/subjective measure 3 7 4 Limited scope of measure 1 0 0 sc-Effective motivation (a) 10 8 11 Absolute performance 0 1 1 Appropriate benchmark 5 2 2 Meaningful reward/penalty 1 3 4 Motivate distributors 0 0 1 Obective performance 1 0 2 Relative performance 3 2 1 sc-Ineffective motivation (a) 6 7 11 Costly to measure 0 0 2 Inappropriate benchmark 2 6 4 Ltd controllability by distrib. 4 0 5 Subjective performance 0 1 0 sc-Positive outcomes (a) 11 4 10 Improvement 2 0 1 Measure causes change 6 1 4 Modify measure or BSC 3 3 5 Conflict/tension 1 2 6 Distributo Interview 7 8 9 Overall BSC rating at time of Y R R interview Overall BSC score at time of 70 61 60 interview sc-Interview protocol (a) 17 16 17 ?benefits of BSC 1 1 1 ?best practices 1 1 1 ?company's measures 1 1 1 ?definition of BSC 1 1 1 ?diagnose-1-hour 1 1 1 ?first-pass fill rate 1 1 1 ?industry involvement 1 1 1 ?objective/purpose of BSC 1 1 1 ?other recommendations 1 1 1 ?personnel reviews 1 0 1 ?relations among measures 1 1 1 ?safety 1 1 1 ?service utilization 1 1 1 ?solve-4-hour 1 1 1 ?training-salary 1 1 1 ?training-technician 1 1 1 ?useful measure 1 1 1 sc-Strategy alignment (a) 11 12 12 Key factors 8 10 6 BSC important for business 2 2 2 Imbalanced market share 1 0 4 Support company strategy 0 0 0 Strategy non-alignment 0 1 5 sc-Effective communication (a) 3 3 2 Routine 2 0 1 Support company culture 0 2 0 Trustworthy 0 0 0 Two-way dialogue 1 1 1 Understandable 0 0 0 sc-Ineffective communication (a) 13 12 10 Not routine 0 0 1 Not support company culture 0 0 0 Not trustworthy 0 1 1 Not understandable 0 1 5 One-way reporting 13 10 3 Most recent overall BSC rating Y R R Most recent overall BSC score 70 61 60 sc-Effective management control (a) 7 4 6 Causality among measures 3 1 2 Comprehensive performance 1 1 1 Effective measurement 2 2 0 Time lag 0 0 1 Weight 1 0 2 sc-Ineffective mgt control (a) 3 2 6 Inaccurate/subjective measure 3 2 5 Limited scope of measure 0 0 1 sc-Effective motivation (a) 13 9 5 Absolute performance 1 1 0 Appropriate benchmark 5 2 2 Meaningful reward/penalty 1 2 1 Motivate distributors 0 2 0 Obective performance 0 2 1 Relative performance 6 0 1 sc-Ineffective motivation (a) 10 15 9 Costly to measure 5 3 1 Inappropriate benchmark 4 6 4 Ltd controllability by distrib. 1 2 3 Subjective performance 0 4 1 sc-Positive outcomes (a) 10 10 11 Improvement 3 0 1 Measure causes change 1 6 3 Modify measure or BSC 6 4 7 Conflict/tension 3 4 2 Distributo Interview Overall BSC rating at time of interview Overall BSC score at time of Totals interview sc-Interview protocol (a) 152 ?benefits of BSC 9 ?best practices 9 ?company's measures 9 ?definition of BSC 9 ?diagnose-1-hour 9 ?first-pass fill rate 9 ?industry involvement 9 ?objective/purpose of BSC 9 ?other recommendations 9 ?personnel reviews 9 ?relations among measures 9 ?safety 9 ?service utilization 9 ?solve-4-hour 9 ?training-salary 9 ?training-technician 9 ?useful measure 9 sc-Strategy alignment (a) 152 Key factors 99 BSC important for business 25 Imbalanced market share 16 Support company strategy 12 Strategy non-alignment 13 sc-Effective communication (a) 33 Routine 14 Support company culture 5 Trustworthy 0 Two-way dialogue 12 Understandable 2 sc-Ineffective communication (a) 92 Not routine 4 Not support company culture 0 Not trustworthy 12 Not understandable 28 One-way reporting 48 Most recent overall BSC rating Most recent overall BSC score Total sc-Effective management control (a) 91 Causality among measures 29 Comprehensive performance 15 Effective measurement 24 Time lag 6 Weight 17 sc-Ineffective mgt control (a) 51 Inaccurate/subjective measure 44 Limited scope of measure 7 sc-Effective motivation (a) 86 Absolute performance 4 Appropriate benchmark 24 Meaningful reward/penalty 16 Motivate distributors 13 Obective performance 8 Relative performance 21 sc-Ineffective motivation (a) 77 Costly to measure 18 Inappropriate benchmark 33 Ltd controllability by distrib. 19 Subjective performance 7 sc-Positive outcomes (a) 87 Improvement 15 Measure causes change 34 Modify measure or BSC 38 Conflict/tension 28 (a)The "sc-" prefix refers to a "supercode," which collects indented codes listed below the supercode. TABLE 3 Summary of Verified Supercode Causal Relations and Associations Causal First Supercode Second Supercode Relations Effective Mgt Control Strategy Alignment 23 Effective Mgt Control Effective Motivation 8 Strategy Alignment Positive Outcomes 28 Strategy Alignment Ineffective Mgt Control Strategy Alignment Effective Motivation Strategy Alignment Ineffective Communication Strategy Alignment Ineffective Motivation Effective Motivation Positive Outcomes 14 Ineffective Mgt Control Conflict/Tension 8 Ineffective Mgt Control Ineffective Motivation 8 Ineffective Mgt Control Ineffective Communication Ineffective Communication Conflict/Tension 16 Ineffective Motivation Positive Outcomes 7 Ineffective Motivation Conflict/Tension 9 First Supercode Associations Effective Mgt Control Effective Mgt Control Strategy Alignment Strategy Alignment 24 Strategy Alignment 14 Strategy Alignment 11 Strategy Alignment 27 Effective Motivation Ineffective Mgt Control Ineffective Mgt Control Ineffective Mgt Control 16 Ineffective Communication Ineffective Motivation Ineffective Motivation TABLE 4 Analysis of Distributor-Response Supercode Proximity and Association Enclosed First Code Second Code by Encloses Effective Mgt Control Aligned with Strategy 12 7 Effective Mgt Control Conflict/Tension 2 0 Effective Mgt Control Effective Communication 1 1 Effective Mgt Control Effective Motivation 2 7 Effective Mgt Control Ineffective Communication 0 0 Effective Mgt Control Ineffective Mgt control 0 0 Effective Mgt Control Ineffective Motivation 0 0 Effective Mgt Control Strategy Non-Alignment 0 0 Effective Mgt Control Positive Outcomes 3 3 Ineffective Mgt Control Aligned with Strategy 2 2 Ineffective Mgt Control Conflict/Tension 3 1 Ineffective Mgt Control Effective Communication 0 0 Ineffective Mgt Control Effective Mgt Control 0 0 Ineffective Mgt Control Effective Motivation 0 1 Ineffective Mgt Control Ineffective Communication 3 9 Ineffective Mgt Control Ineffective Motivation 4 0 Ineffective Mgt Control Strategy Non-Alignment 0 1 Ineffective Mgt Control Positive Outcomes 0 3 Effective Communication Strategy Alignment 4 2 Effective Communication Conflict/Tension 0 0 Effective Communication Effective Mgt Control 1 1 Effective Communication Effective Motivation 4 0 Effective Communication Ineffective Communication 1 0 Effective Communication Ineffective Mgt Control 0 0 Effective Communication Ineffective Motivation 1 0 Effective Communication Strategy Non-Alignment 0 0 Effective Communication Positive Outcomes 2 0 Ineffective Communication Strategy Alignment 3 2 Ineffective Communication Conflict/Tension 8 3 Ineffective Communication Effective Communication 0 1 Ineffective Communication Effective Mgt Control 0 0 Ineffective Communication Effective Motivation 3 2 Ineffective Communication Ineffective Mgt Control 13 4 Ineffective Communication Ineffective Motivation 7 2 Ineffective Communication Strategy Non-Alignment 0 1 Ineffective Communication Positive Outcomes 3 3 Strategy Alignment Conflict/Tension 4 1 Strategy Alignment Effective Communication 2 4 Strategy Alignment Effective Mgt Control 7 12 Strategy Alignment Effective Motivation 6 7 Strategy Alignment Ineffective Communication 2 1 Strategy Alignment Ineffective Mgt Control 2 1 Strategy Alignment Ineffective Motivation 1 1 Strategy Alignment Strategy Non-Alignment 0 1 Strategy Alignment Positive Outcomes 11 8 Strategy Non-Alignment Strategy Alignment 1 0 Strategy Non-Alignment Conflict/Tension 0 0 Strategy Non-Alignment Effective Communication 0 0 Strategy Non-Alignment Effective Mgt Control 0 0 Strategy Non-Alignment Effective Motivation 0 0 Strategy Non-Alignment Ineffective Communication 1 0 Strategy Non-Alignment Ineffective Mgt Control 1 0 Strategy Non-Alignment Ineffective Motivation 2 0 Strategy Non-Alignment Positive Outcomes 0 0 Effective Motivation Strategy Alignment 7 6 Effective Motivation Conflict/Tension 2 3 Effective Motivation Effective Communication 0 3 Effective Motivation Effective Mgt Control 8 2 Effective Motivation Ineffective Communication 2 3 Effective Motivation Ineffective Mgt Control 1 0 Effective Motivation Ineffective Motivation 2 2 Effective Motivation Strategy Non-Alignment 0 0 Effective Motivation Positive Outcomes 6 3 Ineffective Motivation Strategy Alignment 1 1 Ineffective Motivation Conflict/Tension 5 6 Ineffective Motivation Effective Communication 0 1 Ineffective Motivation Effective Mgt Control 0 0 Ineffective Motivation Effective Motivation 2 2 Ineffective Motivation Ineffective Communication 2 7 Ineffective Motivation Ineffective Mgt Control 0 4 Ineffective Motivation Strategy Non-Alignment 0 2 Ineffective Motivation Positive Outcomes 5 1 Conflict/Tension Strategy Alignment 1 3 Conflict/Tension Effective Communication 0 0 Conflict/Tension Effective Mgt Control 0 2 Conflict/Tension Effective Motivation 4 2 Conflict/Tension Ineffective Communication 3 6 Conflict/Tension Ineffective Mgt Control 1 3 Conflict/Tension Ineffective Motivation 8 4 Conflict/Tension Strategy Non-Alignment 0 0 Conflict/Tension Positive Outcomes 0 0 Positive Outcomes Aligned with Strategy 8 9 Positive Outcomes Conflict/Tension 0 0 Positive Outcomes Effective Communication 0 2 Positive Outcomes Effective Mgt Control 3 3 Positive Outcomes Effective Motivation 2 6 Positive Outcomes Ineffective Communication 2 3 Positive Outcomes Ineffective Mgt Control 3 0 Positive Outcomes Ineffective Motivation 1 4 Positive Outcomes Strategy Non-Alignment 0 0 Overlapped First Code by Overlaps Follows (*) Effective Mgt Control 2 0 11 Effective Mgt Control 0 0 1 Effective Mgt Control 0 0 1 Effective Mgt Control 1 1 11 Effective Mgt Control 1 0 8 Effective Mgt Control 0 0 5 Effective Mgt Control 0 0 4 Effective Mgt Control 0 0 0 Effective Mgt Control 1 0 6 Ineffective Mgt Control 0 0 18 Ineffective Mgt Control 0 1 2 Ineffective Mgt Control 0 0 0 Ineffective Mgt Control 0 0 5 Ineffective Mgt Control 0 0 8 Ineffective Mgt Control 2 1 8 Ineffective Mgt Control 0 2 7 Ineffective Mgt Control 0 0 0 Ineffective Mgt Control 0 0 1 Effective Communication 0 0 6 Effective Communication 1 1 0 Effective Communication 0 0 2 Effective Communication 0 0 0 Effective Communication 1 0 5 Effective Communication 0 0 1 Effective Communication 0 0 2 Effective Communication 0 0 1 Effective Communication 0 0 4 Ineffective Communication 0 1 14 Ineffective Communication 1 1 2 Ineffective Communication 0 1 5 Ineffective Communication 0 1 4 Ineffective Communication 0 0 8 Ineffective Communication 1 2 8 Ineffective Communication 1 2 7 Ineffective Communication 0 0 3 Ineffective Communication 0 0 5 Strategy Alignment 0 0 5 Strategy Alignment 0 0 1 Strategy Alignment 0 2 6 Strategy Alignment 1 1 5 Strategy Alignment 1 0 10 Strategy Alignment 0 0 8 Strategy Alignment 2 1 10 Strategy Alignment 0 0 2 Strategy Alignment 1 0 12 Strategy Non-Alignment 0 0 1 Strategy Non-Alignment 0 0 0 Strategy Non-Alignment 0 0 1 Strategy Non-Alignment 0 0 1 Strategy Non-Alignment 0 0 0 Strategy Non-Alignment 0 0 2 Strategy Non-Alignment 0 0 0 Strategy Non-Alignment 0 0 0 Strategy Non-Alignment 0 0 1 Effective Motivation 1 1 12 Effective Motivation 1 0 0 Effective Motivation 0 0 3 Effective Motivation 1 1 14 Effective Motivation 0 0 12 Effective Motivation 0 0 5 Effective Motivation 1 0 2 Effective Motivation 0 0 0 Effective Motivation 1 1 9 Ineffective Motivation 1 2 23 Ineffective Motivation 0 1 5 Ineffective Motivation 0 0 4 Ineffective Motivation 0 0 5 Ineffective Motivation 0 1 6 Ineffective Motivation 2 1 4 Ineffective Motivation 2 0 5 Ineffective Motivation 0 0 2 Ineffective Motivation 1 0 8 Conflict/Tension 0 0 3 Conflict/Tension 0 1 1 Conflict/Tension 0 0 2 Conflict/Tension 0 1 3 Conflict/Tension 1 1 4 Conflict/Tension 1 0 3 Conflict/Tension 1 0 3 Conflict/Tension 0 0 0 Conflict/Tension 0 0 4 Positive Outcomes 0 1 21 Positive Outcomes 0 0 2 Positive Outcomes 0 0 4 Positive Outcomes 0 1 5 Positive Outcomes 1 1 13 Positive Outcomes 0 0 9 Positive Outcomes 0 0 12 Positive Outcomes 0 1 10 Positive Outcomes 0 0 3 Total First Code Precedes (*) Associations Effective Mgt Control 6 38 Effective Mgt Control 2 5 Effective Mgt Control 2 5 Effective Mgt Control 12 34 Effective Mgt Control 4 13 Effective Mgt Control 6 11 Effective Mgt Control 5 9 Effective Mgt Control 1 1 Effective Mgt Control 5 18 Ineffective Mgt Control 7 29 Ineffective Mgt Control 3 10 Ineffective Mgt Control 1 1 Ineffective Mgt Control 5 10 Ineffective Mgt Control 5 14 Ineffective Mgt Control 8 31 Ineffective Mgt Control 5 18 Ineffective Mgt Control 0 1 Ineffective Mgt Control 12 16 Effective Communication 1 13 Effective Communication 3 5 Effective Communication 1 5 Effective Communication 3 7 Effective Communication 4 11 Effective Communication 0 1 Effective Communication 4 7 Effective Communication 1 2 Effective Communication 4 10 Ineffective Communication 10 30 Ineffective Communication 4 19 Ineffective Communication 5 12 Ineffective Communication 8 13 Ineffective Communication 11 24 Ineffective Communication 8 36 Ineffective Communication 4 23 Ineffective Communication 2 6 Ineffective Communication 9 20 Strategy Alignment 3 13 Strategy Alignment 6 13 Strategy Alignment 11 38 Strategy Alignment 11 31 Strategy Alignment 14 28 Strategy Alignment 19 30 Strategy Alignment 22 37 Strategy Alignment 1 4 Strategy Alignment 21 53 Strategy Non-Alignment 2 4 Strategy Non-Alignment 0 0 Strategy Non-Alignment 1 2 Strategy Non-Alignment 0 1 Strategy Non-Alignment 0 0 Strategy Non-Alignment 3 6 Strategy Non-Alignment 0 1 Strategy Non-Alignment 2 4 Strategy Non-Alignment 3 4 Effective Motivation 5 32 Effective Motivation 4 10 Effective Motivation 0 6 Effective Motivation 12 38 Effective Motivation 9 26 Effective Motivation 8 14 Effective Motivation 5 12 Effective Motivation 0 0 Effective Motivation 15 35 Ineffective Motivation 10 38 Ineffective Motivation 3 20 Ineffective Motivation 2 7 Ineffective Motivation 4 9 Ineffective Motivation 2 13 Ineffective Motivation 6 22 Ineffective Motivation 7 18 Ineffective Motivation 0 4 Ineffective Motivation 10 25 Conflict/Tension 6 13 Conflict/Tension 0 2 Conflict/Tension 1 5 Conflict/Tension 0 10 Conflict/Tension 2 17 Conflict/Tension 2 10 Conflict/Tension 6 22 Conflict/Tension 0 0 Conflict/Tension 2 6 Positive Outcomes 11 50 Positive Outcomes 4 6 Positive Outcomes 5 11 Positive Outcomes 5 17 Positive Outcomes 7 30 Positive Outcomes 5 19 Positive Outcomes 1 16 Positive Outcomes 9 25 Positive Outcomes 1 4 (*)Follows or precedes the first code by one line. See footnote 18. FIGURE 1 Representative DBSC Rating and Scores 6 5 4 3 2 1 Distributor Weights, % Y R R R Y Y 4 Y G Y Y G Y 28 6 Y Y Y Y G Y 3 Y Y Y Y Y Y 5 Y Y R Y Y Y 5 G G G G G R 3 G R G R G R 1 Y Y Y Y G Y 55 G G Y Y G G 4 G G Y R Y R 2 Y R R Y Y Y 4 Y R R Y Y G 2 Y Y R Y Y Y 2 G G G Y G G 9 G Y Y Y G G 23 R G G Y G Y 2 R R R Y Y Y 2 R Y Y Y G Y 4 Y G R R G R 1 G G G Y G Y 1 Y Y Y Y G Y 2 Y G Y Y G Y 4 Y G R G G Y 8 Y Y Y Y G Y 3 Y G R G G R 3 Y G R Y G Y 14 Y G R Y G R 100 70 75 61 67 84 64 100 6 Competitive Advantage Y Customer satisfaction Y Traditional market share #1 New market share #2 Y Customer orders, first-time fill rate (a) Y Customer service, problems diagnosed in 1 hour (a) Y Customer service, 4-hour problems solved in 6 hours (a) G Management excellence awards G Adoption of best practices (a) Y Total Competitive Advantage Rating Profitability and Growth G PBIT, % sales G Cash flow from operations, % of sales Y Inventory turnover Y Days sales outstanding Y Service hours utilization (a) G Sales growth G Total Profitability and Growth Rating Corporate Citizenship R Environmental assessement and remediation R Safety (2) R Total Corporate Citizenship Rating Investments in Human Capital Y Employee skill inventory and personal development plans (a) G Industry involvement (a) Y Training (a) Y Total Human Capital Rating Other Y Warranties Y Building condition Y Miscellaneous Y Total Other Y Total - Overall Rating 70 Total - Overall Score (a)Supplied by distributors to the company (22 percent weighting). Other items prepared by the company from financial and satistical reports (78 percent weighting). APPENDIX A Codes and Definitions ?benefits of BSC Question refers to beneficial effects of BSC ?best practices Question refers to submitting a best practice for others to consider adopting ?company's measures Question refers to BSC elements measured by company, not the distributor ?definition of BSC Question about perception of what the BSC is supposed to be ?diagnose-1-hour Question about part of program to accurately diagnose service problem within one hour ?first-pass fill rate Question about measure of ability to fill customer request for parts from available inventory-100 percent is tops ?industry involvement Question about whether distributors are "networking" with customers on a professional basis ?objective/purpose of BSC Question about the objective of the BSC, what the BSC is supposed to achieve ?other recommendations Question about miscellaneous recommendations for BSC improvement ?personnel reviews Question about proportion of employees who have annual performance plans and reviews ?relations among measures Question whether BSC measures are related ?safety Question about safety, one of the BSC measures, relates number of time-loss incidents ?service utilization Question about utilization of service capacity--hours of productive time ?solve-4-hour Question about proportion of standard four-hour repair jobs completed within six hours ?training-salary Question about training and skill achievement of salaried, nontechnicians ?training-technicians Question about training and skill achievement of technicians ?useful measure Question about whether BSC and/or measures are useful for managing the business Absolute performance BSC or individual measure for comparison of performance against a standard of performance, not necessarily relative to other distributors Appropriate benchmark Benchmark is appropriate to achieve red, yellow, or green ratings; challenging but attainable BSC important for The BSC as a whole is important business for managing the business; reference to more than a single measure Causality among measures BSC design reflects cause-and-effect relations among measures Comprehensive performance The BSC is meant to or does provide an overall (financial and nonfinancial) measure of performance Conflict/tension Evidence of conflict or tension caused by the BSC, its elements, or its use Costly to measure Measure is costly/difficult/time consuming to measure or maintain Cross-product subsidy Evidence that costing results in misallocations of cost Effective measurement Measure is hard, verifiable, valid--measures what it says it is measuring Imbalanced market share Emphasis on traditional market share, not new markets Improvement BSC can be/is tool to improve business; evidence or strong belief of improvement of performance Inaccurate/subjective measure Measure does not reflect underlying key activity, not consistently reported over time and across distributors, soft measure, system not capturing measure correctly Inappropriate benchmark Not an appropriate benchmark for achieving red, yellow, or green ratings; not attainable or too easy Inappropriate factor Measure is not important for measuring performance of the business, from either distributor or company perspective Key factors Individual measure is important for managing the measure from either distributor or company perspective; good, important, helpful to measure Limited controllability Measure is largely outside the by distributor control of the by distributor Limited scope of measure Measure does not reflect full extent of important distributor activity; focus too narrow Meaningful reward/penalty Materially affects distributor profits, compensation, or the three-year review for renewal of distributor license Measure causes change Use of measure causes change in distributor action or mindset Modify measure or BSC Argument that the company should revise the BSC measure to be more accurate or appropriate; specific recommendation Motivate distributors BSC motivates distributors to improve measures and/or performance Not aligned with strategy Measure is not important for measuring performance of the business, from either distributor or company perspective Not routine An ad hoc measure, difficult to predict when needed or available Not support company culture Measure works against the company's culture, values, or beliefs Not trustworthy Measure cannot be trusted because of manipulation, bias, or outright cheating that cannot be detected Not understandable Distributors do not understand what the measure is or what it is trying to represent; measure poorly defined Objective performance BSC rating or score is or is supposed to be an objective measure of performance One-way reporting Communication is top-down, dictated policy or prescription; mandatory reporting, no feedback, no dialogue among distributors, no input to company Relative performance Measure or BSC is used to evaluate distributors relative to each other, apart from against a standard of performance Routine Measure is regularly available or used; easy to predict when needed or available Subjective performance Actual performance evaluations are wholly or in part subjective Support company culture Measure reinforces company culture, values, beliefs Support company strategy Measure identifies or reinforces company strategic plans or initiatives Time lag Evidence that downstream effects are lagged from upstream activities or investments; amount of time before a change in one measure is reflected in another downstream measure Trustworthy Measure is trustworthy, reliable and free from manipulation, bias or undetectable cheating Two-way dialogue Communication is open dialogue--sharing of views and ideas among company and distributors, includes feedback Understandable BSC presents measures and performance in clear, understandable format Weight Weight reflects importance to company unless the measure cannot be made objectively
Steps to Assure Coding Reliability and Establish Causality
Insuring Coding Reliability
After agreeing upon the predetermined coding scheme, each of the two researchers coded the first interview using the software tool. After coding the first interview, the researchers met, computer files side by side, to compare coding, resolve differences, and agree on a refined set of codes (reduced from 70 to 54). The researchers then coded the remaining interviews, and mutually resolved any disagreements. In some instances, the resolution was to revise the name or definition of a code. A small number of preset codes were not used and are not reported.
To test coding reliability, several weeks later the researchers jointly recoded three randomly selected interviews. (31) The researchers then noted the number of agreements and disagreements between the first and second codings. The software allows the researcher to code any portion of text-a single word, phrase, sentence, paragraph, and so on. Therefore, the researchers did not count minor differences in boundaries of text blocks as disagreements; rather, a "disagreement" was a different code (or no code) applied to roughly the same block of text. An "agreement" was using the same code for approximately the same block of text. Coding agreement for this test averaged 80.3 percent [agreements/(agreements + disagreements)], ranging from 69 percent to 87 percent across the three interviews. This level of coding reliability is within norms of 80 to 90 percent coding reliability (Miles and Huberman 1994, 64). (32)
Finding Associations among Codes
The qualitative software (Atlas.ti) easily enables queries of proximity relations or associations among coded quotations listed below. Examples in parentheses refer to codes illustrated in Figure 2.
* Coded quotations of one type enclose coded quotations of another type (inaccurate/subjective measure, lines 0075-0079, encloses not understandable, lines 0075-0077).
* Coded quotations of one type are enclosed by coded quotations of another type (not understandable, lines 0075-0077, enclosed by inaccurate/subjective measure, lines 0075-0079).
* Coded quotations of one type overlap coded quotations of another type (causality among measures, lines 0079-0082, overlaps meaningful reward/penalty, lines 0080-0082).
* Coded quotations of one type are overlapped by coded quotations of another type (meaningful reward/penalty, lines 0080-0082, overlapped by causality among measures, lines 0079-0082).
* Coded quotations of one type precede coded quotations of another type by no more than one line (33) (not understandable, lines 0075-0077, precedes not trustworthy, line 0077).
* Coded quotations of one type follow coded quotations of another type by no more than one line (not trustworthy, line 0077, follows not understandable, lines 0075--0077).
The full results of this analysis are in Table 4, and in summary form in Table 3.
Establishing Causality among Codes
Close proximity or association of types of quotations might indicate causality (similar to statistical correlation), but analysis of the context of these measures of proximity is necessary. Miles and Huberman (1994, 146-147) demonstrate that qualitative analysis uses the same rules of causality as statistical analysis. Investigating the context and meaning of associations in qualitative data may reveal causality (for example, between Effective management control--EMC and Strategy alignment--SA) by any of the following observations (the more the better), in rough order of applicability to this study: (34)
* Specificity (a particular link is shown between EMC and SA)
* Consistency (EMC is found with SA in different places)
* Strength of association (much more SA with EMC than with other possible causes)
* Coherence (EMC-SA relationship fits with what else is known about EMC and SA)
* Plausibility (a known mechanism exists to link EMC and SA)
* Temporality (EMC before SA, not the reverse)
* Behavioral gradient (if more EMC, then more SA)
* Analogy (EMC and SA resemble the well-established pattern in other relations)
* Experiment (change EMC, observe what happens to SA)
Using supercode-level queries reduced the number of specific associations dramatically. Queries will find every association of the elements of supercodes, not all of which may be evidence of causality. To focus the investigation on consistent links, the research identified all supercode links with a total of 27 or more "hits" or observed associations, (35) and looked for concentrated evidence of causality between individual or subcoded comments. The number of paired subcode relations rarely exceeded 10 hits. Therefore, to avoid omitting some meaningful sub code relations, the audit was expanded to all supercode relations with 10 or more hits. In some cases, the total number of hits linking two supercodes was widely diffused across their elements, with no consistent patterns at the individual code level. The research did not investigate these diffused associations further. That is, the research conservatively treated consistent, strong (i.e., frequent) relations of specific factors as necessary for establishi ng causation in this study. To qualify as evidence of causality, each of these associations also must demonstrate coherence or be consistent with an explanation of causality.
For example, nearly all cases of consistent, frequent, and specific associations found by the "encloses," "enclosed by," "overlaps," and "overlapped by" operators also presented coherent stories of causality. For example in Figure 2, one can infer causality from the observed relation between causality among measures (lines 0079-0082) and meaningful reward/penalty (lines 0080-0082)--because the DBSC reflects causal relations among measures (part of Effective management control), distributors expect to achieve meaningful rewards by using it (part of Effective motivation). However, some of the associations found by the "precedes" or "follows" operators were only coincidentally proximate. That is, these were associations for which the research could not develop a coherent causal story linking the two codes and were deleted from the counts of evidence of causality.
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|Author:||Malina, Mary A.; Selto, Frank H.|
|Publication:||Journal of Management Accounting Research|
|Article Type:||Statistical Data Included|
|Date:||Jan 1, 2001|
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