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WHO ADOPTS BALANCED SCORECARDS? AN EMPIRICAL STUDY.

Keywords: Balanced scorecard, performance indicators, firm's performance

INTRODUCTION

Since its creation by Harvard Business School Professor Robert Kaplan and business consultant David Norton (1996), the Balanced Scorecard (BSC) has become the most popular performance measurement system used by Fortune 1000 companies over the last two decades. BSC was developed during the course of a yearlong research project involving 12 firms considered to be on the cutting-edge of performance measurement and was initially conceived of as a single performance measurement tool. It has since evolved in such a way that it is now considered to be a major component of a firm's overall strategic management system, in which management processes and systems are aligned with organizational strategy (Kaplan & Norton, 1996, 2001a, 2001b).

Traditionally, organizational performance was measured using financial metrics such as return-on-assets, return-on-capital-employed, or return-on-equity. As technology advanced and society moved into the "Information Age," many organizations' most valuable assets, including human capital and investments in research and development were not captured on the balance sheet. As a result, the traditional financial measures have become less meaningful. Additional information is needed to provide managers with indicators or signals of what financial or organizational performance will be prospectively. Financial measures are always backwards-looking and do not provide any indication about what a firm should do to sustain or improve performance going forward (Kaplan & Norton, 2001a).

The BSC framework is taught in nearly every business school curriculum at both the undergraduate and graduate levels. Anecdotal evidence suggests that since its introduction in 1992, nearly 60% of Fortune 1000 and global firms including publicly-traded and privately-held for-profit corporations, as well as not-for-profit companies, government agencies, educational institutions, and municipalities, have all utilized BSC methodology incorporating strategy-derived financial and non-financial performance measures into their performance evaluation systems (Silk, 1998; Beimann & Johnson, 2007; Rigby & Bilodeau, 2009).

A key premise of the BSC methodology is that performance measures should be derived from the organization's strategy. However, prior research suggests that a firm's strategy can impact its choice of performance measurement systems and also impact organizational performance (Snow & Hrebiniak, 1980; Govindarajan & Gupta, 1985). Although organizational strategy is thought to be a critical element of the BSC, the question of what type of organizational strategy is best suited for or may benefit most from the use of BSC remains largely unexplored. Furthermore, other firm-specific characteristics, in addition to organizational strategy, may determine if a firm will or will not use BSC. These factors include how much of the firm's value creating assets are not included on the balance sheet, i.e., the amount of the firm's intangible assets, firm size, and the amount of divisionalization or decentralization. A firm's recent history of poor financial performance may also precipitate the adoption of a new performance measurement system, such as BSC.

While many firms do utilize BSC, some do not. This study represents an effort to identify the characteristics, in addition to organizational strategy, of firms that are likely to adopt BSC as the preferred system of performance measurement.

LITERATURE REVIEW

Any study involving a firm's choice to undertake or not undertake a particular activity (e.g. to adopt or not to adopt BSC) must first consider the endogenous nature of such choice. Several factors may influence a firm's decision to adopt a BSC. Those factors may also be correlated with the firm's financial performance. Chenhall (2006) argued that "enhanced performance outcomes will depend on how different types of measurement systems best suit, or fit, with an organization's specific context." Based on theory and following prior literature, a model of contextual factors is developed to predict a firm's likelihood to adopt BSC as its performance measurement system. It is posited that a firm's level of intangible assets, strategy, recent financial performance, size and amount of divisionalization are associated with its propensity to adopt BSC.

Intangible Assets

The BSC was initially developed from a study involving firms with large amounts of intangible assets (Kaplan, 2010). The International Accounting Standards Board (IASB) defines an asset as "a resource controlled by the enterprise as a result of past events and from which future economic benefits are expected to flow to the enterprise." Intangible assets are long-term assets that do not have physical substance, yet provide substantial economic benefit to the organization, e.g. patents, copyrights, trademarks, and computer software. Many knowledge-based, technology and service firms cannot record their most valuable assets, such as human capital, internally developed IT systems and expertise, and internally developed goodwill, on their balance sheets. Because these assets are not easily imitable they can provide a source of sustainable competitive advantage for a firm.

However, measuring the value of intangible assets and demonstrating how they ultimately impact the bottom-line are difficult tasks. The BSC and its corresponding strategy maps provide a framework for illustrating how intangible assets (e.g. a highly skilled/trained customer support staff or sales force) affect future financial performance Thus, firms with large amounts of unrecorded intangible assets are conjectured to benefit most from the use of BSC and to be more likely to adopt it (Kaplan & Norton, 2004).
[H.sub.1]:  Firms with more intangible assets are more likely to adopt
            the balance scorecard (BSC), as the preferred system of
            performance measurement.


Strategy

There is no single definition of strategy. Porter (1996) asserted that while operational effectiveness is necessary for strategy, it is not in and of itself "strategy." Strategy requires defining a company's competitive position, including core competencies and areas of competitive advantage, making trade-offs or choices to sustain competitive advantage and aligning organizational activities to achieve strategic goals. In addition, a firm's chosen strategy or strategic positioning should span several years, not just one operating cycle. Kaplan and Norton (1996) defined strategy as "a set of hypotheses about cause-and-effect than can be expressed by a sequence of if-then statements."

Miles and Snow (1978) developed a typology of business strategy in which firms are classified as Defenders, Analyzers, Prospectors, or Reactors. Defender firms often follow a cost-leadership orientation and may be characterized by more organizational stability. They tend to be more vertically integrated, have more formal processes and procedures in place, are slow to change, and are more conservative. Prospector firms seek to develop new products and markets, are more innovative, and are characterized by a more dynamic or uncertain operating environment. They tend to be more decentralized, with few layers of management, and are more entrepreneurial.

Defender and Prospector firms are at opposite ends of the strategy continuum. Reactor firms have no clearly defined strategy. The strategic goals and priorities of these firms change quickly and often, in response to changes in the business environment. Analyzer firms have characteristics of both Defender firms and Prospector firms. Analyzer firms try to maintain a balance between new product innovation and cost containment (Miles & Snow, 1978).

Using the Miles and Snow strategy classifications, Simons (1987) found that the characteristics of accounting control systems differ between Prospector and Defender firms. In particular, the extent to which control systems are tailored to departmental needs, the frequency of change in control systems, and the importance of using informal communication to convey control system information are associated with pursuing a Prospector strategy.

Baines and Langfield-Smith (2003) conducted a structural equation model analysis of the antecedents of management accounting change. They found that changes towards a differentiation strategy, as are characteristic of Prospector firms, are associated with greater reliance on advanced management accounting practices, e.g. BSC. Said, HassabElnabby and Weir (2003) first investigated the performance consequences of non-financial performance measures (NFM) and subsequently investigated factors associated with greater NFM use.

Following, Ittner, Larcker, and Rajan (1997), the authors created a composite measure of organizational strategy where higher values reflect a Prospector strategy and lower values reflect a Defender strategy. Findings indicated that NFM are used more by Prospector firms than by Defender firms. Hendricks, Menor, and Wiedman (2004) surveyed senior executives from Canadian firms to examine the association between several contingency variables, including strategy, and BSC adoption. The measure of strategy was an indicator variable set to 1 if the firm strategy was classified as Prospector or Analyzer based on key informant response to a survey question, and 0 if the firm was classified as pursuing a Defender strategy. Reactor firms were excluded from the analysis. Results indicated that Prospector and Analyzer firms are more likely to adopt BSC than Defender firms.

Naranjo-Gil, Maas, and Hartmann (2009) used survey and archival data from a public hospital in Spain to understand how CFOs determine management accounting innovation. Using a self-reported measure of organizational strategy, hospitals were classified as pursuing a Prospector or Defender strategy. Results showed a positive and significant path between strategy and use of innovative management accounting systems (MAS), such as BSC, indicating that Prospectors use more innovative and sophisticated MAS. Gosselin (2011) surveyed Canadian manufacturing firms and found that firms pursuing a Prospector strategy use more non-financial performance measures and adopt more innovative performance measurement approaches such as BSC.

As prior research found that strategy can be an antecedent of MAS innovation, including the use of BSC, and that Prospector firms are more likely to adopt innovative MAS, it is conjectured that following a Prospector strategy versus a Defender strategy will positively influence the likelihood of adopting a Balanced Scorecard.
H2:  Firms pursuing prospector strategies are more likely to adopt the
     balance scorecard (BSC) more than those pursuing defender
     strategies, as the preferred system of performance measurement.


Financial Performance

While strategy and strategic orientation tend to remain constant over time i.e. to persist, poor financial performance has been shown in prior literature to precipitate innovation and strategic change. In the Management literature, Lant, Milliken, and Batra (1992) study the process of strategic reorientation in a sample of firms from the furniture and computers software industries. They found a significantly negative association between poor financial performance measured by return-on-assets and strategic reorientation, i.e., after a period of poor financial performance, firms are more likely to change strategies.

Said, HassabElnaby, and Weir (2003) investigated factors associated with the use of NFM. In addition to strategy and other factors, they examine the impact of financial distress on NFM use. Using a composite measure of financial distress that includes the probability of bankruptcy from Ohlson (1980), the leverage ratio, and the leverage ratio scaled by Research and Development (R&D), the authors find that distressed firms are more likely to use NFM.

Hendricks, Menor, and Wiedman, (2004) examined return-on-assets (ROA) and return-on-sales (ROS) for the three-year period prior to BSC adoption for a sample of Canadian firms. They found weak evidence in support of the hypothesis that poor financial performance is associated with the decision to adopt BSC. Naranjo-Gil, Maas, and Hartman (2009) investigated how CFOs determine management accounting innovation. Innovative MAS system use was indicated by whether the organization used benchmarking, activity-based costing (ABC) and BSC.

In addition to examining characteristics of the CFO (e.g. age, tenure and educational background), they also examined organizational factors that may impact management accounting innovation (e.g. organizational strategy and historical financial performance). Results from partial least squares (PLS) analysis showed a significant negative path from historical financial performance to innovative MAS use, indicating that poor financial performance led to MAS innovation and use of tools such as BSC.

Hendricks, et al. (2012) examined the likelihood of Canadian firms adopting BSC and found that a firm's ROA in the year prior to adoption was negatively associated with the propensity to adopt, i.e. firms with low or negative ROA were more likely to adopt BSC than firms with high ROA. Firms that had recently experienced losses were more likely to change strategic direction and had a need to find new ways to measure and improve performance to achieve desired outcomes (Westphal & Fredrickson, 2001). Thus, these loss firms are thought to be more likely to adopt BSC.
H3:  Firms with poor prior financial performance are more likely to
     adopt the balance scorecard (BSC), as the preferred system of
     performance measurement.


Firm's Size

Merchant (1981) studied the characteristics of control systems in electronics industry firms and found that size is positively associated with control system sophistication. In a study of Finnish companies, Malmi (1999) found a significant relationship between the firm's size (measured by the number of personnel in each firm) and the likelihood of adopting a new management accounting innovation, specifically activity-based costing.

Hoque & James (2000) surveyed Australian manufacturing firms and investigate the association between BSC usage and firm's size, measured by the number of employees, sales turnover and total assets. BSC usage was measured using a 20-item instrument that includes financial and non-financial performance measures that would be included in a scorecard. Items were rated on a Likert-scale from 1 to 5. Results showed that firm size was positively related to BSC usage.

In Speckbacher, Bischof, and Pfeiffer's (2003) study of firms in German-speaking countries, they found that larger firms were more likely to adopt BSC, but did not find that larger firms were more likely to adopt a specific type of BSC, according their typology of Type I, Type II, and Type III BSC users. Abdel-Kader & Luther (2008) survey United Kingdom food and beverage sector firms and found that large firms adopt more sophisticated management accounting practices.

Duh, Xiao, and Chow (2009) studied Chinese listed firms and found that firm's size, measured as the natural log of total assets, is positively associated with the extent of use of MAC, including the use of a mix of leading and lagging performance indicators explicitly tied to the organization's strategy. Joshi (2011) measured the firm's size by the firm's total assets and found that the use of both financial and nonfinancial performance measures is associated with larger firms. Based on prior literature, organization size is expected to positively impact the likelihood of BSC adoption.
H4:  Large firms are more likely to adopt the balance scorecard (BSC),
     as the preferred system of performance measurement.


Decentralization

In decentralized organizations, decision-making authority is pushed down to lower levels of management rather than being concentrated among a small number of key senior level executives. Decentralized firms require highly developed management accounting systems that provide managers with the information needed for decision-making and management control. Merchant (1981) found that decentralization is associated with more formal control systems.

Abernethy, Bouwens, and Van Lent (2004) surveyed divisional managers of Dutch firms and found that the use of divisional summary performance measures instead of firm-wide measures is positively associated with the degree of decentralization. Abernethy & Bouwens (2005) found that sub-unit managers who are involved in management accounting system design and who have to ability to redeploy human and financial resources are more accepting of management accounting system innovation. In addition to finding a positive association between firm size and management accounting practice sophistication, Abdel-Kader and Luther (2008) found that decentralized firms that delegate responsibility for planning and control activities to business unit managers have more sophisticated management accounting practices than those of centralized firms.

Lee and Yang (2011) studied firms listed on the Taiwan Stock Exchange and their adoption of "Western" MCS. The study found that organic organizations, characterized by greater decentralization, fewer formal rules and a wider control range, are associated with greater use of integrated performance measures, and greater use of measures in each of the four BSC perspectives (financial, customer, internal process and learning & growth) than mechanistic organizations. The study also found that organic organizations have performance measurement systems (PMS) that are at a higher developmental stage (i.e. PMS reflect cause-effect relationships) than mechanistic organizations.

Gosselin (2011) surveyed Canadian manufacturing firms and finds that decentralization is associated with greater use of non-financial performance measures, but not with the adoption of innovative management accounting practices. Based on these findings, decentralization is expected to have a positive influence on the adoption of BSC.
H5:  Decentralized firms are more likely to adopt the balance
     scorecard (BSC) than centralized firms, as the preferred system
     of performance measurement.


HYPOTHESES

In summary, the following hypotheses have been developed and tested:
[H.sub.1]:  Firms with more intangible assets are more likely to adopt
            the balance scorecard (BSC), as the preferred system of
            performance measurement.
H2:         Firms pursuing prospector strategies are more likely to
            adopt the balance scorecard (BSC) more than those pursuing
            defender strategies, as the preferred system of
            performance measurement.
H3:         Firms with poor prior financial performance are more
            likely to adopt the balance scorecard (BSC), as the
            preferred system of performance measurement.
H4:         Large firms are more likely to adopt the balance scorecard
            (BSC), as the preferred system of performance measurement.
H5:         Decentralized firms are more likely to adopt the balance
            scorecard (BSC) than centralized firms, as the preferred
            system of performance measurement.


METHODOLOGY

Sample and Data Collection

The Mergent Online database was used to identify firms that use either of the phrases "balanced scorecard" or "corporate scorecard" or the word "scorecard" in any of their government (i.e. SEC) filings (e.g. 6-K, 10-K, 8-K, DEF 14) from 1993 to 2010. Foreign corporations use the 6-K to disclose information, while U.S. domestic corporations use the 8-K (current report), 10-K/Q (annual/quarterly report), or DEF 14 (proxy statement). Mention of the use of BSC in the proxy statement is most often related to the firm's use of the BSC for compensation in addition to using it to measure organizational performance.

The filing documents were read to confirm that the word or phrase is used in the proper context. If a firm discloses in its SEC filings the use of a balanced scorecard for performance measurement and/or for compensation of the CEO or other executives, it is deemed for the purposes of this study to have implemented a Balanced Scorecard. For example, in the biography of Executive Vice President and Chief Financial Officer Abiola Lawal included in its July 29, 2010 8-K, CAMAC Energy states that "he (Mr. Lawal) worked as a Financial Analyst on the Balanced Scorecard Project in the Financial Planning Department for Walt Disney Company..." CAMAC Energy is not considered a BSC-using firm for the purposes of this study. However, manufacturing company KLA-Tencor Corporation states the following in its proxy statement:
"Though the balanced scorecard has been used with the Company for many
years as a tool for assessing the Company's performance across a broad
range of key areas, fiscal year 2010 represented the first time that
the scorecard was formally incorporated into our executive compensation
program. The balanced scorecard takes into account our strategic
objectives of growth, customer focus, operational excellence and
talent...and applies scores for the Company's performance against a
variety of specific goals within each of those variables. The scorecard
is tracked throughout the year and is reviewed every quarter, then
formally presented to the Compensation Committee and the Independent
Board Members following the conclusion of the fiscal years for
assessment as to the Company's success in achieving the pre-established
annual goals."


A pool of 265 publicly traded potential BSC-adopting firms was initially identified from SEC filings. Additionally, publicly traded companies that have been inducted into the Balanced Scorecard Hall of Fame, or which are listed on Palladium Group's and the Balanced Scorecard Institute's websites are included in the sample of BSC-adopter firms. After excluding firms that do not have a gvkey, firms that did not have the minimum required five years of data to create a strategy score and firms that were missing data for variables needed for analysis, the remaining sample included 155 unique firms, hereafter, BSC-User firms. Table 1 presents the distribution of BSC-User firms by identification source. The majority of user firms, 108, were identified through references to the BSC in their SEC filings. The BSC Hall of Fame, Palladium Group and the BSC Institute were the source of 18, 19, and 10 user firms, respectively. The full (All Firms) model was run with 63,255 firm-year observations from both BSC-User and Non-User firms.

Measurement of Variables:

The BSC is measured as an indicator variable which set to 1 if the firm is one of the 155 publicly traded BSC using sample firms, and 0 otherwise.

Intangible_Assets are resources that generate future economic benefits that are not captured on the balance sheet and are measured as the difference between market value of equity and book value of equity scaled by total assets. Strategy is measured following Higgins, Omer & Phillips (2011) who expand upon Ittner, Larcker & Rajan's (1997) operationalization of the strategy construct. The mean Intangible_Assets measure for BSC-User firms (Non-User firms) is 1.141 (0.878).

Five variables are used to create a strategy score: (1) the ratio of research-and-development (R&D) measured by expenses to sales, (2) the ratio of employees to sales, (3) the market-to-book ratio (MTB), (4) the ratio of advertising expenses to sales, and (5) the PPE intensity ratio (the ratio of property, plant, and equipment (PPE) to lagged assets).

Consistent with prior literature, each ratio is calculated using a rolling average of the previous five years. The inverse of the PPE intensity ratio is used so that for all variables higher scores are associated with a Prospector strategy and lower scores are associated with a Defender strategy. Each ratio used to compute the Strategy Score is intended to capture a different element of firm strategy. The R&D/Sales ratio captures a firm's innovation (or its "propensity to search for new products") and the Employee/Sales ratio captures efficiency. MTB proxies for the firm's growth opportunities, while the Advertising Expense/Sales ratio and PPE intensity ratio are intended to capture the firm's emphasis on sales and marketing and focus on capital assets, respectively.

Each of the variables is subsequently ranked by forming quintiles within each 2-digit SIC industry-year. Firms in the highest quintile receive a score of 5; firms in the second highest quintile receive a score of 4, etc. For each firm-year, the Strategy Score is the sum of the rankings across all five variables such that the highest (lowest) score is 25 (5). Firms with strategy scores at the low end of the continuum are classified as Prospector firms and those at the high end of the continuum are classified as Defender firms. Financial distress is an indicator variable set to 1 if a firm has experienced two consecutive years of negative earnings and 0 otherwise. Size is measured as the number of firm employees reported in Compustat. Decentralization is measured as the number of business segments reported in Compustat.

Statistical Analysis

The following logistic model was used to test hypotheses 1-5, utilizing Stata data analysis software:

Log (p/(1-p)).= [alpha] + [[beta].sub.1]Intangible Assets + [[beta].sub.2]Strategy Score + [[beta].sub.3]Financial Distress + [[beta].sub.4]Size + [[beta].sub.5]Decentralization (1)

or

p = Exp([alpha] + [[beta].sub.1]Intangible Assets + [[beta].sub.2]Strategy Score + [[beta].sub.3]Financial Distress + [[beta].sub.4]Size + [[beta].sub.5]Decentralization) / (1+exp([alpha] + [[beta].sub.1]Intangible Assets + [[beta].sub.2]Strategy Score + [[beta].sub.3]Financial Distress + [[beta].sub.4]Size + [[beta].sub.5]Decentralization)) (2)

Here, p is the probability that a firm is a BSC user.

RESULTS

Descriptive Statistics

Table 1 presents the distribution of BSC-User firms by source.

Hypotheses Testing

The results of the logistic regression analysis concerning a firm's use of Balanced Scorecard (equation 2) are presented in Table 2. The logit regressions are run with all BSC-User identified firms (All Firms), excluding Hall of Fame firms (w/o HOF), with SEC filing identified firms only (SEC Identified), and excluding financial firms (w/o Financials). The models appear well specified as indicated by the Chi-square and ROC values. Firms with a higher level of unrecorded value-adding assets (i.e. with large amounts of intangible assets) are found to be more likely to adopt BSC, in the All Firms model and the w/o Financials model. The w/o HOF and SEC Identified models show no association between a firms' level of intangibles assets and likelihood to adopt BSC. Consistent with prior research, firms with higher strategy scores (i.e. prospector firms) are significantly more likely to adopt BSC than those with low strategy scores (i.e. defender firms) across all models (Said, HassabElnaby, & Weir., 2003; Hendricks, Menor &, Wiedman, 2004; Naranjo-Gil, Maas, & Hartmann, 2009; Gosselin, 2011).

Prior research also indicates that recent poor financial performance is positively associated with the use of BSC (Hendricks, Menor, & Wiedman, 2004, Hendricks, et al. 2012; Naranjo-Gil, Maas, & Hartman, 2009). In contrast with these prior results, across all four models, the study finds firms that have experienced recent financial distress are significantly less likely to adopt BSC than other firms. This may be explained by the fact that firms that have recently experienced losses may be financially constrained and unable to make the financial and human capital investments required to effectively adopt and implement a balanced scorecard performance measurement / strategy management system. Consistent with prior research investigating the effect of size on BSC usage (Hoque & James, 2000; Speckbacher, Bischof, & Pfeiffer, 2003; Abdel-Kader & Luther, 2008; Joshi, 2001), BSC usage is associated with larger firms. Across all models, firms with more business segments (i.e. decentralized firms) are found to be more likely to use BSC.

Industry dummy-variables based on Fama-French 17 industry portfolios are included in the analysis. It is generally found that firms in the following industries are more likely to adopt BSC: Mining & Minerals; Oil & Petroleum Products; Chemicals; Drugs, Soaps, Perfumes & Tobacco; Steel Works, etc.; Transportation; and Utilities. On average, firms in these industries have larger amounts of intangible assets and more business segments than overall averages. In contrast with the proportion of Banking-industry firms in the BSC-User sample, Banks, Insurance and Other Financials are less likely to adopt BSC. Consumer Durables firms, firms in the Automobile and Retail Stores industries are also significantly less likely to adopt BSC than other firms. On average firms in these industries have smaller amount of intangible assets and fewer business segments than overall averages. Differences in the amount of intangible assets and business segments may provide some explanation for the differences in likelihood to adopt BSC for various industries

The first hypothesis proposed that "firms with more intangible assets are more likely to adopt the balance scorecard (BSC), as the preferred system of performance measurement". Data analysis in Table 2 shows that the adoption of the BSC by firms with more intangible assets is significant at the.01 level. Thus, the first hypothesis is supported.

The second hypothesis proposed that "firms pursuing prospector strategies are more likely to adopt the balance scorecard (BSC) more than those pursuing defender strategies, as the preferred system of performance measurement". Higher strategy scores are associated with a Prospector strategy and lower scores are associated with a Defender strategy. Data analysis in Table 2 reveals that firms pursuing prospector strategies significantly adopt the scorecard for measuring their performance at the .01 level. Therefore, this finding support the second hypothesis.

The third hypothesis proposed that "firms with poor prior financial performance are more likely to adopt the balance scorecard (BSC), as the preferred system of performance measurement". Data analysis in Table 2 indicates that the coefficient on the financial distress measure is significant at the .01 level. However, the sign of the coefficient is negative, in contrast to what was predicted. Therefore, the third hypothesis is not supported. The fourth hypothesis proposed that "large firms are more likely to adopt the balance scorecard (BSC), as the preferred system of performance measurement". Data analysis in Table 2 proves that large firms significantly adopt the scorecard as their preferred system of performance measurement at the .01 level. Then, the fourth hypothesis is supported.

The fifth hypothesis proposed that "decentralized firms are more likely to adopt the balance scorecard (BSC) than centralized firms, as the preferred system of performance measurement. Data analysis in Table 2 suggests that decentralized firms significantly adopt the scorecard as their preferred system of performance measurement at the .01 level. As such, the fifth hypothesis is supported.

CONCLUSION

A model to predict the likelihood that a firm will adopt a BSC is proposed and empirically tested. Results from the prediction model indicate that larger, more decentralized firms are more likely to adopt BSC. Firms that have recently experienced losses and firms pursuing a Prospector strategy are less likely to use BSC. Some industries (e.g. Chemicals, Utilities, and Banks) are more likely to adopt BSC, and others (e.g. Consumer Durables and Retail Stores) are less likely to adopt BSC.

This study provides a model predicting the likelihood that a firm will adopt BSC that includes objectively measured determinants and a measure of intangible assets that is not included in prior literature. However, an investigation of additional determinants of BSC adoption may provide a more robust prediction model. For example, Kaplan and Norton (1996) indicated that the most important factor for successful BSC implementation is CEO leadership. Development of a suitable proxy for CEO leadership would strengthen the model. In addition, since findings indicate that some industries are more or less likely to adopt BSC, future research may benefit from focus on specific industries.

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Alicia A. Yancy

University of Houston--Downtown

About the Author:

Alicia A. Yancy is an Assistant Professor of Accounting at the University of Houston--Downtown where she teaches Cost/Managerial Accounting, Intermediate Accounting and Accounting Analysis for Decision-Making. She received her doctorate from the University of Southern California and her research interests are primarily in the area of performance measurement and incentives.
Table 1

Distribution of BSC-User Firms by Identification Source

Source:                         Hall of Fame  Palladium Group

Total organizations identified   159           102
       Foreign                   (87)          (16)
Less:  organizations
       Gov't & Nonprofits        (40)          (36)
       Private Companies          (8)          (11)
       No identifier              (2)
       (gvkey)
Total  BSC Adopters               22            39
Less:  Duplicates                               (3)
Less:  Software/Consulting Co.    (1)
Less:  Insufficient Data                        (2)
Less:  Wrong Context
Less:  Other Exclusions           (3)          (15)
Final Sample                      18            19

Source:                         BSC Institute  SEC Filings  Total

Total organizations identified   122           265           648
       Foreign                   (17)                       (120)
Less:  organizations
       Gov't & Nonprofits        (56)           (1)         (133)
       Private Companies         (17)                        (36)
       No identifier                           (85)          (87)
       (gvkey)
Total  BSC Adopters               32           179           272
Less:  Duplicates                (17)           (6)          (26)
Less:  Software/Consulting Co.    (1)          (13)          (15)
Less:  Insufficient Data          (1)          (20)          (23)
Less:  Wrong Context                           (32)          (32)
Less:  Other Exclusions           (3)                        (21)
Final Sample                      10           108           155

Table 2

Estimates of the Logistic Models Predicting the Adoption of BSC

Variables                           Expected Sign  All Firms

Intercept                                                -4.470 (***)
Intangible Assets                   +                     0.051 (***)
Strategy Score                      +                     0.021 (***)
Financial Distress                  +                    -0.779 (***)
Firm Size                           +                     0.007 (***)
Decentralization                    +                     0.177 (***)
Mining & Minerals                                         0.888 (***)
Oil & Petroleum Products                                  0.347 (**)
Consumer Durables                                        -0.870 (***)
Chemicals                                                 0.779 (***)
Drugs, Soaps, Perfumes, & Tobacco                         0.918 (***)
Steel Works, Etc.                                         1.030 (***)
Fabricated Products                                      -0.121
Machinery & Business Equipment                            0.116
Automobiles                                              -0.397 (*)
Transportation                                            0.511 (***)
Utilities                                                 1.340 (***)
Retail Stores                                             0.079
Banks, Insurance, Other Financials                       -0.640 (***)
Other                                                     0.077
ROC                                                       0.743
Chi-square/F-value                                     (p>0.01)
Number of Observations                               63,225

Variables                                   w/o HOF     SEC Identified

Intercept                               -4.520 (***)      -4.250 (***)
Intangible Assets                        0.050 (**)       -0.004
Strategy Score                           0.025 (***)       0.020 (**)
Financial Distress                      -0.812 (***)      -0.609 (***)
Firm Size                                0.007 (***)       0.004 (***)
Decentralization                         0.177 (***)       0.173 (***)
Mining & Minerals                        0.898 (***)       0.809 (***)
Oil & Petroleum Products                 0.355 (**)       -0.243
Consumer Durables                       -0.871 (***)      -0.950 (***)
Chemicals                                0.795 (***)       0.387 (**)
Drugs, Soaps, Perfumes, & Tobacco        0.920 (***)      -0.367 *
Steel Works, Etc.                        1.030 (***)       0.936 (***)
Fabricated Products                     -0.123            -0.236
Machinery & Business Equipment           0.119            -0.475 (***)
Automobiles                             -0.391 (*)
Transportation                           0.518 (***)
Utilities                                1.350 (***)       0.816
Retail Stores                            0.087            -1.640 (***)
Banks, Insurance, Other Financials      -1.010 (***)      -0.658 (***)
Other                                    0.080            -0.395 (***)
ROC                                      0.733             0.701
Chi-square/F-value                    (p>0.01)          (p>0.01)
Number of Observations              59,177            58,833

Variables                           w/o Financials

Intercept                               -4.500 (***)
Intangible Assets                        0.022
Strategy Score                           0.028 (***)
Financial Distress                      -0.812 (***)
Firm Size                                0.007 (***)
Decentralization                         0.171 (***)
Mining & Minerals                        0.890 (***)
Oil & Petroleum Products                 0.110
Consumer Durables                       -0.894 (***)
Chemicals                                0.490 (**)
Drugs, Soaps, Perfumes, & Tobacco        0.765 (***)
Steel Works, Etc.                        1.010 (***)
Fabricated Products                     -0.152
Machinery & Business Equipment          -0.117
Automobiles                             -0.328
Transportation                           0.252 (#)
Utilities                                1.240 (**)
Retail Stores                           -0.260 (#)
Banks, Insurance, Other Financials
Other                                   -0.058
ROC                                      0.747
Chi-square/F-value                    (p>0.01)
Number of Observations              62,950

(***), (**), (*), and (#) = statistically significant at the 1%, 5%,
10%, and 15% levels, respectively.
BSC Adoption is coded 1 if a firm discloses the use of BSC.
Intangible_Assetsare the difference between the market value of equity
and book value of equity scaled by total assets.
Strategy_Score is the sum of the rankings of five ratios: R&D/Sales,
Employees/Sales, Market-to-Book, Advertising Expense/Sales, and
PP&E/Lagged Total Assets.
Financial_Distress is an indicator variableset to 1 if a firm has
experienced two consecutive years of negative earnings and 0 otherise.
Firm Size is the number of firm employees reported in Compustat.
Decentralization is the number of business segments reported in
Compustat.
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Author:Yancy, Alicia A.
Publication:International Journal of Business, Accounting and Finance (IJBAF)
Article Type:Report
Date:Sep 22, 2017
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