Introducing the First Management Control Systems: Evidence from the Retail Sector

Introducing the First Management Control Systems: Evidence from the Retail Sector Tatiana Sandino sandino@marshall.usc.edu Assistant Professor University of Southern California Abstract Focusing on a sample of US retailers, I study the management control systems (MCS) that firms introduce when they first invest in controls, and identify four categories of initial MCS, which are defined in terms of the purposes these MCS fulfill The first category, “Basic MCS,” is adopted to collect information for planning, setting standards, and establishing the basic operations of the firm The other three categories are contingent on more specific purposes: “Cost MCS” focus on enhancing operating efficiencies and minimizing costs; “Revenue MCS” are introduced to foster growth and be responsive to customers; and “Risk MCS” focus on reducing risks and protecting asset integrity I hypothesize and find that the choice among these categories reflects the firms’ strategy, and that firms that choose initial MCS better suited to their strategy perform better than others Keywords: management control systems; corporate strategy; entrepreneurial organizations; firm growth I want to thank my dissertation committee: Srikant Datar (Co-Chair), Robert Simons (Co-Chair), Robert Kaplan and Alvin Silk as well as Dennis Campbell, Henri Dekker, Fabrizio Ferri, Paul Healy, Susan Kulp, Kenneth Merchant, Mina Pizzini, Edward Riedl, Dhinu Srinivasan, Wim Van der Stede, Christiane Strohm, Ingrid Vargas, Terry Wang, Mark Young, workshop participants at ESADE, Emory University, Harvard University, IESE, INSEAD, Instituto de Empresa, Lancaster University, New York University, University of Southern California, University of Texas at Austin, Washington University in St Louis, and discussants and reviewers at the Global Management Accounting Research Symposium 2004, AAA Annual Meeting 2004, MAS Midyear Meeting 2005, for their comments and suggestions All errors remain my own I Introduction Managerial concerns tend to change frequently in young companies in an early-stage of their growth phase (hereinafter “early-stage” firms) New functions emerge, levels in the management hierarchy multiply, jobs become more interrelated and new coordination and communication needs arise (Greiner 1998) A growing firm confronts not only an internal transformation, but also increasing environmental complexity (Miller and Friesen 1984) As a result, managers of early-stage firms introduce formal management control systems (hereinafter MCS), which are “formal (written and standardized) information-based procedures and statements, used by managers to monitor and influence the behavior and activities in a firm.” (Simons 1994, 5) Such MCS enable managers not only to cope with increasing information needs, but also to avoid loss of control because of lack of monitoring (Child and Mansfield 1972) However, MCS are costly and time-consuming to install and operate As a consequence, early-stage firms are likely to choose their first set of MCS selectively Prior accounting research has studied MCS choices in mature firms, however, the issues underlying the choices of MCS in early-stage firms differ from those confronted by mature firms for three reasons First, mature companies usually have an extensive amount of formal systems already in place, and thus, are less concerned about running “out of control” than early-stage firms.1 Second, the first MCS introduced provide a foundation for the future development of MCS in the firm (Davila 2005, Davila and Foster 2005b, Nelson and Winter 1982) In this respect, while the main concern in a mature company will be how to integrate new MCS with the existing ones, a young firm must consider how the first MCS will affect the choice of future MCS Third, early-stage firms utilize informal control systems more intensely than mature firms (Cardinal et al 2004; Moores and Yuen 2001) and, thus, they might decide to invest only in those formal MCS that liberate managers from routine operations and allow them to informally focus on the firm’s strategy Notwithstanding that MCS are critical to the success, and even the survival, of early-stage firms (Merchant and Ferreira 1985), academic work in this area has been sparse and offers little guidance to practitioners Thus, conditional on the firms’ decision to start investing in MCS, this study examines managers’ choices regarding the first MCS they introduce in early-stage firms (hereinafter referred to as “initial MCS”) The study is conducted in two phases using data from 40 field interviews and 97 responses to a survey directed to early-stage store-based retailers In the first phase, based on the field study, I sought to understand what initial MCS were introduced in early-stage firms and why I found that the initial MCS introduced in early-stage firms could be categorized usefully based on their purpose In the second phase I use the survey data to test: (i) whether the strategy pursued by an early-stage firm significantly determines the firm’s choice of particular categories of initial MCS, and (ii) whether early-stage firms with a better fit between the initial MCS and their strategy experience superior performance The first phase interviews reveal that entrepreneurs characterize initial MCS in terms of the purposes MCS should fulfill, rather than in terms of individual control systems such as budgets, inventory controls, etc., mostly because individual control systems can be used to achieve different purposes (e.g inventory control systems could be used by some firms to learn about customers’ preferences and by other firms to prevent merchandise theft) and it is the purpose that entrepreneurs really care about Four categories of initial MCS, defined in terms of the MCS purposes, emerge from the data: Basic MCS, which constitute a “common-platform” across all firms, are used to collect information for planning and establishing the basic operations; Cost MCS, are introduced to achieve operation efficiencies and cost minimization; Revenue MCS, are used to achieve growth and to learn and respond to the market; and Risk MCS, are used to reduce risks and protect asset integrity It is important to highlight that individual control systems are classified into these categories based on the purpose they fulfill For example, a marketing database used to understand and respond to customer preferences (purpose) would be classified as a Revenue MCS, while a system of internal auditing and transaction tracking used to prevent theft (purpose) would be classified as a Risk MCS The second stage of the study examines whether firms adapt their initial MCS to the firm’s strategy, and the performance consequences of such adaptation (see Figure 1) I predict and find that firms emphasizing differentiation strategies tend to choose as their most important initial MCS a set of Revenue MCS—as well as individual control systems such as marketing databases and sales productivity controls—rather than Cost MCS or Risk MCS For firms emphasizing low cost strategies I hypothesize a more intense use of Cost MCS and Risk MCS, but find only weak evidence for this prediction There are two possible reasons for this: (1) Basic MCS already fulfill some of the information needs required by low cost leaders; (2) Cost MCS and Risk MCS are implemented more broadly than Revenue MCS (i.e most early-stage firms implement at least some Cost MCS and Risk MCS, even if their strategy is not one of “low cost”), perhaps to avoid the risk of failure that most start-ups confront, or to control routine operations that distract managers from informally focusing on strategic decisions Finally, regarding the performance consequences of the choice of initial MCS (bottom of Figure 1), results indicate that a better fit between initial MCS and firm strategy is associated with a higher perceived usefulness of MCS and perceived business performance, as well as higher store and sales growth This study contributes to the management control literature in two ways First, it complements an emerging literature related to the introduction of MCS in early-stage firms This emerging research has focused on the time start-up companies take to adopt formal control systems as well as the determinants of such adoption For example, Moores and Yuen (2001) show that young firms in their early growth stage increase the formality of their MCS, while Davila (2005) and Davila and Foster (2005a and 2005b) find that age, size, the presence of outside investors, a change in CEO, CEO experience, and a planning culture, are positively associated with the rate of adoption and the sequence of introduction of different categories of MCS Second, this study contributes to the contingency research that relates strategy to MCS in mature companies (Langfield-Smith 1997), but which is usually influenced by confounding effects such as the need to integrate new MCS to the existing ones and the need to develop a strategy aligned with previously existing MCS By analyzing the first set of MCS introduced by early-stage firms, this study provides a cleaner setting to understand the causal relationship between strategy and MCS choice Besides contributing to the academic literature on MCS, this study offers important insights to practitioners—entrepreneurs, investors and consultants—about the value and appropriateness of particular categories of MCS for early-stage firms While some studies have suggested that the very implementation of MCS—by inhibiting risk taking and ability to react quickly to changes in the environment—runs contrary to the entrepreneurial spirit (Morris and Trotter 1990; Adizes 1988), managers and investors generally agree that in early-stage, high-growth firms some form of control is needed and the real question is not whether MCS are needed, but which MCS are best suited to the contingencies of each firm The remainder of the paper proceeds as follows: Section develops the research questions, while Section describes sample selection and data collection methods Section focuses on the first stage of the study by developing a categorization of initial MCS Sections and develop the second stage of the study, by investigating the relationship between the choice of particular categories of initial MCS and the strategy pursued by the firm, and the performance implications associated with that choice, respectively Section concludes II Research Questions A number of studies, spanning several disciplines and developed largely on the basis of experience—hereinafter referred to as life-cycle studies—propose that certain categories of MCS are introduced at particular stages of firm growth and suggest that MCS introduced in early-stage firms usually focus on plans, budgets, and incentives (Flamholtz and Randle 2000; Simons 2000; Greiner 1998; Miller and Friesen 1984, 1983; Churchill and Lewis 1983) While highlighting the importance of the firm’s growth stage in the choice and use of MCS, for the most part these studies not consider the role of contingencies within each growth stage, implicitly assuming that all firms in the same growth stage introduce the same types of MCS In contrast, the contingency-based research in managerial accounting shows that large, mature organizations design their MCS as a function of a number of contextual variables, including strategy, environment, technology, organizational structure, and firm size (for a summary of this literature see Chenhall 2003), resulting in differences in the type of information collected.3 Combined, these two avenues of research lead to the first research question: Research Question 1: What types of initial MCS early-stage firms put in place?—Do initial MCS vary across early-stage firms? Another logical question is: what are the determinants of the choice of particular types of initial MCS? Since the 1980s, the contingency literature in managerial accounting has focused on strategy as the most important driver of MCS design Extensive research has documented an association between MCS and strategy in mature firms (see Langfield-Smith 1997 for an overview) In part, strategy has dominated other contingencies because it constitutes the means by which managers can influence all other contextual variables (external environment, technology, etc.) which were previously treated as exogenous (Chenhall 2003) Strategy also gained importance following insights from the organization theory literature suggesting that a strategy supported by the firm’s organization design and control systems could be a powerful source of competitive advantage (Chandler 1962, Porter 1980, Miller and Friesen 1982) I explore the choice of the type of initial MCS by examining the following question: Research Question 2: Are the choices of particular types of initial MCS in early-stage firms associated with the firm’s strategy? Note that the type of initial MCS introduced will not reflect the firm’s strategy if early-stage firms rely heavily on informal communications to support their strategy (Lorange and Murphy 1984; Churchill and Lewis 1983), e.g., if these firms introduce their first MCS mostly to “liberate” management’s time from routine matters so that management can informally focus on the strategy; or if the initial MCS are exclusively intended to reduce the risk of failure typically faced by new organizations (Singh et al 1986; Freeman et al 1983; Stinchcombe 1965) Under any of these scenarios, the type of initial MCS would not relate to the strategy but would instead aim at monitoring non-strategic routine issues or collecting risk-related information A natural follow-up question is related to the performance implications of the choice of the type of initial MCS In the context of mature firms, Chenhall and Langfield-Smith (1998), Simons (1987), and Govindarajan and Gupta (1985) found evidence suggesting that certain combinations of strategies and MCS lead to superior performance In early-stage firms, the adaptation of initial MCS to the strategy may be even more relevant for future performance, since these MCS provide the foundation over which future MCS are developed (Davila 2005, Davila and Foster 2005b) This leads to the third question of this study: Research Question 3: Are business performance and the perceived usefulness of initial MCS related to the fit between the initial MCS introduced and the firm’s strategy? I explore Research Question through field interviews and Research Questions and 3, by using a survey-based database to test hypotheses detailed in sections and respectively III Sample and Data Collection I develop this study using exploratory interviews with experts in entrepreneurship and retailing, as well as a survey-based database for a sample of U.S store-based retailers Focusing on a single industry provides depth to the study and allows me to control for several industryspecific conditions that may be relevant to the introduction of MCS in a company Relative to other sectors, the store-based retail sector presents two major advantages, namely, more variation along the different contingencies that typically affect the choices of MCS (strategy, organizational structure), and more visible control problems associated with the growth of earlystage firms (e.g., an increase in the number of stores increases risk of theft, difficulty in understanding customer needs, problems of ineffective replenishment of inventory, lack of coordination and the need to train employees and align them to the company’s strategy) I base my analysis on two main sources of information First, I utilize information from 18 exploratory interviews that I conducted with professionals with expertise about entrepreneurial control systems and/or the retail sector Second, I use data from a survey of top managers in 97 early-stage retail companies The first section of the survey gathers information on each firm’s strategy and asks about any major changes in strategy since the firm’s inception The second section focuses on the description of the initial MCS introduced by the firm (purpose of the initial MCS, time of introduction of different individual control systems, etc.) Other questions ask managers to self-assess the overall performance of the firm and the usefulness of MCS in the firm’s development, or are designed to obtain a set of control variables After designing and pilot testing the questionnaire, I sent it to the CEOs of U.S based retailers no more than 20 years old5 that distributed their products through at least 20 stores or retail points These criteria were chosen to ensure that the resulting sample was composed of young but growth-oriented firms (i.e excluding “mom and pop” retailers) Through a search in Compustat, One Source, Thomson Research, and Career Search, I identified and contacted 598 firms satisfying these criteria, including 104 publicly traded firms Of the 598 firms targeted, I gathered survey data from 131 (32 public and 99 private), for a response rate of 21.9%.6 In 22 cases, the survey was completed in face-to-face interviews, providing me with an opportunity to explore the reasoning behind the respondents’ answers After eliminating unsuitable responses (see Table 1, Panel A), 97 completed surveys were utilized in the analyses In most cases, the respondent was either the president or the CEO of the firm (see Table 1, Panel B) The average (median) retailer in the sample had 130 (45) stores, and the age of the surveyed firms ranged between and 20 years, averaging 13 years Table 1, Panel C shows that 17% of these retailers emerged as a subsidiary or spin-off of a corporation, and 26% were funded by venture capitalist firms Although most firms pursued their growth internally, 22% were franchisors In terms of industry composition, a chi-square test shows that the sample of respondent firms is not significantly different from the target population (see Table 2, Panel A) Similarly, I find no evidence of differences in size and age between respondents and non-respondents (see Table 2, Panel B).7 Thus, at least with respect to size, age, and industry composition, non-response bias does not appear to be a concern IV Field Study on Initial MCS The first goal of this research—corresponding to Research Question 1—was to explore the types of initial MCS introduced in early-stage firms, i.e the first set of MCS in which the firm made a significant investment.8 This section describes a field study that followed an iterative grounded approach, where I went back and forth between the data collected through interviews and surveys, and the emerging categories of initial MCS (Strauss and Corbin 1998) The section concludes with a summary of the findings, which suggests four categories of initial MCS I started off by consulting publications about retailers and conducting exploratory interviews with retail experts, to identify individual control systems used in the retail industry I came up with a list of 20 individual control systems presented in the first column of Table As I conducted my interviews, I tried to identify which of these specific control systems were most important in early-stage firms.10 However, after conducting a few interviews, it became very clear that interviewees conceived initial MCS in terms of the purposes initial MCS were meant to fulfill, not in terms of individual control systems, since (a) different individual control systems can be used to achieve the same purpose—e.g., a firm trying to learn about customer service could use marketing databases or mystery shoppers to achieve the same purpose—and (b) the same individual control system can be used to achieve different purposes—e.g inventory control systems could be used by some firms to learn about customers’ preferences; by some other firms 10 FIGURE Conceptual Diagram: FIT between the Strategy and (non-basic) Initial MCS a Notes: a Note the (non-basic) Initial MCS exclude the “Basic MCS” category I not test a relation between the firm’s strategy and this category, since “Basic MCS” are a common platform introduced by most early-stage firms, regardless of specific purposes pursued by the firm 33 TABLE Sample Description Panel A: Sample Selection Number of young retail firms targeted Number of respondents (21.9%) Less—Incomplete or invalid surveys Less—Respondents not fitting the selection criteria: - Firms from other industries - Firms older than 20 years - Firms resulting from an acquisition Final Sample 598 131 (13) (5) (8) (4) 97 Panel B: Position of the Respondents • • • • • President Chief Executive Officer President and Chief Executive Officer General Management (VP, Chief Administrative Officer, Director) Finance or Information Management (CFO, CIO, VP Controller, VP Information Systems) • Operations Management (COO, VP related to operations) • Others (Founder, Chairman, Owner) Total Sample Panel C: Descriptive Statistics of the Sample (N=97) Variable Mean Std Dev Lower Quartile SIZE (# Stores) 129.73 211.18 28 AGE (in # years) 13.27 5.18 PUBLIC 0.24 0.43 VC DUMMY 0.26 0.44 SUBSIDIARY 0.17 0.38 FRANCHISE 0.22 0.41 - Median 45 15 - 29 24 21 (30%) (25%) (22%) (7%) 97 (7%) (5%) (4%) (100%) Upper Quartile 125 18 - 34 TABLE Sample Description –Non Response Bias Panel A: Retail Industry Composition Retail Industry Sporting goods stores Building materials and hardware stores Jewelry stores Automotive dealers and gasoline service stations Drug stores Optical goods stores Radio, TV and Computer stores General merchandise stores Stationary, games, hobbies and gift stores Home furnishings and equipment stores Apparel and accessory stores Food stores Eating and drinking establishments Other miscellaneous retail stores Total Target firms # of firms % 0.8% 1.0% 1.2% 1.3% 1.5% 1.8% 11 1.8% 11 17 2.8% 23 3.8% 5.4% 32 7.0% 42 10.9% 65 58.7% 351 1.8% 11 598 100% Sample firms # of firms % 1.5% 1.5% 1.5% 2.3% 0.8% 0.8% 2.3% 3 2.3% 6.1% 6.1% 6.1% 6.9% 58.0% 76 3.8% 131 100% Chi-Square Testa Chi -Square= 10.48 Degrees of Freedom=13 Pr>ChiSq=0.654 Panel B: Differences Between Target and Sample Firms Variable Mean for Difference T-test Wilcoxon Test in means (Pr>t) Respondent Non (Pr>z) Firms Respondents 109.6 121.6 -12.0 0.67 0.48 SIZE (# Stores) 14.2 14.5 -0.3 0.53 0.38 AGE (in # years) Notes: a The chi-square statistic is calculated as Q=∑ i ( f i − ei ) , where fi are the observed frequencies of each ei industry in the sample of respondents (f i = Respondents’ Sample Size* %Responding Firms in Industry “i”) and ei are the expected frequencies based on the industry composition of the target firms ( ei = Respondent’s Sample Size * %Target Firms in Industry “i”) TABLE Introduction of Individual Control Systems Proportion Introduced Initially b Time to Introduce Control System (years) c 35 Individual Control Systems a Mean StdDev Mean Median N StdDev a Quality standards and controls b Policies and procedures 0.762 0.721 0.428 0.450 2.15 2.97 87 92 3.96 4.30 c Pricing system 0.711 0.455 2.43 86 4.10 d Budget controls 0.680 0.469 3.27 89 4.81 e Inventory control systems to optimize stock levels and replenishment f Internal audits, transaction tracking, and checks and balances of information g Cost controls h Codes of business conduct 0.649 0.479 3.63 88 5.33 0.649 0.479 3.60 92 5.02 0.649 0.598 0.479 0.493 2.48 3.24 1.5 80 84 4.13 4.87 i Performance-based compensation systems j Credit rules and controls 0.577 0.557 0.497 0.499 3.85 3.33 83 73 5.10 5.40 k Restrictions to strategic choices (e.g products not to be sold, customers not to be served, etc.) l Key performance indicators 0.546 0.500 2.22 76 3.72 0.536 0.501 3.78 88 4.84 m Sales productivity standards (input-output 0.505 0.502 3.85 83 4.87 measures: sales/employee, sales/square foot, etc.) n Loss prevention/shoplifting controls 0.495 0.502 3.21 77 4.69 o Controls on employee behavior and 0.464 0.501 4.60 86 5.36 development (turnover, training, etc.) p Statement of purpose/mission/credo 0.454 0.500 4.32 83 4.56 q Controls for investment in long term assets 0.453 0.500 4.52 80 5.10 r Mystery shoppers 0.361 0.483 4.23 74 4.81 s Externally oriented information systems, other 0.309 0.464 5.00 64 5.27 than those related to direct customers (e.g Market share data, data from A.C.Nielsen, Information Resources Inc, etc.) t Marketing databases (e.g., Customer 0.257 0.439 5.93 70 5.97 Relationship Management systems, etc.) Source: Survey Data Notes: a The 20 Individual Control Systems were classified in the questionnaire into: Strategy Related Controls (controls k, l, p, and q); Market/Customer Related Controls (controls r, s, and t); Ongoing Operations Controls (controls a, c, d, e, g, j, and m); Personnel Controls (controls i and o); and Risk Minimization Controls (controls b, f, h, and n) b Individual Control Systems are defined as introduced initially if they were introduced in the year (or before the year) when, according to the interviewee, the firm made its first significant investment in Control Systems c Summary measures of number of years from founding date to introduction of each control system Each line includes only the N firms (from a total of 97) that had introduced the system at the time they answered the survey 36 TABLE Logit Regressions Linking MCS Purposes to the Decision to Introduce Individual Control Systems Initially a Dependent Variable Dummy =1 if control system was introduced early Cost controls Quality standards and controls Sales productivity standards Marketing databases Loss prevention/shoplifting controls Internal audits, transaction tracking, and checks and balances of information Policies and procedures Codes of business conduct Credit rules and controls 10 Budget controls 11 Pricing system 12 Inventory control systems 13 Performance-based compensation systems Controls on employee behavior and development 15 Controls for investment in long term assets 16 Externally oriented information systems 17 Restrictions to strategic choices 18 Key performance indicators 19 Statement of purpose/mission/credo 20 Mystery shoppers Intercept p-value COST LIKERT p-value REVENUE LIKERT p-value RISK LIKERT p-value -0.22 -0.86 -0.28 -2.41 -1.91 -0.32 0.38 0.15 0.34 0.00 ChiSq Coefficient Pr>ChiSq Panel A: Strategic and Organization Determinants of Initial MCS (N=61, Count R2=0,56, AdjCount R2=0.21) INTERCEPT -0.120 0.772 0.385 0.292 -0.505 0.189 LOWCOST*(1-SEARCHSTRAT) -0.074 0.722 -0.163 0.409 0.089 0.636 DIFFERENTIATION* (1-SEARCHSTRAT) 0.109 0.546 -0.220 0.169 0.328 0.041 DECENTRALIZATION -0.320 0.072 -0.191 0.241 -0.129 0.399 DIVERSITY -0.107 0.092 -0.030 0.483 -0.077 0.210 SEARCHSTRAT 0.852 0.419 0.451 0.642 0.401 0.652 Panel B: Add Ownership Controls and Industry (N=61, Count R2=0.66, AdjCount R2=0.38) INTERCEPT 0.740 0.409 0.921 0.287 -0.180 0.796 LOWCOST*(1-SEARCHSTRAT) -0.071 0.744 -0.205 0.316 0.135 0.493 DIFFERENTIATION* (1-SEARCHSTRAT) 0.099 0.612 -0.217 0.212 0.316 0.068 DECENTRALIZATION -0.232 0.297 -0.197 0.321 -0.035 0.851 DIVERSITY -0.158 0.096 -0.034 0.586 -0.124 0.160 SEARCHSTRAT 1.033 0.356 0.754 0.463 -0.279 0.770 FRANCHISE 0.399 0.679 -0.686 0.489 1.085 0.243 VCDUMMY -0.198 0.860 -0.844 0.398 0.646 0.512 SUBSIDIARY -0.516 0.641 -0.744 0.421 0.228 0.831 RESTAURANT -1.359 0.276 -0.199 0.863 -1.159 0.256 Source: Survey Data Notes: Dependent Variable: It was constructed utilizing the categorical variable CHOICEMCS, which indicates what initial MCS does the firm emphasize: Risk MCS, Revenue MCS or Cost MCS For details, see Section Strategy Variables: (See details in Section 5) LOWCOST: Composite measure that proxies for the firm’s emphasis on cost leadership It takes higher values for firms emphasizing low costs strategies DIFFERENTIATION: Composite measure indicating the extent to which a firm pursues a differentiation strategy Control Variables: (See details in Appendix 4, Panel A) DECENTRALIZATION: Composite measure from two variables, describing the extent of decentralization in the firm DIVERSITY: Proxy for heterogeneity of activities, measuring the diversity of the assortment offered by the retailer FRANCHISE: Dummy indicating whether a firm grew mostly through franchising (1) or not (0) SEARCHSTRAT: Dummy indicating whether the firm defined its strategy after introducing its MCS (1) or not (0) SUBSIDIARY: Dummy equal to if the firm is/was a subsidiary or spinoff from a larger company, and otherwise VCDUMMY: Dummy on whether the firm received VC funding (1) or not (0) before the introduction of initial MCS RESTAURANT: Dummy equal to if the retail firm is an eating and/or drinking establishment, otherwise 39 TABLE Performance Tests Panel A: Univariate Resultsa Performance Variable PERCPERFORM USEFULMCS Mean for Sub-sample High-Fit Low-Fit FIT=1 FIT=0 3.94 3.50 5.78 4.33 Difference in means T-test (Pr>t) Wilcoxon Test (Pr>z) 0.45 1.45 0.038 0.001 0.064 0.001 SALESGROWTH 0.47 0.24 0.23 0.059 0.032 STORESGROWTH 0.28 0.22 0.06 0.174 0.121 Panel B: Multivariate Results Performance = f (FITi , CEOCHANGEi , EXPERIENCEi , VCDUMMYi , SIZEi , AGEi) Performance Measure b Ordinal Logits PERCPERFORM USEFULMCS CONSTANT p-value OLS Regressions SALES GROWTH STORE GROWTH 0.635 0.031 0.144 0.204 FIT p-value 1.288 0.032 2.209