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Competitive action repertoires and stock risk.

Research in competitive dynamics has long demonstrated a clear link between competitive action and a variety of measures of firm performance, such as profitability, revenue growth, changes in market share and market share rank, reputation, and stock returns (Chen and Miller, 2012; Grimm et al., 2006; Ketchen et at., 2004; Smith et al., 2001). Yet, the relationship between competitive action and one potentially important determinant of stock returns, stock risk, has not been explored in the 25 or more years since the Bettis and Weeks (1987) study of how the stock market reacted to competitive battle between Kodak and Polaroid. Based on a review of relevant literature in competitive dynamics and finance, this is surprising when one considers theory and empirical evidence about the relationship between competitive strategy and stock returns and, separately, the relationship between stock risk and stock returns. In particular, prior research found that characteristics of the firm's set of competitive actions impacts its stock returns (Ferrier and Lee, 2002; Rindova et at., 2010). Further, a rich vein of research in corporate finance has shown that important firm-level organizational characteristics, including strategy, affect the firm's stock risk (Beaver et al., 1970; Bowman, 1980; I Lunada, 1969; Haugan, 1979). So, exploring the strategyrelated antecedents of stock risk is important because the level of risk associated with a firm's stock impacts its price and returns in ensuing time periods (Van Horne, 1980).

These issues motivated the core research question of this study; How does a firm's set of competitive actions influence stock risk? This study extends research in competitive dynamics that has linked competitive action to stock market outcomes.

Observed Strategy, Sense-Giving Signals, and Information Processing Fluency

The process by which investors observe, make sense of, and evaluate competitive strategy rests on their ability to perceive and process a rich variety of signals associated with observed elements of the strategy and other organizational and managerial attributes. Signaling theory has been widely used in the finance literature, mainly in the area of signaling firm quality to investors. For instance, a firm's dividend policy serves as a signal for future earnings (Bhattacharya, 1979; John and Williams, 1985). Similarly, the underpricing of secondary stock offerings serves as a signal of poor firm quality (Cook and Officer, 1996). Signaling has also been used by management scholars to explain how top management team (TMT) legitimacy, as proxied by TMT members' average age and company tenure, serves as a signal for high firm quality which, in turn, reduced IPO underpricing (Cohen and Dean, 2005). Also, the composition of a firm's board of directors signals firm quality and was found to have a positive influence on the IPO values (Certo, 2003; Certo et al., 2001).

This study aims to build a predictive framework that explains how specific structural characteristics of the firm's repertoire (set) of competitive actions carried out in a given time period impacts the stock market's reaction to the firm's observed strategy. As will be discussed more fully below, a firm's repertoire of competitive actions can vary in composition and dynamism, from a set of competitive moves that are stable, predictable, and changing incrementally, to competitive behavior characterized by extreme change, unpredictability, surprise, and disruption (D'Aveni, 1994). Investor/observers must decide whether observed and embedded properties associated with a given firm's competitive action repertoire--relative to that of its rival - signal better future performance for the firm.

Consistent with the actions-as-manifest-strategy view (Grimm et al., 2006), competitive actions serve as strategic signals (Heil and Robertson, 1991; Prabhu and Stewart, 2001) that form the basis for sense-making (Bogner and Barr, 2000; Gioia and Chittipeddi, 1991; Whetten, 1984). In the present study, it is argued that properties associated with the firm's entire repertoire of competitive actions provide valuable sense-giving signals.

Three general properties associated with observed strategic signals enable observers to more easily and quickly identify information which, in turn, increases information processing fluency (Ariely and Garmon, 2000; Berlyne, 1965, 1974; Einhorn and Hogarth, 1986; Mishra et al., 2006). The more similar sets or groupings of signals are to one another, the easier it is for observers to perceive, interpret, and evaluate. Likewise, signals that are familiar to what the observer has experienced in the past will increase information processing and evaluative fluency. Also, processing fluency of a set of signals hinges upon the level of simplicity perceived. Here, a simple set of signals composed of fewer differentiated components is easier to perceive, interpret, and evaluate.

Further, owing to the increase in processing fluency, signal similarity, familiarity, and simplicity also encourage observers to evaluate a set of signals in a more positive light (Lee and Labroo, 2004; Reber et al., 1998; Whittlesea, 1993). These findings are consistent with theory and research in, for example, sense-making (Kiesler and Sproull, 1982; Weick, 1995) and pattern recognition (Simon, 1972; Simon and Kotovsky, 1963; Simon and Sumner, 1968) insofar as how informational processing fluency may be linked to positive observational and evaluative biases.

Sense-Giving Properties of Competitive Action Repertoires

Scholars in competitive dynamics have developed theory and empirical methods centering on conceptualization of firm strategy as observed competitive action, defined broadly as externally-directed, market-based competitive moves carried out with the intent to improve a firm's relative competitive position (Grimm et al., 2006; Smith et al., 2001). Early studies in this research stream focused attention on the action-reaction dyads level of analysis (e.g., Chen et al., 1992), whereby the characteristics of an individual competitive action, for example, are important predictors of a rival's competitive response and, consequently, firm performance. Germane to the present study, Bettis and Weeks (1987) examined how the interplay among individual competitive actions and responses carried out over time influenced shareholder returns and stock risk. In so doing, these authors used a game theoretic framing of competitive battle between Kodak and Polaroid. However, they argue that stock market outcomes may also be associated with each firm's overall competitive strategy.

The present study conceptualizes a firm's overall competitive strategy as the firm's entire set of competitive actions carried out over a significant period of time. In accordance with prior research in competitive dynamics, this is known as the competitive action repertoire level of analysis. Prior research has centered on three core properties of a firm's competitive action repertoire: Conformity (the extent to which the firm's competitive action repertoire is similar to that of rivals), stability (the extent to which the firm's action repertoire is similar to what the firm carried out in previous periods of time), and simplicity (the extent to which the action repertoire consists of a narrow range of types of competitive actions) (Deephouse, 1999; Ferrier et al., 1999; Gnyawali et al., 2006; Miller and Ghen, 1994, 1996a, 1996b). Although a few studies in competitive dynamics have indeed explored the relationship between attributes of competitive strategy like these and stock market outcomes (Ferrier and Lee, 2002; Rindova et al., 2010), no research to date has explored how these attributes of competitive strategy impact stock risk.


The central premise of this study posits that investors will view a particular firm's stock as less risky if they are able to process the information and signals embedded in the firm's competitive action repertoire with greater fluency, speed, and certitude. When confronted with a cacophony of strategic signals and stimuli investors, like any decisionmaker, will generally adopt one of two decision-making and evaluative approaches. On one hand, strategic signals that are dissimilar, unfamiliar, and complex encourage investor/observers to use a systematic decision-processing approach that involves deep cognitive elaboration with regard to the causal links between decision inputs, process, and projected outcomes (Ghaiken et al., 1996). Dissimilarity, unfamiliarity, and complexity naturally disrupt investor/observers' understanding of "cause-effect linkages, industry recipes, and cognitive models" (Bogner and Barr, 2000: 221). This reduces information processing fluency which, in turn, gives rise to greater cause-effect ambiguity and risk (Bogner and Barr, 2000; Mosakowski, 1997). Consequently, information processing effort gives rise to higher levels of risk (Kahneman and Tversky, 1979). This is not to say that investors are less capable of processing, for example, unfamiliar new information; rather, it suggests that processing such information requires more attention and analysis.

On the other hand, competitive action repertoires observed to be similar, familiar, and simple increase informational processing fluency owing to the investors' likely use of a heuristic decision processing approach. Here, investors use simple rules to quickly identify and assimilate new information that is consistent with their current mental models of cause and effect.

Competitive action repertoire conformity. When the largest, most visible competing firms carry out a set of competitive actions that are similar to one another, investors can easily and quickly process the set of signals embedded in the focal firm's competitive action repertoire. Competitive action conformity, therefore, increases information processing fluency owing to investors' use of a similarity decision-making heuristic based on simple rules and prior knowledge. In other words, decision fluency results when signals conform to market norms and referent other firms. Consequently, the interpretation and evaluation of strategic signals will carry less risk. However, when the firm's competitive action repertoire deviates from that of referent rivals, investor/observers are likely to devote more systematic thought and analysis towards understanding, assimilating, and evaluating non-conforming strategic signals. As noted above, this disruption in the decision-maker's mental models of cause and effect prompt these models to be recalibrated. Thus, non-conforming competitive action repertoires will likely be perceived as risky.

Hypothesis 1: Competitive action repertoire conformity will be negatively related to stock risk.

Competitive action repertoire stability. Predictability in behavior is an important signal that establishes an actor's reputation in the eyes of observers (Weigelt and Camerer, 1988). Signals associated with a stable and predictable competitive action repertoire will be readily observed and quickly assimilated into the observer's stock of prior knowledge about the firm. This is facilitated by the observers' use of a familiarity decision heuristic which, in turn, increases information processing fluency and reduces perceived risk.

Unpredictability hampers the formation of an identifiable reputation and reduces processing fluency, thereby increasing signal ambiguity and perceived risk. So, investors are more likely to adopt a systematic decision process to evaluate an unstable, unpredictable competitive action repertoire. Indeed, "the element of surprise [instability] is a key component of a stock's risk premium ..." (Chatterjee et al., 2003: 67, brackets added).

Hypothesis 2: Competitive action repertoire stability will be negatively related to stock risk.

Competitive action repertoire simplicity. Processed by a simplicity decision heuristic, a simple set of competitive actions is naturally easier for investors to perceive, interpret, and understand. A highly complex competitive action repertoire, however, contains a diverse spectrum of strategic signals and information that investors must process more thoughtfully and systematically. Consequently, competitive repertoire complexity dampens information processing fluency. However, whereas signal simplicity provides only a narrow band of strategic information that can be quickly discounted, signal complexity contains a wide band of disruptive, new strategic information that, once processed by investor/observers, may reduce stock risk. So, at the extremes, both very simple competitive action repertoires and those that are highly complex are likely to lead to low risk valuations by investors, albeit for different reasons. Whereas simple strategic signals are processed with great fluency; complex strategic signals contain new information that, while motivating investors to adopt a slower, systematic approach to information processing, may be interpreted as strategically valuable. This implies that a mid-range level of competitive action repertoire simplicity has the highest level of stock risk because strategic signals in this range are not fluently processed, nor do they contain much new information that investors process in their evaluations of future stock market outcomes.

Hypothesis 3: Competitive action repertoire simplicity will exhibit an inverted U-shaped relationship with stock risk.


Sample. The hypotheses were tested on a sample of firms drawn from prior research that ensured that the competitive actions that comprise each firm's competitive action sequence are carried out by head-to-head rivals that compete in a particular industry (Ferrier, 2001; Ferrier et al., 1999; Ferrier and Lee, 2002). The sampling procedure started with all Fortune 500 members that had sales exceeding $500 million and were ranked first or second in their respective industries in terms of market share. Then, to ensure that these firms' competitive actions were directed toward each other in a given industry-market, only the "single business" or "dominant business" firms (i.e., those having Rumelt's (1974) specialization ratios greater than 0.70) were retained in the sample. Resultant pairs of single-business, head-to-head competitors that did not have complete financial data listed in Compustat or were not consistently ranked first or second in market share during the study period (1987-1993) were eliminated from the sample. This matched-pairs sampling process yielded a final sample consisting of a pooled, seven-year cross-sectional data panel involving pairs of single-business, market-leading firms across 35 different industries (70 firms).1 This resulted in 490 firm-years as the unit of analysis.

Independent Variables

Identification of competitive actions. The present study used the competitive actions and competitive action categories developed in prior multi-industry studies (Ferrier, 2001; Ferrier et al., 1999; Ferrier and Lee, 2002). Using structured content analysis, these authors identified and categorized nearly 5,000 competitive actions carried out by firms in the sample into six specific action categories: pricing actions, marketing actions, new product actions, capacity-related actions, service actions, and overt signaling actions. This was accomplished by carefully coding the headlines and abstracts of news reports about each firm's strategy published in the U.S. series of F&S Predicasts into one of the six action categories based on the appearance of a set of corresponding keywords.

To check the reliability of coding, two academic experts in strategic management a professor and a doctoral candidate with extensive industry experience--independently coded a representative sample (N=300) of news headlines into one of the aforementioned action categories. Using Perrault and Leigh's (1989) index of reliability, this categorization approach yielded an index value of 0.91, which suggests a high degree of reliability.

As noted above, a firm's strategy is conceptualized as a competitive action repertoire--the total set of competitive moves carried out by the firm in a given time period (Miller and Chen, 1994). The present study focuses on the three most fundamental and robustly supported attributes of a firm's competitive action repertoire that capture subtle, but important differences associated with the diversity and order of competitive action types carried out by the firm: conformity, stability, and simplicity (cf. Basdeo et al., 2006; Deephouse, 1999; Ferrier, 2001; Ferrier et al., 1999; Miller and Chen, 1994, 1996a, 1996b; Rindova et al., 2010).

Competitive action repertoire conformity. This captures the extent which a firm's set of competitive actions is similar to that of rivals. Optimal matching analysis was used to measure between-firm differences in the set of competitive actions that comprise the two firms' competitive action repertoires, the temporal order of the actions, and the total number of actions carried out (Abbott, 1990; Holmes, 1995). Optimal matching calculates the "distance" between any two sets of action by accounting for the costs of insertions, deletions, and substitutions among all action types (known as INDEL costs) needed to transform one action sequence to exactly match another (Kruskal, 1983). The higher the INDEL costs, the more dissimilar the action repertoires are.

Prior research suggests that competitive actions of different types vary in terms of fundamental characteristics like action magnitude, scope, irreversibility, and implementation requirements (Chen et al., 1992; Grimm et al., 2006). So pairwise INDEL costs among all action types were weighted in accordance with the foregoing action characteristics in mind. For instance, substituting a marketing action for a capacity expansion action carried a substitution weight of 0.80, yet, substituting a marketing for a price cutting action carried a weight of only 0.20 (see Ferrier, 2001: 876).

Using the weighted INDEL matrix, the optimal matching procedure calculated the weighted distance between the focal firm's competitive action repertoire and that of the rival. This is similar to measures used in prior research (Deephouse, 1999; Miller and Chen, 1996b), but also accounts for the temporal order of the actions. The inverse of the annual firm-to-rival distance score derived from optimal matching was used in the analysis as the measure of competitive action repertoire conformity. (2)

Competitive action repertoire stability. To measure the extent to which the firm's set of competitive actions exhibited similar properties--in terms of the types of actions carried out, the number of actions, and temporal order of actions--across time, optimal matching analysis was again used. Here, a high within-firm distance score indicates that the focal firm's set of competitive actions changes over time; low distance scores indicate that the firm's action repertoire exhibits regularities in both composition and temporal order over time. This measure is similar to those used in prior research to capture competitive repertoire inertia (Miller and Chen, 1994) and action sequence unpredictability (Ferrier, 2001; Rindova et al., 2010). The analysis used the inverse of annual average of the firm's distance score as the measure for competitive action repertoire stability.

Competitive action repertoire simplicity. A Herfindahl index that accounts for the weighted diversity among all six action types was used to measure the extent to which a firm's competitive action repertoire consists of a broad range (as compared to a narrow range) of different action types. Firms with high Herfindahl scores carried out a simple competitive action repertoire that consists of just a few action types; low scores indicate that a firm typically carries out a complex action repertoire owing to the relative representative balance among the six possible action types. The analyses used the annual average of competitive action repertoire simplicity.

Dependent Variable

Stock risk. The principal concern of this study is to explore the relationship between the structural properties of a firm's competitive action repertoire and stock risk. Beta serves as the dependent variable that measures the volatility of a firm's stock relative to the overall market. Beta scores greater than 1.0 generally indicate greater risk associated with a particular firm's stock, but also indicates a potential for greater returns. Scores less than 1.0 indicate less volatility and risk, but also less potential for higher returns.

Owing to this study's focus on the entire set of competitive actions carried out by rival firms in a given year, the annualized measure of beta from Compustat was used in the analysis. This was calculated as the annual average of the monthly covariance of the firm's stock returns with the S&P 500 market return divided by the variance of the market returns.

Control Variables

The analysis includes several control variables typically used in risk-related and competitive dynamics research. Data for these variables were obtained from Compustat.

Industry characteristics. A simple industry growth rate for each industry-year (year t) was calculated as the percentage change in industry gross sales from that of the previous year (year t-1) for each 4-digit SIC industry. Industry concentration was calculated using a Herfindahl index for each 4-digit SIC industry for each year over the seven-year time panel. Because industries are likely to exhibit different entry barrier characteristics, industry-level R&D intensity, capital intensity, and advertising intensity were also used as controls in the analysis. R&D intensity is measured as industry R&D spending to industry total sales. Advertising intensity was measured as advertising spending to total sales. Capital intensity was measured as fixed assets to total book assets.

Firm's financial health, size, and market share growth. To capture the firm's general financial health, Altman's Z-score (lagged one year) was used. Although commonly considered an indicator of the likelihood of bankruptcy, Altman's Z-score also serves as a useful indicator of a firm's general financial health, owing to its composite nature and predictive validity (Altman, 1968; Chakravarthy, 1986; Miller et al., 2013). It has also been used as an indicator of general financial distress in prior studies in competitive dynamics (Ferrier et al., 2002). More importantly, Altman's Z-score is a weighted composite of key financial and accounting indicators relating the "fundamentals" that investors often analyze when making decisions about a firm's stock: profitability, revenue, debt/equity, slack resources, and annual average market returns. Owing to its breadth, the use of Altman's Z-score helps establish a firm consistency between the concept of financial health and the measure of it (Miller et al., 2013). It also controls for the firm's prior stock market performance. High Z-scores indicate a condition of strong general financial health and performance; low Z-scores indicate financial distress, which could lead to elevated levels of non-action-related stock risk.

The log of total sales was used to measure firm size. Consistent with prior research, market share growth was calculated as the positive year-to-year change in percent of firm sales to total industry sales in the focal firm's primary industry (Ferrier et al., 1999). Given that the sample consisted of the top two firms in each industry (market share rank), accounting for changes in market share (percent) between head-to-head competitors would control for the influence of changes in market share rank between them.

The descriptive statistics and correlations among the variables used in the analysis may be found in Table 1.


Two-stage ordinary least squares regression was used to test the hypotheses. As indicated in Table 2, all industry and firm controls were entered in stage one. The control-only model was significant (F=5.99, p< 0.001) and included several significant controls on the influence of focal firm stock risk (beta). The competitive action variables were entered in stage two. This model was also significant (F = 7.21; p<0.001) and represented a significant improvement in model fit above that of the control-only model (Change R-square=0.18; F=4.68; p<0.001).

Hypothesis 1 predicted that competitive action repertoire conformity was negatively related to stock risk. This hypothesis was supported. As reported in Table 2, the coefficient for conformity is negative and in the direction predicted (b=-0.50; p<0.001). This suggests that when investors observe that the firm's set of competitive actions is similar to that of a referent rival, they are able to process strategic signals more fluently which, consequently, reduces risk.

Hypothesis 2 was also supported. Higher levels of competitive action repertoire stability were associated with lower levels stock risk (b=-0.26; p<0.05). This suggests that the familiarity associated with the set of actions carried out by the focal firm in successive time periods increases information processing fluency and, like similarity discussed above, reduces stock risk.

Hypothesis 3 predicted a curvilinear, inverted U-shaped relationship between action repertoire simplicity and stock risk. To test this hypothesis, both the linear and squared terms for simplicity were included in stage two of the analysis. The coefficients in Table 2 provide support for this hypothesis. In particular, the significant positive linear term (b=0.73; p<0.001) and the significant negative squared term (b=-0.56; p <0.01) combine to form an inverted U-shaped relationship. This suggests that extreme levels of action repertoire simplicity - both very simple and highly complex--is related to lower levels of stock risk.


Drawing from core ideas in signaling theory informational processing, and competitive dynamics, this study developed and tested a predictive model of stock risk. The study's general finding suggests that stock risk is influenced by specific properties associated with the firm's set of competitive actions carried out over time. More specifically, action repertoires that are similar to that of rivals and stable over time give rise to lower levels of stock risk. Further, results suggest that action repertoires that are neither simple enough, nor are sufficiently complex (i.e., moderate levels of simplicity) are related to higher levels of stock risk.

Whereas prior research predicted and found that these attributes of a firm's competitive action repertoire would, in general, exhibit a negative relationship with various measures of firm performance (e.g., D'Aveni, 1994; Ferrier, 2001; Ferrier et al., 1999; Miller and Chen, 1994; 1996a), this study found that these attributes instead exhibited a significant decrease in stock risk. This highlights an apparent tension between the influences of strategy on different aspects of organizational outcomes. For instance, if competitive action repertoire unpredictability keeps rivals off balance and on the defensive, better performance (including higher stock returns) may result. Yet, the present study found that action repertoire unpredictability (the inverse of stability and inertia) is related to higher levels of stock risk.

The tension may be resolved if one were to consider the preponderance of theory and research within finance that shows a strong correlation between higher risk and higher stock returns that competitive dynamics scholars have heretofore not acknowledged. Indeed, this study stands among the first in many years to empirically test the relationship between important characteristics of a firm's competitive strategy and stock risk (beta).

The study highlights the roles of signaling and information processing. Findings suggest that beyond analyzing the firm's fundamentals, investors take the firm's competitive actions into direct account when evaluating the firm's future earnings potential. To evaluate competitive action repertoires that are similar, familiar, and simple, investor/observers are likely to use a heuristic decision and evaluative process that increase information processing fluency. Absent any significant disruptive information embedded in the action repertoire, investors can quickly evaluate the firm's strategy and, to the extent that this information is already included in investors' mental models of the firm's strategy and future earnings, assign a lower level of estimated risk. However, when the competitive action repertoire is unfamiliar, unpredictable, and complex, investors are more likely to use a systematic decision process. Indeed, despite being experienced and sophisticated in their analyses, investors (particularly institutional investors who manage a wide range of stocks and stock portfolios and have the greatest influence over stock prices and risk) must devote sufficient time and effort to deeply analyze an unfamiliar or unpredictable competitive action repertoire. This new, disruptive information challenges and reshapes investors' mental models of the company's strategy. Such changes are likely to be viewed by investors as carrying higher levels of risk.

This study is not without limitations. It did not directly measure investor perceptions and interpretations of competitive strategy. Future research could develop an experimental research design using, for example, a stock-picking simulation, a scenario planning exercise, or a natural experiment to directly capture investor/observer perceptions of and reactions to competitive actions as they are carried out in real time. Also, the study sample consisted of only the top two firms in each of the 35 industries represented. This was an attribute of the research design to ensure that the pair of firms in each industry was in direct competition and that their competitive actions were directed toward one another. However, it is likely that the competitive actions carried out by smaller firms will have an impact on investors' perceptions of risk and future returns of both smaller firms and the market leaders. Future studies could perhaps focus on a single industry and measure competitive actions of all firms in the industry as a way to control for the overall intensity of the market in which the firms compete. Future research could also fruitfully explore whether attributes of the competitive context beyond observed competitive actions, like managers' perceptions of competitive tension (Chen etal., 2007), interorganizational alliance networks (Gnyawali etal., 2006), nascent versus established markets (Santos and Eisenhardt, 2009), or global rivalry (Yu et al., 2009), affect stock market outcomes.

In sum, this study found that characteristics associated with the firm's repertoire of competitive actions--similarity, familiarity, and simplicity--influenced stock risk. The core explanatory mechanism for the findings center on how these characteristics influence investor/observer information processing fluency when they observe, interpret, and value strategic signals embedded in a set of competitive actions. The study advances competitive dynamics theory in important ways. It motivates the extension of theory beyond the examination of the structural aspects of the competitive marketplace and stock market into the domain of cognition, perception, and subjective evaluation of strategy. It also reinforces the importance of research at the action repertoire level of analysis. Further, it establishes a theoretical and empirical link in the strategy literature between competitive action and stock risk as a potentially important, but underexplored dimension of stock market outcomes.


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Margaret Hughes-Morgan

Assistant Professor of Management

Marquette University

Walter J. Ferrier

Gatton Endowed Associate Professor of Strategic Management

University of Kentucky

(1) A list of firms included in this study are available from the first author.

(2) It should be noted that this measure of the focal firm's competitive action repertoire directly accounts for that of the rival. So, beyond being a key variable of interest, it also serves as an important control variable for the competitive context or market in which the rivals compete.
Table 1
Descriptive Statistics and Correlations

                             Mean      Dev.       1         2

1. Industry Concentration    0.171    0.125

2. Industry Growth           0.195    0.179     0.092

3. Industry Capital          0.856    0.243    -0.522     0.027

4. Industry R&D Intensity    0.060    0.052    -0.462     0.257

5. Industry Advertising      0.061    0.505     0.394    -0.199

6. Firm Financial Health     4.467    2.378    -0.157     0.182

7. Firm Size                 3.831    0.614    -0.252    -0.221

8. Firm Market               0.570    0.497     0.083    -0.117
   Share Growth

9. Action Repertoire        -0.601    0.185     0.202    -0.114

10. Action Repertoire       -0.518    0.187    -0.004     0.127

11. Action Repertoire       -0.698    0.217    -0.221    -0.118

12. Stock Risk (beta)        1.097    0.426    -0.199     0.075

                               3             4             5

1. Industry Concentration

2. Industry Growth

3. Industry Capital

4. Industry R&D Intensity    0.399

5. Industry Advertising      0.024        -0.762

6. Firm Financial Health     0.056         0.320        -0.205

7. Firm Size                 0.283         0.283        -0.242

8. Firm Market              -0.177         0.193         0.058
   Share Growth

9. Action Repertoire        -0.121        -0.209         0.214

10. Action Repertoire        0.015         0.092        -0.235

11. Action Repertoire        0.139         0.260         0.103

12. Stock Risk (beta)       -0.232         0.481        -0.187

                               6             7             8

1. Industry Concentration

2. Industry Growth

3. Industry Capital

4. Industry R&D Intensity

5. Industry Advertising

6. Firm Financial Health

7. Firm Size                 0.200

8. Firm Market              -0.077         0.075
   Share Growth

9. Action Repertoire        -0.202         0.031         0.032

10. Action Repertoire        0.048        -0.100        -0.102

11. Action Repertoire       -0.163         0.190         0.180

12. Stock Risk (beta)        0.057         0.207         0.209

                               9            10            11

1. Industry Concentration

2. Industry Growth

3. Industry Capital

4. Industry R&D Intensity

5. Industry Advertising

6. Firm Financial Health

7. Firm Size

8. Firm Market
   Share Growth

9. Action Repertoire

10. Action Repertoire        0.102

11. Action Repertoire        0.150         0.207

12. Stock Risk (beta)       -0.106        -0.122         0.251

Correlations above 0.182 are significant at the p < 0.05 or better.

Table 2
Results: Focal Firm Stock Risk on Competitive Action
Repertoire Properties

                                   OLS Regression

Stage 1
Firm Size                             0.15 ***
Industry Concentration               -1.32 **
Industry Growth                       0.29 ([dagger])
Industry Capital Intensity            0.42 ([dagger])
Industry R&D Intensity               -2.85 *
Industry Advertising Intensity       -0.38
Firm Market Share Growth             -0.01
Firm Financial Health                -0.01 *
F =                                  5.99 ***
Adj. R-squared =                     0.42

Stage 2
Competitive Action Repertoire        -0.50 ***
Conformity                           (0.14)
Competitive Action Repertoire        -0.26 *
Stability                            (0.15)
Competitive Action Repertoire         0.73 ***
Simplicity                           (0.31)
Competitive Action Repertoire        -0.56 **
Simplicity Squared                   (0.33)

F =
Adj. R-squared =                     0.61
Change in R-squared =                0.18
F of Change =                        4.68 ***

Standard errors reported in parentheses. One-tailed tests,
([dagger]) p<0.10; * p<0.05; ** p<0.01; *** p<0.001.
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Title Annotation:investors action on company decisions
Author:Hughes-Morgan, Margaret; Ferrier, Walter J.
Publication:Journal of Managerial Issues
Article Type:Statistical data
Geographic Code:1USA
Date:Mar 22, 2014
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