10. Organisational Behaviour Competitive Session

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1 Page 1 of 22 ANZAM 2013 INTRODUCTION For forty years, many consultants, academics and authors of strategy texts, management articles and books have urged managers to use stretch objectives to motivate employees and managers and elicit high performance (Collins & Porras, 2002; Menkes, 2011; Peters & Waterman, 1982). Researchers define a stretch goal as an organisational goal with an objective probability of attainment that may be unknown but is seemingly impossible given the current capabilities (Sitkin, See, Miller, Lawless, & Carton, 2011). The critical assumption is that stretch goals increase organisational aspirations and, therefore, organisational performance. Following this advice, senior executives adopt aggressive profit and growth goals for their organisations. For example, Southwest Airlines set the stretch objective of the ten-minute turnaround, the total time at the terminal for each airplane, and reduced operating expenses by 25 percent (Freiberg & Freiberg, 1996). However, despite these and other anecdotal successes invoked to justify and motivate stretch objectives, there is limited systematic evidence for the efficacy of stretch goals on organisational performance. For each Southwest Airline success story, there are examples of failure. For example, in 2002 Toyota set the goal of obtaining a 15 percent share of the global automotive market by 2010 (Esdale Jr & Fiedler, 2010). However, the rapid growth overstretched the company s resources and resulted in quality problems that negatively impacted sales, and the company suffered an 8.7 percent drop in U.S. sales from 2009 to 2010 (Russo & Zhao, 2010). To move beyond anecdotal evidence for and against stretch goals, systemic data collection is required. The relationship between goals and performance is addressed in two lines of literature. First, in organisation theory and strategy, Sitkin et al. (2011) propose theoretical arguments for the facilitative and disruptive effects of stretch goals on performance from cognitive, affective and behavioral perspectives. However, the arguments have not been tested. Also in this same research stream, the behavioral theory of the firm (Argote & Greve, 2007; Cyert & March, 1963) shows that aspiration levels (the operating goals organisations use) are adjusted over time to reflect historical performance or social comparisons. Many studies in this tradition have inferred organisational goals from industry 1

2 ANZAM 2013 Page 2 of 22 average performance (Bromiley, 1991; Greve, 1998, 2003), implicitly assuming that all organisations adopt the same goals (some research has measured goal directly, e.g., Lant, 1992; Mezias, Chen, & Murphy, 2002). Inferring goals or aspiration levels is not always applicable, when an increasing proportion of firms depart from historical performance and social comparisons to set aggressive performance targets. Examples of such firms include GE and Toyota. Gavetti, Greve, Levinthal, & Ocasio (2012) argue that the field needs to incorporate forward-looking decision making and actions made for distant and uncertain benefits. Second, in the literature of organisational psychology, the impact of goal difficulty on performance has been addressed for decades (Locke, 1982). Researchers in this field conclude that performance is a positive function of goal difficulty in general (Locke & Latham, 1990); however, the goal main effect may not hold in complex tasks due to the difficulty of developing high performing strategies when mental models of the task are poor/deficient (Wood, Mento, & Locke, 1987). To investigate the effects of stretch organisational goals on performance, we report the results of an experiment examining four levels of goal difficulty from easy to seemingly impossible stretch goals in a management simulation. The study also investigates the effects of stretch goals on goal commitment, self-efficacy, and risk taking. The findings extend organisation theory on goals by showing that managerial responses to easy, moderate, difficult and stretch goals are contingent on performance against the assigned goals. These mechanisms begin to explain the facilitative and disruptive self-reinforcing performance cycles in response to stretch goals proposed by Sitkin, See, Miller, Lawless, and Carton (2011). THEORETICAL CONTEXT Theory on organisational goals makes specific, explicit assumptions for the responses to goals by organisations with high and low performance levels. For example, when organisational performance approaches bankruptcy, CEOs pay attention to survival rather than other performance goals (March & Shapira, 1992). Essentially, they substitute a survival goal for any previously adopted or assigned performance goals. In contrast, high performance organisations, achieving their assigned goals, do not try to significantly improve performance. Instead, they adopt low risk strategies to maintain 2

3 Page 3 of 22 ANZAM 2013 performance at or slightly above their goal levels. These organisations are motivated to avoid changes that might decrease performance below their assigned goals, rather than taking risks in attempts to exceed their goals. However, there is limited research on the effects of different goal levels on organisational performance (Sitkin et al., 2011). Many organisation theory and strategy studies have inferred aspiration levels from industry average performance or another proxy (Greve, 1998, 2008). Typically, those proxies assume that all organisations in an industry have the same goals. As more managers set stretch performance goals for their organisations, understanding how different goal levels affect performance is increasingly important. Research from organisational psychology sheds light on how goals impact performance at the individual level. However, much of this research compares the effects of challenging and do-your-best goals on performance. Organisations do not adopt do-your-best goals. Instead, studies investigating organisation goals explain how organisations respond to performance that is close to bankruptcy, at or above the assigned goal, or below the assigned goal but safely above bankruptcy. These three contingencies stimulate managerial responses that involve changes in goal commitment, self-efficacy, and risk taking. Increasing goal difficulty influences these mechanisms. The following sections review prior research on these mechanisms and develop three hypotheses for the effects of goal difficulty on each of these three mechanisms. Finally, we combine the effects of the mechanisms to begin to explain the virtuous organisational performance cycle for high performance organisations and the downward organisational performance spiral for low performance organisations described by Sitkin et al. (2011). Goal Commitment Decision makers commitment to assigned goals can either facilitate or disrupt organisational behavior. High goal commitment motivates effort and search, which results in improvements in performance. As a result, the attainment discrepancy gap shrinks and goal commitment increases, leading to more effort and search and improvements in performance. 3

4 ANZAM 2013 Page 4 of 22 Goal commitment is disruptive when assigned goals motivate greater effort and strategy search, but the effort and search results in repeated failures to reach the goals. As a consequence, goal commitment declines, which reduces effort and search. The attainment discrepancy gap increases and traps decision makers in a downward performance spiral (Klein, Wesson, Hollenbeck, & Alge, 1999; Locke & Latham, 1990). Research shows that goal difficulty level affects goal commitment. Generally, difficult goals undermine goal commitment (Locke, Frederick, Buckner, & Bobko, 1984). Reduced goal commitment results in decreased performance (Erez & Zidon, 1984; Klein et al., 1999). Formally, Hypothesis 1: Organisational goal difficulty is negatively related to goal commitment 1. Self-efficacy Self-efficacy, decision makers judgments about the feasibility of achieving their goals, is the second of the three mechanisms examined here that connects goals and performance (Wood & Bandura, 1989; Wood & Locke, 1990). Again, self-efficacy can either facilitate or disrupt organisational performance. Self-efficacy facilitates organisational performance when decision makers experience repeated success at achieving difficult goals, their perceived likelihood of success increases, leading to high motivation, sustained effort and search for ways to further improve performance. Performance continues to improve leading to higher self-efficacy levels. On the other hand, self-efficacy disrupts organisational performance when repeated failures to reach difficult goals cause decision makers to judge themselves unlikely to achieve the goals, reducing self-efficacy and leading decision makers to give up the search for alternative solutions (Wood, Bandura, & Bailey, 1990). As self-efficacy erodes, motivation and effort decline, and performance decreases (Locke & Latham, 1990). Goal difficulty plays a critical role in determining whether self-efficacy facilitates or disrupts organisational performance. In simple, well-structured tasks where a successful strategy is known, an increase in goal difficulty leads to both higher performance and higher self-efficacy (Locke & Latham, 1 Specifically, Goal Commitment EASY > Goal Commitment MODERATE > Goal Commitment DIFFICULT > Goal Commitment STRETCH. 4

5 Page 5 of 22 ANZAM , 2002; Salancik, 1977). However, running an organisation is a complex dynamic task and only a few organisations reach stretch, seemingly impossible, goals. As goal difficulty increases in organisations, more decision makers experience failures, leading to lower self-efficacy. Formally, Hypothesis 2: Organisational goal difficulty is negatively related to reduce self-efficacy 2. Risk Taking The third managerial response connecting goals and performance is risk taking. Risk taking either increases or decreases depending on whether performance is below or above the goal. Decision makers increase risk taking when performance falls below the assigned goal. This relationship is well established in the prospect theory literature (Kahneman & Tversky, 1979; Larrick, Heath, & Wu, 2009). As decision makers become more willing to take risks, they make larger and more frequent changes to existing strategies in an effort to close the attainment discrepancy gap (Greve, 1998). In contrast, when performance is above the aspiration level, decision makers avoid risky actions that might result in reducing performance and continue to use their existing strategies and routines (Larrick et al., 2009; March & Shapira, 1992). Goal difficulty plays a critical role in determining whether risk taking increases or decreases. For example, research shows that challenging goals lead to higher risk taking than do-your-best goals (Larrick, Heath, & Wu, 2009). Formally, Hypothesis 3: Organisational goal difficulty is positively related to risk taking 3. Virtuous Cycles and Downward Spirals The managerial responses described above influence the effects of stretch organisational goals on performance. The dynamics resulting from stretch objectives are complex. For easy, moderate, and attainable goals, managers identify and select successful strategies that they implement with focused effort. They experience positive results, achieving the goal without eroding self-efficacy or goal commitment. When they search for new strategies, local search yields improved strategies, incrementally improving performance. 2 Specifically, Self-efficacy EASY > Self-efficacy MODERATE > Self-efficacy DIFFICULT > Self-efficacy STRETCH. 3 Specifically, Risk Taking EASY < Risk Taking MODERATE < Risk Taking DIFFICULT < Risk Taking STRETCH. 5

6 ANZAM 2013 Page 6 of 22 However, for the typical organisation, stretch goals cause repeated failures that may demotivate managers. They lose their commitment to the goals and have doubts as to their capability to achieve them. In addition, assume that the environment is complex, identifying better strategies is difficult, there are delays in implementation, and the impacts on performance and assessment of the strategy require time. Under such circumstances, organisational stretch goals typically lead to ineffective strategies, increased risk taking and failure. Formally, Hypothesis 4: The relationship between organisational goal difficulty and performance is nonlinear. Easy, moderate, and difficult goals increase performance, but stretch goals decrease performance. METHODOLOGY We use a laboratory experiment to isolate the causal effects of goal difficulty on performance. Participants take the role of the CEO in a simulated organisation, People Express management simulation. The People Express management simulation (Sterman, 1988) is an interactive, computerbased simulation of an airline operating in a competitive market. It has been used in previous research and captures many well-established features of competition between new entrants and incumbents in the airline industry (Bakken, Gould, & Kim, 1992; Graham, Morecroft, Senge, & Sterman, 1992). The experiment compares the effects on performance of combining short (proximal) and long-term (distal) goals, varying the goals from easy, moderate, difficult, and stretch profit goals (four levels in total). Participants were rewarded for achieving the assigned goals through pay-for-performance incentives. Participants and Procedure Participants: 116 undergraduate students from a large university participated in the study. 46 percent of participants were male and the average age of participants was 23 years. Six percent of participants were majoring in economics; 39% were majoring in commerce 4 ; 22% were majoring in engineering; and the rest were majoring in law, medicine, science; or other fields. Participants were randomly assigned to manage a firm with easy ($143.5 million), moderate ($315 million), difficult ($820 million) or stretch ($3,872 million) cumulative profit goals at the end of year Commerce includes management, marketing, accounting, finance, strategy, information system, and organisational behavior. 6

7 Page 7 of 22 ANZAM 2013 Procedure: The experiment was run in laboratory sessions with groups of 25 to 30 participants per session. Each participant followed instructions on an individual computer and managed their simulated firm on their own and completed three simulation rounds. During each simulation round, participants completed a questionnaire three times, measuring goal commitment, self-efficacy, and risk taking on each occasion (i.e., end of year 3, year 7, and year 10) 5. Goal Manipulation: Goal levels were selected to represent easy, moderate, difficult and stretch goals for cumulative profit for year 3 through 10 of the simulation. The average compound growth rates of cumulative profit in the easy, moderate, difficult, and stretch conditions are 29%/year, 38%/year, 50%/year and 71%/year respectively. The difficult goal level is achievable by adopting a set of simple decision rules for price setting, fleet acquisition, hiring, etc. These decision rules are behaviorally realistic, boundedly rational heuristics such as hire enough people to replace employee attrition plus a certain number for each new aircraft acquired. The stretch goal level is by definition seemingly impossible to achieve (Sitkin et al., 2011). Participants were paid $5 for participating in the experiment, $4 for completing the surveys in each simulation and a cash payment of $2 for each year (3-10) in which their cumulative profit met or exceeded the target for that year. Participants attaining their goals in all years of the three simulation rounds would earn $65 (a maximum of $20 in each of the three rounds of the simulation, plus the $5 participation payment). Measures Performance. Cumulative net income (profit) is adopted as the long-term measure of organisational performance. A single measure, rather than multiple performance measures, is adopted to avoid introducing attention effects among multiple goals. Goal Commitment. Prior research shows that commitment to goals plays a critical role in the relationship between goals and performance (Locke, Latham, & Erez, 1988). We adopted five items, as provided in Appendix B, that are well established in prior research. The reliability of this scale achieved high level, indicated by Cronbach alpha of The detailed instructions are in Appendix A. 7

8 ANZAM 2013 Page 8 of 22 Self-efficacy, perceived likelihood of success, is a well established predictor of performance and of the processes that influence organisational learning (Bandura, 1997). The format followed the approach recommended by Bandura (1997), which has been validated in numerous empirical studies. Perceived self-efficacy is the mean score measured on a ten item scale, as provided in Appendix B. The reliability of this scale achieved high level, indicated by Cronbach alpha of Perceived Risk Taking. Prior research shows that perceived risk impacts problem framing and performance (Sitkin & Weingart, 1995). A six-item, task-specific measure of perceived risk was developed based on well-established measures, as provided in Appendix B (Ganzach, Ellis, Pazy, & Ricci-Siag, 2008; Sitkin & Weingart, 1995; Weber, Blais, & Betz, 2002; Weber & Milliman, 1997). The reliability of this scale achieved high level, indicated by Cronbach alpha of RESULTS Hypotheses were tested using the performance data of the third simulation round, after the two learning rounds. For those participants whose firms go bankrupt, I use the data from the quarter in which the simulated organisation goes bankrupt as the measure of performance for that year afterwards. Participants were assigned specific goals from year 3 through year 10 of the simulation, and Hypotheses were tested with 116 observations. Table1 presents the means/medians, standard deviations and correlations for the study variables. Figure 1 shows the performance distribution at the end of Year 10 for each goal level. Hypotheses Tests There are significant differences in goal commitment and self-efficacy between goal conditions, as shown in Table 2 and Table 3 (MANOVA: F Goal_Commitment [3, 105] = 25.77, p < 0.001; F Self-Efficacy [3, 105] = 27.72, p < 0.001). There are marginally significant differences in perceived risk taking between goal conditions (MANOVA: F Perceived_Risk_Taking [3, 105] = 2.15, p = 0.098). Contrast analyses between goal conditions using a Bonferroni correction for family-wise errors are reported in Table 3 that directly test our hypotheses. Hypothesis 1: Organisational goal difficulty is negatively related to goal commitment, is supported. Figure 2 shows goal commitment by goal condition across simulation rounds. Out of a 8

9 Page 9 of 22 ANZAM 2013 maximum score of 10, participants in easy, moderate, difficult, and stretch goal condition reported average ratings of 7.5, 6.4, 4.5 and 3.4 respectively. Pairwise comparisons show that participants assigned easy goals report significantly higher goal commitment than those assigned moderate goals (One-tailed F[1, 53] = 5.57, p = 0.03), participants assigned moderate goals report significantly higher goal commitment than those assigned difficult goals (One-tailed F[1, 54] = 12.27, p < 0.01). Participants assigned difficult goals report higher goal commitment than those assigned stretch goals (One-tailed F[1, 52] = 5.88, p = 0.03). These results indicate significantly support for Hypothesis 1. Hypothesis 2 is partially supported. Figure 3 shows self-efficacy by goal condition across simulation rounds. Out of a maximum score of 100 for self-efficacy, participants in easy, moderate, difficult, and stretch goal conditions report average ratings of 86, 82, 59 and 32, respectively. Pairwise comparisons show that self-efficacy for participants assigned easy goals is not significantly higher than those assigned moderate goals (One-tailed F[1, 53] = 23.57, p = 0.39). As hypothesized, participants assigned moderate goals show significantly higher self-efficacy than those assigned difficult goals (One-tailed F[1, 54] = 10.48, p < 0.01). Also, participants assigned difficult goals show significantly higher self-efficacy than those assigned stretch goals (One-tailed F[1, 52] = 16.76, p < 0.001). Thus, the results show partial support to the hypothesis that when goal difficulty increases, self-efficacy decreases. Hypothesis 3 is only marginally supported. Figure 4 shows risk taking by goal condition across simulation rounds. Out of a maximum perceived risk taking score of 10, participants in easy, moderate, difficult, and stretch goal condition reported average ratings of 5.8, 5.6, 5.7 and 6.8 respectively. Pairwise comparisons show that perceived risk taking for participants assigned easy goals is not significantly lower than those assigned moderate goals (One-tailed F[1, 53] = 0.56, p = 1.0). Similarly, participants assigned moderate goals do not report significantly lower perceived risk taking than those assigned difficult goals (One-tailed F[1, 54] = 0.26, p = 1.0). Participants assigned difficult goals show marginally lower perceived risk taking than those assigned stretch goals (Onetailed F[1, 52] = 3.84, p = 0.09). These results show only marginal support for the hypothesis that perceived risk taking increases when goal difficulty increases. 9

10 ANZAM 2013 Page 10 of 22 Further post hoc analysis shows there is a significant difference in perceived risk taking between stretch goal and all of the other goal conditions combined. A test of difference in perceived risk taking between stretch goal and other goal conditions was conducted using ANOVA. In this case, participants in easy, moderate, and difficult goal conditions are treated as one group. The results show participants in the stretch goal condition reported significantly higher perceived risk taking than participants in other goal conditions (One-tailed F[1,107] = 5.91, p < 0.02). To test Hypotheses 4, Mann-Whitney-Wilcoxon tests examined pairwise differences in performance between the four goal conditions using the Bonferroni correction for family-wise errors. Figure 5 shows median cumulative net income by goal condition across simulation rounds. At the end of Year 10 in the third (final) simulation round, participants assigned easy, moderate, difficult, and stretch goals achieved a median performance of $277, $304, $364, and $195 million in cumulative net income, respectively. Hypothesis 4 is partially supported. The results show that there is no significant difference in median performance between the easy and moderate goal conditions (Mann-Whitney- Wilcoxon Test: Wilcoxon s = 385.0, p = 1.00). Also, there is no significant difference in median performance between the moderate and difficult goal groups (Wilcoxon s W = 383.0, p = 1.0). However, participants assigned stretch goals achieved significantly lower median performance than those assigned difficult goals (Wilcoxon s W = 610.0, p < 0.01). DISCUSSION This research is the first empirical study of which the authors are aware that examines the effect of goal difficulty on organisational performance. The research reports three findings. First, increasing goal difficulty erodes goal commitment and self-efficacy, but increases perceived risk taking. All of these managerial responses reduce organisational capabilities to learn and improve organisational mental models in complex dynamic environments. Second, stretch goals compared with difficult organisational goals undermine performance. Third, the median firm s performance is independent of whether the firm is assigned an easy, moderate or difficult goal. Thus, there is no positive goal main effect but stretch goals undermine performance. 10

11 Page 11 of 22 ANZAM 2013 Goal Difficulty and Managerial Responses Sitkin et al. (2011) argue that stretch goals have either facilitative or disruptive effects on managerial behavior. Stretch goals have a facilitative affect when they motivate managers to increase effort and persistence, and search for and select effective strategies. Stretch goals are disruptive when they generate low goal commitment, threat rigidity and resource diversion. The results show that the disruptive effects dominate the facilitative effects of stretch organisational goals and result in poor organisational median performance. Our findings extend prior goal setting psychology research on the impact of goal difficulty on goal commitment, self-efficacy, and risk taking (Drach-Zahavy & Erez, 2002; Earley, Connolly, & Lee, 1989; Earley & Lituchy, 1991; Klein et al., 1999) to decision makers in organisations. Goal Commitment. Decision makers goal commitment is a negative function of goal difficulty. Participants assigned stretch goals report the lowest level of goal commitment. This finding is consistent with research showing that decision makers reject very difficult goals (Erez & Zidon, 1984). Prior research also shows that there is a positive relationship between goal commitment and performance (Klein et al., 1999). Self-efficacy. Decision makers self-efficacy is a negative function of goal difficulty. Participants assigned stretch report the lowest level of self-efficacy. Prior research shows that performance is a positive function of how confident decision makers are of attaining their goals (Bandura, 1997). Risk Taking. Perceived risk taking is higher for decision makers in the stretch compared with other organisational goal conditions. Consistent with prior research, higher attainment discrepancy motivates decision makers to take higher risks, increasing the size and frequency of strategic changes (Earley et al., 1989). As a result, stretch organisational goals motivate risk-taking and degrade organisational performance. Goal Difficulty and Organisational Performance Organisational performance is not a positive function of goal difficulty. There is no organisational goal main effect on performance between the easy, moderate, and difficult goal conditions. However, we found stretch compared to difficult goals decreased organisational performance. These results are 11

12 ANZAM 2013 Page 12 of 22 consistent with prior research showing mixed results for the effects on performance of goal setting on complex tasks (Chesney & Locke, 1991; Earley et al., 1989; Larrick et al., 2009; Smith, Locke, & Barry, 1990; Wood et al., 1987). While the results do not report a significant positive goal main effect on organisational performance, difficult and stretch organisational goals stimulate a small proportion of organisations to achieve high performance as shown in Figure 1. There appears to be a rule of riches for a few instead of a rule of riches for all. Only a small proportion of the organisations assigned difficult or stretch organisational goals discover high quality strategies. Similarly, in practice, only a few, very well managed firms, for example, GE, Wal-Mart and Toyota are able to benefit from adopting stretch organisational goals. In our experiment, the majority of firms do not derive benefit from adopting stretch goals. This small proportion of firms leads to a right-skewed distribution in performance. This study extends our understandings of the relationship between goal difficulty and performance on complex tasks. Prior research shows that, on a simple task, individual performance is a positive function of goal difficulty as long as decision makers accept the assigned goals (See reviews by Locke and Latham 1990; 2002). However, when stretch goals are perceived as impossible, performance decreases because decision makers reject the assigned goals (Erez & Zidon, 1984). Our results provide empirical evidence to support the argument by Sitkin et al. (2011) that stretch goals undermine the performance of some organisations due to disruptive effects via managerial responses, including self-efficacy, goal commitment, and anxiety. In contrast, the few high performing organisations in the right tail of the performance distribution benefit from the rule for riches for the few and enjoy the self-reinforcing virtuous cycle of high performance, high goal commitment, high self-efficacy, and low risk taking on performance. These few are highly salient in the public media and management development literature, sustaining the myth of the benefits of stretch goals for all. 12

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16 ANZAM 2013 Page 16 of 22 Table 1. Mean/median (standard deviation) and correlations for study variables. Variable Simulation Round Goal Conditions Correlations Easy Moderate Difficult Stretch Sim Round 1 (Pretest) Cum. Net 2 Income Sim Round 2 3 Sim Round 3 294/ 270 (221) 308/ 284 (238) 320/ 277 (202) 315/ 282 (382) 363/ 334 (435) 347/ 304 (295) 323/ 236 (474) 397/ 313 (411) 451/ 364 (490) 338/ 310 (324) - 318/ 282 (321) 0.83*** - 255/ 195 (367) 0.72*** 0.79*** Sim Round (1.8) 7.0 (1.2) 7.0 (1.7) 7.3 (1.5) - (Pretest) Goal 5 Sim Round (1.6) 6.3 (1.7) 4.6 (1.6) 3.6 (1.7) Commitment 0.22* 0.21* 0.28** Sim Round (1.9) 6.4 (1.8) 4.5 (1.5) 3.4 (1.8) *** Sim Round (1.9) 5.4 (1.4) 4.9 (1.6) 4.8 (1.4) - (Pretest) ** Perceived Risk Sim Round (2.2) 5.6 (2.0) 5.9 (1.8) 6.4 (1.6) ** -0.3** 0.59*** - Taking 9 Sim Round (2.1) 5.6 (2.2) 5.7 (2.3) 6.8 (2.0) 10 Sim Round 1 (Pretest) 79 (21) 78 (21) 86 (20) 84 (21) Self-efficacy 11 Sim Round 2 83 (26) 79 (29) 63 (23) 37 (24) 12 Sim Round 3 86 (21) 82 (19) 59 (21) 32 (28) 13 Sim Round 1 (Pretest) * -0.19* * -0.33*** 0.55*** 0.8*** 0.25** *** *** * * *** 0.64*** -0.2* -0.35*** -0.32*** 0.35*** * *** 0.75*** *** -0.32*** 0.22* 0.84*** *** 0.34*** 0.27** 0.37*** 0.23* 0.21* -0.22* -0.21* -0.2* 0.45*** Survival Sim Round ** 0.48*** 0.39*** 0.34*** *** 0.2* *** - 15 Sim Round * 0.27** 0.44*** 0.3** 0.24* * 0.35*** 0.22* 0.45*** 0.61*** N Note: * p<.05 ** p<.01 *** p<.001; Alpha coefficients are shown in parentheses on the diagonal Sample size are different across the variables. Sample size for the variables "Cum. Net Income" and "Survival" is 28 in easy goal, 29 in moderate goal, 30 in difficult goal, and 29 in stretch goal. Sample size for the variables "Goal Commitment", "Perceived Risk Taking", and "Self-efficacy" is 28 in easy goal, 28 in moderate goal, 30 in difficult goal, and 26 in stretch goal for the sim round 1. Sample size for the variables "Goal Commitment", "Perceived Risk Taking", and "Self-efficacy" is 27 in easy goal, 27 in moderate goal, 29in difficult goal, and 26 in stretch goal for the sim round 2. Sample size for the variables "Goal Commitment", "Perceived Risk Taking", and "Self-efficacy" is 28 in easy goal, 26 in moderate goal, 29in difficult goal, and 25 in stretch goal for the sim round 3. 16

17 Page 17 of 22 ANZAM 2013 Table 2. Results of Multivariate Analysis of Variance (MANOVA) test of differences in managerial responses between goal conditions Dependent Mean Square F P-value Variables Goal Commitment < 0.001*** Self-efficacy 15, < 0.001*** Perceived Risk Taking Note: independent variable is Goal Condition. Table 3. Contrasts of pairwise tests of differences in managerial responses between goal conditions Dependent Variable Contrast df df error F P-value Adjusted p-value Goal Easy vs. Moderate * Commitment Moderate vs. Difficult < ** Difficult vs. Stretch * Self-Efficacy Easy vs. Moderate Moderate vs. Difficult ** Difficult vs. Stretch < *** Perceived Risk Taking Easy vs. Moderate Moderate vs. Difficult Difficult vs. Stretch All pairwise tests were one-tailed tests. * p < 0.05, ** p < 0.01, *** p < 0.001, + p <

18 ANZAM 2013 Page 18 of 22 Easy Goal Goal $143.5 Median $277 µ $320 σ $202 (million) Moderate Goal Goal $315 Median $304 µ $347 σ $295 (million) Difficult Goal Goal $820 Median $364 µ $451 σ $490 (million) Stretch goal Goal $3872 Median $195 µ $255 σ $367 (million) 18

19 Page 19 of 22 ANZAM 2013 Figure 1 Performance distributions at the end of Year 10 for each goal condition Easy Moderate Difficult Stretch Easy Moderate Difficult Stretch Mean Goal Commitment 6 4 Mean Self-Efficacy Sim Round 1 (Pretest) Sim Round 2 Sim Round 3 Simulation Round Figure 2 Mean Goal Commitment by Goal Condition for Three Simulation Rounds Sim Round 1 (Pretest) Sim Round 2 Sim Round 3 Simulation Round Figure 3 Mean Self-Efficacy by Goal Condition for Three Simulation Rounds Easy Moderate Difficult Stretch Easy Moderate Difficult Stretch Mean Risk Taking 4 2 Median Cumulative Net Income Sim Round 1 (Pretest) Sim Round 2 Sim Round 3 Simulation Round Figure 4 Mean Risk Taking by Goal Condition for Three Simulation Rounds Sim Round 1 (Pretest) Sim Round 2 Sim Round 3 Simulation Round Figure 5 Median Cumulative Net Income by Goal Condition for Three Simulation Rounds 19

20 ANZAM 2013 Page 20 of 22 Appendix A: Detailed Instructions for Participants The experiment was run in laboratory sessions with groups of 25 to 30 participants per session. Each participant followed instructions on an individual computer and managed their simulated firm on their own and completed three simulation rounds. During each simulation round, participants completed a questionnaire three times, measuring goal commitment, self-efficacy, and risk taking on each occasion (i.e., end of year 3, year 7, and year 10). After all participants had arrived for a session, the experimenter read the instructions aloud. The participants then spent 25 minutes working through a prepared set of introductory exercises and 10 minutes reading a 2-page business case of the simulated firm to become familiar with the management simulation. Then, as a learning goal to increase understanding of the simulated firm, each participant was asked to identify and explain in writing two different strategies to maximize profits in the simulation. Participants subsequently completed one simulation round in which they implemented one of the two strategies. Prior research shows that, in situations where the acquisition of knowledge and skills is required, a learning goal helps participants discover effective strategies (Latham, Seijts, & Crim, 2008; Seijts & Latham, 2000, 2005). Following the simulation round with a learning goal (the learning phase), each participant completed three additional simulation rounds (the testing phase). The first simulation round of the testing phase was the pretest. All participants were set easy goals in the pretest round. In the second simulation round, participants were randomly assigned one of the four goal difficulty levels (i.e., easy, moderate, difficulty, or stretch). Participants completed the third simulation round using the same goal difficulty level they were assigned in the second simulation round. Before each simulation round, participants received a memorandum outlining the performance goals for their simulated firm and the associated payoff table. After reading the memorandum, participants completed the first round of managing their simulated firm (simulation round 1). The simulation continues for 40 quarters or may end earlier if the participant s decisions lead to bankruptcy. After all participants completed the first simulation round, the same procedure was repeated for the second and third simulation rounds. During each simulation round, participants completed a 20

21 Page 21 of 22 ANZAM 2013 questionnaire three times, measuring goal commitment, self-efficacy, and risk taking on each occasion (i.e., end of year 3, year 7, and year 10). 21

22 ANZAM 2013 Page 22 of 22 Appendix B: Survey Questions Goal Commitment The first set of questions focuses on the performance goals outlined in your objective memo you have been given. Note that there are no right or wrong answers; a quick response is generally the most useful. For each of the following statements, please adjust the slider bar to the position that best reflects your opinion (0 = Strongly Disagree, 10 = Strongly Agree). 1.1 It is hard to take the goal in Year 10, Qtr 4 outlined in the memo from the Board of Directors seriously It is unrealistic for me to expect to reach the goal in Year 10, Qtr It is quite likely that the goal in Year 10, Qtr 4 may need to be revised, depending on how things go Quite frankly, I don t care if I achieve the goal in Year 10, Qtr 4 or not I am strongly committed to pursuing the goal in Year 10, Qtr 4. Self-efficacy People often see some risk in situations that contain uncertainty about what the outcome or consequences will be and for which there is the possibility of bad consequences. However, riskiness is a very personal notion, and we are interested in your assessment of how much risk you plan to take in making decisions in the simulation. For the decisions you just made in Year 10, Qtr 2, how much risk are you taking (0 = No Risk, 10 = Extreme Risk)? 2.1 How much risk are you taking in your aircraft purchasing decision? 2.2 How much risk are you taking in your fare decision? 2.3 How much risk are you taking in your decision about the fraction of revenue to spend on marketing? 2.4 How much risk are you taking in your decision about hiring employees? 2.5 How much risk are you taking in your decision about target service scope? 2.6 How much risk are you taking overall across the complete set of decisions? Risk Taking Using the slider scale, please rate how certain you are that you can achieve the performance target in Year 10, Qtr 4 outlined in your objective memo at each of the levels described below. Rate your degree of confidence by recording a number from 0 to 100 using the scale given below: 0 = Cannot Do At All, 50 = Moderately Certain Can Do, 100 = Highly Certain Can Do Can achieve 10% of the annual target Can achieve 20% of the annual target Can achieve 30% of the annual target Can achieve 40% of the annual target Can achieve 50% of the annual target Can achieve 60% of the annual target Can achieve 70% of the annual target Can achieve 80% of the annual target Can achieve 90% of the annual target Can achieve 100% of the annual target 22