MONKEY SEE, MONKEY DO? VICARIOUS LEARNING UNDER IMPLICIT

CONTRACTS

Jongwoon (Willie) Choi

University of Pittsburgh

jchoi@katz.pitt.edu

Gary Hecht

University of Illinois at Urbana-Champaign

ghecht@bus.wisc.edu

Ivo Tafkov

Georgia State University

itafkov@gsu.edu

Kristy L. Towry

Emory University

kristy.towry@emory.edu

June 2013

We thank workshop participants at Michigan State University and Emory University’s

Behavioral Brownbag Series. We are especially grateful for helpful comments from Susanna

Gallani, Joan Luft, Kathryn Kadous, Ranjani Krishnan, Eric Marinich, Amy Swaney, and for

research assistance from Jordan Bable, Eric Chan, and Stuart Smith. We gratefully acknowledge

financial support from our respective universities.

MONKEY SEE, MONKEY DO? VICARIOUS LEARNING UNDER IMPLICIT

CONTRACTS

ABSTRACT

Performance-based contracts often allow for managerial discretion, such that the manager

decides after observing an employee’s performance how that employee will be rewarded or

punished. Importantly, the effects of such performance-based outcomes can extend beyond the

employee(s) directly affected, because such outcomes can be observed by peer employees within

the firm. The net benefit of such vicarious learning as an indirect control depends on the

inferences employees make after observing a peer’s performance-based outcome. In this study,

we use an experiment to investigate whether the inferences observer-employees make depend on

whether the valence of the observed outcome is positive or negative (i.e., a promotion versus a

demotion). Using the setting of a strategic performance measurement system, we test and find

support for a causal model, in which the valence of the observed outcome influences observer-

employees’ inferences and subsequent behavior via their psychological distance from, and their

construal of, the observed outcome. Our results suggest that how observer-employees respond

after observing a peer employee’s performance-based outcome is asymmetric. Specifically,

employees who observe positive outcomes plan actions designed to maximize specific measures

within the strategic performance measurement system, whereas those who observe negative

outcomes plan actions that are more strategy-oriented.

Keywords: vicarious learning; strategic performance measurement systems; psychological

distance; construal level theory

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I. INTRODUCTION

Accounting scholars have traditionally focused on the use of formal controls and explicit

contracts for eliciting desired behavior from employees. However, contracts are typically

incomplete, because it would be impossible to anticipate all possible outcomes and to specify

contingencies. For this reason, firms often allow for discretion in lieu of (or in addition to)

explicit contracts, such that managers decide after observing performance how employees will be

rewarded or punished. Importantly, these performance-based outcomes (e.g., promotions,

demotions, special recognitions, dismissals) are often observable by others in the organization,

and so the potential for such outcomes to influence future behavior spans well beyond the

employees directly affected (Trevino 1992; Butterfield et al. 1996). According to Wood and

Bandura (1989, 362), the observation of others’ outcomes can be invaluable, as “virtually all

learning phenomena resulting from direct experience can occur vicariously by observing

people’s behavior and the consequences of it.” In fact, prior research documents that individuals

potentially learn better from observing others’ experiences, as interpretation of their own

experiences (i.e., successes and failures) is potentially biased (Merlo and Schotter 2003).

Further, managers themselves view their decisions to reward or punish one employee as an

opportunity to signal desired behavior to other employees (Butterfield et al. 1996).

By influencing employee behavior, vicarious learning serves as an important informal

control. However, the effectiveness of this control element might be limited, because the links

between actions and outcomes are likely less obvious when they are the result of managerial

discretion than they would be with explicit contracts. Therefore, it is unclear how performance-

based outcomes will be interpreted affect future actions. In this study, we examine how

employees who observe another employee’s performance-based outcome interpret these

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outcomes, and how these observations affect subsequent behavior. We do so in the context of a

strategic performance measurement system (e.g., The Balanced Scorecard). Such a setting is

particularly relevant for the study of vicarious learning, because it allows for inferences both at a

measurement level (e.g., “My colleague was promoted because she exceeded the target for most

of the measures on her division’s performance scorecard”), and at a strategic level (e.g., “My

colleague was demoted because he failed to move his division in the strategic direction preferred

by management”). In this paper, we propose that the level at which inferences are made will

depend on whether the observed performance-based outcomes are positive (i.e., a promotion) or

negative (i.e., a demotion) in nature.

We rely on social psychology theory to make our predictions. Specifically, we posit that

employees who observe another employee’s positive outcome (e.g., promotion) are motivated to

decrease the psychological distance between themselves and various aspects of the situation,

whereas those who observe a negative outcome (e.g., demotion) are motivated to increase the

psychological distance. Using construal level theory (Liberman et al. 2007; Trope and Liberman

2010), we further predict that psychological distance affects the level at which the situation is

construed. That is, employees who observe positive outcomes will tend to focus their

attributions on the specific performance measures that may have led to the outcome, whereas

those who observe negative outcomes will tend to focus more on underlying strategies and

constructs. As a result, those who observe positive (negative) outcomes will adopt a more

narrow (broad) interpretation and understanding of the outcomes, and this differential

interpretation and understanding will lead to differences in future behavior. Ultimately,

employees who observe positive outcomes will plan actions designed to maximize specific

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measures, whereas those who observe negative outcomes will plan actions that are more

strategy-oriented.

To test our theory, we use an experiment that involves a hypothetical case. In our

experiment, graduate business students (averaging more than 4 years of work experience)

assume the role of a division president within a hypothetical gaming and hospitality firm. The

case describes the firm’s background and organizational structure, a shift in the firm’s strategy,

and the firm’s strategic performance measurement system (i.e., performance scorecard). The

case also describes the performance of a peer who is the president of another division within the

firm. Holding available performance information constant, we manipulate whether the peer

manager is promoted or demoted within the firm. Aware of this outcome, participants assess

their relation to the peer and the event, thereby allowing us to measure participants’

psychological distance from various aspects of the scenario. Then, participants make eight

independent choices between two performance cues, identifying which cue they feel senior

management considered more in their promotion / demotion decision. We strategically designed

these choices to measure the level at which participants construed the situation. Participants also

describe what actions they would take, had they been in the peer’s situation, allowing us to

examine the behavioral implications of vicarious learning.

The results support our predictions. More specifically, a path analysis suggests that the

valence of a performance-based outcome (i.e., promotion versus demotion) influences the

psychological distance from which others observe various aspects of the situation. Thus, those

who observe positive outcomes generate less psychological distance from the situation than those

who observe negative outcomes. In turn, those who observe positive outcomes construe the

situation at a more detailed level, focusing more on performance measures, whereas those who

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observe negative outcomes construe the situation at a broader level, focusing more on strategies

and constructs. The behavioral implications are that employees who observe positive outcomes

change their future behavior to manage specific performance measures that they judge to be

responsible for the rewarded employee’s success. By contrast, employees who observe negative

outcomes are less focused on specific performance measures, but instead think of the strategies

and constructs that the measures are intended to represent, and modify their future behavior to

avoid the strategic blunders to which they ascribe the punished employee’s failures.

These insights are important to the accounting literature, because they imply that

vicarious learning, a potentially important informal control, is asymmetric in nature. That is, by

showing that the valence of an observed outcome can affect the scope of inferences, we

demonstrate that vicarious learning may be broader following a negative performance-based

outcome than following a positive outcome. From a more fundamental perspective, our process-

based model leverages the constructs of psychological distance and construal level, the relation

between which is well-established by prior psychology literature. However, we expand our

understanding of this relation, suggesting that psychological distance can be shaped by

motivational forces and elaborating on related implications for employees’ learning within an

organizational setting.

In addition, our study highlights the substantial role of strategic performance

measurement systems in the learning and growth process. Inherently, such systems offer and

facilitate an integrated perspective of the firm’s performance via a multi-level framework –

usually entailing a strategy-level and a measure-level. Our model suggests, however, that

observer-employees tend to focus on one of the two levels when attributing and explaining

observed performance-based outcomes, and this focus affects their subsequent behavior. Our

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model provides a deeper explanation for this focus, and thus advances academics’ understanding

of the implications of strategic performance measurement systems for employee behavior.

Further, this understanding is important to practitioners, as our model and related findings speak

to the complex relation between the strategic performance measurement system and the

transparency of performance-based outcomes. Specifically, our results suggest that managers

should be aware of the inferences employees make after observing others’ performance-based

outcomes, and this awareness should inform whether they wish to facilitate or inhibit such

observation.

The remainder of this paper is organized as follows: Section II describes our theory and

hypotheses, Section III describes our method, Section IV presents our results, and Section V

concludes.

II. THEORY AND HYPOTHESES

Background

A substantial body of research examines the effectiveness of explicit contracts within

organizations (Lambert 2001; Prendergast 1999). Such contracts can be effective in motivating

desired behavior from employees if all relevant aspects of employee performance can be clearly

identified and contracted upon. However, such contracts are not always feasible or desirable.

For example, all possible contingencies relating to the employment relationship cannot be

anticipated ex ante, and thus cannot be accounted for in the contract. Likewise, measuring

relevant aspects of employee performance may be prohibitively costly such that an explicit and

complete contract is not economically feasible. Moreover, even if such a contract is feasible,

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firms may choose not to offer it because of fairness and reciprocity concerns (Fehr and Schmidt

2000; Kuang and Moser 2009).

For the above reasons, contracts tend to be implicit and informal in nature, allowing for

managerial discretion, such that the manager decides after observing an employee’s performance

how that employee will be rewarded or punished (Bol 2008). For example, many firms’

performance evaluation and reward systems include discretionary bonus pools, in which the size

of the bonus pool is typically determined formulaically (e.g., as a percentage of firm profit), but

the allocation of the bonus pool across employees is left to the supervising manager (Bailey et al.

2011; Baiman and Rajan 1995). Managerial discretion can also affect employee performance

evaluation and compensation via the use of subjective performance measures (Baker et al. 1994)

and the subjective weighting of multiple performance measures (Ittner et al. 2003).

Subjectively-determined performance-based outcomes can include promotions, demotions,

special recognitions, and dismissals.

Importantly, the effects of performance-based outcomes can extend beyond the

employee(s) directly affected because such outcomes can be observed by peer employees within

the firm (Gioia and Manz 1985; Trevino 1992). Such observations can serve as a means of

indirect control because they represent opportunities for vicarious learning. In particular,

observing others’ performance-based outcomes enables observer-employees to draw inferences

about what they can do to achieve a similar outcome in the case of positive performance-based

outcomes like promotions, or to avoid a similar outcome in the case of negative performance-

based outcomes like demotions (Manz and Sims 1981). In turn, employees who observe their

peers’ performance-based outcomes can use those inferences to inform their own future

behavior. As the number of observers of a peer employee’s performance-based outcome

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increases, and/or as the breadth of employee judgments and decisions to which those inferences

can be applied increases, the effects of vicarious learning on firm outcomes can become

increasingly far-reaching and significant.

The net benefit of vicarious learning as an indirect control depends on the inferences

employees make after observing a peer’s performance-based outcome. However, the links

between actions and outcomes are likely less obvious when contracts are implicit and allow for

managerial discretion than when they are explicit. That is, employees who learn vicariously are

rarely perfectly informed, as they are not completely aware of all of the circumstances that lead

to a given performance-based outcome (Trevino 1992). Therefore, it is unclear how

performance-based outcomes will be interpreted by employees who can learn vicariously, and

how those inferences will translate into subsequent behavior.

We study this inference process within the context of strategic performance measurement

systems, which translate the firm’s strategy into a set of performance measures. Aligning firm

strategy with performance measures serves multiple functions, including the communication,

evaluation, and development of strategy (Langfield-Smith 1997; Chenhall 2003). The strategic

performance measurement system setting is particularly useful for studying vicarious learning

because it allows for multiple potential inferences that vary in scope. Specifically, inferences

may reflect a measurement focus (e.g., “My colleague was promoted because she exceeded the

target for most of the measures on her division’s performance scorecard”) or may reflect a

strategy focus (e.g., “My colleague was demoted because he failed to move his division in the

strategic direction preferred by management”).

Within this setting, many different factors likely influence the scope of the inferences

employees draw after observing a peer’s performance-based outcome. In this study, we

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investigate whether the scope of the inferences employees make depends on the valence of their

peer’s performance-based outcome (i.e., whether the observed outcome is positive or negative in

nature). Others have discussed a potential link between the valence of the performance-based

outcome and vicarious learning (e.g., Manz and Sims 1981). However, prior empirical work

examines vicarious learning following negative events (e.g., Trevino 1992; Niehoff et al. 1998;

Denrell 2003; Kim and Miner 2007), and does not systematically investigate the effect of event

valence. Further, these studies focus on the effect of negative events on observer-employees’

justice perceptions, emotions, attitudes, and subsequent misconduct, whereas we focus on the

scope of inferences these observer-employees draw and how this scope influences their

subsequent behavior. In the next section, we leverage psychology literature on psychological

distance and construal level theory to develop a model of the process by which the valence of a

peer’s performance-based outcome affects observer-employees’ inferences and their subsequent

behavior.

Causal Model and Hypothesis

In this subsection, we develop a causal model of how observations of a peer’s

performance-based outcome affect observer-employees’ subsequent behavior. The model is

depicted in Figure 1. Ultimately, the model predicts that within the context of a strategic

performance measurement system, employees who observe a peer’s positive performance-based

outcome will adopt a relatively narrow interpretation of the scenario, and focus their inferences

on the performance measures. As a result, they will plan actions intended to achieve similar

measure-based results. In contrast, the model predicts that employees who observe a peer’s

negative performance-based outcome will adopt a relatively broad interpretation, and focus their

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inferences on the strategic constructs within the firm’s strategy. As a result, they will plan

actions that reflect a more holistic execution of the firm strategy rather than targeting those

specific actions captured by the performance measures. We discuss each of the three links in our

model in greater detail below.

[Insert Figure 1 about here]

Valence of Observed Outcomes and Psychological Distance

Link 1 in our model captures the effects of the valence of a peer’s performance-based

outcome (positive or negative) on an observer-employee’s psychological distance from various

aspects of the observed situation. Psychological distance is a multi-dimensional construct

referring to how individuals subjectively assess the relation between themselves in the here and

now to other people, places, events, and time periods (Liberman et al. 2007; Trope and Liberman

2010). For example, individuals feel psychologically closer to others with whom they know very

well and/or share common hobbies (e.g., a spouse or a good friend) than a complete stranger.

Likewise, individuals planning a family vacation view the trip as a more psychologically

proximal event when it will take place sooner (e.g., tomorrow) rather than later (e.g., next year).1

In this paper, we argue that motivational forces can shape psychological distance. This

argument is logically consistent with a considerable body of literature from social psychology,

which demonstrates that individuals’ desire and motivation to enhance their self-image is a

strong motivational force (Alicke 1985; Tesser 1988; Beach and Tesser 1995). One self-image

enhancement tactic is to associate oneself with success and dissociate from failure. Viewing

oneself as similar to successful others boosts an individual’s perceptions of self-efficacy and

1 The dimensions of psychological distance include (1) temporal distance, (2) spatial distance, (3) social distance,

and (4) hypotheticality. Research finds that these four dimensions of psychological distance are interrelated (Bar-

Anan et al. 2007; Liberman et al. 2007). While we use the label “psychological distance” throughout our paper, the

dimension most relevant to our study is social distance.

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personal control, which induces the individual to pursue and attain more challenging goals

(Langer 1977; Bandura 1986). While associating oneself with success has an image-enhancing

purpose, dissociating from failure has an image-protecting purpose (Wheeler 1966; Snyder et al.

1986; Collins 1996).

Prior research establishes the robustness of individuals’ tendency to associate with

success (generally referred to as “basking in reflected glory”) and dissociate from failure

(generally referred to as “cutting off reflected failure”). For example, Cialdini et al. (1976) find

that more students wear apparel displaying the name of their university on a day following a

victory of the university’s football team than on a day following a defeat. Further, more students

use the pronoun “we” when describing a victory of their team than when describing a loss.

Similarly, in a team problem-solving setting, Snyder et al. (1986) documents that individuals are

more likely to publicize their affiliation with their team (e.g., taking and wearing team-

identifying badges) when they receive favorable feedback regarding their team’s performance

than when they receive no feedback or unfavorable feedback regarding their team’s performance.

Finally, Boen et al. (2002) examine individuals’ tendency to publicize their political party

affiliations following general elections in Belgium, and find that individuals publicize their

associations with a winning political party by displaying posters and lawn signs supporting that

party in front of their houses long after the election. In contrast, individuals conceal their

associations with the losing party by removing such materials soon after the election.

The research described above focuses on individuals’ public behaviors (e.g., clothing

choice, signage, etc.), adopting the perspective that such behaviors are motivated to manage

one’s own social-image (i.e., others’ perceptions of the individual). However, other research

establishes the inextricable relationship between social-image and self-image (Harvey et al.

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1957; Baumeister 1982; Brown and Gallagher 1992). As such, tendencies to “bask in reflected

glory” and “cut off reflected failure” are also motivated by the desire to enhance self-image, and

thus can manifest in private, as well as public, behavior (Cialdini et al. 1976; Snyder et al.

1986).2 Leveraging these literature streams, we posit that individuals who observe a peer’s

performance-based outcome will feel psychologically closer to various aspects of the observed

situation (i.e., the event, the peer, and the environment) when the observed outcome is positive

(e.g., a promotion) than when the outcome is negative (e.g., a demotion).

Psychological Distance and Construal Level

Link 2 in our model captures the effects of psychological distance on the level at which

observer-employees construe the situation, and thus their inferences regarding the circumstances

surrounding their peer’s performance-based outcome. Construal level theory describes how

individuals interpret and mentally represent aspects of their environment (Liberman et al. 2007;

Eyal et al. 2008). The central premise of construal level theory is that individuals can construe

or represent people, events, and objects at different levels, ranging from concrete, low-level

representations that emphasize highly contextualized details, to abstract, high-level,

representations that are less detailed, decontextualized, and more schematic (Lieberman et al.

2007; Trope and Lieberman 2010). That is, moving from a concrete representation to an abstract

representation entails omitting less important features while retaining those central to the item

being construed. For example, consider someone waving her hand at another person. A

concrete, low-level construal of this action might include details about the speed with which the

hand is moving, the size of the hand, etc. In contrast, an abstract, high-level construal of this

2 Similarly, Collins (1996) reviews evidence from existing research on social comparison (i.e., individuals’

assessments of themselves in relation to others), and posits that individuals are more likely to perceive themselves as

being similar to a comparison target in the case of upward comparison (i.e., comparing to someone more successful

than them) than in the case of downward comparison (i.e., comparing to someone less successful than them).

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action might be “being friendly.” As one moves from the concrete to the more abstract construal

of the action, details such as hand-size would likely be omitted from the individual’s active

representation. Importantly, although abstract, high-level representations contain less

information about unique aspects of the object or event being represented, they contain more

information about the general meaning of the object or event and its relation to other objects or

events, and therefore broaden the scope of the item being construed (Trope 1986, 1989; Trope

and Lieberman 2010). Returning to the example of a person waving her hand, the high-level

construal of “being friendly” connects that action to other ways of being friendly (e.g., giving a

hug).

Prior research shows that individuals construe more psychologically distant people,

events, and objects at a higher, more abstract level.3 For example, prior research considers

individuals’ construal of in-groups (i.e., a group the individual identifies with or belongs to)

versus out-groups (i.e., groups the individual is not a part of). In particular, Fiedler et al. (1995)

and Werkman et al. (1999) find that individuals describe members of out-groups, who are more

psychologically distant than members of in-groups, using more abstract terms, and perceive out-

group members as being more homogenous than in-group members. Similarly, Liberman et al.

(2002) find that individuals group objects into fewer, broader categories when thinking about

those objects in a temporally distant scenario than in a temporally-proximate scenario. Finally,

Liviatan et al. (2008) document that, when perceived psychological distance from another person

is large, individuals identify the person’s actions (e.g., reading) at a higher, more abstract level,

focusing on why the actions is performed (e.g., “gaining knowledge”), whereas when the

3 Recent research also shows that psychological distance not only affects construal level, but is also affected by

construal level (Liberman et al. 2007; Stephan et al. 2010). For example, individuals who construe activities at a

high level perceive that activity as occurring farther in the future.

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perceived distance is small, they focus on how the action is performed (e.g., “following lines of

print”).

Based on this evidence, we posit that observer-employees who feel psychologically

closer to various aspects of the situation (i.e., the event, peer, and environment) will construe the

situation at a lower level than individuals who feel more psychologically distant from the

situation. As discussed earlier, a low-level construal includes rich details unique to the particular

person, event, and/or object being construed, while a high-level construal omits contextualized

details and reflects a more general representation of the phenomenon or object being construed.

In our setting, the performance measures contained in the strategic performance measurement

system are a contextualized feature of the setting because the measures are often tailored to an

employee’s (or a business unit’s) particular opportunities, goals, and operations (Kaplan and

Norton 1993, 1996). Furthermore, whether the performance measures indicate that the peer

employee beat, just met, or failed to meet expectations is also unique to the particular situation.

Compared to the strategically-linked performance measures, the firm’s strategy is a less

contextualized, though more central, aspect of the setting because the strategy is meant to apply

to multiple employees or business units within the firm across a multitude of circumstances.

Thus, we posit that observer-employees’ representations of the performance-based outcome will

differ in terms of their emphasis on strategic constructs versus performance measures.

Specifically, we posit that observer-employees will construe their peer’s performance-based

outcome more in terms of the strategically-linked performance measures (strategic constructs) as

the psychological distance between themselves and the peer’s performance-based outcome

decreases (increases).

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Construal Level and Behavioral Focus

The last link in our model, Link 3, captures the implications of observer-employees’

construal of their peer’s performance-based outcome for their future behavior. Construing a

peer’s performance-based outcome essentially entails developing a causal model of the

relationship between the observed employees’ effort and actions and the resulting performance

evaluation and compensation decision made by the supervising manager (Rim et al. 2013). That

is, observer-employees infer what led to the observed outcome, and apply those inferences to

their own situation (Manz and Sims 1981).

Prior research demonstrates that the level at which an individual construes people, events,

and objects influences how that person behaves towards the item being construed. For example,

Stephan et al. (2010) find that individuals act more politely towards others whom they construe

at a high-level than those whom they construe at a low-level. Similarly, Fujita et al. (2006) find

that individuals are better able to exert self-control (e.g., not cheat on their diet) when construing

a situation at a high-level versus a low-level.

Based on the preceding discussion, we posit that when deciding how to act after

observing their peer’s performance-based outcome, observer-employees will leverage their

construal of the observed outcome. Recall that we expect observer-employees construing the

observed outcome at a low-level will emphasize the performance measures in their

representation, while those construing at a high-level will emphasize the firm’s strategy in their

representation. Thus, when deciding how to act in their own situation, observer-employees

adopting a low-level construal of the observed outcome will emphasize actions designed to

influence the performance measures. In contrast, observer-employees adopting a high-level

construal of the observed outcome will consider a broader action set that reflects a more holistic

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execution of the firm strategy, rather than actions designed to influence specific performance

measures.

In summary, we predict that observing a peer’s positive performance-based outcome

induces a process that drives a focus on actions designed to influence performance measures,

whereas observing a peer’s negative performance-based outcome induces a process that drives a

focus on actions that are more strategic. Formally stated, our hypothesis is:

Hypothesis: Employees who observe a peer’s positive performance-based outcome will

focus on actions designed to maximize measures, whereas employees who

observe a peer’s negative performance-based outcome will focus on actions that

are more strategy-oriented.

III. METHOD

Experimental Procedures

We used a 2 x 1 experimental design with outcome valence (negative versus positive) as

a between-subjects factor. We randomly assigned participants to one of the two experimental

conditions (subsequently described in more detail), and provided them with a hypothetical case

to read and complete (see Appendix A). After completing the case, participants responded to

attention-check and demographic questions.4 Each participant received a $5 Amazon gift card

for completing the experiment.

Each participant assumed the role of a division president within a hypothetical gaming

and hospitality company (Seshat Entertainment, Ltd.). The case first provided background

information on the company’s organizational structure. Next, the case described the company’s

recently adopted strategy and the strategic performance measurement system that was designed

4 We divided the experimental materials between two envelopes. The first envelope contained the case and the

second envelope contained the attention-check and demographic questions. Participants were instructed to complete

the first envelope before opening the second.

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to translate this strategy into a set of 14 measures (i.e., a performance scorecard). The case then

informed participants of a recent leadership change that involved another division president

(Spencer Caroyan). In our experimental setting, Spencer served as the participant’s peer in that

both he and the participant were division presidents. Participants in the negative valence

condition learned that Spencer was demoted to an employment position that involved less

managerial responsibility. Participants in the positive valence condition learned that the peer

was promoted to an employment position with more managerial responsibility. Both the

demotion and the promotion involved moving one level within the company’s management

hierarchy from Spencer’s original position. We also informed participants that Spencer had been

a division president for eight years, while the participant had been one for only three years. This

was done to minimize the role of competition in affecting the participants’ future behavior. Such

a consideration is outside the scope of this paper, but its implications for future research are

discussed in the Conclusion.

After learning about Spencer’s performance-based outcome, participants received

information related to Spencer’s performance. Specifically, we provided participants with the

most recent performance scorecard for Spencer’s division, and an article about him that appeared

in an industry trade publication in the previous year. Importantly, all participants received the

same performance information, which we balanced to contain both favorable and unfavorable

cues (and thus, allow for multiple interpretations of the performance-based outcome across both

outcome-valence conditions). For example, the performance scorecard data indicated that

Spencer exceeded, met, and did not meet the company’s expectations for 5, 4, and 5 performance

measures, respectively. The trade publication article revealed that Spencer’s management

choices had provoked both positive and negative reactions among his colleagues. After reading

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the case information, participants responded to questions intended to capture the variables in our

causal model. These questions, which are presented in Appendix B, were held constant across

conditions.

Participants first answered eleven questions designed to assess their psychological

distance from various aspects of the performance-outcome scenario described in the case. Three

questions were designed to capture participants’ psychological distance from the observed

outcome (i.e., the promotion or demotion), and asked participants to assess the extent to which

they could relate to what happened to Spencer, the relevance of the outcome, and the likelihood

that something similar would happen to them. Five questions were designed to capture

participants’ psychological distance from Spencer himself, and asked participants to rate the

similarity between themselves and Spencer (both generally and in terms of work habits and

behavior), familiarity with Spencer, and the likelihood that they would do things in the same way

as Spencer. Finally, three questions were designed to capture participants’ psychological

distance from the company (Seshat), and asked participants to assess the likelihood they would

continue and/or pursue a career with Seshat and the likelihood that they would have responded in

the same way as Seshat senior management. For all eleven questions, participants respond using

7-point Likert scales.

Participants then answered a series of eight questions designed to capture their construal

level. Specifically, we provided participants with 8 pairs of statements about performance cues

that the company’s senior management could have considered in determining Spencer’s

promotion or demotion. Each pairing included one statement emphasizing a strategy-level cue

(e.g., “Adopted a holistic entertainment perspective”) and one statement emphasizing a measure-

level cue (e.g., “Met goal of average customer satisfaction”). Four of the eight pairs focus on

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areas in which the peer manager’s performance was favorable (e.g., “Exceeded goal for new

players’ club membership” vs. “Expanded non-gaming elements”). The other four pairings focus

on areas in which the peer manager’s performance was unfavorable (e.g., “Did not focus on

high-end customers” vs. “Fell short of goal for bet amount per players’ club member”). For each

pairing, participants selected the performance cue they believed senior management likely

considered more in the decision to promote or demote (depending on the condition) Spencer.5

Finally, participants responded to an open-ended question designed to capture their

behavioral focus. Specifically, we asked participants to imagine that the company’s senior

management had yet to finalize Spencer’s performance-based outcome (i.e., to imagine that the

promotion or demotion had not yet occurred), but would do so in the next quarter. Then,

participants were asked to describe the actions they would take to ensure being promoted or to

avoid being demoted (depending on the condition), if they were in Spencer’s situation.

Instrument Validation

As discussed above, we capture participants’ construal level using their choices, across

eight paired comparisons, of the performance cues that they believe senior management likely

considered more in the decision to promote or demote Spencer. Each of these paired

comparisons includes one strategy-level cue and one measure-level cue. Our theory predicts that

the construal level will be higher following a negative performance-based outcome (i.e.,

demotion) than following a positive performance-based outcome (i.e., promotion). Therefore,

this theory would be supported if participants were more inclined to choose strategy-level cues

after observing the demotion and to choose measure-level cues after observing the promotion.

5 Our use of a forced response scale is consistent with prior research on construal level theory (e.g., Vallacher and

Wegner 1989; Liviatan et al. 2008).

19

Before testing this relation, however, it is important to ensure that our instrument was not

constructed in such a way as to bias the results in favor of our causal model and hypothesis. To

achieve this assurance, we conducted two separate exercises to maximize the internal validity of

our instrument. One exercise relates to the balance in the inherent importance of strategy-level

cues and measure-level cues. Another exercise relates to the balance in perceptions of strategy-

level and measure-level performance.

Balance in Perceived Importance of Information Cues

For the instrument to provide a valid test of theory, it is crucial that the inherent

importance of strategy-level cues and measure-level cues is balanced across the cues for which

Spencer’s performance was favorable versus unfavorable. Absent such balance, we could

potentially find results by construction. To understand this concern, suppose that participants

chose performance cues in such a way to justify management’s decisions (i.e., as opposed to as

suggested by our theory). In the negative valence condition, this would involve choosing the

inherently more important cue when the paired comparison involved two cues for which

performance was unfavorable. Similarly, it would involve choosing the inherently less important

cue when the paired comparison involved two cues for which the performance was favorable.6

Though inconsistent with our theory, such a process would lead to a similar pattern of results, but

only if our instrument was systematically unbalanced, such that (1) within the set of unfavorable

performance cues, strategy-level cues were inherently more important than measure-level cues

and/or (2) within the set of favorable performance cues, measure-level cues were inherently more

important than strategy-level cues.

6 By way of contrast, in the positive valence condition, it would involve choosing the inherently more important

factor when the paired comparison involved two factors for which performance was favorable. It would involve

choosing the inherently less important factor when the paired comparison involved two factors for which the

performance was unfavorable.

20

To ensure that the instrument is not systematically unbalanced, we validated it with a

group of 28 graduate business students recruited from the same subject pool that we used for the

main experiment. Our goal was to assess the inherent importance of each of the performance

cues used in the experimental instrument, absent any of the effects driven by the valence of the

observed outcome, our manipulated variable. We provided these participants with a version of

the experimental case that was identical to that used in the main experiment, except that it did not

provide information reflecting the performance-based outcome (i.e., it did not indicate that

management had decided to promote or demote the peer manager). We then gave participants

the sixteen cues that comprised the eight paired comparisons in the main experiment. We

informed participants that for Spencer’s upcoming annual performance review, senior

management would be considering all of these cues. Participants rated how much attention they

thought senior management would pay to each cue using a 7-point Likert scale (1 = none, 7 =

very much). We counter-balanced the order in which the sixteen cues were listed at four levels.

Since cue order did not significantly affect participants’ assessments, we ignore this variable

when discussing our analysis of participants’ assessments.

The pattern of participants’ assessments of importance that we need to rule out is an

interaction, especially one consistent with unfavorable strategy-level cues viewed as more

important than unfavorable measure-level cues and/or favorable measure-level cues viewed as

more important than favorable strategy-level cues. Table 1 presents participants’ mean

importance assessments for cues of each type. We find that the pattern of results is inconsistent

with the pattern we wish to rule out. Specifically, while strategy-level cues generally appear to

be viewed as more important than measure-level cues, this difference is smaller among the

unfavorable performance information than among the favorable information. Further, a

21

regression (not tabulated) with participants’ importance assessments as the dependent variable

and cue level (strategy versus measure), performance information (favorable versus

unfavorable), and the interaction of the two variables as independent variables, indicates that

neither the main effects nor the interaction are significant (all p > 0.45). Based on this analysis,

we conclude that the performance cues in our instrument used to capture participants’ construal

level facilitate an unbiased and valid test of our theory.

[Insert Table 1 about here]

Balance in Perceived Performance

Our theory could also be supported by construction if participants perceive differential

performance across the strategy-level and measure-level. More specifically, if strategy-level

(measure-level) information suggested Spencer performed poorly (well), then participants would

construe the demotion (promotion) outcome in a way that was consistent with – though not

explained by – our theory. To ensure that our instrument is not unbalanced in terms of Spencer’s

strategy-level and measure-level performance, we validated it on this dimension in a manner

similar to our first validation.

Thirty graduate business students recruited from the same subject pool that we used for

the main experiment completed a revised experimental instrument. Our goal was to assess the

balance in the relative performance across the strategy-level and measure-level, as

communicated by the information in the experimental case. Similar to our first validation, we

provided participants with a version of the experimental case that was identical to that used in the

main experiment, except that it did not provide information reflecting the performance-based

outcome (i.e., it did not indicate that Spencer had been promoted or demoted). Participants then

answered one of two questions, depending on the between-subjects condition to which they were

22

randomly assigned. In the strategy-level condition, we asked participants, “To what degree did

Spencer Caroyan implement the company’s strategy?” In the measure-level condition, we asked

participants, “To what degree did Spencer Caroyan achieve his goals for the performance

scorecard measures?” Participants responded using a 7-point Likert scale.7

The pattern of participants’ assessments of performance that we need to rule is a

difference in which participants’ perceptions of Spencer’s performance in the measure-level

condition is higher than that in the strategy-level condition. Indeed, the difference in the mean

response in the measure-level condition (mean = 4.27) is not statistically different from the mean

response in the strategy-level condition (mean = 4.07) (t = 0.59, p = 0.56, two-tailed; not

tabulated). Further, the mean response in each condition is not statistically different than the 4.0

mid-point on the scale (both p-values > 0.10, two-tailed). Based on this analysis, we conclude

that the information in our instrument concerning Spencer’s strategy-level and measure-level

performance is balanced.

IV. RESULTS

Ninety-eight graduate business students from a large university in the Southeastern

United States participated in our main experiment. Fifty-one percent of the participants were

male, and participants’ average age was 28. The average full-time, professional work experience

was more than four years. The experiment was conducted during a regular classroom session,

and took an average of 30 minutes to complete. We included one attention-check question,

which asked participants to recall whether the peer manager had been promoted or demoted.

7 Endpoints in the strategy-level condition were labeled “did not implement any element of company’s strategy” and

“implemented all elements of company’s strategy.” Endpoints in the measure-level condition were labeled “did not

achieve goals on any measures” and “exceeded goals on all measures.”

23

Twelve participants failed this attention check. Results are inferentially identical if those

participants are excluded from the analysis.

Dependent Measures

Psychological Distance

As discussed in Section III, we measure psychological distance via eleven Likert-scale

questions (see Appendix B). We observe a high level of internal consistency across the eleven

questions, as the Cronbach’s alpha for the eleven questions is 0.86, and the intraclass

correlation coefficient (0.86) is statistically different from zero (F96,960 = 7.30, p < 0.01).

However, recall that we designed these questions to capture participants’ psychological distance

from three different aspects of the situation – psychological distance from the observed outcome

(Questions 1 through 3), psychological distance from the peer (Questions 4 through 8), and

psychological distance from the company (Questions 9 through 11). To assess whether we

capture 3 different dimensions, we conduct a factor analysis with varimax rotation.8

Table 2 presents the factor analysis results. Panel A presents mean responses to each of

the eleven questions by valence condition. Importantly, lower factor scores represent greater

psychological distance. As shown in Panel B, the factor analysis yields three factors with an

eigenvalue greater than one, and these three factors account for 66 percent of the cumulative

variation. The factor loadings are consistent with expectations, with Factor 1 representing

psychological distance from the peer, Factor 2 representing psychological distance from the

observed outcome, and Factor 3 representing psychological distance from the company. Thus,

we conclude that the eleven questions capture three distinct dimensions of psychological

8 Varimax rotation is appropriate given that we expect the eleven questions to load onto factors in a particular way.

Specifically, we expect the questions capturing psychological distance from a given dimension (the observed

outcome, the peer, or the company) to exhibit high loadings on one factor and exhibit low loadings on other factors

(Russell 2002).

24

distance. We use participants’ factor scores as our measures of psychological distance, and

include all three measures of psychological distance when testing our causal model.

[Insert Table 2 about here]

Construal Level

As discussed in Section III, we measure participants’ construal level via their choices

across eight paired comparisons, with each pairing including one strategic-level performance cue

and one measurement-level cue. Participants were instructed to choose, within each pair, the cue

that senior management likely considered more in deciding whether to promote or demote the

participant’s peer (Spencer). Our measure of construal level is the number of pairs for which the

participant chose the strategy-level cue as the one that senior management likely considered

more. Thus, this measure ranges from zero to eight, with zero representing the lowest construal

level and eight representing the highest construal level.

Behavioral Focus

Finally, we measure participants’ behavioral focus by coding their responses to the open-

ended question asking them to describe the actions they would take to ensure being promoted (or

avoid being demoted) if they were in Spencer’s position and the final promotion (or demotion)

decision had not been finalized. We first divided these essays into idea units, with each unit

representing one intended future action. The number of idea units per participant ranged from

zero to seven. One author and one independent coder (who was blind to the research question

and hypothesis) then worked independently to classify each idea unit as to whether the conveyed

intended behavior was focused on a strategy-level intended action (e.g., “I will work to expand

gaming options”) or a measurement-level intended action (e.g., “I will ensure that the customer

25

satisfaction measure comes in above the goal”). Selected examples of participants’ responses are

as follows:

Strategy-oriented

“Ensure the growth I promoted was long term rather than just short term focused.”

“Spencer should demonstrate to senior management that his take on the casino business

is a sustainable one.”

“I would make sure to demonstrate that my strategy can be implemented over the long

term and be successful.”

“Continue the same strategy as I have already committed myself to it.”

Measure-oriented

“Improve % of customers that are return customers.”

“Make sure there is bottom line profitability.”

“Raise revenue in all aspects, including dining & night life.”

“I would really highlight to senior management where my ways exceeded expectation.”

Inter-rater reliability in our coding is high (Cohen’s Kappa = 0.86). When testing our

hypothesis and causal model, we take the conservative approach of relying on the independent

coder’s assessments exclusively, though results are inferentially identical if we instead use the

author’s coding. We calculate the percentage of each participant’s idea units that are coded as

being focused on a strategy-level intended action. This percentage is our measure of future

behavior, with higher values representing changes in future behavior that focus on strategy, and

lower values representing changes in future behavior that focus on measures.

Hypothesis Test

Table 3 presents descriptive statistics related to our main dependent measures. Recall our

hypothesis, which predicts that observing positive performance-based outcomes will induce a

26

focus on actions designed to maximize specific measures, whereas observing negative

performance-based outcomes will induce a focus on actions that are more strategy-oriented. We

first test our hypothesis with a simple t-test, comparing the behavioral focus across our two

experimental conditions. Through this test (not tabulated), we find strong support for the

hypothesis. Specifically, the intention to engage in behaviors aimed at the strategy level, as

captured by our behavioral focus measure, is significantly higher in the negative outcome

condition (mean = 76%) than in the positive outcome condition (mean = 43%), (t93 = 4.61, p <

0.01, two-tailed).

[Insert Table 3 about here]

We follow up on this simple test of our hypothesis with a more comprehensive test of our

causal model (originally depicted in Figure 1). Our model – given our specific dependent

measures – is depicted operationally in Figure 2. As noted earlier, lower values of our three

psychological distances measures reflect greater psychological distance. Thus, we expect a

positive relation between the observed performance-based outcome and our three measures of

psychological distance. Likewise, higher values of our construal level measure reflects a higher-

level (i.e., more abstract) construal. Thus, we expect a negative relation between our

psychological distance measures and our construal level measure. Finally, higher values of our

behavioral focus measure reflect a greater focus on strategy (compared to performance

measures). Thus, we expect a positive relation between our construal level and behavioral focus

measures.

[Insert Figure 2 here]

To test our model, we employ structural equations-based path analysis, using AMOS

software. The results of the path analysis are reported in Figure 3. The model provides a good

27

fit for the data, as indicated by a traditional χ2 test (χ

2 = 5.94, p = 0.20) as well as by alternate

measures of fit (Normed Fit Index = 0.96, Bollen’s Relative Fit Index = 0.78, Bollen’s

Incremental Fit Index = 0.99, Tucker Lewis Index = 0.92, Comparative Fit Index = 0.98).

Consistent with our expectations, we find a positive, statistically significant path

coefficient between outcome valence and each of our three measures of psychological distance

(all three p-values < 0.01, two-tailed). Also consistent with expectations, we find a negative

statistical association between our three psychological distance measures and our measure of

construal level (all three p-values < 0.10, two-tailed). Finally, we find a positive association

between our construal level and behavioral focus measures (p < 0.01, two-tailed), which is also

consistent with our expectations. Collectively, the results of our path analysis corroborate our

hypothesis test result, and support our causal model.

[Insert Figure 3 here]

V. CONCLUSION

In this study we develop and find support for a causal-model that explains how managers

and employees learn vicariously within organizations. Our model explains how performance-

based outcomes influence observer-employees’ subsequent behavior. Specifically, employees

who observe another employee’s positive performance-based outcome are motivated to decrease

the psychological distance between themselves and various aspects of the situation. In contrast,

employees who observe a negative performance-based outcome are motivated to increase that

psychological distance. Differences in psychological distance in these scenarios drive observer-

employees’ construal levels. That is, greater psychological distance leads to higher (i.e.,

abstract) construal levels, and thus strategy-level attributions of and explanations for the

28

observed performance-based outcome. Likewise, lower psychological distance leads to lower

(i.e., detailed) construal levels, and thus measure-level attributions of and explanations for the

observed performance-based outcome. Ultimately, the level at which employees develop

attributions and explanations influences future behavior designed to achieve positive

performance-based outcomes and avoid negative performance-based outcomes.

Our study provides insight on the mechanism underlying vicarious learning in

organizations, providing a psychology-based explanation of how employees learn from

observing others’ performance-based outcomes. As an informal control, vicarious learning is

especially important in scenarios characterized by ambiguity (in terms of, for example, strategy

selection, implementation, etc.) and multiple possible course of action. In such scenarios, it is

often difficult to establish formal controls (i.e., complete compensation contracts), and thus

managers must use their discretion in guiding the firm’s learning and growth, often after

employees have made and implemented operational and strategic decisions. In such scenarios,

employees become aware of upper management’s preferences regarding the direction of the firm,

and observation of other employees’ behaviors and related outcomes is a key factor in this

process. Our psychology-based explanation of how employees learn from observing others’

performance-based outcomes contributes to a deeper understanding of this non-trivial process.

Further, in line with previous research (e.g., Ittner et al. 2003; Choi et al. 2012, 2013), our

study provides insight regarding how the firm’s strategic performance measurement system

facilitates and influences employees’ learning and behavior. As suggested by our evidence, the

multi-level nature of strategic performance measurement systems allows for variation in the

interpretation of information made available by the system. Our study highlights how outcome

information influences observer-employees’ inferences related to these multiple levels and

29

advances academics’ understanding of how employees learn from and use strategic performance

measurement systems.

Finally, from a practical viewpoint, our study speaks to the desirability of information

sharing within the organization. That is, managerial preferences regarding observer inferences

should influence whether managers wish to facilitate or inhibit other employees’ observation of

their discretionary evaluation choices. Our results suggest that such actions directly influence

the effectiveness of the firm’s performance measurement and evaluation system.

Our study suggests several avenues for future research. For instance, we test our model

in a stylized setting, the purpose of which is to maintain experimental control and, thus, internal

validity. Of course, this leaves questions regarding the influence of other factors on the

generalizability of our theory beyond settings analogous to that which we use in our study.

Specifically, we focus on the valence of performance-based outcomes as the instigating factor in

our model. We ignore other attributes of these outcomes that could influence the degree to

which they influence subsequent behavior. For instance, whether and to what extent the

observed outcome is in line with expectations may influence the effect of this observation on

psychological distance. As another example, the salience of the performance-based outcome –

perhaps driven by how “extreme” the outcome is (i.e., lack of promotion vs. demotion vs.

termination) – likely influences the extent to which the outcome translates into subsequent

behavior. Other factors relate to psychological distance, a multi-faceted construct in and of

itself. For instance, one dimension of psychological distance is temporal distance – the amount

of time separating related factors. Thus, the amount of time that has passed between the outcome

and its observation, as well as the anticipated amount of time between the observation and the

30

implementation of the subsequent behavior may influence the perspective level adopted by an

observer, and thus subsequent behavior.

Other future research opportunities involve additional aspects of the organizational

ecology. In our study, we intentionally rely on a simplified setting in which there are no direct

implications for observer-employees’ evaluations and/or compensation. However, as is often the

case of peer employees and managers, a promotion for one employee may be “bad news” for an

observer, as that promotion is no longer available to the observer. Similarly, a peer manager’s

demotion (or lack of promotion) may be “good news” for an observer. In such competitive

situations, the direct implications of one employee’s performance-based outcome for observers

may influence the degree and nature of vicarious learning. Future research can investigate the

implications of such a scenario for our model. Finally, the role of upper management is

relegated to providing the performance-based outcome, and we do not model the “management”

of the vicarious learning process. Future research could model scenarios in which perspective

levels (i.e., strategy-level, measure-level) are differentially important, and address questions

related to managers’ propensity to strategically share information to induce a particular type of

vicarious learning. Whether and to what extent employees respond as managers intend, as well

as to what extent they “learn” from managers’ withholding of performance-based outcome

information serve as interesting empirical questions.

31

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35

APPENDIX A

Experimental Materials – Case Information

(same across conditions except where noted)

BACKGROUND INFORMATION

Company Information – Seshat Entertainment, Ltd.

Seshat Entertainment, Ltd. is a gaming and hospitality company that primarily competes in the

commercial casino industry.

Seshat has exhibited steady growth since operations began in 1978, and the company currently

operates 19 properties throughout the United States. These properties are organized

geographically into 4 regions (Northern, Eastern, Southern, and Western). The hierarchy within

each region is determined by property size.

Each property is headed by a president who implements Seshat’s corporate strategy at the

property and manages the overall property operations (see the excerpt of the organizational

structure below). You are a president of the Rising Sun property in the Eastern region, and have

been in this position for 3 years.

Reporting directly to the president are several vice-presidents, who oversee specific areas of

operations (e.g., finance, gaming, security, human resources, etc.). Vice-presidents manage their

areas and respective employees.

Seshat Senior Management

Executive-in-Charge - Northern

Zeus Luxury Resort

Aphina Casino and Spa

Sapphire Reef 3 Other

Properties

Executive-in-Charge - Eastern

Rising Sun

3 Other Properties

Executive-in-Charge - Southern

Tolstoy Resort

Kincaid Hotel & Casino

Walder Casino

3 Other Properties

Executive-in-Charge - Western

6 Properties

36

Strategy and Performance Objectives

Over the past several years, Seshat has been facing increased competition from online and

international gaming companies.

In response, senior management at Seshat shifted the company’s business model towards a more

holistic entertainment perspective. Consistent with this change in focus, Seshat recently adopted

the following mission and overall strategy for the firm:

Deliver the premier experience in gaming, dining, and entertainment to those

seeking luxury and indulgence.

Two key dimensions at the heart of this business model are (1) focusing on the high-end

consumer and (2) expanding the Seshat experience to not only encompass gaming, but also

dining and other nightlife experiences.

Senior management believes that by focusing on the high-end consumer, the firm will naturally

develop a reputation as a luxurious and indulgent destination. Firm management also believes

that the more “holistic” model offers a more appealing product, and captures a greater proportion

of consumers’ entertainment dollars.

With respect to the execution of this strategy, the firm generally operates with a decentralized

structure. That is, property presidents are given extensive autonomy (i.e., decision-making

authority) as to how to implement the strategy.

37

Senior Management has developed a performance scorecard that applies to all properties.

Specifically, Seshat’s performance scorecard includes two performance measures for each

objective. Senior management compiles performance scorecard data each fiscal quarter.

Seshat Strategy Scorecard

Objective

Measure

Performance

Relative to Goal

Gaming 1. Number of new Players’ Club Memberships

2. Bet amount per Players’ Club Member

Nightlife &

Entertainment

1. Average concert / show attendance

2. Nightclub capacity utilization percentage

Customer

Satisfaction

1. Average satisfaction with most recent visit

2. Percentage of customers that are return

customers

Dining 1. Average Zagat rating for all property

restaurants

2. Dining capacity utilization percentage

Operations 1. Compliance audit score

2. Fraud and theft-related losses

Employees 1. Employee satisfaction survey score

2. Employee turnover

Financial

Performance

1. Percent of revenue from non-gaming sources

2. Total profit

Performance on each measure is assessed as “exceeding”, “meeting”, or “not meeting” the

corresponding goal / target. These levels are represented by a +, , and , respectively.

Quarterly goals / targets are determined jointly by Senior Management and Property Presidents

annually.

38

Additional Information (POSITIVE VALENCE CONDITION ONLY)

The following includes information about a recent leadership change, scorecard data, and a

spotlight article about the property where the leadership change took place.

Recent Leadership Change Announcement

For the past 8 years, Spencer Caroyan served as President of the Sapphire Reef Resort Casino,

one of Seshat’s largest properties. In this position, Spencer reported to the Executive-in-Charge

of the Northern Division.

Last month, senior management announced the promotion of Spencer Caroyan to the position of

Executive-in-Charge of Western Region (denoted in the organizational structure below).

As Executive-in-Charge of Western Region, Spencer would oversee the six Seshat properties

located in California, Washington, Arizona, and New Mexico. Spencer would work directly

with and supervise all six property presidents. Further, Spencer would report directly to the

Senior Management Team.

When announcing Spencer’s promotion, the Senior Management Team noted Spencer’s

performance in executing the company’s strategy as the primary factor driving their decision.

Seshat Senior Management

Executive-in-Charge - Northern

Zeus Luxury Resort

Aphina Casino and Spa

Sapphire Reef 3 Other Properties

Executive-in-Charge - Eastern

Rising Sun

3 Other Properties

Executive-in-Charge - Southern

Tolstoy Resort

Kincaid Hotel & Casino

Walder Casino

3 Other Properties

Executive-in-Charge - Western

6 Properties

39

Additional Information (NEGATIVE VALENCE CONDITION ONLY)

The following includes information about a recent leadership change, scorecard data, and a

spotlight article about the property where the leadership change took place.

Recent Leadership Change Announcement

For the past 8 years, Spencer served as President of the Sapphire Reef Resort Casino, one of

Seshat’s largest properties. In this position, Spencer reported to the Executive-in-Charge of the

Northern Division.

Last month, senior management announced the reassignment of Spencer Caroyan to the position

of President of the Walder Casino (denoted in the organizational structure below).

As President of the Walder Casino, Spencer’s day-to-day responsibilities would be much smaller

in scope than those at the Sapphire Reef Resort Casino. Spencer would no longer oversee hotel

and nightclub operations as Walder is the smallest of Seshat’s properties and does not have these

amenities. Further, Spencer would report to the president of another parent property within the

Southern Region.

When announcing Spencer’s reassignment, the Senior Management Team noted Spencer’s

performance in executing the company’s strategy as the primary factor driving their decision.

Seshat Senior Management

Executive-in-Charge - Northern

Zeus Luxury Resort

Aphina Casino and Spa

Sapphire Reef 3 Other Properties

Executive-in-Charge - Eastern

Rising Sun

3 Other Properties

Executive-in-Charge - Southern

Tolstoy Resort

Kincaid Hotel & Casino

Walder Casino

3 Other Properties

Executive-in-Charge - Western

6 Properties

40

Performance Scorecard Information

The following represents the performance scorecard for the Sapphire Reef Resort Casino for the

last year (all four fiscal quarters combined into annual performance).

Sapphire Reef Resort Casino

President: Spencer Caroyan

Fiscal Year 2011

Objective

Measure

Performance

Relative to Goal

Gaming 1. Number of new Players’ Club Memberships +

2. Bet amount per Players’ Club Member

Nightlife &

Entertainment

1. Average concert / show attendance

2. Nightclub capacity utilization percentage

+

Customer

Satisfaction

1. Average satisfaction with most recent visit

2. Percentage of customers that are return

customers

Dining 1. Average Zagat rating for all property

restaurants

2. Dining capacity utilization percentage

+

Operations 1. Compliance audit score

2. Fraud and theft-related losses

Employees 1. Employee satisfaction survey score

2. Employee turnover

+

Financial

Performance

1. Percent of revenue from non-gaming sources

2. Total profit

+

Key: + = Exceeds expectations

= Meets expectations

= Does not meet expectations

41

Entertainment Industry Article

The following is an article that appeared in the March 2010 issues of Gaming & Entertainment

Quarterly, a trade publication specific to the entertainment, gaming, and hospitality industry.

OUR SPOTLIGHT IS ON . . .

SESHAT’S SPENCER CAROYAN

Seshat Entertainment, Ltd.’s 19 properties (and growing) have seen a major overhaul this year. This month, we sit down with Spencer Caroyan, President of Seshat’s Sapphire Reef Resort Casino, and pick his brain about his experience implementing Seshat’s relatively new ‘high-end’ strategy.

In his seventh year as Sapphire Reef’s president, Spencer has been around the block. But, even he was surprised to see Seshat re-group, and implement a new strategy. As a whole, Seshat has been expending a lot of effort in non-gaming areas, trying to increase revenue from dining and other entertainment sources. “I like the overall direction we are going in,” says Caroyan. “But, like Sinatra says, I want to do it “my way!” Those are his words. In ours, it’s apparent that Sapphire isn’t doing things like the 18 other Seshat properties. Seshat’s other sites focused exclusively on opening high-end restaurants with celebrity chefs. While Sapphire (under Caroyan’s guidance) kept its five-star steakhouse, Caldera, it also replaced some of its other dining options with a food court. “It seemed that our minds were closed to an entire market,” reminisced Caroyan. And did it ever work. The dining alone is capturing large crowds, and so are the nickel slots. Caroyan explains: “The economic recession has had a noticeable effect on casino customers. A recent survey conducted by the American Gaming Association shows that 50 percent of surveyed customers limit themselves to under $100 per visit. This prompted us to turn our attention to low stakes games. We just invested a huge sum in new machines. I’m pretty sure that we have the most nickel, dime, and quarter gaming machines – for a casino our size – in the entire country.” To better accommodate the larger crowds, Sapphire Reef’s 1,600-room hotel underwent extensive renovation and conversion work, increasing the total number of rooms to 2,000 and almost doubling the number of villa-style and penthouse rooms. Though smaller than guest rooms at comparable resort properties, they feature exceptional furnishings, thanks to famed interior designer Tanya Gyani.

Which of these

is not like the

others?

42

On the ‘other side’ of the property, Caroyan expanded two of the nightclubs. Additionally, Caroyan eliminated the hefty cover charge at three of the five nightclubs on the property. The nightclubs also promote local talents, rather than spotlight the typical national headliners. Even though Seshat’s strategy expands focus on the non-gaming side of the properties, Caroyan didn’t lose sight of the casino. Given his choices – and the hefty investment that came with those choices – Caroyan decided to reduce the use of personal hosts for guests, concierge services, and free alcohol. Even though it’s unclear how this will affect the high rollers, Caroyan contends that “we needed to be a bit more discerning.” Inside of Seshat Caroyan’s management choices have provoked different reactions. While some of Caroyan’s colleagues describe him as “maverick who thinks outside the box,” others think that “he is focusing on short-term profits and missing the big picture behind company’s business strategy.” Only time will tell if Spencer’s hit the jackpot.

43

APPENDIX B

Experimental Stimuli Used to Capture Psychological Distance, Construal Level, and

Future Behavior

Psychological Distance Psychological distance from the observed outcome (promotion or demotion)

1. To what extent can you relate to what happened to Spencer Caroyan?

2. How relevant is the information provided about Spencer Caroyan to you?

3. How likely is it that something similar to what happened to Spencer Caroyan ever happens to

you?

Psychological distance from the peer

1. Based on the information provided, how similar are you to Spencer Caroyan in terms of work

habits and workplace behavior?

2. Based on the information provided, how similar are you to Spencer Caroyan in general?

3. Based on the information provided, how familiar to you does Spencer Caroyan seem to be?

4. In implementing Seshat’s strategy, how likely is it that you would do things in the same way

as Spencer Caroyan?

5. As stated earlier, you are a president of Seshat’s Rising Sun property. How likely is it that

you would be friends with Spencer Caroyan?

Psychological distance from the company

1. As stated earlier, you are a president of Seshat’s Rising Sun property. How likely are you to

spend the rest of your career with Seshat?

2. Instead of working at Seshat, imagine that you are interested in pursuing a job in the

commercial casino industry. How likely is it that you would apply for a job at Seshat?

3. If you were in the same position as Seshat’s senior management, then how likely is it that

you would respond in the same way?

44

Construal Level

Senior management considered multiple factors, some favorable and some unfavorable, before

deciding to [promote or reassign, depending on condition] Spencer Caroyan. Below are eight

pairs of factors (labeled Option A and Option B). Within each pair, choose the factor that senior

management likely considered more.

Please make your choice for each pair, independent of the other pairs.

Pair

Option A

Option B

Please circle

either A or B for

each pair

1 Exceeded goal for new

players club membership

Expanded non-gaming

elements

A B

2 Fell short of goal for average

Zagat rating

Did not keep tight controls of

operations

A B

3 Adopted a holistic

entertainment perspective

Met goal of average customer

satisfaction

A B

4 Fell short of goal on average

concert attendance

Failed to execute strategy A B

5 Failed to identify target

market

Fell short of goal on fraud and

theft losses

A B

6 Succeeded in executing

strategy

Exceeded goal for dining

capacity utilization

A B

7 Exceeded goal on percentage

of revenue from non-gaming

sources

Sustained financial

performance

A B

8 Did not focus on high-end

customers

Fell short of goal for bet

amount per players’ club

member

A B

45

Future Behavior

Suppose that the senior management at Seshat will wait for the next quarter’s results before

making a final determination about whether to [promote or reassign, depending on condition]

Spencer to [Executive-in-Charge of the Western Region or President of the Walder Casino,

depending on condition].

Imagine yourself in Spencer’s position.

What actions would you take to [ensure being promoted or avoid being reassigned, depending

on condition]?

___________________________________________________________________

___________________________________________________________________

___________________________________________________________________

___________________________________________________________________

___________________________________________________________________

___________________________________________________________________

___________________________________________________________________

___________________________________________________________________

___________________________________________________________________

___________________________________________________________________

___________________________________________________________________

46

FIGURE 1

Hypothesized Process-Based Model

Outcome Valence refers to whether an observed performance-based outcome is positive for a

peer manager (i.e., a promotion) or is negative for a peer manager (i.e., a demotion).

Psychological Distance refers to how individuals subjectively conceptualize the relation between

themselves in the here and now to other people, places, events, and time periods. For example,

individuals feel psychologically closer to others with whom they know very well and/or share

common hobbies (e.g., a spouse or a good friend) than to a stranger.

Construal Level refers to how individuals construe or mentally represent people, events, and

objects, ranging from concrete, low-level representations that emphasize highly contextualized

details, to abstract, high-level representations that are less detailed, decontextualized, and more

schematic. In our model, construal level refers to relative emphasis on firm strategy versus

performance measures, with a low-level (high-level) construal reflecting a greater emphasis on

performance measures (strategy).

Behavioral Focus refers to the extent to which observer-employees’ intended future behavior

emphasizes actions designed to influence performance measures or a broader action set that

reflects a more holistic execution of firm strategy (and not just actions designed to influence the

performance measures).

Outcome

Valence

Psychological

Distance

Construal

Level

Behavioral

Focus

Link 1 Link 2 Link 3

47

FIGURE 2

Operational Model

Outcome

Valence

Psychological

Distance from

Peer

Construal

Level

Behavioral

Focus

+ - +

Psychological

Distance from

Observed

Outcome

Psychological

Distance from

Company

+

+ -

-

48

FIGURE 3

Path Analysis Results

Fit Indices: χ2 = 5.94 (p = 0.20), Normed Fit Index = 0.96, Bollen’s Relative Fit Index = 0.78, Bollen’s

Incremental Fit Index = 0.99, Tucker Lewis Index = 0.92, Comparative Fit Index = 0.98.

Standardized path coefficients are presented.

*** Two-tailed p-value < 0.01

** Two-tailed p-value < 0.05

* Two-tailed p-value < 0.10

Outcome

Valence

Psychological

Distance from

Peer

Construal

Level

Behavioral

Focus +0.44***

+0.77***

Psychological

Distance from

Observed

Outcome

Psychological

Distance from

Company

+0.57***

+0.09*** -0.19*

-0.21**

-0.89***

49

TABLE 1

Instrument Validation

Assessments of the Importance of Performance Informationa

Mean (Standard Deviation)

Performance Informationc

Cue Levelb Favorable Unfavorable

Strategic 5.21

(0.94)

5.15

(1.25)

Measurement 4.94

(0.96)

4.96

(0.97)

__________

a Using a 7-point Likert scale (1 = none, 7 = very much), participants rated how much attention

they thought senior management would pay to each of the sixteen performance information

cues used to capture participants’ construal level in our main experiment.

b Cue Level refers to whether the cues used capture participants’ construal level in our main

experiment reflect a strategic-level cue (e.g., “Expanded non-gaming elements”) or a measure-

level cue (e.g., “Exceeded goal for new players’ club membership”).

c Performance Information refers to whether each of the sixteen cues provided to participants

indicated that the peer’s performance was favorable (e.g., “Exceeded goal for new players’

club membership) or unfavorable (e.g., “Fell short of goal for bet amount per players’ club

member”).

50

TABLE 2

Factor Analysis of Psychological Distance Dimensions

Panel A: Descriptive Statistics by Condition – Mean (Standard Deviation)

Outcome Valencea

Questionb

Positive Negative

Psychological Distance from Observed Outcome

Q1: To what extent can you relate to what happened to

Spencer Caroyan?

3.94 (1.25) 3.41 (1.53)

Q2: How relevant is the information provided about

Spencer Caroyan to you?

4.90 (1.40) 3.63 (1.26)

Q3: How likely is it that something similar to what

happened to Spencer Caroyan ever happens to you?

4.53 (1.08) 3.43 (1.38)

Psychological Distance from Peer

Q4: Based on the information provided, how similar are

you to Spencer Caroyan in terms of work habits and

workplace behavior?

4.49 (1.16) 3.17 (1.34)

Q5: Based on the information provided, how similar are

you to Spencer Caroyan in general?

4.51 (1.02) 3.12 (1.21)

Q6: Based on the information provided, how familiar to

you does Spencer Caroyan seem to be?

4.86 (0.94) 3.49 (1.31)

Q7: In implementing Seshat’s strategy, how likely is it

that you would do things in the same way as Spencer

Caroyan?

4.27 (1.17) 3.04 (1.27)

Q8: As stated earlier, you are president of Seshat’s

Rising Sun property. How likely is it that you would be

friends with Spencer Caroyan?

4.90 (1.23) 3.86 (1.19)

Psychological Distance from Company

Q9: As stated earlier, you are a president of Seshat’s

Rising Sun property. How likely are you to spend the

rest of your career with Seshat?

5.02 (1.07) 3.57 (1.08)

Q10: Instead of working at Seshat, imagine that you are

interested in pursuing a job in the commercial casino

industry. How likely is it that you would apply for a job

at Seshat?

5.43 (0.84) 3.84 (0.99)

Q11: If you were in the same position as Seshat’s senior

management, then how likely is it that you would

respond in the same way?

4.88 (0.93) 3.88 (1.22)

Overall Psychological Distance (all questions) 4.70 (0.56) 3.51 (0.68)

51

TABLE 2 (continued)

Panel B: Principal Component Factors

Factor Eigenvalue Explained Variance (%) Cumulative Explained Variance (%)

1 4.78 43.43 43.43

2 1.40 12.75 56.18

3 1.13 10.26 66.44

4 0.82 7.42 73.86

5 0.69 6.27 80.13

6 0.54 4.95 85.08

7 0.49 4.42 89.50

8 0.43 3.89 93.39

9 0.36 3.26 96.65

10 0.20 1.85 98.50

11 0.17 1.53 100.00

Panel C: Factor Loadings for Three Factors Retained

Question Factor 1 Factor 2 Factor 3

Q1 0.14 0.87 -0.01

Q2 0.03 0.79 0.33

Q3 0.46 0.63 0.03

Q4 0.87 0.12 0.10

Q5 0.82 0.13 0.26

Q6 0.59 0.17 0.30

Q7 0.79 0.17 0.16

Q8 0.65 0.08 0.34

Q9 0.39 0.10 0.63

Q10 0.36 0.10 0.81

Q11 0.08 0.10 0.78

______ a Outcome Valence refers to whether the observed performance-based outcome is positive for the

peer is positive (i.e., a promotion) or is negative (i.e., a demotion).

b

Participants respond to each question using a 7-point Likert-scale. For each question, lower

response values indicate greater psychological distance.

52

TABLE 3

Descriptive Statistics – Mean (Standard Deviation)

Outcome Valencea

Dependent Measure Positive Negative

Psychological Distance from Observed Outcome

(Factor Score)b

0.22 (0.95) -0.23 (1.00)

Psychological Distance from Peer

(Factor Score)c

0.43 (0.81) -0.43 (0.99)

Psychological Distance from Company

(Factor Score)d

0.55 (0.67) -0.56 (0.97)

Construal Levele 3.77 (1.61) 5.53 (1.29)

Behavioral Focusf 0.43 (0.33) 0.76 (0.35)

______ a Outcome Valence refers to whether the observed performance-based outcome is positive for the

peer is positive (i.e., a promotion) or is negative (i.e., a demotion).

b Psychological Distance from Observed Outcome is participants’ factor score based on their

responses to eleven questions capturing psychological distance and factor loadings for Factor 2

(see Table 2).

c Psychological Distance from Peer is participants’ factor score based on their responses to

eleven questions capturing psychological distance and factor loadings for Factor 1 (see Table 2).

d Psychological Distance from Company is participants’ factor score based on their responses to

eleven questions capturing psychological distance and factor loadings for Factor 2 (see Table 2).

e Construal Level is the number of times participants choose the strategy-level cue across eight

paired comparisons in which participants choose the cue within each pair (strategy-level or

measure-level) that they believe senior management likely considered more in deciding whether

to promote or demote the participant’s peer.

f Behavioral Focus is the percentage of participants’ idea units that are focused on a strategy-

level intended action.