MONKEY SEE, MONKEY DO? VICARIOUS LEARNING UNDER IMPLICIT CONTRACTS Jongwoon (Willie...
Transcript of MONKEY SEE, MONKEY DO? VICARIOUS LEARNING UNDER IMPLICIT CONTRACTS Jongwoon (Willie...
MONKEY SEE, MONKEY DO? VICARIOUS LEARNING UNDER IMPLICIT
CONTRACTS
Jongwoon (Willie) Choi
University of Pittsburgh
Gary Hecht
University of Illinois at Urbana-Champaign
Ivo Tafkov
Georgia State University
Kristy L. Towry
Emory University
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.