Effect of a Coach’s Regulatory Focus and an Individual’s Implicit Person Theory on Individual Performance

Abstract

Consistent with the arguments of regulatory focus theory, an experiment revealed that a promotion coaching orientation relative to a prevention coaching orientation had a more positive effect on the performance of recipients following coaching. Moreover, in support of regulatory fit theory, a prevention coaching orientation had a more positive effect on the performance of recipients with implicit fixed beliefs about ability than for those with implicit incremental beliefs. The robustness of these results was supported through replication in a lagged, correlation field study of employees in the production facility of a global company. In addition, in the field study, there was a significant additive component in the effects for promotion-oriented coaching, due to better regulatory fit for employees with incremental beliefs.

Keywords

coaching orientation regulatory focus implicit person theory regulatory fit

At the core of every coaching process is a set of interactions between a coach and the recipient of coaching in which each brings a range of capabilities and predispositions to the interplay. In these interactions, the coach’s orientation toward the learning process is an important part of the coaching context. Of relevance to coaching orientation are developments in motivation theory that draw attention to regulatory focus as a determinant of goal pursuit (Higgins, 1997). Regulatory focus theory identifies two different foci that underpin motivated behavior. These are the pursuit of development in order to succeed and the avoidance of failure through the fulfillment of obligations and responsibilities, which are labeled as promotion and prevention foci, respectively (Higgins, 1998).

Considerable research has investigated how an individual’s self-regulatory focus influences performance (see Higgins, 1998, 2001, for reviews). This research has demonstrated that performance contexts can be framed as promotion oriented or prevention oriented (e.g., Higgins, 1997; Van-Dijk & Kluger, 2004). This finding has led to the proposition that leaders’ regulatory focus could influence leaders’ behavior (Brockner & Higgins, 2001; Kark & Van-Dijk, 2007). Even though employee coaching is a constituent component of leaders’ behavior (Kark & Van-Dijk, 2007; Yukl, 2009), the impact of the regulatory framing during coaching on recipients’ performance has yet to be examined. This is an important consideration because, despite the prevalence of coaching in organizations (Latham, Almost, Mann, & Moore, 2005), empirical examinations of coaching have not clearly demonstrated its positive influence on performance (Wageman, 2001). Moreover, investigations of the effects of coaching that do not consider differences in the interactions between coaches and recipients may be masking the positive and negative effects of moderators, such as the regulatory focus of the coach. Coaching that focuses recipients’ attention on the promotion of success versus prevention of failure may have different consequences on their performance.

Theories of person–situation interaction (e.g., Mischel & Shoda, 1995) suggest that contextual factors, such as a coach’s regulatory focus, do not affect individuals as if they are blank slates. Individuals have beliefs and attitudes that filter and interpret the messages they receive from their environmental contexts. A recipient’s belief that seems likely to influence reactions to the regulatory focus of a coach is the belief regarding the changeability of the skills to be developed. As the ultimate purpose of coaching is to increase the knowledge, skills, and abilities of individuals (Mace, 1950), that is, to enable learning (D’Abate, Eddy, & Tannenbaum, 2003), recipients’ belief that their abilities are changeable should influence any impact of the coaching they receive on their performance. Consequently, a second purpose of our research is to examine whether implicit person theories of ability moderate the association between the regulatory framing of coaching and the performance outcomes from coaching.

A final purpose of our research is to examine the effectiveness of coaching through the perspective of regulatory fit theory (Higgins, 2000, 2005, 2006). While interaction theories predict a person–situation interaction, they do not specify the nature of the interaction. Regulatory fit theory states that performance is highest when the regulatory demands and opportunities of the context match the individual’s motivational orientation. In our study, we suggest that the fit between the regulatory framing of coaching by the coach, in promotion versus prevention terms, and recipients’ beliefs about improvement versus demonstration of their competence influences the effectiveness of coaching.

Our research contributes to the small body of extant empirical studies on the determinants of coaching effectiveness, which is currently dwarfed by the voluminous practitioner literature on coaching in the workplace. The scholarly literature offers many definitions and descriptions of the contexts and formats of coaching and of the characteristics of the interaction between a coach and a recipient. Our investigative lens is focused on the interactions between a trainee and a coach in the context of helping recipients to develop a skill (Study 1) and coaching that is given by supervisors to employees on an ongoing basis in a manufacturing facility (Study 2). Across both coaching contexts examined, we investigate how the orientation of the coach in his or her framing of the learning process and the interaction between the regulatory framing of coaching and recipients’ implicit beliefs about ability influence the outcomes of their interactions. Previous research has not considered the impacts of framing and beliefs in the interactions between coaches and trainees, which are at the heart of all coaching processes, as possible theoretical explanations for the effectiveness of coaching. By focusing on the application of regulatory focus (Higgins, 1997, 1998), implicit ability (Dweck, 1996, 1999), and regulatory fit theories (Higgins, 2000, 2005, 2006) to the interactions between coaches and trainees, we seek to expand the scope of existing theory and clarify the conditions under which coaching can positively influence performance, as called for by Feldman and Lankau (2005). Nevertheless, we caution that the characteristics of the coaching contexts and interactions we examine do not match all extant definitions and characteristics of coaching (e.g., Feldman, 2001).

We begin with an overview of coaching. We then explain the theoretical bases used to develop our hypotheses and the hypotheses we tested.

Coaching

Since Mace (1950) introduced the concept of coaching to management, there has been much confusion about what coaching means, as definitions and operationalizations vary in the published research literature (e.g., Carson, Tesluk, & Marrone, 2007; Heslin, VandeWalle, & Latham, 2006). Coaching, in common with other developmental practices, is a goal-directed interaction between one developer (e.g., coach) and one or more learners (e.g., recipients of coaching; D’Abate et al., 2003). There is also agreement that coaching is a way of relating and communicating with others (Evered & Selman, 1989) and that it refers to what leaders (e.g., managers, supervisors, team leaders) do in their everyday interactions with subordinates and team members to influence the results obtained by them (Morgeson, 2005; Wageman, 2001).

What occurs when coaches communicate and interact with recipients is not well understood because the behaviors that constitute coaching are specific to the organizational context in which coaching occurs (Wageman, 2001). Moreover, the number of individuals with whom coaches simultaneously interact and the regularity of interactions between coaches and recipients are open questions (D’Abate et al., 2003). For example, one of the behavioral categories used by Wageman (2001) to assess coaching performed by leaders of teams is “problem solving consultation” (p. 566). The study included assessments of how often problem-solving consultation and other behavioral categories were performed by leaders, but it was unclear how often specific behaviors within each behavioral category, such as “teaching the group to use a problem solving process” (p. 565), were performed. It is also unclear whether the coaching behaviors were enacted with groups or with individual members of teams reporting to a leader.

The context-specific nature of coaching notwithstanding, there are characteristic actions that coaching has in common with other developmental interactions and the related learning process. Goal setting, providing feedback, and practical application, as well as modeling and observing, are frequently mentioned as coaching behaviors intended to enhance a recipient’s ability and motivation (D’Abate et al., 2003). Others, who equate coaching and learning, include work by Evered and Selman (1989). They noted that “great” coaches perceive themselves foremost as teachers. Similarly, Ellinger and Bostrom (2002) found that managers perceived coaching to be synonymous with facilitating learning on the part of their subordinates. For this reason, how coaches frame the learning process and the associated behaviors is important because coaching is a situational prime that can induce recipients to pursue the same achievement goal in one of two quite different ways (Higgins, 1997, 1998).

Regulatory Focus

Higgins’s (1997, 1998) theory of regulatory focus describes the different ways in which individuals pursue goals and what are experienced as desirable (positive) or undesirable (negative) outcomes. In Higgins’s theory, the motivational orientations adopted in respect to goals lead individuals to use one of two different means to accomplish the same achievement goal. The first regulates the achievement of rewards, focusing individuals on positive outcomes, and hence is labeled a promotion focus. The second regulates avoidance and focuses individuals on negative outcomes and therefore is labeled a prevention focus. Individuals with a promotion regulatory focus typically pursue novel ideas and practices to achieve personal hopes, wishes, and challenging aspirations. They tend to be eager and opportunistic in their goal pursuit activities. Contrasted with this approach is that of individuals with a prevention focus. They typically tend to avoid actions that could prevent them from fulfilling normative expectations, duties, and responsibilities. Consequently, they tend to be vigilant and cautious in their goal-pursuit activities. In short, people with a promotion focus strive to attain desired, positive end states, such as success, whereas those with a prevention focus are motivated to avoid undesired, negative ends states, such as failure (e.g., Shah, Higgins, & Friedman, 1998).

Kark and Van-Dijk (2007) and Brockner and Higgins (2001) have suggested that the actions (behavior) and language (communication) of leaders or other influential people in a person’s environment inspire a focus on either positive (promotion) or negative (prevention) end states. Both foci can be functional, as effective performance in an organization often requires both eagerness (promotion) to succeed as well as vigilance (prevention) to avoid failure. For example, assembly line employees need to make high-quality products as quickly as they can while minimizing errors that would lead to rejection of the product. Problem solving requires generating and sifting through many alternatives before finding an appropriate solution. Consequently, promotion and prevention foci are required for effective performance. In an empirical examination of these notions among professional employees as diverse as loan underwriters and accountants, the stronger the prevention regulatory focus of individuals, the higher their in-role performance, while the stronger the promotion regulatory focus of individuals, the higher their creative and helping behaviors (Neubert, Kacmar, Carlson, Chonko, & Roberts, 2008). Although not mentioned by those authors, the prevention focus was, presumably, related to in-role performance for the respondents because the nature of their jobs requires vigilance for performance effectiveness. While these findings reveal the relationship of these two regulatory orientations that individuals bring to bear on performance, what remains unexamined in a work context is whether the coaching that leaders provide to recipients framed with a promotion versus a prevention orientation influences performance. Evidence from laboratory studies by Higgins and his colleagues is suggestive.

Findings from experiments indicate that prevention framing leads to better but slower performance than does promotion framing on tasks requiring vigilant attention (Förster, Higgins, & Bianco, 2003). Promotion framing causes better and faster performance than does prevention framing on tasks requiring eagerness to try many alternatives (Friedman & Förster, 2001).

In the context of coaching, investigations of employee coaching allude to but have not explicitly examined how the framing of coaching with a promotion versus a prevention orientation influences performance outcomes. Wageman (2001) referred to the everyday interactions that leaders have with team members as leaders’ coaching intended to influence members to take responsibility for their performance. By describing coaching as a way to influence individuals’ normative expectations, Wageman implicitly framed coaching with a prevention orientation. Tewes and Tracey (2008) developed an on-the-job posttraining supplement that required managers to coach themselves to reinforce earlier interpersonal skills training. The self-coaching program required them to set goals to either continue or start effective behaviors, as well as to set goals to reduce the frequency of ineffective behaviors. This self-coaching was implicitly framed with a promotion orientation (continue or start to perform behaviors to achieve successful future performance) supplemented with a prevention orientation (minimizing behaviors that obstruct effective performance). The implicitly framed prevention coaching behaviors assessed by Wageman were unrelated to overall performance. However, the implicitly framed promotion plus prevention self-coaching assessed by Tewes and Tracey was related positively to posttraining performance. In summary, some coaching interventions have implicitly framed behaviors with these two regulatory orientations. Moreover, both promotion and prevention regulatory orientations are positively associated with performance, although evidence supporting the former association is stronger than the latter. Accordingly, we hypothesized the following:

Hypothesis 1: Coaching framed with a promotion or prevention regulatory focus is positively related to performance, although the association between promotion-framed coaching and performance is stronger than the association between prevention-framed coaching and performance.

Interaction theories (e.g., Mischel & Shoda, 1995) state that individuals’ behaviors are influenced not only by the situation but also by the personal qualities they possess. Therefore, the influence of the framing of the learning process in coaching on performance may be enhanced or tempered by what recipients believe about their ability to acquire the knowledge and skills that are being taught by the coach.

Implicit Person Theories

Dweck’s (1996, 1999) implicit person theories framework refers to the conceptions or beliefs that individuals hold about specific abilities, such as intelligence or problem solving. In brief, Dweck’s theory, which has received extensive empirical support (e.g., Butler, 2000; Plaks & Stecher, 2007), states that individuals differ in the degree to which they view abilities as either fixed-trait-like entities (entity theory) versus dynamic states that can be changed over time through developmental activities, experience, and new strategies (incremental theory). These beliefs or theories about ability have been shown to influence how people respond to interventions designed to improve their performance (Van-Dijk & Kluger, 2004).

People who hold entity beliefs see abilities and other personal characteristics as stable. Hence, effort and persistence to develop strategies for achieving difficult outcomes are often viewed as relatively futile or as evidence of lack of ability. Failure, setback, and errors, or other feedback that suggests substandard performance, are also interpreted as evidence of lack of ability for performing the task effectively. Relatedly, because they view performance as an indicator of ability, goals are often viewed as something they must achieve in order to demonstrate to themselves and others that they possess ability. Hence, these people are vigilant for possible errors or failures, which they try to avoid, that may block their attainment of a goal. They are particularly attentive to and motivated by situational cues that highlight the avoidance of mistakes that would prevent goal attainment.

In contrast to those who hold an entity view, people who have an incremental conception of ability believe that skills can be increased through effort and persistence, and they commit to challenges such as a high goal so that they can improve their portfolio of skills (Dweck & Leggett, 1988). Setbacks are more likely to be viewed by them either as opportunities to learn or as reflecting a need to work harder or change strategy, rather than as an indicator of ability or reason to withdraw from a task. Incremental theorists are more likely to experiment with alternative strategies in the hope of learning more about a task, even when doing so may undermine their short-term performance (Wood & Bandura, 1989a). People with an incremental view of ability are attentive to and are motivated by situational cues (e.g., feedback) that highlight their need for improvement. Compared with their counterparts with entity beliefs about the same ability, those with an incremental belief are more likely to view goals as a target to strive for and their performance as an indicator of progress.

In summary, the behavior of entity theorists is directed by normative expectations, whereas incremental theorists are guided by their self-defined expectations. Since needs are a determinant of regulatory focus (Higgins, 1997), the need to fulfill normative expectations versus personal development places entity versus incremental theorists in different regulatory foci (cf. Brebels, De Cremer, & Sedikides, 2008). Because entity theorists’ beliefs about the fixedness of their ability motivates them to minimize errors so as to avoid a negative outcome—failure to demonstrate their competence—their beliefs place them in a regulatory mode that is consistent with a prevention focus (Van-Dijk & Kluger, 2004).¹ In contrast, because incremental theorists’ beliefs about the malleability of their ability motivates them to improve so as to attain a positive outcome—success—their beliefs place them in a regulatory mode that is consistent with a promotion focus (Van-Dijk & Kluger, 2004). Extension of regulatory focus to regulatory fit theory suggests how the framing of coaching interacts with recipients’ implicit beliefs to influence their performance.

Regulatory Fit

Regulatory fit refers to the degree to which the demands and opportunities people experience in the performance of task activities sustains the motivational orientations of their regulatory focus (Higgins, 2005). The central proposition of the regulatory fit argument is that individuals are more engaged and get more value out of an activity when their regulatory focus is matched to the regulatory demands and opportunities of the context. Thus, an individual with a promotion focus will remain more engaged and get greater value out of a task in which the context and other related task demands support an eager and opportunistic striving for the goal in which improvements, learning, and other forms of progress are emphasized. In contrast, an individual with a prevention focus will be more engaged and gain more intrinsic value from the activity when performance, minimization of errors, demonstration of capability, and other forms of responsible behavior are stressed.

Regulatory fit enhances the value associated with the performance of a task as opposed to the value associated with the attainment of a goal. The two are separate (Avnet & Higgins, 2003; Higgins, 2000, 2005). However, on many tasks, engagement and the intrinsic value of activities are usually associated with persistence and better performance (Higgins, 2006). Therefore, regulatory fit is often associated with high performance, particularly on tasks where engagement, attention, and persistence contribute to outcomes, such as acquiring a skill, processing new information, or complying with regulations and advice (Freitas, Liberman, & Higgins, 2002; Hong & Lee, 2008; Lee & Aaker, 2004).

Based on the preceding discussion, we tested a second hypothesis. The arguments underlying it are based on the effects of the regulatory fit of a coach’s framing of the learning process with the implicit beliefs about ability of the recipient.

Hypothesis 2: Individual implicit theories of ability moderate the association between the regulatory framing of coaching and the performance effects of coaching.

Regulatory Orientation of Coaching and Fit

Their differing conceptions of ability place recipients in either a prevention or a promotion regulatory focus (Van-Dijk & Kluger, 2004) in their approach to a task. At the same time, a coach’s motivational orientation (i.e., regulatory focus) in the coaching process creates a context that facilitates and sustains either a prevention or a promotion focus. As a result, individuals with an entity belief about ability typically have a better regulatory fit with coaches who provide a prevention-oriented context, while individuals with an incremental belief about ability typically have a better regulatory fit with coaches who provide a promotion-oriented context. This is because regulatory fit leads to greater engagement in the learning task (Higgins, 2005) and thus enhances the effectiveness of the coaching process.

Both Higgins’s (1997, 1998, 2005) and Dweck’s (1999) theories state that individuals are particularly attentive to and motivated by contextual cues that support their predisposition to adopt either a prevention (entity) or a promotion (incremental) orientation when working on achievement tasks, such as learning a new skill that is complex for them. In the interactions between a coach and a recipient, the communications from the coach provide most of the contextual cues that orient the recipient to the learning process. These include communications of standards or goals; descriptions and demonstrations of appropriate behaviors; and the provision of feedback on achievements, errors, and gaps between actual performance and standards.

It is through the communication of standards, description of actual performance, and feedback that a coach can create a context that encourages and sustains either a promotion or a prevention focus (Kark & Van-Dijk, 2007). The creation of either context is a product of the language and framing of standards, behaviors, and feedback by a coach in his or her communications to the recipient.

A coach with a prevention orientation typically stresses goal attainment in order to avoid failure and hence may make statements to the effect that “if you cannot reach this goal, then you are not cut out for this task and might as well give up.” When describing behaviors and strategies, a coach may emphasize the recipient’s obligation to “get it right.” When providing feedback, the coach may highlight errors and shortfalls as well as how to correct them. During interactions with the recipient, a coach’s language may include words that stress prevention such as “poorly,” “mistakes,” “problems,” and “to be avoided,” words that have been shown to prime a prevention orientation (cf. Hong & Lee, 2008; Lee & Aaker, 2004; Shah et al., 1998). For entity theorists, the framing of goals and behaviors, interpretation of feedback, and language of such a coach match their own expectations of the task (e.g., not making mistakes, getting it right) and the type of guidance they seek in order to minimize errors and avoid failure (Dweck, 1999). This conforms to their entity beliefs, sustains their prevention regulatory focus, and increases their attentiveness to the coach and their engagement in the task (Higgins, 2005).

Incremental theorists look for and are receptive to task framings and feedback that stress mastery over performance, and feedback that encourages them to discover strategies and assesses their progress on the task. A coach who focuses recipients’ attention on the goal of not making mistakes in order to avoid failure is inconsistent with incremental theorists’ belief that mistakes and learning from them are signs of progress. A mismatch between their expectations and a preventive coach likely leads to less engagement with the coach and task.

A coach with a promotion orientation typically describes goals as challenges to be overcome so as to achieve success. Comments are made to the effect, “Let’s see if you can attain this goal” and “Focus on your improvement and progress over time.” When describing behaviors and strategies, the coach emphasizes the need to “test different approaches” and “see what happens if you try this.” When providing feedback, this coach highlights progress and strategies that work versus those that do not. During coaching interactions with a recipient, the coach’s language tends to include words such as “better,” “improve,” and “positive,” words that have been shown to prime a promotion orientation (cf. Hong & Lee, 2008; Lee & Aaker, 2004; Shah et al., 1998).

For incremental theorists, the promotion framing of goals and behaviors, interpretation of feedback in terms of progress and what works, plus the language of a promotion-oriented coach matches their expectations (e.g., trying different strategies, making progress) of the type of guidance they seek in order to master the task (Dweck, 1999). The framing and language of a promotion-oriented coach conforms to beliefs that sustain the promotion regulatory focus of incremental theorists. This increases their engagement with the task and their learning from the coach (Higgins, 2005). For entity theorists, a mismatch with their expectations makes the promotion-oriented coach less rewarding. Coaching that focuses recipients’ attention on the goal of improving contradicts entity theorists’ beliefs that they cannot improve. This leads to less engagement with the coach and a less successful outcomes from the coaching process. Based on regulatory fit theory, the following hypotheses were tested:

Hypothesis 3a: Individuals with an entity belief about ability have higher performance than those who hold an incremental belief when their coach displays a prevention orientation.

Hypothesis 3b: Individuals with an incremental belief about ability have higher performance than those who hold an entity belief when their coach displays a promotion orientation.

Two studies were conducted to test these hypotheses. The first was a laboratory experiment that allowed us to isolate the promotion versus prevention framing of coaching of recipients performing a problem-solving task. The second was a correlation study conducted in an organizational setting that assessed the framing of supervisors’ coaching relationships with their subordinates.

Study 1

Method

Sample

The sample consisted of 118 undergraduate university students in a human resource management course in a large university in Canada. Of the students who reported their sex, 57 (48%) were female and 62 (52%) were male. Their mean age was 21.44 years (SD = 2.25), and their mean grade point average (GPA) was 3.27 (SD = 0.56) on a 4.0 scale.

The Task

Consistent with Wageman (2001), who identified “teaching the group to use a problem solving process” (p. 565) as a coaching behavior in the Fortune 500 organization where she conducted her research, the task that the participants performed was a fishbone diagram. This problem-solving tool is widely used to generate and organize information in the identification of “root causes” or potential solutions to problems in organizational settings (Wood, Cogin, & Beckmann, 2009). The word fishbone refers to the appearance of the diagram, which has a head where the main issue or problem is recorded and spines that are labeled with major categories of root causes or solutions. Specific causes or solutions are recorded on the spines under each category to which they best fit. Spines containing a specific cause or solution may be extended to include subcauses or subsolutions. The process is dynamic in that the categories and sets of causes or solutions within each category may be reorganized periodically and then redrawn on a clean fishbone diagram. There are specific rules for the construction of fishbone diagrams, which are “Clearly define the problem (Head),” “Clearly identify the problem categories/causes/solutions (large bones),” “Correctly pursue each line of causality back to its root cause” (small bones), and “Organize categories into subcategories clearly and logically.”

Completed fishbone diagrams can be assessed and scored for how well the rules have been applied and the overall quality of the analyses, based on how well the specific causes or solutions are organized within each of the categories and subcategories on the final diagram. In addition to providing a clear set of standards for the assessment of performance, the rules are relatively straightforward to teach and master in one coaching session. Because fishbone diagrams are used in problem solving on a wide range of tasks in organizations (Rudin, 1990), coaching people on the use of the tool has practical relevance (Latham & Lee, 1986).

Procedure

Participants were randomly assigned to one of four conditions in a 2 (coach’s prevention vs. promotion focus) × 2 (participants’ incremental vs. entity beliefs) design. After random assignment to the two coaching conditions, we primed rather than assessed dispositional implicit beliefs to enable us to have a fully crossed, between-participants factorial design. Considerable evidence indicates that the effects of entity versus incremental beliefs about ability are consistent across trait measures and situationally primed beliefs (e.g., Hong, Chiu, Dweck, Lin, & Wan, 1999). The priming was done by having participants read a description of the determinants of intelligence that stressed either fixed, innate determinants for those in the entity belief condition or development determinants for those in the incremental belief condition. Participants then completed a set of questions that provided a manipulation check on the priming of entity versus incremental beliefs.

Coaching of an individual on how to create fishbone diagrams followed immediately after the participants finished the questionnaire on the entity versus incremental beliefs. During the coaching session, depending upon the condition to which they were randomly assigned, participants observed a coach using either a prevention or a promotion focus when coaching a confederate on how to produce a fishbone diagram. Observing a recipient versus being the direct recipient of another’s actions does not have significantly different consequences on the expended effort following action (Spencer & Rupp, 2009). Hence, the performance of the participants should be influenced by the coaching interaction they observed in a similar manner to them being the direct recipient (cf. Avery, Richeson, Hebl, & Ambady, 2009).

Groups of participants observed the individual receiving coaching. This was done for three reasons. First, on and off the shop floor and in and outside the classroom, employees and students of management benefit from the role-modeling effects of observing a coach provide coaching to another individual. This is because role modeling is an effective way to enhance learning of new skills (Wood & Bandura, 1989b). Second, presenting coaching in this way allowed for the demonstration of the behaviors that are frequently reported as constituting learning-related coaching behaviors—teaching, providing feedback, providing practical application, observing, and modeling (D’Abate et al., 2003). Third, demonstrating coaching with a live model gave the participants an opportunity to observe the behavioral interactions between the coach and recipient and listen to the communication between the two as the coaching relationship developed. This permitted participants to experience how coaches relate to and communicate with others (Evered & Selman, 1989).

Throughout the coaching session, the coach and the individual who was directly coached stood in front of the group of participants and recorded all work on a whiteboard that was clearly visible to the participants (teaching, role modeling, and observing). The participants completed each of the steps required to create a fishbone diagram immediately following the coaching of that step (providing practical application). The participants were able to question the coach at any time during the coaching demonstration (teaching). After the model had completed the diagram, the coach posed questions and gave feedback (providing feedback) that was consistent with the coaching orientation of the experimental condition.

To hold nonmanipulated conditions constant, the coach and the model were the same two people in both the prevention and the promotion focus coaching conditions. The model was a female student, similar in age to the participants. The coach, who was older than the students, was also female.

The session ended with the participants completing another fishbone diagram that was returned for objective assessment. After the participants had submitted their fishbone diagrams, each one was given an information sheet that provided a debriefing of the experiment.

Manipulations and Measures

Entity–incremental beliefs were induced by asking participants to read a passage, developed by Hong et al. (1999), that described the properties and determinants of intelligence as either fixed and innate or developmental, respectively. For example, the entity prime included statements such as, “The results of research show that intelligence is not influenced by education or developmental opportunities and that intelligence is fixed long before people reach adulthood.” In the incremental prime, developmental statements were made about intelligence such as, “The results of research show that intelligent people are the product of their environments and their abilities are continually being shaped, even in adulthood.”

As a manipulation check on the effectiveness of the priming manipulation, participants completed a questionnaire regarding their beliefs about ability immediately after they had read the brief article on intelligence. The eight-item questionnaire, developed by Dweck (1999), included four items that assessed entity beliefs (e.g., “You can learn new skills, but you really can’t change your basic abilities”) and four items that assessed incremental beliefs (e.g., “You can increase your basic abilities”). Entity items were reverse coded to reflect an incremental orientation, and the eight items were averaged to give a measure of beliefs ranging from strong entity (low) to strong incremental (high) beliefs. This scale had adequate internal validity. The Cronbach’s alpha coefficient was .81.

The manipulation of the beliefs about ability was tested with a single-factor ANOVA with the two levels of the prime (entity or incremental) as a between-participant’s factor and scores on the entity–incremental belief scale as the dependent variable. Participants who received the incremental prime had significantly higher scores (M = 3.37, SD = 1.53) on the entity–incremental scale than participants who received the entity prime (M = 0.02, SD = 2.00), F(1, 116) = 106.01, p < .0001, η² = .48. Thus, the manipulation of beliefs about ability was effective.

Coaching orientation was manipulated through the two scripts that the coach learned and then applied in the relevant coaching condition. In the prevention focus condition, the script used negative referents based on the recommendations of Shah et al. (1998). For example, when reviewing the example of a fishbone diagram from the confederate recipient of coaching in front of the participants, the coach asked questions such as, “Can you tell me what you did poorly, what mistakes you made?” The coach also made evaluative statements such as, “Here are the main problems I found with how you used the fishbone technique.” These statements were repeated when the participants were asked to review their own fishbone diagrams during the coaching demonstration session.

In the promotion focus coaching condition, the script followed by the coach contained positive referents in comments to the confederate recipient and subsequently to the participants. These included statements such as, “Can you tell me what you could do better?” “What could you do to improve your use of the fishbone technique?” “Here is what I found positive with how you used the fishbone technique.” In both coaching conditions, the participants were encouraged to comment openly on both the confederate’s diagram and/or their own diagrams. Participants’ comments were summarized by the coach in a manner consistent with the motivational focus of the experimental condition, for example, “That is an improvement that can be made” (promotion focus) or “That is a mistake that can be avoided” (prevention focus).

To assess the manipulations of the coaching conditions and to check on the relative effectiveness of the coaching in the two conditions, videotapes were made of the coach. Two independent raters, who were unaware of the purpose of the experiment, evaluated the videotapes of the coach’s performance. Two aspects of the coaching were assessed, namely, the effectiveness of the coaching and the motivational orientation of the coach.

The effectiveness of each coaching session was assessed using a five-item questionnaire (e.g., “The coach clearly explained the fishbone technique”; α = .95). Each item was accompanied by a 5-point Likert-type scale (1 = strongly disagree, 5 = strongly agree). Responses to the five items were averaged to give a mean coaching effectiveness score. Ratings of the coach’s motivational orientation were based on the mean of two items for assessing prevention focus (e.g., “The coach focused on not making mistakes”; α = .84) and two items for assessing promotion focus (e.g., “The coach approached the task positively”; α = .97). Each item was assessed on a 5-point Likert-type scale (1 = hardly at all, 5 = a lot).

A t test revealed no difference in coaching effectiveness between the two coaching conditions (M = 4.80, SD = 0.28, for prevention; M = 4.30, SD = 0.99, for promotion), t₍₂₎ = .69, ns. Thus, any observed differences were not due to the quality of the coaching. Directional t tests on the manipulations checks for the coaching orientations showed that the coach’s focus was more prevention oriented (M = 4.00, SD = 1.41) than promotion oriented (M = 2.25, SD = 1.76), t₍₁₎ = 7.00, p < .05, in the prevention focus condition. Similarly, the coach’s focus was more promotion oriented (M = 5.00, SD = 0.00) and less prevention oriented (M = 1.75, SD = 0.35), t₍₁₎ = 13.00, p < .01, in the promotion focus condition. Thus, the coaching manipulation was effective.

Performance was assessed by two raters who independently evaluated the completed fishbone diagrams of participants. All identifiers were removed. The two raters not only were blind to the hypotheses but also were unaware of the ability beliefs or coaching orientation condition in which each fishbone diagram was produced. The raters had been trained in the rules for producing effective fishbone diagrams and had previously evaluated several examples of both high- and low-quality fishbone diagrams correctly. The two raters first recorded their independent evaluations of the quality of the fishbone diagrams and then developed a consensus evaluation for each diagram. The intraclass correlation (ICC) value for the three sets of ratings was .74. The performance scores used in the analyses were a combination of the three ratings, which were calculated by first calculating the average for the two independent ratings and then calculating the average of that score and the consensus rating. We combined these two commonly used procedures for reducing errors in the performance ratings so as to increase the reliability of this measure (Sulsky & Balzer, 1988). The correlation between the mean value of the two independent ratings and the consensus rating was .78 (ICC = .98).

Control variables included the participants’ age, sex, GPA, number of rules for using the fishbone technique correctly recalled, and number of categories and subcategories of causes or solutions generated. A questionnaire regarding these variables was completed after the coaching demonstration session had been completed but prior to participants’ completion of the fishbone diagrams that were subsequently evaluated by the two independent raters. Statistical control of these variables, along with the comparison of the effectiveness ratings of the coaching in each condition, ensured that the effect of the coach’s promotion versus prevention focus, as well as participants’ primed beliefs about ability, could be isolated. This made it possible to determine with greater confidence the effects of the coach’s motivational orientation on participants’ performance.

Results

Table 1 shows the means, standard deviations, intercorrelations, and reliability coefficients.

Table 1

Study 1: Correlations Among Variables in Laboratory Study

	M	SD	1	2	3	4	5	6	7	8
1. Coaching orientation	0.55	0.50	—
2. Implicit person theory prime	0.50	0.50	−0.08	—
3. Age	21.42	2.24	−.08	.15	—
4. Sex	0.46	0.50	.02	−0.01	−.19*	—
5. Grade point average	3.26	0.56	.07	.08	.02	.20*	—
6. Number of rules correct	2.35	1.38	.27**	−.05	.06	.11	.22*	—
7. Categories and subcategories	15.77	5.02	−.20*	.04	−.01	−.05	.07	.07	—
8. Performance	4.34	0.59	.34**	−.15	−.07	.08	.06	.16	.43**	—

Note: Coaching orientation: 0 = prevention, 1 = promotion; implicit person theory prime: 0 = entity, 1 = incremental. N = 118.

p < .05, two-tailed. **p < .01, two-tailed.

The main and interaction effects predicted in Hypotheses 1 and 2 were initially tested through a 2 × 2 ANOVA. This was followed by F tests for the planned comparisons predicted in Hypotheses 3a and 3b. The ANOVA revealed a significant main effect for coaching orientation. Promotion coaching orientation resulted in higher performance, F(1, 112) = 29.90, p <.0001, $η_{p}^{2}$ = .22, than did prevention coaching. The predicted interaction between the coaching orientation and participants’ primed beliefs about ability was also significant, F(1, 112) = 6.61, p <.05, $η_{p}^{2}$ = .06, thus supporting Hypothesis 2. Planned comparisons indicated that this interaction was due to the fact that under the prevention coaching focus, the participants primed with entity beliefs had higher performance, F(1, 50) = 7.30, p < .01, $η_{p}^{2}$ = .14, than those primed to have an incremental belief, thus supporting Hypothesis 3a. Hypothesis 3b was not supported. There was no significant difference between participants primed with incremental and entity beliefs under the promotion coaching focus. These results are summarized in Table 2 and depicted in Figure 1. The cell means from the ANOVA analysis are shown in Figure 1.

Table 2

Laboratory Study: Interactive Effects of Coaching Orientation and Participants’ Implicit Person Theory on Participants’ Performance

	Performance
	df	F	Partial η²
Covariates
Age	1	0.08	.00
Sex	1	1.62	.02
Grade point average	1	0.10	.00
Number of rules correct	1	0.01	.00
Categories and subcategories	1	50.99***	.33
Independent variables
Coaching orientation	1	29.70***	.22
Implicit person theory (primed)	1	4.22*	.04
Coaching Orientation × Implicit Person Theory	1	6.61*	.06
Error	104

Note: Coaching orientation: 0 = prevention, 1 = promotion; implicit person theory prime: 0 = entity, 1 = incremental.

p < .05, two-tailed. ***p < .001, two-tailed.

Figure 1

Laboratory Study: Interactive Effects of Coaching Orientation and Participants’ Implicit Person Theory on Participants’ Performance

Discussion

The results of Study 1 provide support for regulatory focus theory (Higgins, 1998) and the interaction perspective (Mischel & Shoda, 1995), as well as partial support for the regulatory fit theory (Higgins, 2005) that underpinned Hypotheses 3a and 3b. Participants who were exposed to promotion regulatory-focused coaching outperformed those who were exposed to prevention regulatory-focused coaching. This extends regulatory focus theory by demonstrating that when performance requires both eagerness (to generate possible solutions) and vigilance (to identify appropriate solutions), exposure to a promotion regulatory focus has greater effects on performance than does exposure to a prevention regulatory focus. The interaction perspective was also supported, as we found a medium effect size, $η_{p}^{2}$ = .06 (Cohen, 1988).

For regulatory fit arguments to provide a complete explanation of the interactive effects of coaching orientation and recipients’ conceptions of ability would have required four sets of differences, two of which were supported. First, for comparisons of entity primed beliefs within each of the coaching orientation conditions, (a) participants with the entity beliefs should have performed better than those with incremental beliefs in the prevention-focused coaching condition. And they did. In addition, (b) participants with entity beliefs should have performed less well than those with incremental beliefs in the promotion-focused coaching condition. This did not occur. Second, in comparisons of participants with either incremental or entity beliefs, respectively, between the prevention orientation and promotion orientation coaching conditions, (a) participants with incremental beliefs should have performed less well under prevention-oriented coaching than under promotion-oriented coaching, which they did. In addition, (b) participants with entity beliefs should have performed better under prevention-oriented coaching than under promotion-oriented coaching. This did not occur.

Thus, the arguments for regulatory fit were supported by two of the four possible outcomes. Participants with entity beliefs responded much more positively to prevention-oriented coaching, which was a good fit with their prevention regulatory focus than did the participants with an incremental belief. For the latter, the priming that placed them in a promotion-oriented regulatory focus was a poor fit with a prevention coaching orientation. In addition to lack of fit, their lower performance could be because participants who were primed to believe they were free to learn how to perform without having to be concerned about mistakes were then faced with the situational cue of the prevention-orientated coach who was communicating the message that mistakes need to be avoided (i.e., approach behavior is unwanted). Behavior stimulus interaction theory (Veling, Holland, & van Knippenberg, 2008) suggests that the response conflict generated specifically in contexts where situational requirements signal the undesirability of approach behavior (i.e., elicited by the incremental prime) is resolved by deeming the approach behavior to be less attractive. The perceived undesirability of approach behavior plus the pressure exerted by the coach to avoid mistakes may have combined to suppress performance.

Regulatory fit theory, however, was not supported with regard to participants with entity beliefs and a coach with a promotion focus. In the promotion-oriented coaching condition, it appears that the positive, developmental approach overcame the primed entity belief such that all participants benefited regardless of their entity or incremental beliefs. However, those with an incremental belief performed much worse in the prevention-oriented coaching condition, which was a poor fit with their regulatory focus, than they did in the promotion-oriented coaching condition, a good fit. The performance levels of those with entity beliefs did not differ across the two coaching orientation conditions.

In summary, it appears that individuals with incremental beliefs are somewhat more responsive to regulatory fit with their coaches’ regulatory orientation and learn more under a promotion-focused coach than under a prevention-oriented coach. In contrast, individuals with entity beliefs appear to learn equally well under both prevention- and promotion-oriented coaches. This may be the product of better regulatory fit under prevention-oriented coaching and due to a change in their primed beliefs about ability under the promotion-oriented coach.

Previous studies with student samples have contained a high proportion of participants with an incremental conception of ability as their default belief (Tabernero & Wood, 1999; Wood & Bandura, 1989a). Thus, it is possible that many of the participants in the promotion coaching condition who had been primed with an entity belief reverted to their dispositional incremental belief under the coach’s developmental encouragement. What is not clear from the current study is what happens if the entity beliefs are more deeply held and are not the product of a contextual prime of the type used in the current study. This and other issues were addressed in Study 2. Further implications of Study 1 are discussed following the second study.

Study 2

Starting with a laboratory experiment provides confidence regarding the internal validity of the causal effects of coaching orientation and recipients’ conceptions of ability on coaching effectiveness. Nevertheless, a potential limitation of this laboratory experiment is a lack of ecological validity. Unlike a work setting, the participants’ performance in the laboratory experiment had no positive or negative consequences for them other than the satisfaction derived from performing effectively or the dissatisfaction from performing poorly. In addition, as mentioned, the fact that the conceptions of ability were primed may have influenced the results for the entity belief prime in the promotion-oriented coaching condition. Consequently, a field study was conducted with employees to determine whether these results were replicable when the conceptions of ability were the dispositional beliefs of the employee/recipient and the coaching orientation was integral to the on-the-job coaching style of the supervisor/coach. The coaches in this study were supervisors who were trained and tasked with coaching their subordinates as part of the company’s leadership practices. This second study thus allowed us to examine Feldman and Lankau’s (2005) suggestion to investigate the influence of leadership (coaching) styles on employee outcomes when coaching is a formal part of supervisors’ job duties. We examined the same three hypotheses tested in the laboratory experiment.

Method

Overview

Study 2 was a lagged correlation field study. Data were collected from a Malaysian production factory of a Fortune 500 manufacturer of mobility solutions. The human resources manager of this production facility was approached for the study because the company had been described in the practitioner literature as an exemplar in the introduction of coaching to supervisory roles on an on-going basis. In addition, this organization was described as having a coaching culture throughout its global operations. Accordingly, supervisors as well as employees in the organization had received training from the company about using coaching as a way to relate to each other on a daily basis. We did not assess the supervisors’ specific coaching behaviors or their impact since previous research has examined the association between specific coaching behaviors and performance (Carson et al., 2007; Tewes & Tracey, 2008; Wageman, 2001) and our research interests was on the regulatory framing of coaching rather than on the behaviors per se.

Focus Group Interviews

To have a clear understanding, however, of what constituted coaching in the Malaysian production facility of this organization, the first author and a research assistant asked 33 employees, in focus groups, to describe what coaching meant to them. The focus groups were conducted at the same time but separately from the administration of surveys assessing employees’ perceptions of their supervisors’ coaching orientations and their own performance effectiveness following coaching. The focus group employees’ descriptions of what coaching meant to them follows. The measures of coaching orientation and performance effectiveness are described in a subsequent section.

As examples of effective coaching by their supervisors, the employees in the focus groups described teaching/giving direction about how to improve productivity; showing/teaching step-by-step procedures; giving clear explanations, saying “work faster” to motivate; briefing (setting production goals, giving feedback on number of rejects, number of faults in rejects) operators before the start of a work session and at the end of a shift; giving feedback and praise; showing charts and performance indicators such as attendance, output quality, productivity, and explaining goals to line operators as a group; dialogue on a one-to-one basis; and monitoring the hourly output target.

Employees also described giving wrong information, not paying attention, not understanding problems, not following up during the day to monitor progress, and communication breakdowns as examples of ineffective coaching by their supervisors. These descriptions of coaching support the notion that, for these employees, coaching was pervasive throughout each working day; these descriptions are consistent with behaviors identified in research conducted in the United States as constituting coaching (D’Abate et al., 2003; Evered & Selman, 1989).

Sample

The sample consisted of 249 assembly line employees. The mean age of the employees was 29.35 years (SD = 7.53); 98% were female. The mean number of years they had worked full-time was 9.59 (SD = 6.73).

Procedure and Measures

The entity–incremental beliefs about ability were collected from employees 9 months before the assessments of their supervisor’s coaching orientation and employee performance. Participants’ dispositional implicit person theories, including their conceptions of ability, were assessed using a general four-item scale developed and validated by Levy, Stroessner, and Dweck (1998). A sample item is, “People can change even their most basic qualities.” As in the manipulation check questionnaire used in Study 1, all four items were averaged to give a single strong entity (low) to strong incremental (high) belief scale. The Cronbach’s coefficient alpha of the disposition belief scale was .78.

Nine months after reporting their conceptions of ability, the employees rated their supervisor’s coaching orientation in their relationships with their subordinates on a Likert-type scale with five items that assessed levels of promotion orientation (e.g., “This supervisor often encouraged me to think about how I could achieve success at work”) and four items that assessed levels of prevention orientation (e.g., “This supervisor often encouraged me to worry that I would fail to accomplish my work goals”) over the preceding 9 months. The nine items were adapted to a coaching context from Lockwood, Jordan, and Kunda’s (2002) measure of self-regulatory focus. Cronbach’s coefficient alphas for the five items on levels of promotion orientation coaching and four items on levels of prevention orientation coaching were .92 and .82, respectively.

Performance effectiveness was rated by the employees on a five-item scale (e.g., “Coaching has led me to solve problems more effectively”). The five items were averaged to give a performance effectiveness scale with a coefficient alpha of .94. A self-report measure was used to assess the effect, if any, coaching had on employees since employees’ actions, including performance, are informed by their own interpretations of their environments (Salancik & Pfeffer, 1978), including, for example, how their supervisors frame the coaching they give employees. In addition, asking supervisors to assess the effectiveness of their coaching on subordinates’ performance could have produced strong socially desirable responses, as these supervisors worked in an organization that trained them in coaching and expected them to be effective as coaches (Paulhus, 2002).

Data Analyses

The interaction hypotheses were tested with hierarchical regression with performance effectiveness as the dependent variable. The entity–incremental belief and coaching orientation scores were entered first, followed by the interaction term for those two variables. Following Aiken and West (1991), all three predictor variables were centered to minimize multicollinearity. The interactions were graphed, as described by Aiken and West, who recommended depicting low and high values of the predictor variable at 1 standard deviation below and above the mean. Performance effectiveness was regressed separately on the two types of regulatory focus. This is because a test of the regulatory fit hypothesis requires examining the interaction of the entity–incremental belief scale separately for the prevention and promotion scales since there is no theoretical or empirical reason to suggest that these two regulatory foci are at opposite ends of the same continuum (i.e., negative scores on prevention focus do not equal positive scores on promotion focus) and is consistent with previous research (Lockwood et al., 2002; Neubert et al., 2008).

Results

A confirmatory factor analysis was conducted to test the measurement model for measures of the four focal variables, namely, (a) promotion coaching orientation, (b) prevention coaching orientation, (c) entity–incremental implicit beliefs, and (d) performance effectiveness following coaching. This analysis revealed that when each item was forced to load only on its respective factor, the four-factor model fitted the data well (comparative fit index [CFI] = .96, Tucker-Lewis index [TLI] = .94, root mean square error of approximation [RMSEA] = .06), whereas a single factor model did not (CFI = .61, TLI = .50, RMSEA = .18, Δχ² = 943.78, df = 8, p < .0001). These results provide evidence of the discriminant validity of the scales used in this study.

The means, standard deviations, intercorrelations, and Cronbach’s alpha coefficients for the variables are shown in Table 3. The results of the regression analyses are shown in Table 4

Table 3

Study 2: Correlations Among Variables

	M	SD	1	2	3	4	5	6	7
1. Promotion coaching orientation	3.88	0.77	(.92)
2. Prevention coaching orientation	3.51	0.80	.42**	(.82)
3. Implicit person theory	4.08	0.91	.14*	.05	(.78)
4. Age	29.35	7.53	.08	−.12	.02	—
5. Sex	0.98	0.13	.09	.11	−.04	.07	—
6. Years worked	9.59	6.73	.06	−.07	−.01	.84**	.08	—
7. Performance effectiveness	3.83	0.85	.56**	.23**	.04	.10	.01	.11	(.94)

Note: Reliabilities are shown on the diagonal in parentheses. Sex: 0 = male, 1 = female; implicit person theory: low values = entity, high values = incremental. N = 249.

p < .05, two-tailed. **p < .01, two-tailed.

Table 4

Field Study: Interactive Effects of Coaching Orientation and Employees’ Implicit Person Theory on Employees’ Performance Effectiveness

	Performance Effectiveness
	Model 1				Model 2
Criterion Variables	β	ΔF	R ²	ΔR²	β	ΔF	R ²	ΔR²
Step 1: Predictor variables		55.20***	.31	.31		7.28**	.06	.06
Promotion coaching orientation	.56***
Prevention coaching orientation					.23***
Implicit person theory	−.04				.03
Step 2: Interaction term		6.19*	.33	.02		4.55*	.07	.02
Promotion Coaching Orientation × Implicit Person Theory	.13*
Prevention Coaching Orientation × Implicit Person Theory					−.13*
F	39.64***				6.44**
df	3, 245				3, 245
N	249				249

Note: Model 1: coach’s promotion coaching orientation and employees’ implicit person theory as predictor variables; Model 2: coach’s prevention coaching orientation and employees’ implicit person theory as predictor variables.

p < .05, two-tailed. **p < .01, two-tailed. ***p < .001, two-tailed.

In support of the first hypothesis, both promotion-oriented (β = .56, p < .0001, 95% confidence interval [CI] = .51 < β < .74) and prevention-oriented (β = .23, p < .0001, 95% CI = .14 < β < .40) coaching had a positive effect on performance effectiveness. However, the standardized beta values indicate that coaching framed in promotion terms had a larger association with performance effectiveness than did coaching framed in prevention terms.

The interaction terms for entity–incremental beliefs and coaching orientation were significant and positive for promotion-oriented coaching (β = .13, p < .05, 95% CI = .03 < β < .27) and significant and negative for prevention-oriented coaching (β = –.13, p < .05, 95% CI = –.28 < β < –.01). These results support Hypothesis 2. The interactions are shown in Figure 2. The additional explanatory importance of the interaction terms is consistent with findings in behavioral science research (Cohen, 1988). The effect sizes (.03 and .02 for promotion vs. prevention coaching orientation by implicit person theory interactions when converted to the effect size, f²; Aiken & West, 1991) are larger than the median effect size of .002 reported by Aguinis, Beaty, Boik, and Pierce (2005).

Figure 2

Field Study: Interactive Effects of Coaching Orientation and Employees’ Implicit Person Theory on Employees’ Performance Effectiveness

To further analyze the interactions and to test Hypotheses 3a and 3b derived from regulatory fit theory, simple slopes analyses were conducted (Aiken & West, 1991; Cohen, Cohen, West, & Aiken, 2003). The simple slope analysis for the prevention coaching focus showed that it had a significant positive relationship with performance effectiveness for subordinates with an entity belief (β = .38, p < .0001, 95% CI = .21 < β < .59) but was not significantly related to the performance of subordinates who had an incremental belief (β = .13, ns, 95% CI = –.03 < β < .30). Only subordinates with an entity belief responded positively to a prevention coaching focus, thus replicating the result obtained in the laboratory. This result supports Hypothesis 3a.

These analyses also showed that a promotion coaching orientation had a significant positive relationship with performance effectiveness for all subordinates who had either incremental beliefs (β = .69, p < .0001, 95% CI = .60 < β < .92) or entity beliefs (β = .45, p < .05, 95% CI = .34 < β < .64). While all subordinates benefited from a promotion coaching focus, the standardized beta values indicate that the relationship was significantly more pronounced for employees with incremental beliefs (z = 2.79, p < .01).² Therefore, promotion-oriented coaching is beneficial for all subordinates, thus replicating the result obtained in the laboratory. In addition, there was an added benefit due to the better regulatory fit for subordinates with a dispositional incremental theory and promotion-oriented coaching. This result supports Hypothesis 3b.

Discussion

Two studies, one a laboratory experiment involving students of and future recipients of employee coaching, and the second, a lagged correlation field study involving employees in a global organization, yielded results that provide robust support for the positive effect of promotion-oriented coaching for recipients with entity or incremental beliefs about ability. These results also demonstrate that regulatory fit between the framing of coaching and recipients’ implicit beliefs is associated with performance effectiveness. In particular, across both studies, under prevention-oriented coaching, recipients with an entity belief about ability benefited from better regulatory fit relative to their colleagues with incremental beliefs. These two sets of results were evident for both primed and dispositional entity–incremental beliefs about ability, for manipulated and naturally occurring coaching orientations, on a short-term task in a laboratory, and in a 9-month assessment in an organization. In the organizational setting, the positive performance related to promotion-oriented coaching for all employees was supplemented with an additional effect due to the better regulatory fit of employees with dispositional incremental beliefs relative to those with entity beliefs. This supplemental effect of regulatory fit under promotion-oriented coaching was not evident in the laboratory setting. An explanation for this result is the altering of the primed entity beliefs in that experiment. Since previous studies have found student samples to contain higher proportions of students who have dispositional incremental beliefs relative to those with dispositional entity beliefs (e.g., Tabernero & Wood, 1999), the primed entity beliefs of our student participants may not have been as deeply held and, hence, may not have been as influential on their performance as the were the more deeply held dispositional entity beliefs of our field study participants. This result has implications for the long-run uses of promotion-oriented coaching that are discussed later.

While we could not examine the coaching (behavioral) orientation of a coach and its interaction with recipients’ implicit beliefs about ability in all the different contexts (e.g., organizational change), formats (e.g., life coaching, executive coaching), and characteristics (e.g., one-on-one support) of coaching interactions described in the literature (e.g., Feldman & Lankau, 2005; Hall, Otazo, & Hollenbeck, 1999), our laboratory and field studies, nevertheless, make several contributions to the coaching literature. Whether the context is the learning of a new skill or ongoing performance maintenance and improvement, our studies reveal that the behavioral orientation of the coach does interact with the recipient’s implicit person theories to influence the latter’s performance. Thus, our two studies provide support for Higgins’s (1997, 1998, 2000, 2005, 2006) regulatory focus and regulatory fit theories of motivation as a framework for training coaches who need to help recipients develop a new skill or improve day-to-day performance. They provide support for Kark and Van-Dijk’s (2007) conceptual argument that subordinates do perceive leaders as enacting different regulatory foci in their interactions with followers. Contradictory findings regarding the effectiveness of coaching on employee behavior suggest that moderator variables, such as those that affect regulatory fit, should be taken into account when assessing coaching effectiveness. Heslin et al. (2006), for example, have shown the importance of taking into account the beliefs about ability held by the coach. The present study has shown the necessity of taking into account the beliefs about ability held by the individual who is being coached. The role of moderator variables is discussed further when we consider implications for future research and practice.

A second contribution of these two studies is that they build on Heslin et al.’s (2006) results in that they suggest that promotion-oriented coaching should be the preferred approach when the beliefs or other characteristics of the recipient are unknown or when coaching a heterogeneous group. In Heslin et al.’s study, coaches with an incremental belief about ability were more effective than those with an entity-based view. If, as we have argued for the recipients in the two present studies, entity and incremental beliefs place individuals in a prevention and a promotion focus, respectively, then Heslin et al.’s results are consistent with our finding that promotion-focused is better than prevention-focused coaching.

Our notion that implicit beliefs place individuals in a promotion versus prevention focus is consistent with Van-Dijk and Kluger (2004) but is different from traditional conceptions of regulatory fit (e.g., Higgins, 2000). Just as Judge (2007) suggested that regulatory fit theory can extend person–organization fit theory, our findings in support of our fit hypotheses suggest that the concept of regulatory fit can be extended to include fit between the regulatory focus of the situation and individuals’ beliefs about their abilities.

The current results also have potential implications for practice, albeit these need to be subjected to further research before making prescriptions. We offer recommendations, although the interaction effect sizes found in the present field study are small according to guidelines offered by Cohen (1988). They are, nevertheless, larger than the median effect size reported by Aguinis et al. (2005). Although it may be tempting to dismiss small effect sizes as unimportant, there is agreement that small effect sizes do have theoretical and practical significance (Aguinis et al., 2005; Cohen, 1988), and the tendency of the behavioral sciences to harshly judge them may undermine the contribution of such findings to scientific progress and managerial practice (Breaugh, 2003). Medicine, in contrast, is less harsh. Rosenthal (1990) reported that a placebo-controlled experiment was discontinued because it was deemed unethical to not give a new drug to control patients, as the drug was found to decrease heart attacks by 4% (r = .04, effect size r² = .0016). This effect size is much smaller that the interaction effect sizes we found in our research yet was deemed of practical significance.

The results of this research have implications for theory and practice. Specifically, they support the recommendation from regulatory fit theory that people should be matched to their contexts in order to maximize the potential for motivation, learning, and performance. For example, there are work contexts, such as auditing, where error avoidance, strict adherence to routines, and meeting required standards are nonnegotiable features of a task. In such contexts, coaching, as well as incentives, systems, and processes, is more likely to present a prevention orientation in which individuals with entity beliefs are likely to be a better fit than those with incremental beliefs. A practical implication of the regulatory fit model is to recruit people with entity beliefs about ability for tasks. To test these implications would require examining the engagement, motivation, learning, and performance of people with entity beliefs compared with those with incremental beliefs in work settings where task characteristics require a preventive orientation. In addition, individuals with different implicit theories could be assessed on tasks that require either a prevention or promotion regulatory focus (Van-Dijk & Kluger, 2004) who report to leaders who are supportive (promotion regulatory focused) versus controlling (prevention regulatory focused; Kark & Van-Dijk, 2007) to examine a possible triple interaction among organizational, leadership, and individual characteristics.

As noted above, coaching orientation has not been reported in the literature as being part of the training of coaches. This also was not the case in the coaching training program for the supervisors assessed in Study 2. Therefore, as in Study 1, coaching orientation can be considered a separate dimension that contributes to the effectiveness of the coaching process. The practical significance of this finding is that coaching orientation needs to be taken into account in training programs.

Limitations

Each of the two studies has limitations, but in many instances, the limitations in one study were countered by the design feature of the other. The limitations of the laboratory experiment, as noted earlier, included lack of ecological validity due to the contrived nature of the setting and the use of students who were recruited to participate in it. The limitations of the field study included the correlation design, which meant that causal inferences could not be drawn. Another limitation was the use of self-assessments by subordinates for performance effectiveness following coaching. These limitations were partly countered by the strengths of the laboratory experiment where the results were consistent with those obtained from the employees. In addition, the measure of an employee’s implicit person theories preceded the self-report measures by 9 months. Moreover, the relationship between supervisors’ coaching orientations and their employees’ implicit person theories involved a complex interaction that is not easily explained by common method bias (Evans, 1985).

Some argue that a critical feature of coaching that differentiates it from other developmental practices such as training, teaching, supervising, performance management, and leading is that there is a one-on-one interaction between the coach and recipient (e.g., Feldman, 2001; Hall et al., 1999; London, 2003). Thus, another limitation of our research is that, even in our field study, which occurred in an organization described as having a coaching culture, we could not ascertain how many of the coaching interactions (e.g., dialogue on a one-to-one basis) that occurred between employees and their managers were one-on-one in nature. Thus, the effects we observed may be moderated by variations in the contexts, formats, or interaction characteristics of coaching. Future research needs to investigate this.

In addition, because coaching shares features with other developmental interactions in which feedback and insights aimed at guiding and inspiring improvements in performance is given, it is plausible that the psychological processes we examined in our research could also be applicable to other developmental practices. This possibility needs to be examined in future research. Finally, to advance our theoretical understanding of why coaching is effective, it may also be worthwhile, in future research, to more clearly differentiate behaviors that constitute coaching from those that are definitive of other developmental interactions.

In spite of the limitations inherent to each of the two studies, the results from two different designs and two different samples are consistent. As McGrath (1982) argued, “Using multiple methodological probes to gain substantive convergence by methods . . . compensate for one another’s vulnerabilities” (p. 80).

Footnotes

Acknowledgements

This article was accepted under the editorship of Talya N. Bauer. This research is partially supported by a GRF grant (CityU 148708) from the Research Grants Council of the Hong Kong S.A.R. and a grant from the Social Sciences and Humanities Research Council of Canada awarded to Christina Sue-Chan and Robert E. Wood, as well as a grant to Gary P. Latham from the Social Sciences and Humanities Research Council of Canada. We thank Madeline Wu for her assistance on this project. We also thank Kwok Leung for his comments on a previous version of this article.

Notes

References

Aguinis

Beaty

J. C.

Boik

R. J.

Pierce

C. A.

2005. Effect size and power in assessing moderating effects of categorical variables using multiple regression: A 30-year review. Journal of Applied Psychology, 90: 94-107.

Aiken

L. S.

West

S. G.

1991. Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.

Avery

D. R.

Richeson

J. A.

Hebl

M. R.

Ambady

2009. It does not have to be uncomfortable: The role of behavioral scripts in Black–White interracial interactions. Journal of Applied Psychology, 94: 1382-1393.

Avnet

Higgins

E. T.

2003. Locomotion, assessment, and regulatory fit: Value transfer from “how” to “what.” Journal of Experimental Social Psychology, 39: 525-530.

Breaugh

J. A.

2003. Effect size estimation: Factors to consider and mistakes to avoid. Journal of Management, 29: 78-97.

Brebels

De Cremer

Sedikides

2008. Retaliation as a response to procedural unfairness: A self-regulatory approach. Journal of Personality and Social Psychology, 95: 1511-1525.

Brockner

Higgins

E. T.

2001. Regulatory focus theory: Implications for the study of emotions at work. Organizational Behavior and Human Decision Processes, 86: 35-66.

Butler

2000. Making judgments about ability: The role of implicit theories of ability in moderating inferences from temporal and social comparison information. Journal of Personality and Social Psychology, 78: 965-978.

Carson

J. B.

Tesluk

P. E.

Marrone

J. A.

2007. Shared leadership in teams: An investigation of antecedent conditions and performance. Academy of Management Journal, 50: 1217-1234.

10.

Cohen

1988. Statistical power analysis for the behavior sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

11.

Cohen

1983. Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

12.

Cohen

West

S. G.

Aiken

L. S.

2003. Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.

13.

D’Abate

C. P.

Eddy

E. R.

Tannenbaum

S. I.

2003. What’s in a name: A literature-based approach to understanding mentoring, coaching, and other constructs that describe developmental interactions. Human Resource Development Review, 2: 360-384.

14.

Dweck

C. S.

1996. Capturing the dynamic nature of personality. Journal of Research in Personality, 30: 348-362.

15.

Dweck

C. S.

1999. Self-theories: Their role in motivation, personality, and development. Philadelphia, PA: Psychology Press.

16.

Dweck

C. S.

Leggett

E. L.

1988. A social-cognitive approach to motivation and personality. Psychological Review, 95: 256-273.

17.

Ellinger

A. D.

Bostrom

R. P.

2002. An examination of managers’ beliefs about their roles as facilitators of learning. Management Learning, 33: 147-179.

18.

Evans

M. G.

1985. A Monte Carlo study of the effects of correlated method variance in moderated multiple regression analysis. Organizational Behavior and Human Decision Processes, 36: 305-323.

19.

Evered

R. D.

Selman

J. C.

1989. Coaching and the art of management. Organizational Dynamics, 32: 16-32.

20.

Feldman

D. C.

2001. Career coaching: What HR professionals and managers need to know. Human Resource Planning, 24: 26-35.

21.

Feldman

D. C.

Lankau

M. J.

2005. Executive coaching: A review and agenda for future research. Journal of Management, 31: 829-848.

22.

Förster

Higgins

E. T.

Bianco

A. T.

2003. Speed/accuracy decisions in task performance: Built-in trade-off or separate strategic concerns? Organizational Behavior and Human Decision Processes, 90: 148-164.

23.

Freitas

A. L.

Liberman

Higgins

E. T.

2002. Regulatory fit and resisting temptation during goal pursuit. Journal of Experimental Social Psychology, 38: 291-298.

24.

Friedman

R. S.

Förster

2001. The effects of promotion and prevention cues on creativity. Journal of Personality and Social Psychology, 81: 1001-1013.

25.

Hall

D. T.

Otazo

K. L.

Hollenbeck

G. P.

1999. Behind closed doors: What really happens in executive coaching. Organizational Dynamics, 28: 39-53.

26.

Heslin

P. A.

VandeWalle

Latham

G. P.

2006. Keen to help? Managers’ implicit person theories and their subsequent employee coaching. Personnel Psychology, 59: 871-902.

27.

Higgins

E. T.

1997. Beyond pleasure and pain. American Psychologist, 52: 1280–1300.

28.

Higgins

E. T.

1998. Promotion and prevention: Regulatory focus as a motivational principle. Advances in Experimental Social Psychology, 30: 1-46.

29.

Higgins

E. T.

2000. Making a good decision: Value from fit. American Psychologist, 55: 1217-1230.

30.

Higgins

E. T.

2001. Promotion and prevention experiences: Relating emotions to nonemotional states. In Forgas

J. P.

(Ed.), Handbook of affect and social cognition: 186-211. Mahwah, NJ: Lawrence Erlbaum.

31.

Higgins

E. T.

2005. Value from regulatory fit. Current Directions in Psychological Science, 14: 209-213.

32.

Higgins

E. T.

2006. Value from hedonic experience and engagement. Psychological Review, 113: 439-460.

33.

Hong

Lee

A. Y.

2008. Be fit and be strong: Mastering self-regulation through regulatory fit. Journal of Consumer Research, 34: 682-695.

34.

Hong

Chiu

Dweck

C. S.

Lin

D. M.

Wan

1999. Implicit theories, attributions and coping: A meaning system approach. Journal of Personality and Social Psychology, 77: 588-599.

35.

Judge

T. A.

2007. The future of person–organization fit research: Comments, observations, and a few suggestions. In Ostroff

Judge

T. A.

(Eds.), Perspectives on organizational fit: 419-445. New York: Lawrence Erlbaum.

36.

Kark

Van-Dijk

2007. Motivation to lead, motivation to follow: The role of the self-regulatory focus in leadership processes. Academy of Management Journal, 32: 500-528.

37.

Latham

G. P.

Almost

Mann

Moore

2005. New developments in performance management. Organizational Dynamics, 34: 77-87.

38.

Latham

G. P.

Lee

T. W.

1986. Goal setting. In Locke

E. A.

(Ed.), Generalizing from laboratory to field settings: 101-117. Lexington, MA: Heath.

39.

Lee

A. Y.

Aaker

J. L.

2004. Bringing the frame into focus: The influence of regulatory fit on processing fluency and persuasion. Journal of Personality and Social Psychology, 86: 205-218.

40.

Levy

Stroessner

Dweck

C. S.

1998. Stereotype formation and endorsement: The role of implicit theories. Journal of Personality and Social Psychology, 74: 1421-1436.

41.

Lockwood

Jordan

C. H.

Kunda

2002. Motivation by positive or negative role models: Regulatory focus determines who will best inspire us. Journal of Personality and Social Psychology, 83: 854-864.

42.

London

2003. Job feedback: Giving, seeking, and using feedback for performance improvement. Mahwah, NJ: Lawrence Erlbaum.

43.

Mace

M. L.

1950. The growth and development of executives. Boston: Harvard Business School, Division of Research.

44.

McGrath

J. E.

1982. Dilematics: The study of research choices and dilemmas. In McGrath

Martin

Kulka

(Eds.), Judgment calls in research: 69-102. Beverly Hills, CA: Sage.

45.

Mischel

Shoda

1995. A cognitive-affective system theory of personality: Reconceptualizing situations, dispositions, dynamics, and invariance in personality structure. Psychological Review, 102: 246-268.

46.

Morgeson

F. P.

2005. The external leadership of self-managing teams: Intervening in the context of novel and disruptive events. Journal of Applied Psychology, 90: 497-508.

47.

Neubert

M. J.

Kacmar

K. M.

Carlson

D. S.

Chonko

L. B.

Roberts

J. A.

2008. Regulatory focus as a mediator of the influence of initiating structure and servant leadership on employee behavior. Journal of Applied Psychology, 93: 1220-1233.

48.

Paulhus

D. L.

2002. Socially desirable responding: The evolution of a construct. In Braun

H. I.

Jackson

D. N.

Wiley

D. E.

(Eds.), The role of constructs in psychological and educational measurement: 49-69. Mahwah, NJ: Lawrence Erlbaum.

49.

Plaks

J. E.

Stecher

2007. Unexpected improvement, decline, and stasis: A prediction confidence perspective on achievement success and failure. Journal of Personality and Social Psychology, 93: 667-684.

50.

Rosenthal

1990. How are we doing in soft psychology? American Psychologist, 45: 775–777.

51.

Rudin

1990. Simple tools solve complex problems. Quality, 29(4): 50-51.

52.

Salancik

G. J.

Pfeffer

1978. A social information processing approach to job attitudes and task design. Administrative Science Quarterly, 23: 224-253.

53.

Shah

Higgins

E. T.

Friedman

R. S.

1998. Performance incentives and means: How regulatory focus influences goal attainment. Journal of Personality and Social Psychology, 74: 285-293.

54.

Spencer

Rupp

D. E.

2009. Angry, guilty, and conflicted: Injustice toward co-workers heightens emotional labor through cognitive and emotional mechanisms. Journal of Applied Psychology, 94: 429-444.

55.

Sulsky

L. M.

Balzer

W. K.

1988. Meaning and measurement of performance rating accuracy: Some methodological and theoretical concerns. Journal of Applied Psychology, 73: 497-506.

56.

Tabernero

Wood

R. E.

1999. Implicit theories versus the social construal of ability in self-regulation and performance on a complex task. Organizational Behavior and Human Decision Processes, 78: 104-127.

57.

Tewes

M. J.

Tracey

J. B.

2008. An empirical examination of posttraining on-the-job supplements for enhancing the effectiveness of interpersonal skills training. Personnel Psychology, 61: 375-401.

58.

Van-Dijk

Kluger

A. N.

2004. Feedback sign effect on motivation: Is it moderated by regulatory focus? Applied Psychology: An International Review, 53: 113-135.

59.

Veling

Holland

R. W.

van Knippenberg

2008. When approach motivation and behavioral inhibition collide: Behavior regulation through stimulus devaluation. Journal of Experimental Social Psychology, 44: 1013-1019.

60.

Wageman

2001. How leaders foster self-managing team effectiveness: Design choices versus hands-on coaching. Organization Science, 12: 559-577.

61.

Wood

R. E.

Bandura

1989a. Impact of conceptions of ability on self-regulatory mechanisms and complex decision making. Journal of Personality and Social Psychology, 56: 407-415.

62.

Wood

R. E.

Bandura

1989b. Social cognitive theory of organizational management. Academy of Management Review, 14: 361-364.

63.

Wood

Cogin

Beckmann

2009. Managerial problem solving: Frameworks, tools, techniques. Sydney: McGraw-Hill.

64.

Yukl

2009. Leadership in organizations (7th ed.). Englewood Cliffs, NJ: Prentice Hall.