Interdependent Tripartite Efficacy Perceptions and Individual Performance: Case Study of a Boys’ Basketball Team

Abstract

Tripartite efficacy refers to the beliefs of the individuals within a dyad regarding personal abilities (self-efficacy), the partner’s abilities (other-efficacy), or relation-inferred self-efficacy. This efficacy model has recently gained popularity in sports research (Jackson, Whipp, & Beauchamp, 2013), although there has not been any longitudinal research on efficacy beliefs and performance within this complex intra-dyad tripartite efficacy model. In a case study, we examined six individual players on a high school basketball team to explore any longitudinal changes in these tripartite efficacy beliefs through a season of play. On seven data collection periods, players completed the Basketball-Tripartite Efficacy Measure, and their game performance statistics were analyzed with an objective basketball individual performance formula. We found similar variations between participants’ other-efficacy beliefs and the dyad partner’s basketball performance score as well as between self-efficacy and individual performance score. Observational data from this case study lend some support to spiraling of self-efficacy and performance from repeated successes or failures and to perceived efficacy-performance plateaus that have been previously demonstrated in controlled experimental research. Importantly, this study suggests the presence of other-efficacy beliefs in their relationship to other-performance and to spiraling relationships between other-efficacy beliefs and other-performance, which have not been demonstrated previously.

Keywords

Self-efficacy other-efficacy RISE efficacy spirals longitudinal

Introduction

Because of the interdependent nature of individuals in groups, two-person interactions (dyads) within a group have particular relevance to group effectiveness in multiple domains (Hare, 1994; Katz, Lazer, Arrow, & Contractor, 2004). In sports, the importance of dyads has been specifically acknowledged in sports teams such as soccer and basketball (Darnis-Paraboschi, Lafont, & Menaut, 2005; Wickwire, Bloom, & Loughead, 2004). Dyads are the smallest team unit, consisting of two communicating members who rely on one another for personal and mental support in social and task-based pursuits (Wickwire et al., 2004); they can be represented in sports by coach–athlete, coach–coach, and athlete–athlete pairings. Athlete–athlete dyads in larger sports teams have become a critical area of study due to the interdependent relationship needed for successful performances (Bourbousson, Sève, & McGarry, 2010; Habeeb, Eklund, & Coffee, 2017; Vilar, Araújo, Davids, & Button, 2012). Further, evidence suggests that stronger dyadic relationships are associated with greater overall team effectiveness (Wickwire et al., 2004).

When examining intra-dyadic relationships, the tripartite efficacy framework—which encompasses three distinct types of efficacy beliefs an individual within a dyadic pair may hold, self-efficacy, other-efficacy, and relation-inferred self-efficacy (RISE; Jackson, Gucciardi, & Dimmock, 2011; Jackson, Knapp, & Beauchamp, 2008; Jackson et al., 2013; Lent & Lopez, 2002)—is of interest. Most of the research on efficacy beliefs has focused on self-efficacy or a person’s belief about his or her own ability to perform particular behaviors or skills (Bandura, 1986, 1997; Besharat & Pourbohlool, 2011; Hepler & Chase, 2008; Lent, Schmidt, & Schmidt, 2006; Yeo & Neal, 2006). Self-efficacy has also been applied to an understanding of a group’s collective sense of efficacy (i.e., a group of athletes’ beliefs in their ability as a team to perform well) by combining the self-efficacy beliefs of individual team members to establish an aggregate self-efficacy of the group (Bandura, 1986, 1997; Habeeb et al., 2017; Heuzé, Raimbault, & Fontayne, 2006). Other-efficacy, an individual’s belief about the ability of his or her partner, and RISE, an individual’s beliefs about how his or her efficacy is perceived by the partner, are the two other aspects of tripartite efficacy beliefs. Other-efficacy and RISE should be examined in dyadic pairs of interdependent sports teams because the strength of these pairs has been found to be related to team cohesion and success (Habeeb et al., 2017; Jackson et al., 2008; Lent & Lopez, 2002; Wickwire et al., 2004).

These three efficacy components have been found to influence and predict variations in personal performance of tasks such that higher self- and other-efficacy beliefs have been related to higher task performance and greater enjoyment, when compared with other-efficacy also relating to self-efficacy across various domains (Dunlop, Beatty, & Beauchamp, 2011; Jackson et al., 2013; Jackson, Gucciardi, Lonsdale, Whipp, & Dimmock, 2014; Saville & Bray, 2016). For example, Jackson et al. (2013) reported that adolescents’ other-efficacy beliefs about their physical education teachers and their RISE beliefs significantly predicted their self-efficacy beliefs in their own physical education ability and their own physical activity skills outside the physical education class. Dunlop et al. (2011) found that participants who received negative other-efficacy beliefs from their partners in a dance video game performed significantly worse than participants who received positive other-efficacy beliefs from their partners. These negative-feedback participants also reported lower subsequent self-efficacy beliefs following the feedback. Finally, in a recent study of youth athletes, engaging in RISE-related coaching behaviors with players was associated with greater RISE and self-efficacy beliefs for the athletes (Saville & Bray, 2016). In summary, this research suggests that other-efficacy beliefs can influence an individual’s sense of self-efficacy and, possibly, performance.

Research examining the relationship between efficacy beliefs and performance has demonstrated disagreement about whether the direction of the potential effect is from positive beliefs to positive performance or the reverse (Feltz, 1982; Heggestad & Kanfer, 2005; Ritchie & Williamon, 2012; Shea & Howell, 2000). Even though the direction of impact is unclear, most research suggests a positive relationship between the two variables. A meta-analysis of 114 studies found a weighted average correlation of .38 in favor of a moderate significant positive relationship (Stajkovic & Luthans, 1998), though sport psychology studies were not part of this meta-analysis. Although no research has examined the tripartite efficacy components and performance in a way that that could provide a clear answer to the question of causal direction, several studies of this tripartite model have examined athlete–athlete dyads (Jackson et al., 2008, 2011), student–professor dyads (Jackson et al., 2013), and how relational efficacy beliefs interact with each other (Jackson, Myers, Taylor, & Beauchamp, 2012; Lent & Lopez, 2002). Given the positive relationship between efficacy variable and performance shown through Stajkovic and Luthans’ (1998) meta-analysis, an important next step in needed research is to analyze how any or all components of tripartite efficacy might influence performance.

Another complicated concept in the efficacy–performance relationship is the concept of efficacy spirals. Bandura (1986) stated that typically, “successes raise efficacy appraisals; repeated failures lower them” (p. 399) and after “a strong sense of efficacy is developed through repeated successes, occasional failures are unlikely to have much effect on an individual’s efficacy capabilities” (p. 399). These ideas exemplify efficacy spirals, meaning the phenomenon occurring from a pattern of consecutive increases (or decreases) in both perceived efficacy and performance over a minimum of three task attempts (Lindsley, Brass, & Thomas, 1995; Shea & Howell, 2000). Efficacy spirals have been observed by researchers as changes in efficacy and performance following success or failure (Feltz, 1982; Lindsley et al., 1995; Shea & Howell, 2000). For example, Locke, Frederick, Lee, and Bobko (1984) demonstrated with individuals completing a brainstorming task that past performance had a major influence on future performances. Although there is evidence of variance in self-efficacy beliefs, no prior research has addressed how task performance might relate to variations in efficacy beliefs directed toward others.

In summary, available research on the constructs included in the tripartite efficacy framework indicates that these beliefs are likely to influence each other and possibly performance (e.g., Dunlop et al., 2011; Jackson et al., 2008, 2014). Further, efficacy-performance spirals occur based on results of past performances (e.g., Lindsley et al., 1995; Shea & Howell, 2000). Limited research has addressed correlates of changes in tripartite efficacy components longitudinally, such as during a competitive sports season. Most past research focused on RISE beliefs from one individual (e.g., coach) without considering how teammates’ beliefs may relate to individual performance (Jackson et al., 2014). A research gap remains regarding how tripartite efficacy beliefs change and relate to performance over time, particularly when considering teammates’ RISE. In the current study, we explored tripartite efficacy changes longitudinally within a case study analysis of an interscholastic varsity basketball team over the duration of a single season. Drawing on previous literature, our primary two-part research question was the following: (a) Will tripartite efficacy beliefs of these interscholastic basketball players fluctuate over time; and, if so, (b) will these efficacy variations relate to basketball performance?

Method

Participants

One high school boys’ basketball team in the Midwestern United States was the focus of this case study, with data collected from six senior players (one participant left the team midway through the season) identified by the head coach before the season as players who were expected to play the most game minutes over the course of the season. The participants identified as African American (n = 3), Other or Mixed (n = 2), and Caucasian (n = 1), with an average of 3.17 (SD = 0.98) years on the team. The basketball positions of the participants included three guards and three forwards with an average age of 17.5 years (SD = 0.6).

Measures

Demographic information

We used a demographic questionnaire to collect information on participants’ age, race, position, number of years on the team, and grade in school.

Basketball tripartite efficacy beliefs

We created the Basketball-specific Tripartite Efficacy Measure (B-TEM), based on the Basketball Self-Efficacy Questionnaire developed by Hutzler and Shemesh (2012), to investigate tripartite efficacy beliefs of high school basketball players. Participants taking the Basketball Self-Efficacy Questionnaire rate their efficacy in their ability to execute basketball-specific skills on a Likert scale from 1 (very poor) to 5 (very high). A sample item is, “The level of my self-confidence I have in shooting the ball into the basket at critical times in the game is . . .” After receiving permission to modify the scale, an initial list of tripartite items was determined with the assistance of five basketball experts (i.e., two high school coaches and three former collegiate basketball players). They were asked to give a list of 10 necessary skills for a basketball player to have in order to be successful in competition. This list of skills was themed by the primary researcher and a secondary outside basketball expert and then reduced to five key skills. These skills comprised the five questions of the B-TEM: (1) ability to properly respond mentally in a game, (2) perform fundamental basketball skills at a high level, (3) physically perform at a high level, (4) impact the play of teammates, and (5) be a solid defender. Participants were instructed to “answer the following questions about how you feel at this current moment in the season.” The stems to the questions varied depending on which factor of tripartite efficacy was being measured (Habeeb et al., 2017; Jackson et al., 2013), resulting in three subscales: self-efficacy, other-efficacy, and RISE. For self-efficacy, the stem was, “I believe that I (player number __)…,” for other-efficacy, it was “I believe that (player number ___)…,” and for RISE, it was “I believe that (player number __) thinks that I….” The B-TEM was then pilot tested with five starters of two collegiate intramural basketball teams (n = 10). Cronbach’s alpha coefficients confirmed internal consistency for all subscales with the pilot sample: self-efficacy (SE) = .94, other-efficacy (OE) for all players ranging from .72 to .98, and RISE at .94.

The final B-TEM questionnaire consists of five items each for individual self-efficacy, other-efficacy, and RISE from the other teammates. Thus, participants answered 55 total questions: 5 about self-efficacy, 25 about other-efficacy (a set of B-TEM items for each of five teammates), and 25 about RISE (a set of B-TEM items for each of five teammates). All items were answered using a 7-point Likert scale, with answers ranging from 1 (extremely unconfident) to 7 (extremely confident). The original scale created by Hutzler and Shemesh (2012) was only a 5-point scale, but the B-TEM utilized a 7-point scale to allow for more observable variation among scores. The full B-TEM format is given in the Appendix. Cronbach’s alpha coefficients with the current sample confirmed internal consistency of most items (Nunnally, 1978). Each OE-# refers to other-efficacy of a particular player (i.e., Player 1, Player 2, Player 3, Player 4, Player 5, and Player 6). The alphas were as follows: SE = .86, OE-1 = .79, OE-2 = .80, OE-3 = .66, OE-4 = .60, OE-5 = .75, OE-6 = .88, and RISE = .94. Two of the alpha coefficients were near the acceptable cutoff (OE-3 = .66; OE-4 = .60), which reflected the other-efficacy beliefs of teammates directed toward Players 3 and 4. However, this variation in the Cronbach’s alpha coefficient scores was likely caused by the participants’ scores on one specific item (i.e., belief in teammate’s ability to impact the play of his teammates) that varied substantially toward these specific players during multiple points in the season. It is believed that the numerous variations in the responses for this item, combined with the relative stability of the responses for the other four items in the other-efficacy scales, could be the cause for these lower alpha coefficients of the scores toward these participants.

Individual performance

The performance of each individual player was measured by an Objective Basketball Individual Performance formula (OBIP) used by Bray and Whaley (2001). The formula is Performance = Shot% (Points +Rebounds + Assists + Steals) − Turnovers + 10. Higher scores indicated better overall performance. The OBIP was shown by Bray and Whaley to be an effective measure of performance regardless of the type of team, offense, or player position. Data for the OBIP formula were acquired from the official box score reported during every game. Copies of the official box score were obtained from the head coach at the weekly meeting.

Season reflection

After the season ended, each participant completed a short interview to gain insight about what happened throughout the season. This process consisted of participants viewing the charts of their results and then expanding and explaining any interesting trends in the data.

Procedure

Following ethical approval from our university’s institutional review board, participants and parents provided informed assent and consent, respectively. During the first step of data collection, each participant completed a demographic questionnaire in person and then completed the online version of the B-TEM, which took 10 to 15 minutes. For each subsequent data collection, participants completed the online survey individually on a computer in the head coach’s office during practice time. The participants were pulled from practice individually to complete the survey. The participants completed the survey seven times over the course of the season; the first time was the day before the first game. Each subsequent test was approximately every 2 weeks, except the fourth data collection, which was 4 weeks after the previous data collection point due to inclement weather. These specific dates of data collection are indicated in Table 1. Participants were debriefed during their final exit interview.

Figure 1.

The standardized scores for Player 1 across the seven data collection periods. The first data collection period occurred prior to the first game, which is why no OBIP score is available at D1. SE: self-efficacy; RISE: relation-inferred self-efficacy; OE: other-efficacy; OBIP: objective basketball individual performance.

Team and season context

The competitive season for this team consisted of 22 games over 14 weeks; however, due to weather cancellations, the final number of games was 19. The team practiced for about 1 hour and 45 minutes four to five times a week after school. The team’s preseason consisted of 2 weeks of practice prior to the first game. The team finished the regular season with a record of 2–17 or two wins and 17 losses, down from the previous season. Prior to the beginning of the season, the high school was voted to be closed following the end of the school year and merge with their cross-town rivals. The season studied, in effect, was the team’s final season as a school.

It is also important to note that one of the players stopped playing with the team prior to the end of the season having competed in 8 of the 19 games, and having completed five of the seven data collections. The phrase data collection period (D) describes the time between two data collection points. Additionally, the third data collection period (D3) was extended to cover 28 days, because of severe winter weather, which caused eight practice cancellations. The fourth data collection period (D4), which covered 14 days, was also affected by inclement winter weather resulting in the cancellation of two games and four practices. The team only played one game during this data collection period.

Analysis

The research design of this study was a nonexperimental descriptive case study. A case study, defined by Yin (2009), is an empirical enquiry that investigates a contemporary phenomenon within its real-life context. This type of methodological approach was ideal because case studies allow for detailed examination at multiple time points across a time period to determine what is truly occurring in the field and compare the results to what established theory and previous research suggests will be found (Anderson, Miles, Mahoney, & Robinson, 2002; Yin, 2009). To answer the research questions, (a) will tripartite efficacy beliefs of the interscholastic basketball players fluctuate over time; and, if so, (b) will variations in these efficacy beliefs relate to performance, we visually examined the variation of graphed data points in efficacy beliefs and performance across players throughout the season.

First, Cronbach’s alpha coefficients were conducted to assess reliability for each subscale and descriptive statistics were assessed through Statistical Package for the Social Sciences version 19 (Chicago, IL). Then, individual performance statistics for each game were entered into Microsoft Excel 2007 to produce a single score representing each player’s game performance using the OBIP formula. Together, these data were used to create simple line graphs (Kazdin, 2011) in Excel, plotting the means for each variable at each data collection point per person. Thus, each of the six participants had a total of three charts representing their season data: (1) self-efficacy scores and RISE from each teammate; (b) other-efficacy scores for each teammate; and (c) individual performance (OBIP) scores.

Through the line graphs, we used visual analysis to examine the fluctuation patterns between tripartite efficacy variables and performance across the season, and any substantial variations (Kazdin, 2011). We were unable to find prior studies analyzing nonintervention based data, so we utilized methods adopted for single-case designs. This consisted of examining changes in means and changes in trends across time (Kazdin, 2011). Substantial longitudinal variations between data collection points were present when a 14.29% or greater variation for the B-TEM subscales was observed. This threshold was determined to be substantial because it reflected average changes of a single level in the variables on the B-TEM (e.g., self-efficacy improving from 5 to 6 is a substantial increase of 14.29%). Substantial variations in OBIP scores were established as scores differed one or more standard deviations from the participant’s OBIP season average. Variations meeting the threshold in any of these variables, whether positive or negative, were considered substantial. Additionally, when the term match is used in any form in the following section, it refers to similar substantial changes found in those specified variables during the same data collection period. It should be noted that this term does not encompass statistical meaning but was used to guide our discussion of changes in the results.

To examine the trends in efficacy beliefs to see if they paralleled similar changes in OBIP scores, we took the following steps. We first examined individual self-efficacy and RISE scores to see how they matched with OBIP scores. We would see how the change in self-efficacy and RISE compared with changes in OBIP. Next, we compared OE scores to see how they related to that individual’s OBIP. For example, we examined OE scores directed at Player 1 in relation to Player 1’s OBIP to see if an increase in OE was associated with an increase in OBIP.

The graphs presented within this manuscript are on a standardized scale. This was done to ease readability by putting all scores on the same graph. RISE and OBIP scores were averaged at each data collection point as well as OE scores directed at that player. In other words, RISE at D1 reflects the average of Player 1’s five RISE scores at D1. The OE score at D1 reflects the mean standardized score of all OE scores directed at Player 1 at D1. OBIP at D1 is the average from games played during that data collection period. Self-efficacy contained only measurements from that individual. However, full charts graphed with separate raw scores are available from the second author.

Finally, each individual player confirmed the accuracy of the results from the B-TEM and OBIP formula during the final season reflection. Since each interview included specific questions that were unique to that particular player based on their profile, traditional qualitative analytic procedures were not appropriate. However, quotes are included where applicable to give more insight into the data.

Results

Descriptive data for the participants’ B-TEM scores are represented in Table 1 and the individual OBIP scores are represented in Table 2. These data provide the full set of scores for each player. The questions examined in this study were as follows: Do tripartite efficacy beliefs of interscholastic basketball players fluctuate over time, and if so, are the variations related to basketball (task) performance? The results for the first part of this research question included data on trends of individuals’ self-efficacy and RISE beliefs in relation to their individual game performance scores. There were two participants who had matching relationships between three of the variables at multiple data points, as well as one additional participant who had a matching relationship between RISE and individual performance across multiple data points. Player 1 reported the strongest examples of the simultaneous variance in his self-efficacy, RISE, and performance scores over the course of the season. To serve as an example, his scores are graphically depicted in Figure 1, however, graphs and data for all players is available through the online supplemental material. More specifically, during D1, Player 1 reported substantial drops in his RISE beliefs for four of his five teammate relationships, which followed a series of three substantially poor game performances. In D2, Player 1 reported a general increase in his RISE scores with every teammate, two of which were substantial increases. Over the course of D2 and D3, Player 1 also reported a substantial 14.29% increase in his self-efficacy and a 17% increase in his RISE with Player 3, as well as consistently improving game performances. Player 1 stated about these increases: “[I] had more confidence in myself and other people and I really thought we could just get better.” Following D2, there was a split in the RISE beliefs of Player 1 where he reported self-efficacy scores and Player 3-RISE scores that varied nearly identically over the rest of the season. Player 3 also demonstrated matching variations in his self-efficacy, RISE scores, and game performance during D1, D2, D3, and D5. He also stated that he was “playing with confidence” during these periods and as a result felt good about himself and believed his teammates perceived him to be more capable. Finally, Player 4’s data showed matches in his RISE beliefs and individual performance during D2 and D3. Players 2, 5, and 6 demonstrated limited variation in their self-efficacy and RISE beliefs but still had substantial variations in their performances. Player 2 indicated that changes in OE and RISE were related to performance. He stated that, “everything our coaches have been saying to us is really starting to stick, so everyone is starting to perform at the level they know they can perform.”

Table 1.

Participants’ B-TEM scores across the basketball season.

		Score
Player	Date	SE-1	RISE-2	RISE-3	RISE-4	RISE-5	RISE-6	OE-2	OE-3	OE-4	OE-5	OE-6
Player 1	11/25	5.8	5.2	5.8	5.4	4.6	5.2	5.4	5.8	5.8	5	4.6
	12/10	5.2	3 (−31.43%)	4.6 (−17.14%)	2 (−48.57%)	4	4 (−17.14%)	5.4	5.2	5.4	3.6 (−20%)	4.4
	12/20	5.4	4 (+14.29%)	5.2	4 (+28.57%)	4	4.8	5	5.8	5.6	4.6 (+14.29%)	4.6
	1/17	6.2	4	6.4 (+14.29%)	4	4	4	6 (+14.29%)	7 (+17.14%)	5.2	5	5
	1/30	5.6	4.4	5.8	4	4.2	5 (+14.29%)	4.8 (−17.14%)	6 (−14.29%)	5.6	4.6	5.8
	2/13	6.8 (+17.14%)	4	7 (+17.14%)	4	4	4 (−14.29%)	5.6	6.8	6.6 (+14.29%)	5.8 (+17.14%)	5
	3/3	6.6	4	6 (−14.29%)	4	4	–	5.6	7	5.8	4.6 (−17.14%)	–
		SE-2	RISE-1	RISE-3	RISE-4	RISE-5	RISE-6	OE-1	OE-3	OE-4	OE-5	OE-6
Player 2	11/25	6.8	6.6	6	7	6.6	6	6.4	6.6	7	6.4	6.4
	12/10	6.6	6	6.6	7	6.6	6.6	5 (−20%)	6.4	7	6.2	5.2 (−17.14%)
	12/20	7	6.4	6	6.6	6.2	6.2	5.2	5.6	6.6	5.4	5.2
	1/17	7	6	6	6.4	5.6	6	6	7 (+20.0%)	6.4	6	4.2 (−14.29%)
	1/30	7	6	6	7	6	6.2	6	5.8 (.17.14%)	7	5.4	4
	2/13	6.8	6.2	7 (+14.29%)	7	6.2	4 (−32.43%)	6	7 (+17.14%)	7	6	2 (−28.57%)
	3/3	7	6.6	6.4	6.6	6.6	–	6.2	6.2	6.6	6.4	–
		SE-3	RISE-1	RISE-2	RISE-4	RISE-5	RISE-6	OE-1	OE-2	OE-4	OE-5	OE-6
Player 3	11/25	6.4	6	6	6	7	6	6.2	6	6.8	5.6	5.2
	12/10	6	6	6.4	6.4	5.8 (−17.14%)	5.6	5.8	6	6	5.6	5.6
	12/20	7 (+14.29%)	7 (+14.29%)	5.8	6.2	5.4	5.2	6	5.8	6.2	5.4	4.6 (−14.29%)
	1/17	7	6.2	6	6.8	6	6	6	5.4	6	5.6	4.8
	1/30	7	6	6	6.2	5.2	6	5.8	6	6	5.4	6 (−17.14%)
	2/13	6 (−14.29%)	6	5 (−14.29%)	6	5.2	3.8 (−31.43%)	6	5 (−14.29%)	6	5.2	3.4 (−37.14%)
	3/3	6.2	6	6 (+14.29%)	6	5.8	–	6	5	6	5.2	–

		SE-4	RISE-1	RISE-2	RISE-3	RISE-5	RISE-6	OE-1	OE-2	OE-3	OE-5	OE-6
Player 4	11/25	6.6	6.2	6.2	6.2	6.2	6.2	6.6	6.2	6.6	5.4	6.2
	12/10	7	7	7	7	7	7	5.6 (−14.29%)	7	6.6	5.8	6.6
	12/20	7	6 (−14.29%)	6 (−14.29%)	6.2	6.2	6 (−14.29%)	6	6 (−14.29%)	6.6	6.2	5.2 (−20.0%)
	1/17	7	7 (+14.29%)	7 (+14.29%)	7	7	7 (+14.29%)	5.6	6.6	6.8	6.4	6.6 (+20.0%)
	1/30	6.8	6.8	6.8	6.8	6.8	6.8	6.6 (+14.29%)	6.4	6.8	6.2	6.6
	2/13	7	6.8	6.8	7	6.8	6.8	6.6	6.6	7	6.4	6.4
	3/3	7	7	7	7	7	–	6.8	6.6	7	6.4	–
		SE-5	RISE-1	RISE-2	RISE-3	RISE-4	RISE-6	OE-1	OE-2	OE-3	OE-4	OE-6
Player 5	11/25	6.8	6.6	6.6	6.6	6.6	6.6	6.6	6.4	7	6.8	6.8
	12/10	6.2	6.2	6.8	6.6	6.8	6.8	6.4	6.8	7	7	6.8
	12/20	6.6	6.6	6.6	6.6	6.6	6.6	6.6	6.8	6.8	7	5.6 (−17.14%)
	1/17	6.8	6.4	6.6	6.8	6.8	6.8	6.8	6.8	7	6.8	6.6 (+14.29%)
	1/30	–	–	–	–	–	–	–	–	–	–	–
	2/13	6.8	6.8	6.8	6.6	6.6	6.8	6.8	6.8	6.8	6.8	6.8
	3/3	6.8	6.8	6.8	6.4	6.4	–	6.8	6.8	7	6.8	–
		SE-6	RISE-1	RISE-2	RISE-3	RISE-4	RISE-5	OE-1	OE-2	OE-3	OE-4	OE-5
Player 6	11/25	6.2	5	5.8	5.8	5.6	5.6	5.75	5.4	5.8	6.2	5.8
	12/10	1.8 (−62.86%)	3.2 (−25.71%)	2.2 (−51.43%)	3 (−40.0%)	2.2 (−48.57%)	2 (−51.43%)	4.8	3.6 (−25.71%)	4.4 (−20%)	5.8	4.2 (−22.86%)
	12/20	6.2 (+62.86%)	4	5.8 (+51.4%)	6 (+42.86%)	5.2 (+42.86%)	5 (+42.86%)	5.2	5.2 (+22.86%)	4.8	5.4	4.2
	1/17	6	5.8 (+25.71%)	6	5.6	6.2 (+14.29%)	5	4.4	5.6	5.8 (+14.29%)	6	4
	1/30	5.8	5.6	5.4	4.6 (−14.29%)	6	5	4.2	5.2	5.6	5.8	4.8
	2/13	–	–	–	–	–	–	–	–	–	–	–
	3/3	–	–	–	–	–	–	–	–	–	–	–

Note: Scores with a substantial variation from the previous score are marked bold and with percent change in parentheses. The scores of Player 6 are unverified, hence the lack of bold to identify substantial changes. Percent change remains. SE: self-efficacy; RISE: relation-inferred self-efficacy; OE: other-efficacy.

Table 2.

Participants’ objective basketball individual performance scores throughout the season.

Period	Date	W/L	Player 1	Player 2	Player 3	Player 4	Player 5	Player 6
P1	11/26	L	5*	20.11	5.5*	17.64*	13	15.67*
	11/30	L	7.33	13.125	14.23	7*	7*	10.2
	12/3	W	25.8*	23.5*	22.625*	22.7*	15.75	12
	12/7	L	4.875*	19.85	4.27*	13.35	10	10.8
P2	12/13	L	10.4	13.25	27.75*	13	10.5
P2	12/14	L	8	11.67*	15.31	11	17.8*	10
P3	12/20	L	9	17.5	18	11.67	10.67
	12/21	L	10.57	18	13	8*	8*	14*
	12/28	L	13.83	19	17.5	15.5	10.25
	12/30	W	15.8	25.4*	25.15*	16
	1/11	L	15	12	24.26*	13.3	10.8	10
P4	1/24	L	17.5	16.07	10.6	16.88	18	9*
P5	2/1	L	10.25	8*	13.17	14
	2/7	L	8	11.25*	13.16	12.45
	2/8	L	12.2	16.5	11.6	10.11	16.125
P6	2/15	L	13.8	11.67*	8.69*	13.67	17.33
	2/22	L	15	23.38*	22	8*	9.17
	2/26	L	20*	14	18.74	11.57	13.67
	2/28	L	28*	23*	15.95	16	21.75*
Avg			13.18	16.7	15.87	13.25	13.11	11.46
SD		2–17	6.341	4.977	6.506	3.785	4.24	2.293

Note: Scores outside of one positive or negative standard deviation are marked bold with an asterisk.

It also appeared that other-efficacy beliefs were partially related to performance because all participants demonstrated at least one similar variation in the other-efficacy beliefs directed at them from their teammates, which matched their individual performances in some way. Player 2’s previous statement about everyone performing at their potential corroborates this finding as well. However, there were a small number of collection points that did not match and the variations were not reflected throughout the entire season for every participant. Key examples of support were demonstrated particularly in the results of Player 3. During D1, Player 3 had two games with substantially lower OBIP scores and one game that was very near the substantially lower cut-off, all which were followed by a general decrease in the OE beliefs directed toward him from three players. During D3, all of Player 3’s teammates reported an increase in their OE beliefs toward him following Player 3 posting three consecutive games that were above his season OBIP mean. Additionally, during D4, three players reported decreases in their OE of Player 3 following the single game when Player 3’s performance dropped nearly a full standard deviation below the season mean, indicating a clear link between OE and task performance. The remainder of the participants also displayed supporting results; Player 1 (D1, D2, D3), Player 2 (D2 and D3), and Player 5 (D1, D2, and D5) had OE scores directed at them from their teammates that coincided with their individual game performances. For example, Player 4 noted about Player 1 that, “he could have been playing better, because I know how he can play.” Player 4 had limited variation in the OE beliefs directed at him, but he also had limited variation in game performance and reported the lowest standard deviation of his OBIP scores (SD = 3.78) for the entire team. Finally, the OE beliefs toward Player 6 did not necessarily reflect his game performances; yet, the teammates’ OE beliefs did reflect his practice attendance during D2 and D3. The head coach stated that Player 6 began missing practice during D2 and the OE of three of his teammates toward him illustrated a substantial drop during this time, as well as an increase during D3 when Player 6 began to attend practice more consistently. This aligns with Player 1’s comments that his scores toward himself and other players varied based on how they practiced. The OE scores from the other participants toward Player 6 also showed a general decrease during D5, which reflected Player 6’s departure from the team prior to the end of season.

Discussion

Research examining self-efficacy and performance spirals has demonstrated the longitudinal variation of both variables up to a certain point at which skill mastery seems to overrule occasional failure (Bandura, 1986; Feltz, 1982; Lindsley et al., 1995; Locke et al., 1984; Shea & Howell, 2000). However, most of this research has been basic and theoretical in nature, with little application to field performance (Lindsley et al., 1995; Shea & Howell, 2000). The results of this study indicated that tripartite efficacy beliefs exist between team members and are capable of longitudinal variation. Furthermore, the presence of efficacy-performance spirals within the other-efficacy and RISE components of the tripartite efficacy model were found.

There were multiple data collection periods where similar changes in self-efficacy, RISE, and performance occurred for two of the six participants, while a third participant displayed similar changes in his RISE scores in relation to his performance, partially supporting the theory of efficacy-performance spirals. These examples of self-efficacy and RISE illustrate possible instances of efficacy-performance spirals in a competitive situation. However, there were also three participants who reported contradictory results; thus, the results of this study are mixed and indicate that more in-depth research is warranted. The variations present in the study are congruent with variations hypothesized by Bandura (1986). Four participants demonstrated stable self-efficacy beliefs, which could suggest that they had reached a mastery phase, a phase indicated by progression past the plateauing point where failures affect their self-belief (Bandura, 1986). The two participants who demonstrated variations in their self-efficacy and individual performances had variations that reflected spiral variation patterns. These results provide some support for the idea that efficacy-performance spirals occur up until the point of mastery, after which the efficacy-performance plateau begins and performance failures will not substantially affect subsequent performance (Bandura, 1986; Feltz, 1982; Locke et al., 1984; Shea & Howell, 2000). However, mastery was not measured in this study and therefore these interpretations must be made with caution and researched directly in the future. Again, it is important to note that these efficacy-performance spirals only occurred in half of our participants, which limits the support toward this concept.

Additionally, the level of RISE variation demonstrated by the participants illustrates that personal efficacy beliefs can show variance, indicating that although individuals may have consistently stable beliefs in their own abilities, they may not have the same stable belief about how their competence is perceived by their teammates. This somewhat contrasts Jackson et al. (2014) who found that group RISE scores predicted individuals’ confidence in their own abilities in interdependent sports teams and physical education students. Overall, the results of the current study demonstrated the possible presence of self-efficacy performance spirals in a field setting that have been previously demonstrated in laboratory research (Coffee & Rees, 2011; Coffee, Rees, & Haslam, 2009; Feltz, 1982; Shea & Howell, 2000).

Understanding how belief in self and teammates change throughout a season and how those changes relate to team dynamics have implications for performance. Each teammate has a relationship and those combined express the group’s dynamics. By understanding the relationship between the other-efficacy beliefs and performance, as well as how this relationship among teammates changes, a deeper understanding of how group dynamics vary over time can occur. This understanding could then allow for further investigation of the stability of individual relationships, thus enhancing team-building interventions specifically designed to facilitate the growth of the individual relationships between players and beyond the team collectively. For example, many team-building exercises are pair based. With an understanding of which players might have lower other-efficacy scores toward teammates, these teammates could be grouped together in team-building activities to help build cohesion between players. Additionally, recent research has found that RISE-related coaching behaviors are associated with greater RISE and self-efficacy (Saville & Bray, 2016). Although some of the behaviors studied were specific to coaching, the idea of vocally demonstrating confidence is one of the behaviors that can be adapted by athletes. Thus, players could be taught how to effectively communicate confidence in their teammates. Further, a recent study that implemented a self-determination theory-based intervention with high school physical education teachers found an increase in students’ peer-RISE (Sparks, Lonsdale, Dimmock, & Jackson, 2017). This provides further support that coaching behaviors can have an influence on athletes’ RISE. In turn, these strategies could help reduce some of the efficacy spirals that possibly result from a drop in RISE, although that causal relationship is still unclear.

Although a positive relationship between self-efficacy and performance has been demonstrated (Bandura, 1997; Stajkovic & Luthans, 1998), this study illustrates the possibility of a similar relationship between other-efficacy and other-performance as well. Furthermore, these results partially demonstrate longitudinal variations in other-efficacy beliefs that are similar to the literature of self-efficacy longitudinal changes (Coffee & Rees, 2011; Coffee et al., 2009; Lindsley et al., 1995; Shea & Howell, 2000). In addition to the longitudinal variations in other-efficacy, the possible presence of spiral relationships between other-efficacy and the others’ performance was exhibited for some of the players, reflecting a pattern that has been demonstrated in self-efficacy and performance relationships (Feltz, 1982; Locke et al., 1984; Shea & Howell, 2000). However, despite illustrated changes in other-efficacy beliefs following player performance, it is unclear if the changes in other-efficacy beliefs following good or bad performance toward a player would similarly affect the other’s subsequent performances, which would reflect a true efficacy-performance spiral (i.e., efficacy beliefs and performance affecting change in each other cyclically). Nonetheless, this effect could be in the form of in-game behavior made by the teammates toward the specific other (Dunlop et al., 2011; Jackson et al., 2013) or may cause changes in the other’s own self-efficacy (Jackson et al., 2012), both which could lead to changes in the other’s performance. For example, teammates could be more willing to trust that a specific player with whom they had a higher level of other-efficacy would perform at a high level. This trust could possibly result in the player ignoring his or her own chances to score and actively looking to get that particular teammate the ball in certain situations, in effect further enhancing that player’s performance. This increased level of other-efficacy from one’s teammates could also promote that player to have higher self-efficacy and RISE beliefs about oneself, resulting in taking more chances and performing more confidently (e.g., Player 3’s increases in OBIP score, RISE beliefs, and OE beliefs from teammates during D3, and subsequent drop during D4).

Finally, there is limited evidence on the results of plateaus in other-efficacy beliefs and performance relationships compared with self-efficacy and performance research (Feltz, 1982; Locke et al., 1984; Shea & Howell, 2000). It is unclear if there is a mastery phase of other-efficacy and other-performance, similar to self-efficacy, in that other-efficacy beliefs toward a player would no longer vary following that player’s failures or success. It is plausible that such a mastery phase could possibly be reached after multiple seasons of teammates playing together and establishing knowledge of teammates’ performance capabilities, but future research would need to confirm this possibility.

Limitations and future research

There were some limitations that should be considered when examining these results. First, this research utilized a case study approach whereby results are not generalizable to other individuals or scenarios. Further, the patterns that supported previous theory and research were only present to varying degrees among the players, limiting the overall empirical support for the tripartite model. Although these preliminary results from a field setting provide an interesting starting point, they need be examined with a larger sample consisting of more sports teams or across multiple seasons. This sample should be large enough to allow for more advanced quantitative analyses of the data to answer testable hypotheses. Second, due to the length of the assessment and multiple data collections, test fatigue was a possibility. Third, there was a missed data collection period that occurred during D3 due to inclement weather, resulting in a 4-week data collection period. Therefore, the substantial changes may have occurred gradually during this time and not have been substantial when examined as 2-week variations instead of 4-week variations. Fourth, by pulling participants out of practice to complete assessments, it is possible that participants may have hastily completed the assessments. This may have resulted in the participants being more focused on returning to practice than accurately completing the questionnaires.

Despite these shortcomings, this study contributes to the literature in several ways. First, it is the first study to our knowledge to examine the fluctuations in tripartite efficacy throughout the duration of a season. Second, it studied tripartite efficacy among real-life youth teammates, whereas a majority of research has focused on physical education students (Jackson et al., 2012, 2014), elite athlete dyads (Jackson et al., 2008), collegiate dyads (Habeeb et al., 2017), or coach–athlete dyads (Jackson et al., 2011). Third, it provided some evidence in a field-based setting of self-efficacy performance spirals and highlighted that although individuals’ self-efficacy may remain stable over time, they may not hold similar beliefs about how teammates perceive their efficacy.

Future research examining plateauing in the other-efficacy and performance relationships should highlight the benefits for interdependent sports teams that have the same players competing together for multiple seasons. Wickwire et al. (2004) examined relationships between the members of elite athlete dyads and how these working relationships are formed. However, no research has examined these relationships as they are forming. Examples of such research could include tracking the relationships of baseball and softball pitcher and catchers or collegiate offensive football lineman as they progress through their collegiate playing careers over multiple seasons and how their other-efficacy toward their fellow teammates change. This research could even pinpoint when other-efficacy and performance plateaus occur during a growing relationship. Finally, given the inconsistent evidence regarding efficacy-spirals, this study provides an impetus to further explore antecedents and the occurrence of efficacy-performance spirals.

In addition, it is possible for future studies to examine a number of relationships demonstrated in this research. This study provided examples of changes in other-efficacy related to others’ performances. Future research examining the effect of other-efficacy beliefs on the others’ performance should be pursued to examine an additional facet of efficacy-performance spirals. Even though the majority of participants reported limited variations in their self-efficacy beliefs, which appeared to demonstrate the presence of efficacy-performance plateaus (e.g., Feltz, 1982; Locke et al., 1984; Shea & Howell, 2000), further examination of other-efficacy and RISE beliefs would enhance the knowledge concerning the multiple components that play a role in creating and enhancing team dynamics, and how these components affect the performance of group members. Additionally, researchers can investigate athletes’ other-efficacy and RISE scores in relation to the team’s record and examine if there are differences in athletes’ other-efficacy scores after losing and winning.

Conclusion

Understanding how team members’ beliefs in a teammate may change in relation to the performance of that teammate, as well as how individuals’ perceptions of their teammates efficacy beliefs toward themselves may also change with their own performance, is a crucial element in understanding individual and group performances. Thus, this study specifically sought to expand on previous literature by examining tripartite efficacy between the members of a high school basketball team over the course of the season, comparing the results to previous theoretical and basic research concerning efficacy-performance spirals. The results indicated that tripartite efficacy beliefs exist between members of an interdependent sports team and are capable of longitudinal variation. Furthermore, the presence of efficacy-performance spirals within the other-efficacy and RISE components of the tripartite efficacy model appeared to exist. By understanding these relationships, sports psychology consultants, coaches, and other influential stakeholders can identify key relationships that need bolstering, or identify specific relationships, which are key to the performance of the group. Such an understanding could lead to methods that promote efficacy and performance via these relationships. Further research is needed to better understand the interaction and causal relationship between intra-dyadic tripartite efficacy beliefs and the relationship that these tripartite efficacy beliefs have with the performances of individuals and their teammates. For now, this case study provides support for the existence of these constructs and allows a glimpse into how they may function in relationship to sports performance during the course of an entire season.

Supplemental Material

Supplemental material for Interdependent Tripartite Efficacy Perceptions and Individual Performance: Case Study of a Boys’ Basketball Team

Supplemental material for Interdependent Tripartite Efficacy Perceptions and Individual Performance: Case Study of a Boys’ Basketball Team by Joseph M. Stonecypher, Lindsey C. Blom, James E. Johnson, Jocelyn H. Bolin and Robert C. Hilliard in Psychological Reports

Footnotes

Appendix: Basketball-Tripartite Efficacy Assessment (B-TEM)

Acknowledgments

The authors would like to thank Alisha Sink for her feedback on earlier versions of this manuscript.

Article Notes

Supplemental Material

Supplemental material for this article is available online.

References

Anderson

A. G.

Miles

Mahoney

Robinson

(2002) Evaluating the effectiveness of applied sport psychology practice: Making the case for a case study approach. The Sport Psychologist 16: 432–453. doi:10.1123/tsp.16.4.432.

Bandura

(1986) Social foundations of thought and action: A social cognitive theory, Englewood Cliffs, NJ: Prentice-Hall Inc.

Bandura

(1997) Self-efficacy: The exercise of control, New York, NY: W.H. Freeman and Company.

Besharat

M. A.

Pourbohlool

(2011) Moderating effects of self-confidence and sport self-efficacy on the relationship between competitive anxiety and sport performance. Psychology 2: 760–765. doi:10.4236/psych.2011.27116.

Bourbousson

Sève

McGarry

(2010) Space-time coordination dynamics in basketball: Part 1. Intra- and inter-couplings among player dyads. Journal of Sports Sciences 28: 339–347. doi:10.1080/02640410903503632.

Bray

C. D.

Whaley

D. E.

(2001) Team cohesion, effort, and objective performance of high school basketball players. The Sport Psychologist 15: 260–275. doi:10.1123/tsp.15.3.260.

Coffee

Rees

(2011) When the chips are down: Effects of attributional feedback on self-efficacy and task performance following initial and repeated failure. Journal of Sports Sciences 29: 235–245. doi:10.1080/02640414.2010.531752.

Coffee

Rees

Haslam

(2009) Bouncing back from failure: The interactive impact of perceived controllability and stability on self-efficacy beliefs and future task performance. Journal of Sports Sciences 27: 1117–1124. doi:10.1080/02640410903030297.

Darnis-Paraboschi

Lafont

Menaut

(2005) A social-constructivist approach in physical education: Influence of dyadic interactions on tactical choices in an instructional team sport setting. European Journal of Psychology of Education 20: 171–184.

10.

Dunlop

W. L.

Beatty

D. J.

Beauchamp

M. R.

(2011) Examining the influence of other-efficacy and self-efficacy on personal performance. Journal of Sport & Exercise Psychology 33: 586–593. doi:10.1123/jsep.33.4.586.

11.

Feltz

D. L.

(1982) Path analysis of the causal elements in Bandura’s theory of self-efficacy and an anxiety-based model of avoidance behavior. Journal of Personality and Social Psychology 42: 764–781. doi:10.1037/0022-3514.42.4.764.

12.

Habeeb

C. M.

Eklund

R. C.

Coffee

(2017) It depends on the partner: Person-related sources of efficacy beliefs and performance for athlete pairs. Journal of Sport & Exercise Psychology 39: 172–187. doi:10.1123/jsep.2016-0348.

13.

Hare

A. P.

(1994) Types of roles in small groups: A bit of history and a current perspective. Small Group Research 25: 433–448. doi:10.1177/1046496494253005.

14.

Heggestad

E. D.

Kanfer

(2005) The predictive validity of self-efficacy in training performance: Little more than past performance. Journal of Experimental Psychology: Applied 11: 84–97. doi:10.1037/1076-898X.11.2.84.

15.

Hepler

T. J.

Chase

M. A.

(2008) Relationship between decision-making self-efficacy, task self-efficacy, and the performance of a sport skill. Journal of Sport Sciences 26: 603–610. doi:10.1080/02640410701654280.

16.

Heuzé

J. P.

Raimbault

Fontayne

(2006) Relationships between cohesion, collective efficacy and performance in profession basketball teams: An examination of mediating effects. Journal of Sport Sciences 24: 59–68. doi:10.1080/02640410500127736.

17.

Hutzler

Shemesh

(2012) Self-efficacy, task and ego orientation, and family support in wheelchair and able-bodied basketball players. Therapeutic Recreation Journal 46: 73–90.

18.

Jackson

Gucciardi

D. F.

Dimmock

J. A.

(2011) Tripartite efficacy profiles: A cluster analytic investigation of athletes’ perceptions of their relationship with their coach. Journal of Sport and Exercise Psychology 33: 394–415. doi:10.1123/jsep.33.3.394.

19.

Jackson

Gucciardi

D. F.

Lonsdale

Whipp

P. R.

Dimmock

J. A.

(2014) “I think they believe in me”: The predictive effects of teammate- and classmate-focused relation-inferred self-efficacy in sport and physical activity settings. Journal of Sport & Exercise Psychology 36: 486–505. doi:10.1123/jsep.2014-0070.

20.

Jackson

Knapp

Beauchamp

M. R.

(2008) Origins and consequences of tripartite efficacy beliefs within elite athlete dyads. Journal of Sport & Exercise Psychology 30: 512–540. doi:10.1123/jsep.30.5.512.

21.

Jackson

Myers

N. D.

Taylor

I. M.

Beauchamp

M. R.

(2012) Relational efficacy beliefs in physical activity classes: A test of the tripartite model. Journal of Sport & Exercise Psychology 34: 285–304. doi:10.1123/jsep.34.3.285.

22.

Jackson

Whipp

P. R.

Beauchamp

M. R.

(2013) The tripartite efficacy framework in high school physical education: Trans-contextual generality and direct and indirect prospective relations with leisure-time exercise. Sport, Exercise, and Performance Psychology 2: 1–14. doi:10.1037/a0030169.

23.

Katz

Lazer

Arrow

Contractor

(2004) Network theory and small groups. Small Group Research 35: 307–332. doi:10.1177/1046496404264941.

24.

Kazdin

A. E.

(2011) Single-case research designs: Method for clinical and applied settings, 2nd ed. New York, NY: Oxford University Press.

25.

Lent

R. W.

Lopez

F. G.

(2002) Cognitive ties that bind: A tripartite view of efficacy beliefs in growth-promoting relationships. Journal of Social and Clinical Psychology 21: 256–286. doi:10.1521/jscp.21.3.256.22535.

26.

Lent

R. W.

Schmidt

(2006) Collective efficacy beliefs in student work teams: Relation to self-efficacy, cohesion, and performance. Journal of Vocational Behavior 68: 73–84. doi:10.1016/j.jvb.2005.04.001.

27.

Lindsley

D. H.

Brass

D. J.

Thomas

J. B.

(1995) Efficacy-performance spirals: A multilevel perspective. Academy of Management Review 20: 645–678. doi:10.2307/258790.

28.

Locke

E. A.

Frederick

Lee

Bobko

(1984) Effect of self-efficacy, goals, and task strategies on task performance. Journal of Applied Psychology 69: 241–251. doi:10.1037/0021-9010.69.2.241.

29.

Nunnally

J. C.

(1978) Psychometric theory, 2nd ed. New York, NY: McGraw-Hill.

30.

Ritchie

Williamon

(2012) Self-efficacy as a predictor of musical performance quality. Psychology of Aesthetics, Creativity, and the Arts 6: 334–340. doi:10.1037/a0029619.

31.

Saville

P. D.

Bray

S. R.

(2016) Athletes’ perceptions of coaching behavior, relation-inferred self-efficacy (RISE), and self-efficacy in youth sport. Journal of Applied Sport Psychology 28: 1–13. doi:10.1080/10413200.2015.1052890.

32.

Shea

C. M.

Howell

J. M.

(2000) Efficacy-performance spirals: An empirical test. Journal of Management 26: 791–812. doi:10.1016/S0149-2063(00)00056-8.

33.

Sparks

Lonsdale

Dimmock

Jackson

(2017) An intervention to improve teachers’ interpersonally involving instructional practices in high school physical education: Implications for student relatedness support and in-class experiences. Journal of Sport & Exercise Psychology 39: 120–133. doi:10.1123/jsep.2016-0198.

34.

Stajkovic

A. D.

Luthans

(1998) Self-efficacy and work-related performance: A meta- analysis. Psychological Bulletin 124: 240–261. doi:10.1037/0033-2909.124.2.240.

35.

Vilar

Araújo

Davids

Button

(2012) The role of ecological dynamics in analysing performance in team sports. Sports Medicine 42: 1–10. doi:10.2165/11596520-000000000-00000.

36.

Wickwire

T. L.

Bloom

G. A.

Loughead

T. M.

(2004) The environment, structure, and interaction process of elite same-sex dyadic sport teams. The Sport Psychologist 18: 381–396. doi:10.1123/tsp.18.4.38.

37.

Yeo

G. B.

Neal

(2006) An examination of the dynamic relationship between self-efficacy and performance across levels of analysis and levels of specificity. Journal of Applied Psychology 91: 1088–1101. doi:10.1037/0021-9010.91.5.1088.

38.

Yin

(2009) Case study research: Designs and methods, 4th ed. Thousand Oaks, CA: Sage.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.09 MB