Role ambiguity, role conflict, team conflict, cohesion and collective efficacy in sport teams: A multilevel analysis

Accepted Manuscript Role Ambiguity, Role Conflict, Team Conflict, Cohesion and Collective Efficacy in Sport Teams: A Multilevel Analysis F.M. Leo, I. González-Ponce, P.A. Sánchez-Miguel, A. Ivarsson, T. García-Calvo PII:

S1469-0292(15)00046-1

DOI:

10.1016/j.psychsport.2015.04.009

Reference:

PSYSPO 997

To appear in:

Psychology of Sport & Exercise

Received Date: 30 October 2014 Revised Date:

2 April 2015

Accepted Date: 4 April 2015

Please cite this article as: Leo, F.M., González-Ponce, I., Sánchez-Miguel, P.A., Ivarsson, A., García-Calvo, T., Role Ambiguity, Role Conflict, Team Conflict, Cohesion and Collective Efficacy in Sport Teams: A Multilevel Analysis, Psychology of Sport & Exercise (2015), doi: 10.1016/ j.psychsport.2015.04.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Running Head: GROUP PROCESS IN SPORT TEAMS

1

ACCEPTED MANUSCRIPT

Role Ambiguity, Role Conflict, Team Conflict, Cohesion and Collective Efficacy in Sport Teams: A Multilevel Analysis * Leo, F. M., ** González-Ponce, I., *Sánchez-Miguel, P. A., ***Ivarsson, A., and **García-

RI PT

Calvo, T. * Faculty of Teacher Training. University of Extremadura ** Faculty of Sport Sciences. University of Extremadura

AC C

EP

TE D

M AN U

Halmstad University

SC

*** Center of Research on Welfare, Health and Sport. School of Health and Welfare.

Author Note

Correspondence to: Francisco M. Leo Marcos. Faculty of Teacher Training. University of Extremadura. C/ Avenida de la Universidad, S/N, C.P.: 10003, Cáceres, Spain. Telf: +34 927 257049. Fax: +34 927257051. E-mail: [email protected]

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

1

1

Role Ambiguity, Role Conflict, Team Conflict, Cohesion, and Collective Efficacy in Sport

2

Teams: A Multilevel Analysis

3

RI PT

4 5 6

SC

7 8

M AN U

9 10 11 12

16 17 18 19 20 21 22 23 24 25

EP

15

AC C

14

TE D

13

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

2

Abstract

2

This study examines how perceptions of role ambiguity, role conflict, team conflict, and

3

cohesion can predict collective efficacy in sports teams. The participants were professional

4

female and male football players, who participated in the First and Second Divisions in

5

Spain. We adopted a longitudinal perspective, taking measures at the beginning, the middle,

6

and the end of a sport season. Multilevel modelling analysis showed that perceptions of team

7

conflict and cohesion, at the interpersonal and interteam levels, can predict changes in

8

collective efficacy. However, individual perceptions of role ambiguity and role conflict were

9

not relevant in establishing a team’s confidence. These results suggest interesting practical

11

SC

M AN U

10

RI PT

1

applications for coaches and sports psychologists in the professional sphere. Keywords: conflict, efficacy beliefs, football, group processes, role .

AC C

EP

TE D

12

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

3

1

Role Ambiguity, Role Conflict, Team Conflict, Cohesion, and Collective Efficacy in Sport

2

Teams: A Multilevel Analysis

3

According to Bandura (1997), it is important to emphasise conviction and confidence in players’ abilities to generate high-outcome expectations. When athletes believe in their

5

possibilities and have conviction in their performances, the possibilities of obtaining a higher

6

performance in the game may be increased (Myers, Payment, & Feltz, 2004). In team sports,

7

where interdependence is very high, not only self-efficacy is important, but also the

8

confidence of each player in the team’s performance (Beauchamp, 2007; Fransen et al., 2012;

9

Myers et al., 2004). In other words, confidence in group abilities may be more relevant than

11

SC

M AN U

10

RI PT

4

individual strengths (Fransen et al., 2012; Son, Jackson, Grove, & Feltz, 2011). Identifying variables that would influence collective efficacy may be relevant to optimising the performance in sports teams (Beauchamp, 2007). Thus, this study aims to

13

extend the scientific literature regarding group processes. Specifically, with a sample of male

14

and female professional football players, the work aims to examine the importance of role

15

ambiguity, role conflict, team conflicts, and team cohesion in perceptions of the collective

16

efficacy level, and how these interactions can fluctuate during a competitive season.

17

Taking the self-efficacy theory developed by Bandura (1997) as a conceptual

EP

TE D

12

framework, collective efficacy was conceptualized as an extension of said theory. Despite

19

this fact, alternative definitions of the construct have emerged in the literature, which have

20

focused on its interactive factors (assignment, coordination, and integration). Thus, some

21

authors, such as Zaccaro, Blair, Peterson, and Zazanis (1995), defined collective efficacy as

22

“a sense of collective competence shared among individuals when allocating, coordinating,

23

and integrating their resources in a successful concerted response to specific situational

24

demands” (p. 309). How team members develop this collective efficacy is similar to self-

25

efficacy, through sources of efficacy information (Bandura, 1997). At least some of the

AC C

18

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

4

sources of collective efficacy should be analogous to self-efficacy, namely past performance

2

accomplishments, vicarious experiences, verbal persuasion, and physiological states, but

3

these sources should be focused at the group level (Bandura, 1997; Feltz, Short, & Sullivan,

4

2008). Moreover, researchers have suggested several specific antecedents of this variable,

5

such as leadership, team size, motivational climate, and group cohesion (Beauchamp, 2007;

6

Chow & Feltz, 2007).

RI PT

1

Some of the variables that have been associated with self-efficacy are role ambiguity

8

(Beauchamp, Bray, Fielding, & Eys, 2005; Eys & Carron, 2001), defined as the lack of clear

9

and coherent information respecting a particular function, and role conflict (Beauchamp &

M AN U

SC

7

Bray, 2001), which refers to the presence of incongruent expectations within the

11

performances (Beauchamp & Bray, 2001; Kahn, Wolfe, Quinn, Snoek, & Rosenthal, 1964;

12

Tubre & Collins, 2000). However, we do not know if these variables can affect the

13

development of collective efficacy. In accordance with this issue, Chow and Feltz (2007)

14

noted that within group environments, if players have a greater clarity of understanding of

15

their roles, this may contribute to confidence in the team´s ability to solve specific situations

16

in the competition.

EP

17

TE D

10

According to the self-efficacy theory, Bandura (1997) pointed out that efficacy influences the course of action an individual chooses, the amount of effort expended, the

19

degree of perseverance demonstrated, and the thought patterns regarding performance.

20

Furthermore, when environmental demands become high, verbal persuasion is demanding or

21

past experiences are negative, efficacy beliefs may suffer (Bandura, 1997). For example, if an

22

athlete fails to clearly understand what his or her primary role functions are, he or she will be

23

unsure about the accuracy of the cognitive representations that guide behaviour, and may

24

subsequently underestimate team capabilities. To summarise, when a person perceives a lack

25

of clear information or contradictory information associated with his or her role, collective

AC C

18

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

5

1

efficacy related to these behaviours is also likely to suffer (Bandura, 1997). Thus, role

2

ambiguity and role conflict could be predicted to have a negative relationship with collective

3

efficacy beliefs (Chow & Feltz, 2007). As indicated above, another concept having a strong relationship with collective

5

efficacy is team cohesion (Heuzé, Raimbault, & Fontayne, 2006; Kozub & McDonnell, 2000;

6

Leo, García-Calvo, Parejo, Sánchez-Miguel, & Sánchez-Oliva, 2010; Paskevich, Brawley,

7

Dorsch, & Widmeyer, 1999). This term has been defined as “a dynamic process that is

8

reflected in the tendency for a group to stick together and remain united in the pursuit of its

9

instrumental objectives and/or for the satisfaction of member affective needs” (Carron,

M AN U

SC

RI PT

4

Brawley, & Widmeyer, 1998, p. 213). This concept is situated within the cohesion conceptual

11

model by Carron et al. (1998), which identified 4 dimensions on two different levels. The

12

first level refers to task cohesion – which reflects the degree to which team members work

13

together to achieve common purposes – and social cohesion, which reflects the degree that

14

team members have empathy with each other and enjoy comradeship with the group (Carron,

15

Widmeyer, & Brawley, 1985; Carron et al., 1998); the second level identifies the attraction to

16

the group – the perception of how the group satisfies the athlete’s needs and personal aims –

17

and group integration – which means the perception about how the group works in unity.

18

Therefore, four different dimensions can be identified: group integration-task (GI-T), group

19

integration-social (GI-S), individual attraction to the group-task (ATG-T), and individual

20

attraction to the group-social (ATG-S). These dimensions can influence team performance

21

(Carron, Colman, & Wheeler, 2002) as well as different psychological variables (Heuzé et al.,

22

2006; Kozub & McDonnell, 2000; Leo et al., 2010; Paskevich et al., 1999). Concerning the

23

present study's focus on collective efficacy, group processes can contribute to a team’s sense

24

of shared confidence. A team’s ability to communicate with each other, cooperate, and strive

25

together can be a clear source of information to get more confidence in the group’s ability to

AC C

EP

TE D

10

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

6

achieve a higher performance (Chow & Feltz, 2007). If we consider that collective efficacy

2

refers to confidence in performing group tasks, task cohesion dimensions – ATG-T and GI-T

3

– may have a stronger relationship with collective efficacy, as has been shown in previous

4

studies (Kozub & McDonnell, 2000; Leo et al., 2010; Paskevich et al., 1999). Nevertheless, if

5

players are comfortable with their teammates and show empathy to each other, it also seems

6

to promote confidence among the athletes but in a lesser extent.

RI PT

1

Despite the fact that previous studies have examined the positive aspects of

8

functioning group processes (e.g., group cohesion), maladaptive behaviours that can appear

9

within a sport group have been allocated less importance (Paradis, Carron, & Martin, 2014).

M AN U

SC

7

Investigating the changes of both positive (e.g., cohesion) and negative processes (e.g.,

11

conflict) can help to broaden our understanding of group dynamics (Marks, Mathieu, &

12

Zaccaro, 2001; Sullivan & Feltz, 2001) and, specifically, their effects on perceptions such as

13

collective confidence (Gully, Incalcaterra, Joshi, & Beaubien, 2002; Tekleab, Quigley, &

14

Tesluk, 2009). Team conflict, defined as “a process in which one party perceives that its

15

interests are being opposed or negatively affected by another party” (Wall & Castiller, 1995,

16

p. 517), is a multidimensional concept (task and relationship). Accordingly, task conflict

17

refers to “disagreement among group members about the content of the tasks being

18

performed, including differences in viewpoints, ideas, and opinions”, and relationship

19

conflict refers to the “interpersonal incompatibility among members, which typically includes

20

tension, animosity, and annoyance among members within a group” (Jehn, 1995, p. 258).

21

Theories of team development (Gersick, 1988; Tuckman, 1965) have suggested that conflicts

22

and team cohesion have a great influence on the capacity of the team members to effectively

23

interact over time. According to these models, dealing successfully with conflicts can assist

24

the development of confidence, as a result of strategies used by players (Holt, Knight, &

25

Zukiwski, 2012), which include meetings, conversations, agreements, and negotiations. That

AC C

EP

TE D

10

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

7

is to say, when teams have successfully overcome the conflict, the cohesion created between

2

team members is greater, which in turn leads to higher team confidence and greater group

3

efficacy (Tekleab et al., 2009). Thus, conflicts and cohesion interact to shape the efficacy

4

within a team. Nevertheless, most of the studies regarding conflict and cohesion (for

5

example, Holt et al., 2012; Paradis et al., 2014) have analysed these variables in isolation and

6

did not focus on how they were related to the functioning of the team (Tekleab et al., 2009). All these antecedents discussed previously – role ambiguity, role conflict, team

SC

7

RI PT

1

conflict or cohesion – are dynamic, so they are understood as changing over time (Bandura,

9

1997; Carron & Eys, 2012; Paradis et al., 2014). Therefore, the perception of the players

M AN U

8

regarding expectations of group effectiveness can also vary over time (Fransen et al., 2015).

11

In fact, some studies corroborate that there is usually a decrease in collective efficacy

12

throughout the season (Heuzé et al., 2006; Leo, Sánchez-Miguel, Sánchez-Oliva, Amado, &

13

García-Calvo, 2012). Thus, analysing the fluctuation of collective efficacy over time can

14

provide more information on how these efficacy expectations are developed. Also, we can

15

learn if the antecedents are related to these fluctuations, in comparison to past cross-sectional

16

studies.

17

The present study

EP

Due to the close relationship between the players’ role and the degree of attraction and

AC C

18

TE D

10

19

integration among members in a sporting group, this study aims to establish how both

20

constructs could be applied in order to predict collective efficacy (Carron & Eys, 2012).

21

Moreover, this work aims to join two similar concepts that deal with interpersonal

22

relationships in the group, such as conflicts and cohesion, which work together when

23

establishing relationships within a group (Marks et al., 2001). To achieve this purpose, a

24

longitudinal study was conducted, with the aim to help monitor how players’ perceptions of

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

1

confidence levels could change during a season, and was examined through a multilevel

2

approach.

8

The main aim of the study was to examine the predictive capacity of several

4

psychosocial variables – role ambiguity, role conflict, team conflict, and team cohesion – on

5

collective efficacy. Ultimately, this research aims to extend the current literature with regard

6

to changes in group processes that appear in a multilevel analysis of male and female

7

professional football teams during the length of one playing season. In this regard, and

8

according to previous studies, it was hypothesised that role ambiguity (Beauchamp et al.,

9

2005; Eys & Carron, 2001), role conflict (Beauchamp & Bray, 2001), and team conflict

M AN U

SC

RI PT

3

10

negatively (Tekleab et al., 2009), as well as cohesion factors positively (Heuzé et al., 2006;

11

Leo et al., 2010), would emerge as antecedents that influence perceptions of collective

12

efficacy.

13

16

Participants

The participants were professional male and female football players, who all belonged

EP

15

Method

TE D

14

to one of 20 federate teams that participated in group four of the second division of the

18

Spanish Men’s Football League or one of 16 federate teams that participated in the first

19

division of the Spanish Women’s Football League.

20

AC C

17

From an original sample of 626 questionnaires collected at stage 1, 45 (7.18%) were

21

excluded due to invalid completion of the questionnaires (i.e., the questionnaires were not

22

fully completed). At the middle of the season (stage 2), 26 (4.56%) questionnaires from a

23

total of 575 were excluded. At the end of the season (stage 3), from a total of 613

24

questionnaires, 37 questionnaires (6.03%) were excluded.

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

1

9

Thus, at the beginning of the season (Time 1), we recruited a total of 581 players with a mean age of 24.51 years (SD = 3.73; range = 15–39 years old; 356 men and 225 women)

3

and an average football experience of 14.01 years (SD = 5.16). In the middle of the season

4

(Time 2), a total of 549 players from the original sample were recruited, with a mean age of

5

23.98 years (SD = 4.84; range = 15–37 years old; 319 men and 230 women) and an average

6

football experience of 14.25 years (SD = 4.84). At the end of the season (Time 3), there were

7

a total of 576 players from the original sample, with a mean age of 23.97 years (SD = 4.83;

8

range = 15–37 years old; 339 men and 237 women) and an average football experience of

9

14.46 years (SD = 5.25).

SC

M AN U

10

RI PT

2

Finally, from the overall sample, 351 players (202 male and 149 female) completed the 3 assessments, 110 individuals (85 male and 25 female) completed only the first

12

measurement, 46 players (33 male and 13 female) accomplished only the second assessment

13

and 93 participants (70 male and 23 female) completed only the third measurement.

14

Furthermore, 70 players (43 male and 27 female) completed the first and second

15

measurements, 50 individuals (26 male and 24 female) completed the first and third

16

assessments and finally, 82 individuals (41 male and 41 female) completed the second and

17

third measurements.

EP

TE D

11

19 20

AC C

18 Instruments

Role ambiguity. To assess role ambiguity, we used a 12-item scale adapted from the

21

instrument developed by Beauchamp, Bray, Eys, and Carron (2002), that measures various

22

dimensions − scope of responsibilities (3 items), behaviours in fulfilling role responsibilities

23

(3 items), evaluation of role performance (3 items), and consequences of not fulfilling role

24

responsibilities (3 items). In this paper, we are interested in the higher-order dimension

25

factors and not in the lower-order dimension factors. An example of role ambiguity includes

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

10

“I am clear about the different responsibilities that make up my role”. Players responded to

2

all items on a 9-point scale ranging from strongly disagree (1), to strongly agree (9). Thus,

3

higher ratings of agreement indicated greater role clarity and, hence, less role ambiguity. The

4

confirmatory factor analysis (CFA) of the data taken at the beginning of the season offered

5

support for this factor structure showing acceptable model fit, χ2 = 80.16, df = 16, χ2 /df =

6

3.01, CFI = .97, IFI = .97, RMSEA = .07, SRMR = .03. Furthermore, Cronbach’s alpha values

7

were deemed acceptable, for all the four subscales, α = .82, .83, .82, and .81 respectively, and

8

the full scale, α = .83. The CFA and Cronbach’s alpha of the data from the middle and the

9

end of the season showed similar values to those obtained at the beginning of the season in all

11

SC

M AN U

10

RI PT

1

instruments (see Table 1).

Role Conflict. To assess role conflict, we used a 6-item scale adapted from the instrument developed by Beauchamp and Bray (2001). Examples of role conflict include “I

13

am sometimes provided with conflicting information of what my role is”. Responses were

14

rated on a 5-point scale ranging from strongly disagree (1) to strongly agree (9). Thus, higher

15

ratings of agreement indicated greater role conflict. The CFA results with data from our study

16

confirmed a factor structure showing acceptable model fit, χ2 = 19.94, df = 9, χ2 /df = 2.21,

17

CFI = .98, IFI = .98, RMSEA = .04, SRMR = .03. Furthermore, Cronbach's alpha coefficient

18

was acceptable for the full scale, α = .73.

EP

AC C

19

TE D

12

Team Conflict. To assess team conflict, we used a 6-item scale developed by Jehn

20

(1995) and adapted for Tekleab et al. (2009) that measures two dimensions: task conflict (3

21

items) and relationship conflict (3 items). An example of a question related to the task

22

conflict dimension is “How frequently were there conflicts about ideas on your team?”

23

Players responded to all items on a 9-point scale ranging from never (1) to always (9). The

24

CFA results with data from our study confirm this two-factor structure showing acceptable

25

model fit, χ2 = 16.43, df = 8, χ2 /df = 2.05, CFI = .99, IFI = .99, RMSEA = .04, SRMR = .02.

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

11

1

Furthermore, Cronbach's alpha coefficients were acceptable, obtaining values of .79 for task

2

conflict and .85 for relationship conflict. Group Cohesion. The Short Spanish version of the GEQ (Carron et al., 1985)

4

developed by Leo, González-Ponce, Sánchez-Oliva, Pulido, and García-Calvo (in press) was

5

used to assess team cohesion. This inventory of 12 items comprises four factors, GI-T (3

6

items, i.e., “Team members are united in their efforts to reach their performance goals in

7

training sessions and matches”), GI-S (3 items, i.e., “Team members would like to spend time

8

together in situations other than training and games”), ATG-T (3 items, i.e., “On this team, I

9

can do my best”), and ATG-S (3 items, i.e., “The team is one of the most important social

M AN U

SC

RI PT

3

groups I belong to”). Responses were rated on a 9-point scale ranging from strongly disagree

11

(1) to strongly agree (9). The CFA results with data from our study confirm this four-factor

12

structure showing acceptable model fit, χ2 = 178.09, df = 48, χ2 /df = 2.71, CFI = .94, IFI =

13

.94, RMSEA = .06, SRMR = .04. Furthermore, Cronbach's alpha coefficients were

14

acceptable, obtaining values of .79 for GI-T, .74 for GI-S, .74 for ATG-T, and .71 for ATG-

15

S.

Collective Efficacy. To assess collective efficacy, the “Cuestionario de Eficacia

EP

16

TE D

10

Colectiva en Fútbol” (CECF; in English, “The Football Collective Efficacy Questionnaire”),

18

developed by Leo, Sánchez-Miguel, Sánchez-Oliva, Amado, and García-Calvo (2014) was

19

used. This instrument starts with a stem phrase (i.e. “Our team’s confidence in our capability

20

to…”) and has a total of 26 items that refer to certain football situations (i.e., “…resolve

21

game situations in the attacking phase”), which are grouped into a single factor. Responses

22

were rated on a 5-point scale ranging from bad (1) to excellent (5). The CFA results with data

23

from our study confirm that all 26 items were grouped into a single factor, χ2 = 33.91, df = 9,

24

χ2 /df = 2.76, CFI = .97, IFI = .97, RMSEA = .06, SRMR = .04. Furthermore, Cronbach's

25

alpha coefficient was acceptable for full scale, α = .81.

AC C

17

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

1 2

12

Procedure We used a longitudinal correlational design. We carried out three assessments at three time points: within three weeks of the beginning of the sport season (T1), at the middle (T2)

4

and at the end of the season (T3), separated by a 20-22 week interval between each

5

measurement wave.

6

RI PT

3

The study received ethical approval from the University. All participants were treated according to the American Psychological Association ethical guidelines regarding consent,

8

confidentiality, and anonymity of responses. Also, the measurement plan was announced to

9

underage athletes and their parents, who decided on their children’s participation in the study.

M AN U

SC

7

Questionnaires were matched over time using a coding system to protect confidentiality.

11

Research assistants read the instructions first and encouraged participants to ask questions if

12

they had any doubts that needed to be clarified. Participants completed the questionnaires in

13

the changing room before a training session. Participants completed the questionnaires

14

individually within 15-20 minutes, in the absence of their coach, supervised by the research

15

assistants and under non-distracting conditions.

16

Data Analysis

17

Multilevel modelling

EP

Multilevel modelling (MLM: Heck, Thomas, & Tabata, 2010) was used to analyse the

AC C

18

TE D

10

19

intercept and change in collective efficacy among the participants and its relationship with

20

different psychological variables. The MLM analyses were performed in SPSS version 20.0

21

Multilevel modelling (i.e. hierarchical linear model) which aims to analyse data that contains

22

an inherent hierarchical structure (Chou, Bentler, & Pentz, 1998). For example a series of

23

repeated measured data at level 1 could be nested within individuals at level 2 (Heck et al.,

24

2010). In the present study the data contains three levels. The first level of the data contains

25

individual scores of collective efficacy during 3 stages of measurement (within subject level).

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

1

At the second level the collective efficacy scores are nested within individuals (between

2

subjects). Last, at the third level, the individuals are nested into teams (between teams).

3

13

To evaluate the model fit for MLMs three different model fit information criteria were used – 2 LL, Akaike’s Information Criterion (ACI) and the Schwarz Bayesian Information

5

Criterion (BIC). In the model selection lower values on all criteria are equivalent to a better

6

model fit (Heck et al., 2010).

7 8

10

Descriptive Statistics

M AN U

9

Results

SC

RI PT

4

The means and standard deviations for the participants for all study variables are

11

presented in Table 1. We examined data normality, obtaining skewness values between -1.62

12

and 1.28, and kurtosis values between -.47 and 3.29.

13

Regarding means, in general participants reported scores above the midpoint of the scale for role ambiguity, cohesion, and collective efficacy. Participants also reported scores

15

for role conflict and team conflict that were under the midpoint of the scale.

16

Multilevel models

EP

TE D

14

A series of multilevel models were used to analyse initial level and change in

18

collective efficacy, over the three stages of measurement. Moreover, the multilevel models

19

were used to investigate the predictive associations of a number of psychosocial variables at

20

the initial level and change in collective efficacy. In line with recommendations from Field

21

(2009) an empty model, without any predictors, was initially tested (Baseline model).

22

Intraclass correlations (ICC) suggested that 19% of the variance in collective efficacy, over

23

the three measurement stages, could be explained due to differences between teams (level 3)

24

while 17% could be attributed to differences between people (level 2). In the second model,

25

time was included as a level 2, fixed effect covariate (model A). Following the fixed effect

AC C

17

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

14

model, time was included as a level 2 covariate on both the fixed and random slope (model

2

B). In model C all psychosocial variables were included as fixed effect level 2 predictors.

3

Furthermore, in the last model, fixed interaction effects between time and specific

4

psychosocial variables were included, all at level 2 (model D).

5

RI PT

1

The result showed that intercept varied significantly between participants (empty

model). Including time as a fixed effect covariate generated an improved model fit, BIC =

7

2938.16 (model A). More specifically the result indicated that the collective efficacy

8

significantly decreased by 0.21 units per stage. By adding time as a random level 2 effect

9

predictor the model fit was improved, BIC = 2914.37 (model B). In this model the result

M AN U

SC

6

showed that collective efficacy slopes varied significantly over time, β = .02, p < .001. In the

11

fourth model the psychosocial predictors of change in collective efficacy were included

12

(model C). The inclusion of the psychosocial level 2 predictors improved the model fit, BIC

13

= 2405.59. Time was still a significant predictor as both a fixed, β = -.09, p < .001, and

14

random effect, β = -.02, p < .001, for the second level. Of the added psychosocial predictors,

15

the four significant predictors of change in collective efficacy were: relationship conflict, β =

16

-.03, p < .05; task conflict, β = -.06, p <.001; task integration, β = .10, p < .001; and task

17

attraction, β = .07, p < .001. The results show that individual perceptions of role ambiguity

18

and role conflict do not appear to predict the levels of collective efficacy. Nevertheless,

19

perceptions about the group´s task cohesion and team conflict are statistical significant

20

predictors. Also, task dimensions are stronger predictors in comparison to social factors.

21

Moreover, integration is stronger than attraction within cohesion. Adding three interaction

22

terms into the model (model D) did not improve model fit, BIC = 2421.80.

23

AC C

EP

TE D

10

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

1 2

15

Discussion This study examined how players’ perceptions of role ambiguity, role conflict, team conflict, and team cohesion can explain collective efficacy within teams during their playing

4

season. A multilevel analysis with male and female professional football players was

5

conducted. Generally speaking, it was confirmed that some group processes examined in the

6

study, such as conflict and team cohesion, can explain the fluctuations of collective efficacy

7

during a playing season. In other words, whereas perceptions of ambiguity and role conflict

8

did not emerge as determinants, the bonds or conflicts within a team showed as elements that

9

could influence confidence levels, as revealed by players in the teams. Thus, the initial

M AN U

SC

RI PT

3

10

hypothesis was only partially confirmed, but this is still indubitably significant, as it may

11

offer some practical implications for coaches in high-performance sports.

12

In the results, it was observed that collective efficacy scores fluctuated during the season, appearing as a significant decrease near the end of the season. These results are

14

consistent with those previously suggested by other authors (Magyar, Feltz, & Simpson,

15

2004), where researchers found that the degree of players’ union varied during a season,

16

affecting confidence or vice versa (Heuzé et al., 2006; Leo et al., 2010).

EP

17

TE D

13

When the different psychological variables were included as predictors in the changing of the collective efficacy, it was observed how the measurement model improved its

19

adjustment. In this regard, time was still a significant predictor, which suggests that

20

arguments defended by other authors in previous research can be corroborated by this

21

outcome. As time passes, perception of collective efficacy can certainly vary in the

22

progressive achievement of the aims set or the non-fulfilment of the expectations created

23

(Fransen et al., 2015; Leo et al., 2012). Notwithstanding, psychological variables added to the

24

model give us a possible justification of these fluctuations. Both team conflict factors (task

25

conflict and relationship conflict) and task cohesion factors (integration and attraction to the

AC C

18

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

16

1

task) significantly influenced the changes of the collective efficacy during the playing season.

2

Nevertheless, role ambiguity and role conflict did not suffice to explain the changes shown in

3

the perception of collective efficacy. Generally speaking, it was revealed that individual perceptions regarding each

RI PT

4

player’s role, and considering both ambiguity and conflict, did not affect the perception of

6

collective efficacy. This can be explained by the fact that despite players perceiving conflict

7

or lack of clarity in the functions they have to perform, their perceptions about the group’s

8

abilities are not affected. That is to say, one can perceive that he/she needs greater clarity in

9

the information given, with respect to the tasks to be performed, and at the same time still

M AN U

SC

5

have confidence in the group’s capabilities. As was indicated in the introduction, the

11

interdependence in team sports is very high, and therefore, perceptions about individual´s

12

issues is not enough to explain perceptions within a group (Bandura, 1997; Fransen et al.,

13

2012; Son et al., 2011). Up until now, studies had only found positive relationships between

14

role ambiguity and role efficacy, two variables associated with individual perceptions

15

regarding their behaviours (Beauchamp et al., 2002; Beauchamp et al., 2005; Eys & Carron,

16

2001), but not with group variables such as collective efficacy. Furthermore, the degree of

17

concordance in the conceptualisation between variables is different. While one is defined as

18

ambiguity or conflict in the tasks that individual players´ must perform (individual level), the

19

other is defined as confidence to develop group tasks (group level). The higher the

20

concordance in conceptual definition, such as cohesion and team conflict with collective

21

efficacy, the greater the relationship that can be obtained (Myers, Paiement, & Feltz, 2007).

22

Taking the variables related to global perceptions within a group into account, it was

AC C

EP

TE D

10

23

observed that both conflict and team cohesion explained the changes in the collective

24

efficacy. Whereas team conflict negatively predicted a between-person level of collective

25

efficacy, team cohesion encouraged a positive prediction. More specific, players who

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

17

perceived greater conflict levels in their teams also demonstrated a lower confidence level in

2

the group’s capabilities (Tekleab et al., 2009); nevertheless, players who perceived higher GI-

3

T and ATG-T showed greater collective efficacy levels (Heuzé et al., 2006; Kozub &

4

McDonnell, 2000; Leo et al., 2010). It is suggested that individual attraction to the group is

5

an individual perception, but as it refers to the way that players are attracted to a group, it

6

therefore also has a close relationship with the group and collective perception. In contrast,

7

the players’ perceptions of role ambiguity or role conflict have no group dimension, as both

8

refer only to the player in question (i.e., how clearly he or she perceives the functions

9

required of him or her). However, the results showed that GI-T, a cohesion factor that relates

11

SC

M AN U

10

RI PT

1

more to the group, is a stronger predictor than individual ATG-T.

Furthermore, it is important to note that the variables related to negative issues, such as team conflict (relationship and task), were statistically significant; notwithstanding,

13

regarding the variables associated with positive aspects such as team cohesion, only task

14

dimensions (GI-T and ATG-T) were statistically significant in the model. This suggested to

15

us that whereas social conflicts are relevant to confidence in the group’s capabilities (Paradis

16

et al., 2014), the increasing of social cohesion had relatively little influence on the players’

17

perceptions (Kozub & McDonnell, 2000; Leo et al., 2010). Nevertheless, when conflicts

18

appeared in interpersonal relationships, this seemed to be an important factor in decreasing

19

players’ trust (Holt et al., 2012). Lastly, results revealed that for players whose main aim is

20

performance, the task-performing aspects of the group emerged as very relevant.

EP

AC C

21

TE D

12

Finally, if we observe the last of the models created, where the interactions between

22

the independent variables of the study were included, it was shown that the adjusted model

23

did not improve. That is to say, notwithstanding that task cohesion and task conflict still

24

significantly predicted collective efficacy, the rest of the variables and the interactions

25

conducted did not show significant values in the prediction. This corroborated previous

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

18

1

postulates, where it was found that individual perceptions were not sufficient to explain the

2

perceptions in a group (Bandura, 1997; Beauchamp, 2007; Fransen et al., 2012; Son et al.,

3

2011). A limitation of our study was that the findings, although longitudinal with three

RI PT

4

measurements across the playing season, were correlational, and no causal inferences could

6

be drawn. Nevertheless, our results were consistent with theoretical predictions and previous

7

empirical research concerning the association between team dynamics variables and

8

collective efficacy (Hampson & Jowett, 2012; Heuzé et al., 2006; Leo et al., 2010). A second

9

limitation is related to the relatively small number of level-3 units (n = 36 teams) in the

M AN U

SC

5

multilevel analysis. A small number of units on a specific level in a MLM analysis could

11

influence the statistical power of the study and could therefore also have an impact on the

12

results (e.g., Hayes, 2006; Snijders, 2005). Another limitation of this study was that it relied

13

exclusively on self-reports, and thus, to some extent, our findings are subject to potential

14

influences of shared method variance. Future longitudinal research in this area would do well

15

to obtain records of objective markers of team conflict and team cohesion through objective

16

records (e.g., observation instruments). Finally, the generalisability of our findings to other

17

population samples and sports should be made with caution, as our sample comprised players

18

from a particular sport (i.e., football), and from a particular country (i.e., Spain).

EP

AC C

19

TE D

10

Despite the aforementioned limitations, we believe that this work makes a unique

20

contribution to the literature by examining the concurrent predictive effects of individual

21

perceptions (role ambiguity and role conflict) and collective perceptions (team conflict and

22

cohesion) within the team on collective efficacy in semi-professional sport at three different

23

levels over a 12-month period. Future research could build upon this work by developing an

24

intervention programme to enhance cohesion and to reduce conflicts within teams (Carron &

25

Eys, 2012; Holt et al., 2012). Then, through an experimental study, we could observe whether

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

changes in these variables have the intended effect on collective efficacy. One more

2

recommendation for future research is to investigate the potential differences, in both

3

intercept and slopes, between males and females as well as between amateurs and

4

professionals.

RI PT

1

5 6

AC C

EP

TE D

M AN U

SC

7

19

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

1

20

References

2

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

3

Beauchamp, M. R. (2007). Efficacy beliefs within relational and group contexts in sport. In S. Lavallee & D. Jowett (Eds.), Social psychology in sport (pp. 181–193). Champaign,

5

IL: Human Kinetics.

6

RI PT

4

Beauchamp, M. R., & Bray, S. R. (2001). Role ambiguity and role conflict within interdependent teams. Small Group Research, 32, 133–157.

8

doi:10.1177/104649640103200202

Beauchamp, M. R., Bray, S. R., Eys, M. A., & Carron, A. V. (2002). Role ambiguity, role

M AN U

9

SC

7

10

efficacy, and role performance: Multidimensional and mediational relationships

11

within interdependent sport teams. Group Dynamics: Theory, Research, and Practice,

12

6, 229–242.

13

Beauchamp, M. R., Bray, S. R., Fielding, A., & Eys, M. A. (2005). A multilevel investigation of the relationship between role ambiguity and role efficacy in sport. Psychology of

15

Sport and Exercise, 6, 289–302. doi:10.1016/j.psychsport.2004.03.002 Carron, A. V., Brawley, L. R., & Widmeyer, W. N. (1998). The measurement of

EP

16

TE D

14

cohesiveness in sport groups. In J. L. Duda (Ed.), Advances in sport and exercise

18

psychology measurement (pp. 213–226). Morgantown, WV: Fitness Information

19 20 21 22 23

AC C

17

Technology.

Carron, A. V., Colman, M. M., & Wheeler, J. (2002). Cohesion and performance in sport. A meta analysis. Journal of Sport & Exercise Pshycology, 24, 168–188.

Carron, A. V., & Eys, M. A. (2012). Group dynamics in sport (4th ed.). Morgantown, WV: Fitness Information Technology.

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

1

21

Carron, A. V., Widmeyer, W. N., & Brawley, L. R. (1985). The development of an

2

instrument to assess cohesion in sport teams: The Group Environment Questionnaire.

3

Journal of Sport Psychology, 7, 244–266. Chou, C., Bentler, P. M., & Pentz, M. A. (1998). Comparisons of two statistical approaches

RI PT

4

to study growth curves: The multilevel model and the latent curve analysis. Structural

6

Equation Modeling: A Multidisciplinary Journal, 5, 247–266.

7

doi:10.1080/10705519809540104

8

SC

5

Chow, G. M., & Feltz, D. L. (2007). Exploring new directions in collective efficacy and sport. In M. Beauchamp & M. Eys (Eds.), Group dynamics advances in sport and

10

exercise psychology: Contemporary themes, (pp. 221-248). New York: Routledge.

11

Eys, M. A., & Carron, A. V. (2001). Role ambiguity, task cohesion, and task self-efficacy.

M AN U

9

Small Group Research, 32, 356–373. doi:10.1177/104649640103200305

13

Feltz, D. L., Short, S. E., & Sullivan, P. J. (2008). Self-efficacy in sport. Champaign, IL:

14

Human Kinetics.

TE D

12

Field, A. (2009). Discovering statistics using SPSS. (3rd ed.). London: Sage.

16

Fransen, K., Decroos, S., Vanbeselaere, N., Vande Broek, G., De Cuyper, B., Vanroy, J., &

EP

15

Boen, F. (2015). Is team confidence the key to success? The reciprocal relation

18

between collective efficacy, team outcome confidence, and perceptions of team

19 20 21

AC C

17

performance during soccer games. Journal of Sports Sciences, 33, 219-231.

doi:10.1080/02640414.2014.942689

Fransen, K., Vanbeselaere, N., Exadaktylos, V., Vande Broek, G., De Cuyper, B.,

22

Berckmans, D., …Boen, F. (2012). “Yes, we can!”: Perceptions of collective efficacy

23

sources in volleyball. Journal of Sports Sciences, 30, 641–649.

24

doi:10.1080/02640414.2011.653579

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

1 2

22

Gersick, C. J. G. (1988). Time and transition in work teams: Toward a new model of group development. Academy of Management Journal, 31, 9-41. Gully, S. M., Incalcaterra, K. A., Joshi, A., & Beaubien, J. M. (2002). A meta-analysis of

4

team-efficacy, potency, and performance: Interdependence and level of analysis as

5

moderators of observed relationships. Journal of Applied Psychology, 87, 819-832.

6

doi:10.1037//0021-9010.87.5.819

Hampson, R., & Jowett, S. (2012). Effects of coach leadership and coach-athlete relationship

SC

7

RI PT

3

on collective efficacy. Scandinavian Journal of Medicine & Science in Sports. (Online

9

Version of Record published before inclusion in an issue) doi:10.1111/j.1600-

11 12 13 14 15

0838.2012.01527.x.

Hayes, A. F. (2006). A primer on multilevel modeling.Human Communication Research, 32, 385-410. doi:10.1111/j.1468-2958.2006.00281

Heck, R. H., Thomas, S. L., & Tabata, L. N. (2010). Multilevel and longitudinal Modeling with IBM SPSS. New York: Routledge.

TE D

10

M AN U

8

Heuzé, J. P., Raimbault, N., & Fontayne, P. (2006). Relationships between cohesion, collective efficacy and performance in professional basketball teams: an examination

17

of mediating effects. Journal of Sports Sciences, 24, 59–68.

18

doi:10.1080/02640410500127736

20 21

AC C

19

EP

16

Holt, N. L., Knight, C. J., & Zukiwski, P. (2012). Female athletes’ perceptions of teammate conflict in sport: Implications for sport psychology consultants. The Sport Psychologist, 26, 135-154.

22

Jehn, K. A. (1995). A multimethod examination of the benefits and determinants of

23

intragroup conflict. Administrative Science Quarterly, 40, 256–282.

24

Kahn, R. L., Wolfe, D. M., Quinn, R. P., Snoek, J. D., & Rosenthal, R. A. (1964).

25

Occupational stress: Studies in role conflict and ambiguity. New York: John Wiley.

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

23

1

Kozub, S. A., & McDonnell, J. F. (2000). Exploring the relationship between cohesion and

2

collective efficacy in rugby teams. Journal of Sport Behavior, 23, 120–129. Leo, F. M., García-Calvo, T., Parejo, I., Sánchez-Miguel, P. A., & Sánchez-Oliva, D. (2010).

4

Interacción de la cohesión en la eficacia percibida, las expectativas de éxito y el

5

rendimiento en equipos de baloncesto [Interaction of cohesion and perceived efficacy,

6

success expectations and performance in basketball teams]. Revista de Psicología del

7

Deporte, 19, 89–102.

SC

9

Leo, F. M., Sánchez-Miguel, P. A., Sánchez-Oliva, D., Amado, D., & García-Calvo, T. (2012). Evolution of perceived cohesion and efficacy over the season and their

M AN U

8

RI PT

3

10

relation to success expectations in soccer teams. Journal of Human Kinetics, 34, 129–

11

138. doi:10.2478/v10078-012-0072-y

12

Leo, F. M., Sánchez-Miguel, P. A., Sánchez-Oliva, D., Amado, D., & García-Calvo, T. (2014). Análisis de los procesos grupales y el rendimiento en fútbol semiprofessional

14

[Analysis of the group process and the performance in semiprofessionalsoccer].

15

Revista Internacional de Medicina y Ciencias de la Actividad Física y del Deporte,

16

14(53), 153-168.

19

EP

18

Leo, F. M., González-Ponce, I., Sánchez-Oliva, D., Pulido, J. J., & García-Calvo, T. (in press). Adaptation and validation in spanish of the Group Environment Questionnaire

AC C

17

TE D

13

(GEQ) with professional athletes. Psicothema.

20

Magyar, M. T., Feltz, D. L., & Simpson, I. P. (2004). Individual and crew level determinants

21

of collective efficacy. Journal of Sport and Exercise Psychology, 26, 136–153.

22

Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. (2001). A temporally based framework and

23

taxonomy of team processes. Academy of Management Review, 26, 356–376.

24

doi:10.5465/AMR.2001.4845785

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

1

Myers, N. D., Paiement, C. A., & Feltz, D. L. (2007). Regressing team performance on

2

collective efficacy: Considerations of temporal proximity and concordance.

3

Measurement in Physical Education and Exercise Science, 11, 1-24. Myers, N. D., Payment, C. A., & Feltz, D. L. (2004). Reciprocal relationships between

RI PT

4

24

5

collective efficacy and team performance in women’s ice hockey. Group Dynamics:

6

Theory, Research, and Practice, 8, 182–195. doi:10.1037/1089-2699.8.3.182

Paradis, K. F., Carron, A. V., & Martin, L. J. (2014). Athlete perceptions of intra-group

8

conflict in sport teams. Sport and Exercise Psychology Review, 10, 4-18. Paskevich, D. M., Brawley, L. R., Dorsch, K. D., & Widmeyer, W. N. (1999). Relationship

M AN U

9

SC

7

10

between collective efficacy and cohesion: Conceptual and measurement issues. Group

11

Dynamics: Theory, Research, and Practice, 3(3), 210–222. doi:10.1037/1089-

12

2699.3.3.210

13

Snijders, T. A. B. (2005). Power and sample size in multilevel modeling. In B. S. Everitt & D. C. Howell (Eds.), Encyclopedia of Statistics in Behavioral Science, (pp. 1570-

15

1573). Chicester: Wiley.

Son, V., Jackson, B., Grove, J. R., & Feltz, D. L. (2011). “I am” versus “we are”: Effects of

EP

16

TE D

14

distinctive variants of self-talk on efficacy beliefs and motor performance. Journal of

18

Sports Sciences, 29, 1417–1424. doi:10.1080/02640414.2011.593186

19

Sullivan, P. J., & Feltz, D. L. (2001). The relationship between intrateam conflict and

20

AC C

17

cohesion within hockey teams. Small Group Research, 32, 342-355.

21

Tekleab, A. G., Quigley, N. R., & Tesluk, P. E. (2009). A longitudinal study of team conflict,

22

conflict management, cohesion, and team effectiveness. Group & Organization

23

Management, 34, 170–205. doi:10.1177/1059601108331218

24 25

Tuckman, B. W. (1965). Developmental sequence in small groups. Psychological Bulletin, 63, 384–389.

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

ACCEPTED MANUSCRIPT

1

25

Tubre T. C. & Collins, J. (2000). Jackson and Schuler (1985) Revisited: A meta-analysis of

2

the relationships between role ambiguity, role conflict, and job performance. Journal

3

of Management, 26, 155–169. doi:10.1177/014920630002600104

5 6

Wall, J. A., & Castiller, R. R. (1995). Conflict and its management. Journal of Management, 21(3), 515–558. doi:10.1177/014920639502100306

RI PT

4

Zaccaro, S. J., Blair, V., Peterson, C., & Zazanis, M. (1995). Collective efficacy. In J. E. Maddux (Ed.), Self-efficacy, adaptation, and adjustment: Theory, research, and

8

application (pp. 305–328). New York: Plenum Press.

SC

7

AC C

EP

TE D

M AN U

9

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

26

ACCEPTED MANUSCRIPT

1

Table 1

2

Means, Standard Deviations, and Cronbach’s Alpha Coefficients of All Study Variables

SD

Α

M

SD

Role Scope

7.87

1.13

.82

7.65

1.22

Role Behaviour

7.91

1.05

.83

7.68

1.17

Role Evaluation

7.50

1.24

.82

7.18

1.41

Role Consequenses

7.95

1.18

.82

7.75

Role Conflict

1.69

.63

73

1.95

Relationship Conflict

1.78

.90

.85

2.33

Task Conflict

2.41

1.14

.79

GI-Social

7.22

1.45

ATG-Social

6.81

1.53

ATG-Task

SD

α

.80

7.50

1.33

.85

.81

7.50

1.37

.90

.82

6.96

1.65

.87

7.58

1.47

.89

.79

.82

2.16

.85

.84

1.27

.88

2.62

1.41

.91

3.02

1.39

.85

3.23

1.35

.82

74

6.65

1.82

.84

6.67

1.87

.87

.71

7.11

1.64

79

6.97

1.78

.80

M AN U

.83

AC C

4

M

1.27

7.36

1.35

.79

6.84

1.71

.85

6.64

1.82

.81

7.63

1.25

74

6.75

1.62

.71

6.49

1.90

.74

3.91

.51

.77

3.56

.64

.81

3.50

.67

.84

EP

Collective Efficacy

Time 3 (n = 576)

TE D

GI-Task

α

SC

M

Variable List

3

Time 2 (n = 549)

RI PT

Time 1 (n = 581)

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

27

ACCEPTED MANUSCRIPT

1

Table 2

2

Parameter Estimates (SE) for the performed multilevel linear models Nullmodel Model A Model B Model C Model D 3.65** (.05)

4.06** (.06) -.21** (.01)

Time

4.06** (.05) -.21** (.01)

Role Scope

Role Evaluation

M AN U

Role Consequences Role Conflict Relationship Conflict Task Conflict GI-Social

TE D

GI-Task ATG-Social

EP

ATG-Task

3.08** (.27) -.15 (.09) -.02 (.02) -.03 (.02) .02 (.01) -.004 (.03) -.005 (.02) -.03* (.01) -.03 (.02) -.02 (.02) .10** (.01) -.01 (.02) .07** (.01) -.02 (.01) .004 (.003) .01 (.01)

SC

Role Behaviour

2.79** (.13) -.09** (.01) -.02 (.02) -.03 (.02) .02 (.01) .02 (.01) -.005 (.02) -.03* (.01) -.06** (.01) .01 (.01) .10** (.01) .02 (.01) .07** (.01)

RI PT

Fixedeffects Intercept (p-value)

Time*Role Conflict

AC C

GI-Social *ATG-Social

Time* Role Consequences RandomEffects Residual

Intercept TEAM Intercept ID

.26** (.01) .08** (.02) .07** (.01)

.22** (.01) .08** (.02) .09** (.01)

.21** (.01) .06** (.02) .04* (.01) .02** (.002)

.17** (.01) .03* (.01) .03* (.01) .01* (.002)

.17** (.01) .03* (.01) .03* (.01) .01* (.002)

3093.38

2900.79

2877.00

2278.76

2272.59

Intercept ID + time OverallModel Test -2LL

Running Head: COLLECTIVE EFFICACY IN PROFESSIONAL SPORT

28

ACCEPTED MANUSCRIPT 2910.79 2938.16

2887.00 2914.37

2312.76 2405.59

2312.59 2421.80

TE D

M AN U

SC

RI PT

3101.38 3123.28

EP AC C

1

AIC BIC Note. * = p < .05. ** = p < .00

ACCEPTED MANUSCRIPT

Highlights We examined the predictive capacity of group processes on collective

RI PT

efficacy. We used professional football players in a longitudinal design.

Team conflict and cohesion can predict changes in collective efficacy.

SC

Role ambiguity and role conflict did not emerge as relevant to team

AC C

EP

TE D

M AN U

confidence.