Do We Share the Same View? Validating the Shared Mental Models in Team Sports Questionnaire

Abstract

Shared mental models (SMMs) can exert a positive influence on team sports performance because team members with SMMs share similar tasks and team-related knowledge. There is currently insufficient sports research on SMMs because the underlying theory has not been adapted adequately to the sports context, and different SMMs measurement instruments have been used in past studies. In the present study we aimed to externally validate and determine the construct validity of the “Shared Mental Models in Team Sports Questionnaire” (SMMTSQ). Moreover, we critically examined the theoretical foundation for this instrument. Participants were 476 active team athletes from various sports. While confirmatory factor analysis did not support the SMMTSQ’s hierarchical model, its 13 subfactors showed a good model fit in an explorative correlative approach, and the model showed good internal consistency and item–total correlations. Thus, the instrument’s subfactors can be applied individually, even while there are remaining questions as to whether other questionnaires of this kind are an appropriate means of measuring SMMs in sport.

Keywords

shared mental models team sports questionnaire

Introduction

Team sports consist of many complex and interdependent members’ actions, and athletes must master them collectively to perform well together. Examining an example scenario, the “no look” pass in handball, a backcourt player passes the ball to the pivot player, without looking directly at this target player. If both players understand the situation in the same way, the pivot is ready for the pass, can catch the ball, and can score successfully. However, without this shared understanding, the pivot is not prepared for the pass and attempted teamwork fails. To solve this situation successfully, both players must understand the task (blind pass to the pivot) and both must have necessary knowledge (awareness of players position, game strategy) about the other team member. This example illustrates a shared mental model (SMM) between team players. In general, SMMs are defined “as knowledge structures held by members of a team that enable them to form accurate explanations and expectations for the task, and, in turn, to coordinate their actions and adapt their behavior to demands of the task and other team members” (Cannon-Bowers et al., 1993). They include both a similar understanding of the task (task-related knowledge) and an understanding of the strengths and weaknesses of the other team members (team-related knowledge; Eccles & Tenenbaum, 2004). Team members use their SMMs to adapt their actions and coordinate them into a common act (Hänsel & Baumgärnter, 2014). However, there are special demands in sports, as sports call for rapid actions as situations change constantly, and there is seldom enough time for planning (Araújo et al., 2006). To meet these demands as a team, expert players draw on SMMs (Eccles, 2019).

SMMs have previously been studied in various contexts such as the military (see Cooke et al., 2004, for uninhabited air vehicles), industrial and organizational psychology (DeChurch & Mesmer-Magnus, 2010a), and sports (Filho et al., 2016; Giske et al., 2015). Empirical studies and meta-analyses have shown that SMMs play a central role in team performance (DeChurch & Mesmer-Magnus, 2010b; Mohammed et al., 2010). Typically, team performance has been operationalized as task performance, completion of tasks, or the abilities required for the tasks; and it has been measured both subjectively and objectively (DeChurch & Mesmer-Magnus, 2010a). Moreover, a meta-analytic review by DeChurch and Mesmer-Magnus (2010a) showed that SMMs can explain more variance than team members’ motivational states and behavioral processes.¹

In order to replicate and extend SMMs research to the sports context, there is a need for a valid measure of sport teams’ SMMs. As Mohammed et al. (2010) reported, there is no universal or theoretically sound method for measuring SMMs in general settings, complicating the application of SMMs to sports. To date, most research has depended upon questionnaires administered to team members (Cooke et al., 2000), just as many other team constructs in sports have been measured with questionnaires (e.g., Eys et al., 2019). In order to transfer SMMs into team sports, Gershgoren (2012) developed the Shared Mental Models in Team Sport Questionnaire (SMMTSQ), incoporating the specifics of team sport. Unfortunately, this tool is only available through Gershgoren’s doctoral dissertation, and it has not yet been critically reviewed by different research groups to affirm its validity and appropriate theoretical foundation for its intended purpose. Furthermore, additional research is needed to determine whether this questionnaire is applicable across different types of team sports. Critical research examinations of available questionnaires are common, as seen by the Group Environment Questionnaire (Carron et al., 1985), critically reviewed by several researchers (e.g., Eys et al., 2007; Ohlert, 2012; Ohlert et al., 2015). The better validated the questionnaire, the more confident we are that it incorporates and reflects our understanding of its relevant underlying construct. Only after first confirming the validity of the SMMTSQ, can we address whether it can sufficiently measure SMMs (Cooke et al., 2000). If however, SMMs are too complex to be measured with a single questionnaire, then new research methods for studying SMMs would need to be developed (McNeese et al., 2015). Hence, in the present study, we aimed to verify the validity and replicate the SMMTSQ’s goodness of fit to the SMM construct through independent research. We expected our results to serve as a basis for further discussing the theoretical suitability of questionnaires like the SMMTSQ for measuring SMMs in the sport context.

Shared Mental Models

Shared mental models (SMMs) are based, first, on each player’s mental model and, second, on the degree to which these mental models are shared among team members (Langan-Fox et al., 2004). At the individual level, mental models are defined as internal mental representations of objects, actions, situations, or people (Wilson, 2000). They help individuals describe, explain, and predict situations (Rouse & Morris, 1986) without needing to examine every detail, permitting a comparison of the given situation to an internal mental model for quicker decision making (Wilson, 2000). This process is particularly relevant in fast action sports, because individual mental models allow simple, fast, and flexible decision making. When individual team members’ mental models are the same or similar, the result is a shared mental model—an SMM—that facilitates the understanding and anticipation of a team’s next actions, allowing individuals to adapt their own actions to another’s movements (Cannon-Bowers et al., 1993; Eccles, 2019). Given similar SMMs, team members will interpret situations similarly and, thereby, form similar expectations (Reimer et al., 2006).

The working definition of SMMs used in this study states that, in sports, individual team members have similar internal mental models of the task and of the team, resulting in good collective action on the field (Eccles & Tenenbaum, 2004). In this case, the SMMs of team members overlap, are similar, or are complementary (Cannon-Bowers & Salas, 2001). This working definition is analogous to other frequently used constructs such as “team mental models” (Filho & Tenenbaum, 2012; Mohammed et al., 2010) or “shared knowledge” (Eccles & Tenenbaum, 2004). However, SMMs contrast with interactive team cognition (ITC), because ITC describes team cognitions as thoughts that first emerge on the team level (Cooke et al., 2013). The theory of SMMs assumes that team members have already developed shareable individual mental models. On the group level, the similarity of these individual mental representations creates SMMs.

Various questionnaires currently used to measure SMMs have resulted in ambiguous empirical findings. For example, Giske et al. (2015) found a positive correlation between SMMs and a performance-related component, namely role clarity. However, Giske et al.’s (2015) findings were based on exploratively self-developed items as the measurement method. These items measure three types of SMMs: a general SMM, a training-specific SMM, and an opponent-specific SMM. Filho et al. (2015) also found a positive relationship between SMMs and perceived performance in female and male college soccer players. However, their finding was based on the Team Assessment Diagnostic Measure (TADM)—a broader, non-sport-specific questionnaire originally designed and validated to measure SMMs for occupational work teams (Johnson et al., 2007). Their finding was not replicated in an explorative multilevel analysis with the same instrument (Filho et al., 2014). In sum, there are not enough studies of SMMs to speak of exhaustive knowledge in this area and one reason why is, that the literature is lacking a clear measurement method for SMMs.

As none of the previous approaches may have been sufficiently based on underlying sport-specific constructs, Filho et al. (2014) used field observations, semistructured interviews, and the documentary method to measure SMM components over several measurement periods. The authors divided SMMs—as in the present working definition—into individual task-related and team-related knowledge that mutually influence each other (Filho et al., 2014). In another study, Gershgoren et al. (2013) used semistructured interviews, observations, and documents to examine the SMMs components from the perspective of two soccer coaches. They found that SMMs could be differentiated into (a) game intelligence, with subfactors such as anticipation and creativity, and (b) game philosophy, with subfactors such as agreement among the players and agreement between the coaches and the players. They obtained similar results from 12 semistructured interviews with football players and coaches (Gershgoren et al., 2016). As in the first study, data on SMMs components were grouped according to game intelligence and game strategy (similar to game philosophy), and they added efficacy beliefs. Game intelligence included (a) anticipatory abilities, (b) creativity, (c) knowing each other’s abilities, and (d) experience in sports (similar to the study from Gershgoren et al., 2013). Game intelligence enables athletes to coordinate their actions in different situations. Game strategy (similar to game philosophy in the study from Gershgoren et al., 2013) included (a) taking advantage of the abilities of the players and hiding their weaknesses (i.e., maximizing abilities), (b) agreement on players’ positioning, (c) a balance in terms of personal and professional characteristics, and (d) consistency over the course of the game. Overall, game strategy should provide athletes with strategic guidance for their actions within a game. The authors also reported that three different types of efficacy beliefs emerged from the data: (a) self-, (b) other, and (c) collective efficacy. In general, these results can be adapted to the proposed working definition as follows: Contents must be task-related as in game intelligence (Filho et al., 2014; Gershgoren et al., 2013) and team-related as in game strategy (Filho et al., 2014, Gershgoren et al., 2013). In addition, the present working definition is expanded to include empirical findings that SMMs include content on both one’s own and others’ efficacy beliefs (Gershgoren et al., 2013). This was the basis for developing the “Shared Mental Models in Team Sports Questionnaire” (SMMTSQ; Gershgoren, 2012) that is currently the only questionnaire to combine both these definitional components of SMMs for the specific sports context.

Shared Mental Models in Team Sports Questionnaire

The SMMTSQ is an English-language questionnaire developed by Gershgoren (2012) consisting of 50 items organized into three scales and 13 subscales. Respondents rate items on 5-point Likert scales (see Table 1). SMMTSQ items were first formulated on the basis of qualitative determinations of their ability to identify aspects of SMMs in sports and measure components of the working definition of SMMs (Gershgoren, 2012). The instruments’ main three factors are comprised of 13 subfactors as detailed in Table 2 and listed here. The Situational Cognition scale contains task-related knowledge in the SMMs on the four subfactors Anticipation, Creativity, Experience, and Knowing each other’s abilities. The General Cognition scale contains team-related knowledge in the SMMs with six subfactors, Agreement among the players, Agreement on players’ positioning, Agreement between coaches and players, Maximizing abilities, Team goals, and Balance. The Efficacy Beliefs scale has three subfactors: Self-efficacy, Other efficacy, and Collective efficacy. The final SMMTSQ item is a global, summary item (“Overall, to what degree are the players on your team in sync with each other on the field/court/ice”).

Table 1.

Overview of the SMMTSQ’s Structure and Scales.

Higher-order factors	Subfactors	M	SD	Omega h	Cronbachs α
	G1 – Agreement among the players	3.85	0.68	0.73	0.72
	G2 – Agreement on players positioning	3.50	0.66	0.65	0.72
GeneralCognitions	G3 – Agreement between the coaches and players	3.76	0.81	0.76	0.77
	G4 – Maximizing abilities	3.48	0.81	0.66	0.70
	G5 – Team goals	3.48	0.66	0.73	0.83
	G6 – Balance	3.41	0.65	0.60	0.61
	S1 – Anticipation	3.33	0.71	0.77	0.80
Situational	S2 – Creativity	3.30	0.74	0.81	0.83
Cognitions	S3 – Experience	3.52	0.85	0.60	0.69
	S4 – Knowing each other abilities	3.60	0.71	0.71	0.78
	E1 – Self-efficacy	3.60	0.69	0.78	0.80
Efficacy Beliefs	E2 – Other efficacy	3.65	0.70	0.68	0.71
	E3 – Collective Efiffcay	3.90	0.72	0.77	0.79

Table 2.

Description of All Items Within the current study of the SMMTSQ (Gershgoren, 2012).

Item		Subfactor	N	M	SD	DP*
	Part A – Please indicate your team’s opinion on the following matters
	Now, please rate the degree to which your teammates (not you personally!!)…
1	Find that the game plans used maximize the team’s ability to perform	G4	470	3.59	0.80	0.47
2	Agree on the personnel in the line-up	G2	473	3.69	0.85	0.48
3	Agree that the coach’s game plans result in a balanced composition of players’ abilities	G6	473	3.59	0.87	0.41
4	Believe that the coach’s game plans capitalize on individual players’ strengths	G4	474	3.66	0.90	0.48
5	Agree that the positions on the field/cour/ice are occupied by the right team members	G2	470	3.76	0.88	0.56
6	Think that the workload on the field/court/ice is distributed appropriately among the players	G6	472	3.39	1.01	0.32
7	Believe that the coach’s game plans compensate for individual players’ weaknesses	G4	473	3.29	0.93	0.43
8	Consider that each player on the field/court/ice is the best team member to fulfill the requirements of his/her position	G2	475	3.25	0.90	0.46
9	Think that the players’ capabilities complement one another harmonically	G6	473	3.57	0.88	0.42
10	Believe that the coach’s game plans enable the team to achieve its full potential	G4	470	3.40	0.95	0.59
11	Agree that each player on the field/court/ice ist he most capable team member fort he role he/she is assigned to	G2	470	3.30	0.92	0.53
12	Think that the tactical, technical, physical, and mental abilities of the team members are being balanced	G6	471	3.09	1.00	0.44
	To what degree do the players on your team:
13	Share similar team goals	G5	475	4.05	0.93	0.67
14	Possess realistic beliefs about their own physical abilities	W1	471	3.58	0.89	0.51
15	Trust in each other’s abilities	W2	473	3.69	0.89	0.47
16	Believe in the team´s ability to perform well	W3	475	4.04	0.82	0.66
17	Consider that the team´s goal represent their own goals	G5	473	3.72	1.01	0.61
18	Possess realistic beliefs about their own tactical abilities	W1	475	3.55	0.89	0.68
19	Believe that each player can fulfill his/her role on the team	W2	473	3.56	0.93	0.57
20	Believe in the teamßs ability toreach ist objectives	W3	472	3.92	0.86	0.62
21	Find the teamßs goals to be realistic	G5	474	4.10	0.87	0.59
22	Possess realistic beliefs about their own technical abilities	W1	474	3.71	0.86	0.67
23	Believe that each player is the right person within the team’s composition	W2	475	3.44	0.94	0.47
24	Believe in the team’s ability to outperform other teams regardless of the other teams’ skill-level	W3	474	3.82	0.94	0.50
25	Agree among themselves on the team’s goals	G5	472	3.82	0.95	0.72
26	Possess realistic beliefs about their own mental abilities	W1	473	3.56	0.85	0.61
27	Believe that each player possesses the skills required to perform well	W2	475	3.91	0.95	0.48
28	Believe in the team’s ability to overcome difficulties and obstacles	W3	475	3.82	0.96	0.60
29	Agree with head coach on the team´s goals	G5	473	3.89	0.93	0.56
	Part B – Please provide your own opinion on the following matters
	To what degree do you agree with your head coach on:
30	The team´s playing philosophy	G3	475	3.76	1.02	0.60
31	The team’s general playing formation	G3	475	3.75	0.97	0.60
32	The offensive/defensive orientation of the team	G3	475	3.76	0.96	0.59
	To what degree do you agree with your teammates on the following issues?
33	The team’s playing philosophy	G1	475	3.84	0.90	0.49
34	The team’s general playing formation	G1	475	3.85	0.84	0.53
35	The offensive/defensive orientation of the team	G1	475	3.86	0.80	0.59
	On the field/court/ice, to what degree do you consider your teammates to be:
36	Able to anticipate each other’s actions	S1	472	3.40	0.86	0.59
37	Able to gain an advantage using their creativity	S2	470	3.40	0.90	0.61
38	Familiar with the strengths of the other team members	S4	475	3.78	0.97	0.63
39	Experienced in this sport	S3	475	3.91	0.95	0.53
40	Able to modify their actions according to other players’ movements	S1	475	3.41	0.90	0.59
41	Able to predict the actions of the creative players on the team and respond accordingly	S2	475	3.24	0.94	0.70
42	Familiar with the weaknesses of the other team members	S4	473	3.76	0.88	0.58
43	Experts in this sport	S3	475	3.14	0.99	0.53
44	Able to predict the upcoming movements/actions of other team members	S1	471	3.30	0.87	0.68
45	Able to synchronize their own actions with those of the creative players on the team	S2	475	3.25	0.87	0.66
46	Able to adjust their actions to take advantage of their teammates strengths	S4	474	3.45	0.87	0.61
47	Able to read what is about to happen during competition and prepare for this in advance	S1	474	3.20	0.95	0.62
48	Able to use their creativity to think and act outside the box for the benefit of the team	S2	475	3.30	0.91	0.68
49	Able to adjust their actions to compensate for their teammates weaknesses	S4	474	3.42	0.91	0.63
	Last, please answer the following item
50	Overall, to what degree are the players on your team in sync with each other on the field/court/ice		469	3.77	0.87

Note: *DP = Discriminatory power (correlation item with scale).

SMMTSQ Item content was validated through review by two scientific experts. In addition, the construct validity, reliability, and criterion validity were tested statistically (for more information see Gershgoren, 2012). Cronbach’s alpha for the intercorrelation between superordinate factors was good with values ranging between .91 and .94. For the 13 subfactors, values ranged between .76 and .90. Test-retest reliability was good with values between .80 and .84. For criterion validity, the SMMTSQ correlated significantly with the Team Assessment Diagnostic Measure for work teams, r = .76, with correlations between the individual scales also significant (r = .49 to .76).

Aim of the Present Study

As noted, the SMMTSQ (Gershgoren, 2012) is the first and only questionnaire purporting to measure SMMs in a sport-specific context that combines SMM components into one questionnaire. Gershgoren (2012) reported that the SMMTSQ is based on a complex model with a hierarchical structure, and that the component parameters indicated satisfactory reliability and validity. We sought first to replicate the instrument’s validity and, secondly to examine its universal suitabilty, independent of different sports and gender. Likewise, we tested the instrument’s assumed demographic correlates with both task-related SMMs (age, experience in sports, league level) and team-related SMMs (number of seasons played in the team and participation in training sessions). With these results in hand, we sought to critically review whether questionnaires of this type are suitable for measuring sport SMMs.

Method

Participants and Procedure

A total of 476 active female (n = 218, 45.9%) and male team athletes (n = 25, 54.1%) participated in the present online study. They practiced the following sports: volleyball (36.4%), handball (30.7%), soccer (19.4%), basketball (8.8%), hockey (1.1%), and other (3.6%). Their average age was 27.18 years (SD = 8.28), they averaged 15.40 years (SD = 8.09) experience in their respective sport, they had played on their team for an average of 5.01 seasons (SD = 4.53), and they trained an average of 2.06 (SD = 0.80) times per week. We recruited this sample through sports associations that were asked to contact their members via e-mail, homepage, calls for tenders, and their social networks. The survey was also advertised via snowballing, starting with sports groups’ social networks. We applied various recruitment criteria. For example, participants had to be at least 16 years old, play a team sport, and currently play actively in a league. If participants played several team sports, they were asked to select just one to consider while responding to the study and to indicate this for control purposes. Participation in the online survey was voluntary and could be terminated at any time. Participants also agreed to our anonymous use of their data. To motivate participation, 20 vouchers were raffled off after completion of the study. Anonymity was guaranteed by participating in the raffle via a separate questionnaire.

In line with the declaration of Helsinki and the institutional ethical committee, participants were informed of the aim of the study and its general procedures and were guaranteed anonymity and that obtained data would be used exclusively in aggregated fashion for research purposes. After giving their informed consent, participants answered general questions (e.g., type of sport, age, length of team membership, league level) and completed the SMMTSQ. The structure of the individual scales was contained within the questionnaire and therefore remained the same for all participants (see Table 2). Of the original 1,330 athletes who accessed the link, about one half (675) started the survey. Data were analyzed from the almost 70% (476) of these respondents who completed the survey sufficiently to yield a very low missing value rate (averaging less than 1%).

Data Analysis

We analyzed data using SPSS.24 and R. Before testing the underlying model with confirmatory factor analysis (CFA), we checked the model’s structural assumptions. These data showed no collinearity; all item correlations were below r < .65. However, Kolmogorov–Smirnov tests indicated, that responses of all items deviated significantly (p < .01) from a normal distribution. Thus, we calculated a CFA with a robust maximum likelihood estimator (RMLR) (see Bühner, 2006), in order to address the violation of a normality assumption (Wu & Kwok, 2012). We tested the model fit with the robust comparative fit index (RCFI), robust root mean square error of approximation (RRMSEA), and standardized root mean square residual (SRMR). The model-fit was acceptable at RCFI ≥ .9 and RRMSEA ≤ .06 (Hu & Bentler, 1999). We measured internal consistency with McDonald’s omega h and Cronbach’s α. We set levels of statistical significance at p < .05. Effect sizes for the relations with external criteria were expressed by r-values, while effect sizes for the MANOVA were expressed by partial eta square (η_p²).

Results

Descriptive Statistics

Table 1 summarizes the participants’ mean scores (and standard deviations) on the 13 scales, ranging from 3.30 to 3.90 (0.65 ≤ SD ≤ 0.85). The internal consistency of the individual scales was satisfactory to good (.60 ≤ Ω h ≤ .81, or 61 ≤ Cronbach’s α ≤ .83). Table 2 shows the descriptive statistics on all 50 items and their classifications to the respective scale. All items were answered by between 469 and 475 respondents. Mean scores on the individual items varied between 3.09 and 4.10 (0.77 ≤ SD ≤ 1.02). The discriminatory power was acceptable to good at .41 ≤ r ≤ .72 for all items with one exception (Item 6, r = .32).

Factorial Validity

We tested a three-factor hierarchical model with RMLR estimators. This model could not be confirmed, as the RCFI was lower than the needed criterion, χ² = 6020.80, df = 1081, p < .001, RCFI = .88, RRMSEA = .05, 90% CI [.05, .06], SRMR = .07. This poor model fit was underlined by significant intercorrelations with a large effect size between the three postulated superordinate factors (p < .001, .57 ≤ r ≤ .66), as well as significant correlations with all 13 subfactors (p < .001, .32 ≤ r ≤ .90) (see Table 3).

Table 3.

Intercorrelations With All Higher-Order Factors With Subfactors of the SMMTSQ.

	SC	EB	G1	G2	G3	G4	G5	G6	S1	S2	S3	S4	W1	W2	W3	Item 50
GC	.565^**	.662^**	.721^**	.693^**	.763^**	.784^**	.669^**	.773^**	.519^**	.541^**	.345^**	.556^**	.425^**	.625^**	.590^**	.581^**
SC		.605^**	.521^**	.361^**	.341^**	.351^**	.490^**	.479^**	.895^**	.901^**	.784^**	.851^**	.393^**	.567^**	.540^**	.478^**
EB			.538^**	.461^**	.320^**	.499^**	.659^**	.536^**	.548^**	.567^**	.439^**	.552^**	.762^**	.851^**	.852^**	.495^**
G1				.396^**	.509^**	.382^**	.498^**	.469^**	.475^**	.497^**	.319^**	.501^**	.376^**	.479^**	.500^**	.513^**
G2					.438^**	.518^**	.308^**	.529^**	.337^**	.350^**	.228^**	.340^**	.285^**	.511^**	.354^**	.354^**
G3						.558^**	.349^**	.461^**	.292^**	.338^**	.164^**	.378^**	.178^**	.323^**	.285^**	.403^**
G4							.440^**	.621^**	.312^**	.349^**	.189^**	.375^**	.305^**	.470^**	.460^**	.404^**
G5								.434^**	.474^**	.463^**	.331^**	.439^**	.485^**	.504^**	.645^**	.463^**
G6									.457^**	.440^**	.317^**	.449^**	.350^**	.545^**	.443^**	.469^**
S1										.828^**	.589^**	.693^**	.395^**	.493^**	.471^**	.407^**
S2											.565^**	.728^**	.374^**	.505^**	.513^**	.419^**
S3												.536^**	.288^**	.443^**	.373^**	.324^**
S4													.327^**	.530^**	.513^**	.503^**
W1														.489^**	.487^**	.287^**
W2															.620^**	.459^**
W3																.493^**
Item 50

Note: For the correlations Spearma’s Rho was calculcated. **Correlations are significant on the niveau 0.01 (2-sides). GC = General Cognitions, SC = Situational Cognitions, EB = Efficacy Beliefs.

Furthermore, the 13 assumed subfactors also correlated significantly with each other (p < .001, .16 ≤ r ≤ .83) and they each correlated significantly with the summary Item 50 (“Overall, to what degree are the players on your team in sync with each other on the field/court/ice?”), regardless of what scale they belonged to (p < .001, .29 ≤ r ≤ .51). Therefore, we tested a more economical, correlative model of the 13 subfactors without the three superordinate factors. This correlative model showed a satisfactory model fit, χ² = 6020.80, df = 1081, p < .001 RCFI = .90, RRMSEA = .05, 90% CI [.04, .05], SRMR = .06. Hence, we used this model and dropped the three superordinate factors and analyzed the previous 13 subfactors as correlative factors with equal ranking.

Relations With External Criteria

For further analyses, we calculated the sum scores of the 13 factors. Table 4 shows the individual correlations between the 13 factors and age, experience in sport, number of seasons already played with the team, participation in training sessions, and league level. Overall, age correlated negatively with 10 of the 13 factors with small effect sizes (p < .001, -.18 ≤ r ≤ -.12). Experience in sports correlated significantly with the following three factors: Agreement among the players (G1) (p < .001, r = -.14), Balance (G6) (p < .001, r = -.12) and Knowing each other’s abilities (S4) (p < .05, r = -.12). There was also a significant correlation between the number of seasons in the team and the two factors Agreement on players positioning (G2) (p < .05, r = .11) and Agreement between the coaches and the players (G3) (p < .001, r = .16). Participation in training sessions correlated significantly with the four factors Anticipation (S1), Creativity (S2), Experience (S3), and Knowing each other’s abilities (S4) (p < .05, .09 ≤ r ≤ .15). There was also a significant correlation with the factor Team goals (G5) (p = .02, r = .11). The league level correlated significantly with the two factors Anticipation (S1) (p = .02, r = -.11) and Agreement between the coaches and the players (S3) (p = .001 r = -.15).

Table 4.

Correlations of the SMMTSQ’s 13 Factors With Descriptive Variables.

	ES	NS	PT	A	LL	G1	G2	G3	G4	G5	G6	S1	S2	S3	S4	W1	W2	W3
ES	1.000	.293**	−.072	.548**	.028	−.139**	−.053	−.077	−.082	−.060	−.119**	−.073	−.055	.022	−.118*	−.058	−.082	−.028
NS		1.000	−.109*	.282**	.101*	.041	.106*	.125**	.031	−.091	−.020	−.003	.025	−.020	.053	−.063	−.028	−.024
PT			1.000	−.228**	−.402**	.054	.006	−.015	.038	.112*	.085	.149**	.127**	.134**	.094*	.083	.058	.038
A				1.000	.016	−.120**	−.031	.035	−.073	−.149**	−.143**	−.176**	−.164**	−.142**	−.153**	−.137**	−.144**	−.159**
LL					1.000	.001	−.009	.012	.032	.024	−.040	−.108*	−.073	−.146**	−.045	−.009	−.027	.035

Note: ** Correlations are significant on the niveau 0.01 (2-sides). * Correlations are significant on the niveau 0.05 (2-sides). ES = Experience in sport, NS = Number of seaons with the team, PT = Participation in training sessions, A= Age, LL = League level.

Gender Differences

Using Pillai’s trace, a multiple analysis of variance (MANOVA) revealed a gender difference collectively over the 13 factors, V = 0.06, F(13,462) = 2.32, p < 01, η_p² = .06. After Bonferroni corrections, differences were evident specifically on the factors Anticipation (S1), p = .02, and Creativity (S2) p = . 01. In both cases, male athletes scored higher than female athletes. There were no gender differences on the other 11 factors.

Sports Differences

Using Pillai's trace, the MANOVA showed a difference between sports, V = 0.31, F(9,13,234) = 1.62, p < .01 η_p² = .04. After Bonferroni corrections, the factor Team goals (G5) differentiated between soccer and hockey, p = .02, with soccer players scoring higher than hockey players. The factor Balance (G6) differentiated between volleyball and basketball, p = .03, and volleyball and handball, p = .03; and, in both cases, volleyball players estimated the balance in the team to be higher. Finally, the factor Collective Efficacy (W3) differentiated between hockey and soccer, p = . 05, with soccer players again scoring higher. The other 10 subfactors showed no differences between different sports.

Discussion

Shared mental models (SMMs) are thought to make an important contribution to good team performance in various sports. To further fuel research in this area, we aimed, in the present study, to independently validate the SMMTSQ, which is based on a hierarchical model structure with three superordinate factors and 13 subfactors. We were unable to confirm this model structure. This result and our finding of high correlations between the postulated superordinate factors and the 13 subfactors suggest that there were not three independent factors in a hierarchical structure as assumed, but rather 13 factors—originally postulated by the authors as subfactors—that are interrelated without a hierarchical structure. We further explored this result with a model, where all 13 factors correlated to one another. This model showed a satisfactory model fit. Therefore, all factors equally represent what athletes perceive as relevant parts of SMMs.

Our analyses of criterion validity showed, that the individual factors applied to both genders with few exceptions (gender differences were found for two factors: Anticipation and Creativity) and across various sports (sports differences were found for three factors: Team goals, Balance, and Collective efficacy). Future research should examine where these differences stem from and whether they differently impact SMMs outcomes, such as coordination and performance. For now, researchers and practioners who want to use the scales across genders and team sports should apply these five scales with caution.

We examined the correlates of age, experience in sports, number of seasons already played with the team, participation in training sessions, and league level. Of particular note, we found that participation in training sessions correlated significantly with four factors: Anticipation, Creativity, Experience, and Knowing each other’s abilities. Thus, the more athletes trained, the stronger these SMMs aspects became. These factors were originally combined into situational cognitions and therefore represented SMMs that were helpful to game decisions.

Limitations and Directions for Future Research

Within the current study our external validation effort was limited to a confirmation of the postulated structure. We did not further replicate the findings from Gershgoren (2012) by examining the SMMTSQ’s concurrent validity with other team constructs (e.g., group cohesion) or its predicitve validity with other measures (e.g., team performance). Furthermore, the original validation was based on collegiate athletes, while our sample was older and had already played with the team for about five seasons. Bearing these limitations in mind, our overall findings suggest a different model fit for the SMMTSQ than the postulated one, and our results raise several questions regarding the accuracy of the purported questionnairés' model structure, including the following. Does the SMM construct have a hierarchical model structure with superordinate summarizing factors? Are these hierarchical ideas redundant to the 13 individual scales or do the subscales satisfactorily describe the construct? Even if the scales can be grouped in terms of content, our failure to replicate the grouping to superordinate factors statistically lends no support to this model structure. Perhaps the SMMTSQ reflects a specific perception of SMMs by athletes and coaches, but the associated factors are of equal weight and therefore cannot be grouped. The original SMMTSQ was based on expert interviews with athletes and coaches (Gershgoren et al., 2016) as well as interviews with coaches alone (Gershgoren et al., 2013) about their perceptions of component aspects of SMMs. To construct the SMMTSQ, these qualitative results were compared with a theoretical definition of SMMs. The current study replicated these earlier findings and provided quantitative evidence that this perception of SMMs was quite consistent and well reflected metaknowledge, but a hierachical understanding of SMMs through the SMMTSQ’s theoretical structure may be unjustifiably complex. Nonetheless, at a simpler level our results demonstrate that SMMs can be adapted to the sports context.

In reviewing the questionnaire critically, the SMMTSQ reflects the players’ perceptions and metaknowledge, focusing primarily on team-related SMMs and asks globally about task-related SMMs. For example, this questionnaire can adequately survey the philosophy of the game (see Items 30 and 33) or the offensive versus defensive orientation of the team (see Items 32 and 35) at a global level of task-related SMMs. However, this global focus is less situationally task-related than first thought. In theory, task and team related knowledge are building blocks of SMMs (Eccles & Tenenbaum, 2004), and the implicit SMMs definition used for the SMMTSQ extended to include efficacy beliefs may have no incremental explanatory value for SMMs. In this respect, the various relevant components of SMMs require further theoretical and empirical clarification, and the instrument may need modification.

Furthermore, while the SMMTSQ represents meta-knowledge of SMM, in this study we could not determine whether SMMs guide and influence decision making in different situations (Langan-Fox et al., 2004). Theoretically, team members can adapt their SMMs to meet various situations so that they can be flexible during a game (Bourbousson et al., 2011). However, it is hard for questionnaires like the SMMTSQ to consider dynamic and flexible SMMs adaptations. In the handball “no look” pass example discussed earlier, we might use the questionnaire’s Anticipation Scale to measure the extent of an individual player’s general anticipatory abilities to expect and adapt to a pass possibility, but only if the player is aware of and can self-report personal anticipatory abilities. It is possible that players are not aware of this specific anticipatory ability, even though they routinely make correct decisions within a game or correctly adapt to their fellow player’s intent. Thus, such situational and concrete task-related SMMs are hard to assess by questionnaires in two respects: (a) It is difficult for questionnaires to capture the nearly infinite numbers of different team play scenarios through general scales; and, (b) players may resort to SMMs implicitly, without full personal awareness. Additionally, players might believe they possess explicit SMMs when game play reveals that these presumed SMMs do not actually guide their game behavior. Implicit measurement methods, as originally proposed by McNeese et al. (2015), would seem to offer another way to investigate SMMs. Such methodological advances might provide a better method to investigate SMMs and permit more accurate explorations of the gap between implicit and explicitly perceived SMMs.

Conclusion

In sum, as questionnaires have been commonly used measurement tools in research on SMMs (Cooke et al., 2000), we tested the SMMTSQ on a large sample of athletes from various team sports. The present study indicated that this questionnaire can be used to explicitly measure perceived SMMs as there were correlates of relevant behaviors on several separate scales; but further research is needed to (a) critically examine each of the 13 correlative SMMs measurement scales so as to determine whether a more economic assessment approach might be possible; and (b) resolve new questions as to whether questionnaires of this type can viably measure SMMs in accordance with their underlying theory. Questionnaires cannot capture a player’s implicit action-guidance, considered critical to the SMM construct. Thus, we suggest, that future researchers might develop and validate a SMM measurement tool that captures implicit knowledge and awareness in order to complement the SMMTSQ’s emphais on explicit SMM information.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Charlotte Raue

Note

Author Biographies

Charlotte Raue: PhD, student in the German Research Foundation (DFG) within the research training group 1712-2 "Trust and communication in a digitized world" and at the Institute of Sport and Exercise, Department of Sport and Exercise Psychology at the University of Muenster.

Dennis Dreiskaemper: PhD, Senior researcher at the Institute of Sport and Exercise Science, Department of Sport and Exercise Psychology at the University of Muenster.

Bernd Strauss: PhD, Full professor at the Institute of Sport and Exercise Science and Head of the Department of Sport Psychology at the University of Muenster.

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