International Journal of Hospitality Management 33 (2013) 19–28
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International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman
The temporal priority of team learning behaviors vs. shared mental models in service management teams Priyanko Guchait a,∗ , Katherine Hamilton b,1 a b
Conrad N. Hilton College of Hotel and Restaurant Management, University of Houston, 229 C.N. Hilton Hotel & College, Houston, TX 77204-3028, United States The Pennsylvania State University, 330G Information Sciences and Technology Building, University Park, PA 16802, United States
a r t i c l e
i n f o
Keywords: Team/shared mental models Team/group cognition Learning behavior Service management teams
a b s t r a c t This study investigated the temporal priority of shared mental models (SMMs) on team learning behaviors using a longitudinal study design. Twenty-seven ﬁeld-based teams (173 participants) performing a restaurant management task participated in the study. Panel data on SMMs and learning behaviors were collected in two waves across the 16-week lifespan of the teams. Results from cross-lagged models showed that team learning behaviors had a positive effect on the formation of shared mental models, whereas shared mental models did not predict team learning behaviors. Additionally, SMMs and team learning behavior had a signiﬁcant positive effect on team performance. The results of the current study contributed to the team literature by showing that team processes (team learning behavior) may impact the development of SMMs, which consequently impacts team performance. The current work also demonstrates that teamwork is essential for success of hospitality organizations and suggests ways to improve team effectiveness. Implications of these results for research and practice are discussed. © 2013 Elsevier Ltd. All rights reserved.
1. Introduction The performance of customer-contact employees or service employees has long been recognized as a signiﬁcant determinant of customer perceptions of service quality provided by service organizations (Bitner, 1990; Hartline and Jones, 1996; Gould-Williams, 1999). Customers rely on employee competence, responsiveness, and interpersonal skills while assessing service quality. Good employee performance has been linked with increased customer perceptions of service quality, whereas poor employee performance has been linked with increased customer complaints and brand switching (Zeithaml et al., 1996). However, recently, service organizations which include hospitality organizations are focusing on organizational work teams to increase the effectiveness of service delivery, enhance service quality, and organizational competitiveness (Hu et al., 2009). Recent studies have assessed team/group effectiveness by measuring customerperceived service quality and customer loyalty (De Jong et al., 2008; Salanova et al., 2005). Researchers have highlighted several beneﬁts of front-line service management teams including more efﬁcient use of knowledge and experience of those employees who are closest to the customer, and increased learning, adaptability, and
∗ Corresponding author. Tel.: +1 713 743 2433. E-mail addresses: [email protected]
, [email protected]
(P. Guchait), [email protected]
(K. Hamilton). 1 Tel.: +1 814 863 8851. 0278-4319/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijhm.2013.01.004
productivity (De Jong et al., 2008; Batt, 1999; Cohen et al., 1997). Because of the increased adoption of teams in service operations there is a need to investigate factors that inﬂuence team effectiveness (De Jong et al., 2008). Service researchers have suggested that to enhance teamwork there is a need to encourage better communication and interactions among members (team processes) (Moultrie et al., 2007). Scholars have also linked knowledge sharing in teams with team performance in the hospitality industry (Magnini, 2008; Hu et al., 2009). Given the importance of team processes in the success of hospitality teams, the current work examined team learning behavior as a team process variable that involves communication, interactions, and knowledge sharing in teams. Furthermore, hospitality researchers have also noted the value of shared understanding of rules, norms, expectations, roles, values, perceptions, and interaction patterns to facilitate team performance (Hu et al., 2009). Therefore, shared mental models (SMMs), which include shared understanding among members about taskwork (e.g., rules, expectations, performance requirements, and work goals), and teamwork (i.e., how the team should work together, which involves having a shared understanding about roles/responsibilities, values, skills of teammates, and interaction patterns), and which has been considered to be a signiﬁcant predictor of team performance (DeChurch and Mesmer-Magnus, 2010b), is examined in the current context of hospitality teams. Based on the critical role of these variables to hospitality team effectiveness, the purpose of this study is to test a bi-directional (two-way) relationship between SMMs and team learning
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behaviors using a longitudinal study design. Through doing so, we hope to contribute to the literature in multiple ways. First, we will examine the effects of SMMs over time. Time plays a critical role in the formation of team cognition (e.g., SMMs). Whereas individuals have individual cognitive structures and internal cognitive processes to organize those structures, teams have team cognitive structures and use external processes to organize those structures (Cooke et al., 2004). Given the complexity of the cognitive structures in a team, team cognition takes time to develop through the interplay between team knowledge and team processes (Cooke et al., 2004). However, little empirical research has been conducted in this area. Indeed, in a meta-analysis on the effects of team cognition on team effectiveness, DeChurch and Mesmer-Magnus (2010a) concluded that more research is needed that examines the reciprocal relationship between team processes/behaviors and the resultant team cognitive structures over time. Similarly, Pearsell et al. (2010) have suggested that increased interaction across time among team members are likely to create richer and more overlapping connections in teams’ cognitive structures. The authors recommended that researchers examine the bi-directional relationship between team actions and the formation of emergent states (Pearsell et al., 2010). Second, the current study will better position team learning behaviors in the team nomological network by empirically testing its relationship with SMMs. Team learning behavior is a continuing process of reﬂection and action, which involves asking questions, seeking feedback, experimenting, reﬂecting on results, and discussing errors or unexpected outcomes of actions (Edmondson et al., 2007; Edmondson, 1999). It refers to the group interaction activities through which individuals in teams acquire, share, and combine knowledge (Argote et al., 1999), to adapt and improve (Gibson and Vermeulen, 2003). Despite the connection of the construct with learning and knowledge development (cf. Edmondson, 1999), it has yet to be linked to cognitive outcomes (e.g., SMMs). This will be addressed in the current study through examining its relationship with one of the most developed types of collective cognition, i.e., SMMs (Mathieu et al., 2008). Third, the current work will examine the proposed relationships with service-management teams. Recent team cognition researchers have called for studies with decision-making, project management, and service management teams to extend the generalizability of SMM theory (e.g., Mohammed et al., 2010). This need exists because most studies on SMMs have been conducted using action teams (e.g., military and aviation control teams) (Mohammed et al., 2010; Chou et al., 2008). This gap will be addressed in the current study as service-management teams will be investigated in a restaurant setting. 2. Development of hypotheses The relationship between SMM and team learning behavior can be explained through the I-P-O theoretical framework (Kraiger and Wenzel, 1997). This framework suggests that team inputs (I) lead to the formation of team processes (P), which result in team outcomes (O) (Hackman, 1987). The SMM literature has been dominated by a unidirectional view of the I-P-O framework, in which existing SMMs have been viewed as an input which has led to the formation of various team processes (e.g., Banks and Milward, 2007; Marks et al., 2000, 2002; Mathieu et al., 2000, 2005; Stout et al., 1999). However, a second theoretical lens can be borrowed from the communications literature in explaining the relationship between SMM and team processes. This literature states that team processes, such as communication, lead to the formation of SMM through building common ground or shared understanding (Clark and Brennan, 1991). Given that both perspectives are equally likely, the current study proposes competing hypotheses to evaluate the temporal
precedence of SMMs on team processes (i.e., team learning behaviors) and vice versa. In other words, the current work investigates if team learning behavior leads to SMMs or SMMs leads to team learning behaviors. 2.1. The effect of shared mental models on team learning behaviors Although the effect of SMM on team learning behaviors has not been explicitly tested, SMM have been linked to multiple team processes (Marks et al., 2000, 2002; Mathieu et al., 2000, 2005). Two of the most commonly examined team processes in the SMM literature are team communication and coordination. In a larger study investigating the effects of leader brieﬁngs and team interaction training on the formation of mental models, communication processes, and team performance, Marks et al. (2000) found that higher sharedness in team interaction mental models resulted in more efﬁcient communication processes. In a later study, Marks et al. (2002) also showed that shared team interaction mental models predicted team coordination and backup processes. Mathieu et al. (2000, 2005) have also found support for the link between SMMs and team processes. Mathieu et al. (2000) examined the relationship between shared taskwork and teamwork mental models and a mix of team processes that included strategy formation, coordination, cooperation, and communication. Whereas taskwork mental models include work goals and performance requirements, teamwork mental models include interpersonal interaction requirements, skills of teammates, and shared values (Mohammed et al., 2010; Cannon-Bowers and Salas, 2001). Mathieu et al. (2000) found that both shared taskwork and shared teamwork mental models independently predicted the use of team processes. A similar study by Mathieu et al. (2005) found that the formation of shared taskwork mental models also resulted in the use of more effective team processes. In these empirical studies (i.e., Mathieu et al., 2000, 2005; Marks et al., 2000, 2002), arguments supporting the effect of SMMs on team process are linked to early theoretical developments that are rooted in the classic I-P-O framework (Klimoski and Mohammed, 1994; Kraiger and Wenzel, 1997). These authors argue that when teams develop the input of SMMs they have a shared/common understanding of what is expected of them and are better able to synchronize their actions (Kraiger and Wenzel, 1997). Having such cognitive synchronization among team members is likely to improve their ability to coordinate their actions (cf. Kraiger and Wenzel, 1997). Early conceptualizations of SMMs by Klimoski and Mohammed (1994) have also argued that having team members be on the same page (i.e., having SMMs) are more likely to lead to an increased use of effective communication processes, strategy and coordinated use of resources, and interpersonal relations or cooperation. The central processes included in team learning behavior overlap with the critical processes identiﬁed as outcomes in the SMM empirical and theoretical literatures (Mathieu et al., 2000, 2005; Marks et al., 2000, 2002; Klimoski and Mohammed, 1994; Kraiger and Wenzel, 1997). Team learning behavior incorporates multiple team process variables, of which the two central processes are communication and coordination (Edmondson et al., 2007; Edmondson, 1999). According to Edmondson (1999), the construct can be broken down into (a) communication processes such as open communication (e.g., discussing differences of opinion openly) and communication frequency (e.g., seeking continuous feedback and information) and (b) coordinated use of resources (i.e., acquire, combine, and share unique knowledge). Based on these theoretical arguments and the fact that SMM of taskwork and teamwork knowledge have both been empirically linked to communication and coordination processes, it is expected that:
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Hypothesis 1a. Shared taskwork mental models will have a positive effect on the engagement in team learning behaviors. Hypothesis 1b. Shared teamwork mental models will have a positive effect on the engagement in team learning behaviors. 2.2. The effect of team learning behaviors on shared mental models Even though the norm in the SMM literature has been to evaluate the effect of SMM on team processes, the results have not been consistent (e.g., Banks and Milward, 2007; Stout et al., 1999). For example, Banks and Milward (2007) conducted a lab experiment to investigate the effect of SMMs and shared procedural knowledge on team process and performance. Similar to Mathieu et al. (2000), a mix of team processes were assessed that included strategy formation, coordination, cooperation, and communication. However, Banks and Milward (2007) failed to ﬁnd a signiﬁcant effect for either taskwork or teamwork SMMs in predicting team processes or team performance. The authors concluded that the effect of SMMs on team processes were not compatible with the traditional I-P-O framework (Banks and Milward, 2007). Despite this conclusion, no alternate approach to the framework was proposed. Two known exceptions in the SMM literature that have examined the reverse effect of team processes on the prediction of SMMs are Stout et al. (1999) and Levesque et al. (2001). Stout et al. (1999) conducted a lab experiment with undergraduate student teams to evaluate the relationships among planning, shared mental models, communication, and performance. The authors found that planning increased shared mental models, the efﬁciency of their communication strategies, and team performance. On the other hand, Levesque et al. (2001) found in a ﬁeld study of student MBA teams that the level of role differentiation in the team predicted the extent of team interaction which was associated with higher levels of SMM development among team members. Despite the differences in the nature of the sample and team task, both Stout et al. (1999) and Levesque et al. (2001) found that the team processes planning and communication, respectively, predicted the formation of SMMs. The ﬁndings from Stout et al. (1999) and Levesque et al. (2001) are consistent with early theoretical arguments made by Klimoski and Mohammed (1994). According to Klimoski and Mohammed (1994), interaction is a primary means through which team mental models become similar over time. The more team members interact, the more likely they are to develop a common frame of reference and a shared mental model among its members (Klimoski and Mohammed, 1994). Depending on the nature of their communication, a shared understanding is likely to develop about team goals and related tasks, work habits and patterns, and member’s expertise. These arguments are consistent with the communications literature which has argued that the use of effective communication strategies can help build common group or shared understanding among group members (Clark and Brennan, 1991). Other research on team communication has included similar arguments. For example, Innami (1992) proposed that verbal communication is the key driver for the emergence of group belief structures. Similarly, Donnellon et al. (1986) suggested that communication mechanisms, associated with shared meaning and organized action, are a key predictor of shared understanding among team members. The effects of communication on building shared knowledge and understanding is also evident in the literature on transactive memory system (TMS), which has been referred to as the second most developed stream of literature on structured cognition (e.g., DeChurch and Mesmer-Magnus, 2010a,b; Ilgen et al., 2005). In the TMS literature, a wide body of research has consistently shown that communication volume and frequency predict TMS development
(e.g., Jackson and Moreland, 2009; Kanawattanachai and Yoo, 2007; Lewis, 2004; Peltokorpi and Manka, 2008). Communication is a key component of team learning behaviors (Edmondson et al., 2007; Edmondson, 1999). Team learning behaviors involve the use of team communication processes, communication frequency, and coordinated use of resources (Edmondson, 1999). Given that past theoretical and empirical research has supported the effect of communication in the formation of shared mental models, it is expected that: Hypothesis 2a. Team learning behaviors will have a positive effect on the formation of shared taskwork mental models. Hypothesis 2b. Team learning behaviors will have a positive effect on the formation of shared teamwork mental models. 2.3. The effect of team learning behaviors and shared mental models on team performance The inﬂuence of SMMs on team performance has been shown in earlier studies (e.g., Mathieu et al., 2005, 2010). SMMs allow team members to interpret information in a similar manner, explain situations similarly, and have similar expectations about future events (Mohammed et al., 2010; Rouse et al., 1992). This shared understanding enables the team to coordinate actions and adapt behavior to task demands, leading to enhanced decisionmaking and higher performance (Cannon-Bowers et al., 1993). Both taskwork and teamwork SMMs have been linked with team performance (DeChurch and Mesmer-Magnus, 2010a). Studying hospitality teams, Hu et al. (2009) examined the relationship between team culture and service innovation performance. Team culture was referred to as an emergent and simpliﬁed set of rules, norms, expectations, and roles which are shared and performed by team members (Earley and Mosakowski, 2000). This emergent team culture was referred to as a mental model which offers members of the group a group speciﬁc sense of identity (Hu et al., 2009). Therefore, although different, the team culture construct is somewhat similar to the SMMs construct described here. Although, Hu et al. (2009) examined team culture in hospitality teams, their analyses were done at the individual level. Similarly, earlier studies have demonstrated the relationship between team processes and team performance (Mathieu et al., 2005). Mathieu et al. (2000) linked team communication, team strategy, and team cooperation with team performance. Hospitality studies have also noted the importance of team processes such as team communications and interactions for improved team performance (Hu et al., 2009). Studies focusing on knowledge and information sharing demonstrated that well-developed team processes result in better coordination and superior team performance (Banks and Millward, 2000). Both theoretical and empirical studies in hospitality have linked knowledge sharing and group/organizational learning with organizational performance (Magnini, 2008; Yang, 2010). Knowledge sharing refers to the process by which individuals mutually exchange their knowledge and generate new knowledge collaboratively (Magnini, 2008). Hospitality researchers have also suggested that knowledge sharing (by individual employees) results in group/organizational learning which ultimately results in greater organizational effectiveness (Yang, 2010). Additionally, scholars have found support for a positive relationship between team learning behaviors and team performance (Savelsbergh et al., 2009; Edmondson, 2002, 1999). Team learning behaviors have been linked with important outcomes such as reduced costs and increased team performance (Savelsbergh et al., 2009; Edmondson et al., 2001), improved product and service quality (Chan, 2002), and increased customer satisfaction (Edmondson, 1999). Team learning behaviors help teams understand their environment and their customers,
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adapt to changes, detect and correct errors, discover unexpected consequences of previous actions, coordinate member’s actions effectively, and improve quality and efﬁciency of their work (Bresman, 2010; Edmondson, 1999). Team learning behavior thus appears central for team performance. Team performance in the hospitality industry, particularly in a food-service context involves quality and timeliness of deliverables (food and service), meeting customer’s needs and management team’s goals (e.g., making proﬁt, meeting deadlines, keeping production costs low). Based on this body of theoretical and empirical research, we expect that shared taskwork mental models (taskwork SMM), shared teamwork mental models (teamwork SMM), and team learning behaviors will have a positive impact on team performance. The following hypotheses are proposed: Hypothesis 3a. Shared taskwork mental models will have a positive effect on team performance. Hypothesis 3b. Shared teamwork mental models will have a positive effect on team performance. Hypothesis 3c. Team learning behavior will have a positive effect on team performance.
be best described as self-managing teams (SMTs) (De Jong et al., 2008). SMTs refer to groups of interdependent employees who are collectively responsible for decision-making and developing work routines, planning, and monitoring team performance (De Jong et al., 2008). Such teams are ideal for use in this study because their members mainly rely on individuals within the team to accomplish their tasks. These teams are comparable to any real-life team in the hospitality industry (or in a restaurant) as the team membership remained consistent throughout the semester and each team was held mutually accountable for outcomes, that is, food service to the public. Moreover, a team member could be ﬁred from the team due to poor performance, and customers behaved in the same manner as in restaurants based on how happy or unhappy they are with the food and service quality. Furthermore, all the students in the course were graduating after the completion of this course, and most of them were starting as entry level managers in hospitality ﬁrms. Therefore, based on the context, the work experience of the participants in the hospitality/restaurant industry and in teams, their age, and based on their current standings in their career, the teams are comparable to any newly established management teams in the hospitality/restaurant industry. 3.3. Procedure
3. Methodology 3.1. Participants One hundred and seventy-three undergraduate students (27 teams) enrolled in a senior food production and service management course in a large northeastern university in the United States participated in the study. The sample was 78 percent Caucasian and 53 percent female. The average age of the sample was 22.37 with a range from 20 to 47 years. Team size ranged from 4 to 9 members with an average of 7. The average team grade point average (G.P.A.) was found to be 3.05. Participants self-reported their overall G.P.A. as an indicator of their ability and motivation in the team task. G.P.A. was aggregated to the team-level using the mean. Members reported their prior work experience (1.00 = very low; 5 = very high) which was aggregated to the team-level using the mean. Most participants had high experience working in teams (M = 4.48, SD = .36), moderately high experience working in restaurants (M = 3.78, SD = .79), moderate experience working in the ‘back-of-the-house’ (M = 2.98, SD = .83), moderately high experience working in the ‘front-of-the-house’ (M = 3.63, SD = .64), moderate experience working as managers/supervisors (M = 2.92, SD = .57), and high overall work experience (M = 4.22, SD = .40). This distribution in demographics is common among entry level food service or restaurant managers (BLS, 2012). According to Cohen (1988), sample size is positively related to the statistical power of an inferential test. Based on this problem the alpha level for this study was relaxed from ˛ = .05 to ˛ = .10 so that marginal effects could be interpreted (Cohen, 1988). 3.2. Team task: food service restaurant The team’s task involved planning and supervising the preparation and service of meals in a restaurant setting that was open to the public for two nights twice during the semester. Each team provided service to 90–140 customers for each dinner. The main goal of the team was to ﬁrst create a marketable theme restaurant, a full business plan, and then develop, produce, and evaluate an authentic dining experience. Team tasks involved both technical-administrative duties, such as budgeting, purchasing, menu planning, and cost accounting, as well as leadership duties, namely decision-making, developing strategy, and supervising employees. Based on these responsibilities, the teams can
Data were collected at two different time points. One problem with collecting SMM data across multiple time points is that the repetitive interruption caused by the measurement may interfere with the development of SMMs and add errors to the measure (Rentsch and Small, 2007). In order to reduce this potential ﬂaw in our design we carefully selected time points that were turning points in the team’s life cycle. This idea is consistent with Gersick’s (1989) idea of team development, which suggests that teams’ progressions through their lifespan varies based on their external deadlines and pressures. Such external pressures often have a strong impact on the cognitive and behavioral processes that take place in a team (Gersick, 1989). Time 1 was at 6 weeks into the teams’ 16-week lifespan. This time period was selected, because it occurred 1 week prior to the teams’ ﬁrst meal delivery or performance episode, and therefore represented the teams’ ﬁrst planning stage. At Time 1, the ﬁrst round of data was collected on taskwork SMM, teamwork SMM, and team learning behavior. Time 2 was at 14 weeks into the teams’ 16-week lifespan. Time 2 represented the teams’ second planning stage. During the second planning stage, the teams go back and analyze their performance on ﬁrst meal delivery, then perform ﬁnancial analysis, market analysis, decide on the changes for their second meal delivery and set it up. At this time, a second round of data was collected on taskwork SMM, teamwork SMM, and team learning behavior. Finally, instructors rated the team performance after the teams’ ﬁnal meal delivery which was at 16 weeks. 3.4. Measures 3.4.1. Shared mental models (SMM) SMM refer to “team members’ shared, organized understanding and mental representation of knowledge about the key elements of the team’s relevant environment” (Mohammed & Dumville, 2001, p. 90). Taskwork SMMs was measured by the extent to which team members had a common understanding about the team’s taskwork such as goals and performance requirements (Mohammed et al., 2010). Teamwork SMMs on the other hand was measured by the extent to which team members have common understanding about how to work in a team such as communication patterns and shared values (Cannon-Bowers and Salas, 2001). Consistent with several previous studies, SMMs were evaluated using questionnaires (Knight et al., 1999; Mathieu et al., 2006; Rapert et al., 2002;
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Smith-Jentsch et al., 2005). Questionnaires require little cognitive effort to complete, which make them easy to administer in ﬁeld settings over multiple time points (cf. DeChurch and MesmerMagnus, 2010a; Mohammed et al., 2010). The development of questionnaire items followed the recommendations of Mohammed and Hamilton (2012) by ﬁrst conducting a thorough team task analysis (Lorenzet et al., 2003). Given that SMMs are contextual in nature, knowing what content to measure is a key step in the measurement process (Mohammed and Hamilton, 2012). This important step in the process is discussed in detail below. The team’s task was analyzed through conducting interviews, observations, and questionnaires. Twenty semi-structured 30-min interviews were used to gather data from members of three different teams. These three teams were not included in the study sample. Questions focused on understanding the nature of the team’s task and interaction among team members. Course instructors, responsible for reviewing the work of the service teams, were also interviewed to validate the team members’ responses. Observations were made both during planning (e.g., theme development, menu development, pricing, forecasting, pre-production reporting, post-production reporting) and execution stages of the teams (when the teams managed the meals). Observations focused on gathering data on how team members communicated, coordinated, and shared information, delegated tasks, trained employees, managed time, forecasted, ensured service quality, and handled customer complaints. Finally, based on the data gathered in the interviews and observations, questionnaires were generated on the various types of teamwork knowledge that were observed. Participants were asked to rate the relative importance of items to team effectiveness. Responses were provided on a scale of 1 (not at all important) to 10 (very important). The ﬁnal measure of taskwork and teamwork SMMs were based on ten and ﬁfteen questionnaire items, respectively. The taskwork items focused on the technical-administrative duties of team whereas the teamwork items focused on the leadership/team coordination duties. All items were measured on a ﬁve point scale (1 = strongly disagree; 5 = strongly agree). A sample taskwork SMM item was “Our team members are in agreement about how best to manage the staff during our meal night.” The internal consistency reliability estimate was .85 for Time 1 and .89 for Time 2. A sample teamwork SMM item was “Our team members communicate openly with each other.” The internal consistency reliability estimate was .92 for Time 1 and .94 for Time 2. To estimate the level of within-team variability, responses were indexed using within-team standard deviation (Harrison and Klein, 2007). High standard deviation indicated high team variability and less similarity of SMM within teams. Therefore, teamwork SMM and taskwork SMM similarity scores were computed by subtracting the standard deviation from 1 (1 − SD) to reverse the scale so that greater values represented higher levels of teamwork SMM and taskwork SMM similarity. 3.4.2. Team learning behavior Team learning behavior was measured by the extent to which the team engages in activities to obtain and process data that allow it to adapt and improve. Examples of such activities are information sharing, discussion about errors, and seeking feedback (Edmondson, 1999). To measure team learning behavior, 12 items were used from the team learning behavior scale developed by Edmondson (1999). Slight edits were made to these items to ensure that they related to the team task used in the current study. A sample item from the scale was, “In our team, people discuss ways to prevent and learn from mistakes.” A 5-point Likert scale was used with anchors ranging from 1 (strongly disagree) to 5 (strongly agree). The internal consistency reliability of the scale was .83. Justiﬁcation for aggregation of the individual items to the
team was based on rwg and ICC estimates (Kozlowski and Klein, 2000). The mean rwg for team learning behavior was found to be .88, with 93% of the estimates above the acceptable .70 threshold (James et al., 1984). ICC 1(.21) and ICC 2 (.59) was also found to be acceptable (Bliese, 2000) and signiﬁcant at the .01 level. Based on these estimates, team members’ responses were aggregated to the team level. 3.4.3. Team performance Team performance was measured with ﬁve items asking about the quality and timeliness of deliverables and about meeting customer needs and goals. Four items were used from a team performance scale developed by Lewis (2004). Minor adjustments were done to some items based on the context. The current study added an item to the existing scale as suggested by the course instructors – “the team paid attention to detail.” All items were measured on a ﬁve point scale (1 = strongly disagree; 5 = strongly agree). A sample item would be “The team did a good job of meeting the customer’s needs.” Internal consistency of the scale items (˛ = .92) was found to be acceptable. All ﬁve items loaded strongly on one factor (standardized loading ranged from .75 to .95); all measurement items showed statistically signiﬁcant loadings at alpha level of .001. The average variance extracted (AVE) was also over .5 indicating (convergent) validity. All survey questionnaire items are presented in Appendix. 4. Results 4.1. Correlations and change over time All analyses were conducted at the team level. Table 1 displays the team level correlations and descriptive statistics of all the key variables assessed in the study. Among the types of SMMs examined, teamwork and taskwork SMMs were signiﬁcantly positively correlated at Time 1 (r = .67, p < .05) and at Time 2 (r = .40, p < .10). However, the degree of overlap between the variables was not large enough to consider them redundant. Taskwork SMMs at Time 2 was signiﬁcantly positively correlated with team learning behavior at Time 1 (r = .43, p < .05) but not with team learning behavior at Time 2 (r = .02, p > .10). On the other hand, teamwork SMMs at Time 2 was signiﬁcantly positively correlated with team learning behavior at Time 1 (r = .39, p < .10) and at Time 2 (r = .41, p < .05). 4.2. Data analysis The current study used panel data to investigate the temporal priority of team learning behaviors in predicting SMMs and vice versa. Panel data is a type of longitudinal data that “consists of information gathered from the same individuals or units at several different points in time” (Finkel, 1995, p. 1). Panel data are particularly useful for establishing the temporal order of effects through the estimation of cross-lagged models (Finkel, 1995). Based on the recommendations of Finkel (1995) for cross-lagged models Y2 (dependent variable at Time 2) should be regressed onto X1 (independent variable at Time 1) after the effect of Y1 is controlled (dependent variable at Time 1). The resulting analysis represents the effect of X1 on the changes in Y over time. Temporal precedence is evaluated based on the strength of the coefﬁcients observed for each cross-lagged model. Following these recommendations, the current study used hierarchical regression analysis to test the proposed hypotheses, while controlling for the effects of the dependent variable at Time 1 in each model. 4.3. Test of hypotheses Hypothesis 1a stated that taskwork SMM would have a positive effect on engagement in team learning behaviors. As shown
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Table 1 Descriptive statistics and intercorrelations at the team level. Mean
Time 1: Independent and dependent variables 1. Teamwork SMM .50 2. Taskwork SMM .45 3. Team learning behavior 3.57
.19 .17 .31
Time 2: Independent and dependent variables 4. Teamwork SMM .47 5. Taskwork SMM .46 6. Team learning behavior 3.60 7. Team performance 3.66
.15 .15 .28 .75
.18 .12 .03 .21
.22 .16 .10 .47*
.39† .43* .57* .66**
.40† .41* .25
Note: SMM, shared mental model. * p < .05. ** p < .01. † p < .10. Table 2 Hierarchical regressions of shared mental models on team learning behavior. Variables Time 1
Team learning behavior Time 2 ˇ
Step 1 Team learning behavior Step 2 Team learning behavior Task SMM Step 1 Team learning behavior Step 2 Team learning behavior Team SMM
.55** .56** .03 .56** .57** −.11
Note: *p < .05; †p < .10; SMM, shared mental model. ** p < .01.
in Table 2, after controlling for team learning behavior at Time 1, taskwork SMM was not found to have a signiﬁcant impact on team learning behavior at Time 2 (ˇ = .03, p > 10). Similarly, Hypothesis 1b stated that teamwork SMM would be positively related to team learning behavior. The effect of teamwork SMM was not found to have a signiﬁcant impact on team learning behavior at Time 2 (ˇ = −.11, p > .10), after controlling for team learning behavior at Time 1. Therefore, neither Hypothesis 1a nor Hypothesis 1b was supported. Hypothesis 2a stated that team learning behavior would be positively related to taskwork SMM. As shown in Table 3, team learning behavior at Time 1 was found to have a signiﬁcant impact Table 3 Hierarchical regressions of team learning behavior on shared mental models. Variables Time 1
Taskwork SMM Time 2 ˇ
Step 1 Taskwork SMM Step 2 Taskwork SMM Team learning behavior
on taskwork SMM at Time 2 (ˇ = .53, p < .05), after controlling for taskwork SMM at Time 1. Hypothesis 2b stated that team learning behavior would be positively related to teamwork SMM. Consistent results were found across these hypotheses, such that team learning behavior at Time 1 also had a signiﬁcant impact on teamwork SMM at Time 2 (ˇ = .41, p < 10), after controlling for teamwork SMM at Time 1. Both Hypotheses 2a and 2b therefore received support. These ﬁndings are summarized in Fig. 1. Finally, the inﬂuence of taskwork SMM, teamwork SMM, and team learning behavior on team performance was tested. Taskwork SMM was found to be signiﬁcantly positively related to team performance (ˇ = .48, p < .05), supporting Hypothesis 3a. Teamwork SMM was not found to be signiﬁcantly positively related to team performance (ˇ = .32, p > .10), therefore, Hypothesis 3b was not supported. Furthermore, team learning behavior was found to be signiﬁcantly positively related to team performance (ˇ = .51, p < .05), supporting Hypothesis 3c. 5. Discussion The purpose of this study was to verify the temporal priority of SMMs on team learning behaviors using a longitudinal study design. We proposed competing hypotheses on the directionality of the relationship between SMMs and team learning behaviors. Our ﬁndings show that team learning behaviors at Time 1 positively predicted SMMs at Time 2, whereas SMMs at Time 1 did not have a signiﬁcant impact on team learning behaviors at Time 2. Additionally, taskwork SMM and team learning behavior had a positive effect on team performance. This pattern of results suggests that, at least in the early stages of team formation, team processes (such as learning behaviors) may be more predictive of team cognition (SMMs) than vice versa. This idea is consistent with the fact that cognition takes time to develop in teams (cf. Cooke et al., 2004). Whereas individual cognitive processing takes place internally, team cognitive processing
.17 .01 .53**
Time 1 Taskwork SMM
Time 2 0.01
Time 3 0.48*
Teamwork SMM Time 2 0.53**
ˇ Step 1 Teamwork SMM Step 2 Teamwork SMM Team learning behavior
Team Learning Behavior
.15 .06 .41†
Note: *p < .05; SMM, shared mental model. ** p < .01. † p < .10.
Team Learning Behavior
Fig. 1. Summary of results from hypothesized model (coefﬁcients represent standardized beta weights). Note. SMM, shared mental model; **p < .01; † p < .10.
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occurs externally through team members actively engaging in team processes to coordinate their knowledge. The results show that the sub-processes associated with team learning behaviors, i.e., discussing differences of opinion openly, seeking continuous feedback and information, and coordinating cognitive resources, are instrumental in getting team members on the same page for both taskwork and teamwork knowledge. The implications and limitations of these ﬁndings are discussed below. 5.1. Theoretical implications Two theoretical perspectives were pitted against each other in the current study. The ﬁrst suggested that SMMs would play the role of an input and lead to the engagement in team learning behaviors. This perspective is grounded in the classic I-P-O model (Kraiger and Wenzel, 1997) and has proliferated current empirical research on SMMs (e.g., Marks et al., 2000, 2002; Mathieu et al., 2000, 2005). The second perspective explored was adopted from the communications literature. This perspective suggested that effective communication strategies/team processes could help to build common group or shared understanding among group members (Clark and Brennan, 1991). SMMs are therefore viewed as an outcome to the use of effective team processes. The results of the current study lend greater support to the latter theoretical perspective than the former. These ﬁndings are consistent with the conceptual frameworks put forth by Ilgen et al. (2005) and Marks et al. (2001), which suggest that a reciprocal relationship exists between team inputs and team processes. Based on these ﬁndings, we encourage researchers to expand their perspectives on the role of SMMs on team processes by adopting conceptual models beyond the typical I-P-O framework. We also encourage researchers to broaden their measurement of SMMs. Findings such as the ones presented in the current study are often masked in the ﬁeld due to the limited number of longitudinal studies that exist on SMMs. Given the cognitive effort required to complete some SMM measures, the construct is not often evaluated across multiple time points (Mohammed et al., 2010). However, it is only through heeding the call for more longitudinal research in the ﬁeld (cf. DeChurch and Mesmer-Magnus, 2010b; Mohammed et al., 2010), that we will be able to adequately capture the dynamic nature of SMMs. 5.2. Practical implications First, the ﬁndings that team cognitions (SMMs) inﬂuence team performance, has implications for hospitality managers. Providing support to earlier studies (Mathieu et al., 2000, 2005, 2010), the current work demonstrated that SMM enhances team performance. Taskwork and teamwork shared mental models help team members to be on the same page which helps team members to predict teammates’ behavior and potential needs; as a result, it is easier for team embers to obtain necessary support from teammates easily. Such support improves members’ timeliness and efﬁciency which ultimately results in increased quality of outcomes (Chou et al., 2008), that is, service quality. This ﬁnding has important implications for restaurant managers. Prior hospitality research has suggested that when employees have a shared understanding of service standards (i.e., taskwork SMM) and teamwork (i.e., teamwork SMM), it is easier for service providers to receive support from coworkers, which creates positive service atmosphere, consequently improving restaurant service and guest satisfaction (Susskind et al., 2007). Therefore, restaurant managers need to focus on the development of shared mental models in service management teams in restaurants. Second, the ﬁndings that team learning behaviors inﬂuence team performance, has implications for hospitality managers. Research in team dynamics has demonstrated the link between
team processes (communication and cooperation) and team performance (Mathieu et al., 2005), and team learning and team performance (Van der Vegt et al., 2010; Edmondson, 1999). Hospitality researchers have also noted the importance of team processes such as better communications and interactions among team mates to facilitate the performance of hospitality teams (Hu et al., 2009). Hospitality scholars have also explained how knowledge sharing facilitates the transformation of individual knowledge to group and organizational knowledge, which in turn results in the advancement of group/organizational learning and eventually greater organizational effectiveness (Yang, 2010; Magnini, 2008). Therefore, hospitality studies have associated group processes and group learning with group/organizational effectiveness. Employee and group performance in hospitality industry is directly linked to customer perceived service quality and customer loyalty (Salanova et al., 2005). Therefore, the ﬁndings of the current study that team learning behavior improves team performance, has important implications for hotel/restaurant managers. Hospitality managers need to focus on the development of effective team processes such as team learning behaviors to improve restaurant service, guest satisfaction, and loyalty. The ﬁnding that team learning behaviors were more predictive of SMMs, than vice versa, has strong implications for team selection. Along with the use of self-managed teams, selective hiring was considered as one of the most important HRM practices in high performance work organizations in the hospitality and tourism industry (Kusluvan et al., 2010). When putting together selfmanaging service-management teams, it may be more important to focus on predictors of team processes as opposed to SMMs. Predictors of team processes have ranged from individual team member characteristics (e.g., personality variables), to team-level factors (e.g., task structure), to organizational and contextual factors (e.g., organizational design features and environmental complexity) (cf. Mathieu et al., 2008). Predictors speciﬁc to team learning behaviors include team psychological safety (i.e., the collective belief that the team is safe to take interpersonal risks) and team efﬁcacy (i.e., shared perceptions that the group’s effort will lead to successful performance) (Edmondson, 1999). Variables such as these should be taken into account in the creation of service management teams to help ensure that its members engage in team learning behaviors, which will help in the formation of SMMs. The ﬁndings from this study also have implications for team training. Along with the use of self-managed teams, and selective hiring, extensive training was also recognized as an important HRM practice for high performance hospitality organizations (Hinkin and Tracey, 2010). If team learning behaviors are more predictive of SMMs, than vice versa, then training strategies that focus on the development of teamwork processes should be more effective at building SMMs than strategies that focus on general taskwork knowledge. Such an effect is consistent with a recent meta-analysis of 168 independent studies on team training, which found that the impact of team training on cognitive outcomes was moderated by the content of the training (Salas et al., 2008). According to the meta-analysis, the relationship between training type and cognitive outcomes of training was .30 for taskwork training content (e.g., cross-training) and .52 for teamwork training content (e.g., team interaction training) (Salas et al., 2008). Hospitality organizations using self-managing service management teams should therefore consider implementing a teamwork-focused training strategy in the early stages of the team’s development to help with the formation of team processes and thus SMMs. Overall, the ﬁndings have implications for human resource management issues in the hospitality and tourism industry. A recent review of human resource management issues in the hospitality and tourism industry by Kusluvan et al. (2010), showed a lack of adoption and implementation of progressive, high-performance,
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or high-involvement HRM practices by the industry. For this reason, the hospitality and tourism industry has a bad reputation of poor HRM practices (Kusluvan et al., 2010; Lucas, 1996). However, increasing number of hospitality and tourism organizations are being recognized for their effective HRM practices (Hinkin and Tracey, 2010). High performance work organizations are characterized by effective HRM practices involving selective hiring, extensive training, self-managed teams, information sharing, and group based compensation based on performance (Kusluvan et al., 2010). The current work conducts research in a context which is very similar to a real-life restaurant setting. The study demonstrates how teamwork can result in improved outcomes in restaurant units. The paper also demonstrates factors that enhance team effectiveness, including shared understanding about taskwork and teamwork, and team learning behaviors. Hospitality/restaurant companies can use this framework to select, train, and develop effective service management teams for competitive advantage. 5.3. Limitations and future research As with any research, decisions regarding the design of the study resulted in methodological tradeoffs. First, SMMs were evaluated using questionnaires. Questionnaires are one of the popular SMM measures (e.g., Knight et al., 1999; Mathieu et al., 2006; Rapert et al., 2002; Smith-Jentsch et al., 2005). These measures were selected for this study because they require little cognitive effort to complete, which makes them easier to be administered in ﬁeld settings and over multiple points in time (cf. DeChurch and Mesmer-Magnus, 2010b; Mohammed et al., 2010). However, SMMs elicited through questionnaires may have a different relationship with team processes than SMMs elicited through concept maps or similarity ratings. A meta-analysis of 23 independent studies on SMMs has shown that the way in which SMMs are measured moderates the nature of the relationship between SMMs and team outcomes (DeChurch and Mesmer-Magnus, 2010b). The authors found that measures which capture both the content and structure of SMMs have a stronger relationship with team processes than measures that only capture content (DeChurch and MesmerMagnus, 2010b). According to the meta-analysis, the relationship between SMM and team process was weak and non-signiﬁcant when measured using questionnaires ( = −.05), but signiﬁcant and notably stronger when measured using similarity ratings ( = .27) or concept maps/card sorting ( = .17) (DeChurch and MesmerMagnus, 2010b). A reason for these differences may be the fact that questionnaires are restricted to only being able to capture the content of shared knowledge, whereas concept maps and paired similarity ratings are able to assess both content and structure (Mohammed and Dumville, 2001). Given this limitation of questionnaires, it is expected that the results of this study may be stronger with the use of other types of SMM measurements, such as concept maps and similarity ratings. However, future research in this area is warranted. Second, the results of the study may be limited by the sample size. According to Cohen (1988), sample size is positively related to the statistical power of an inferential test. Based on this problem the alpha level for this study was relaxed from ˛ = .05 to ˛ = .10 so that marginal effects could be interpreted (Cohen, 1988). Despite this adjustment the statistical conclusion validity of this study is supported by the differences in the strengths of the effect sizes observed. Critics of signiﬁcance testing suggest that the magnitude of the effect size provides more reliable information than p values (e.g., Oakes, 1986; Schmidt and Hunter, 1997). Effect size is not as sensitive to differences in sample size as p values (Oakes, 1986). The results from the current study show that the effect size for the impact of SMMs on the use of team learning behaviors was weak (task SMM: ˇ = .03, p > .10; team SMM: ˇ = .11, p > .10). On the other
hand, team learning behaviors had a strong effect on the formation of SMMs (task SMM: ˇ = .53, p < .05; team SMM: ˇ = .41, p < 10.). The stark difference between these effect sizes strengthens the validity of our ﬁndings. However, given the fact that some researchers have recommended the use of both p values and effect size estimates to determine both the probability and strength of the observed effect (e.g., Denis, 2003), future research is needed that attempts to replicate the ﬁndings of the current study with a larger sample. Third, the current study collected panel data across two waves of time. These two waves were carefully selected to be meaningful and were based on key turning points in the team’s life cycle. However, it is possible that the sharedness of mental models may have changed considerably between the two data points selected. Little is known about the stability of SMMs over time (Mohammed et al., 2010). For example, Langan-Fox (2003) has proposed that teams engage in three distinct phases in the formation of SMMs. These are the declarative phase (in which ﬁrst impressions are used to learn the initial rules of the team), knowledge compilation phase (in which team members ﬁgure out the ‘shortcuts’ to the rules), and ﬁnally the expert/proceduralized knowledge phase (in which knowledge becomes implicit and performance becomes automatic). Expert teams are considered to have stronger and more stable shared mental models than teams in the declarative and knowledge compilation phases. To the authors’ knowledge, there has been no direct empirical test of this model. It is therefore not clear when SMMs change or the frequency with which they change. Future research is needed in investigating the directionality of the relationship between team processes and SMMs across several time periods to see if and when the strengths of the effects between the two sets of variables vacillate across time. Finally, the current work used service-management teams to test the proposed relationships, thereby extending the generalizability of the SMMs theory. Most prior work in this area focused on military teams, aviation control, and undergraduates performing laboratory tasks (Lim and Klein, 2006; Mathieu et al., 2000). Responding to recent calls from scholars (Chou et al., 2008; Mohammed et al., 2010) about the need to conduct SMM studies with decision-making teams, project management teams, and service management teams, the current study adds to the literature. Although, the context of the current study is restaurant management setting, we believe that the ﬁndings will hold in any decision-making, project management, knowledge-worker teams or service management teams. Therefore, future studies need to test the relationships in other contexts (e.g., departmental teams in hotels and restaurant unit management teams) and with other team types.
Appendix. Taskwork shared mental models . Our team members 1. are in agreement about how best to manage the staff during our meal night 2. have similar understanding about how best to serve the guest 3. are in agreement about how best to ensure the highest quality food and beverage 4. have a common understanding about how best to ensure the service standards are maintained 5. are in agreement about how best to ensure we meet the time goals 6. have a shared understanding about how best to ensure we meet our sales goals 7. are in agreement about how best to handle potential “crises” that may arise during our night 8. are in agreement about how best to ensure we have sufﬁcient inventory and sufﬁcient replacement 9. have a common understanding about how best to train our employees 10. are in agreement about how best to ensure food cost is managed efﬁciently
P. Guchait, K. Hamilton / International Journal of Hospitality Management 33 (2013) 19–28 Teamwork shared mental models . Our team members 1. work well together 2. accept decisions made by the general manager 3. communicate openly with each other 4. agree on decisions made in the team 5. back each other up in carrying out team tasks 6. are aware of other team members’ abilities 7. trust each other 8. treat each other as friends 9. value achievement orientation 10. value being efﬁcient 11. value being cost-effective 12. value being highly organized 13. value being precise 14. value being result oriented 15. value paying attention to detail For both constructs, the respondents were instructed to answer each item based on the extent of agreement in the team. Team learning behavior 1. This team regularly takes time to ﬁgure out ways to improve its work performance 2. In our team, people discuss ways to prevent and learn from mistakes 3. We regularly take time to ﬁgure out ways to improve our work processes 4. Problems and errors in our team are never communicated to the appropriate people so that corrective action can be taken 5. In this team, someone always makes sure that we stop to reﬂect on the team’s work process 6. People in this team often speak up to test assumptions about issues under discussion 7. My team keeps instructors informed about what we plan and accomplish 8. Team members go out and get all the relevant work information they possibly can. 9. This team actively reviews its own progress and performance 10. This team ignores feedback from instructors 11. This team asks for help from instructors when something comes up that team members don’t know how to handle 12. This team does its work without stopping to consider all the information team members have Team performance 1. The team’s deliverables were of excellent quality 2. The team managed time effectively 3. The team met important deadlines on time 4. The team did a good job of meeting the customer’s needs 5. The team paid attention to detail
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