Int. J. Learning and Change, Vol. 6, Nos. 1/2, 2012
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The influence of complexity and uncertainty on self-directed team learning David Gray Department of Marketing and Management, Macquarie University, Sydney, Australia Email:
[email protected] Abstract: To help increase the effectiveness of self-directed teams, this paper studies the attitudes and behaviour of self-directed team members during the course of a computer simulated marketing strategy game. Self-directed teams are used widely throughout organisations yet receive little scrutiny when they undertake a task which is subject to conditions of multi-period complexity and uncertainty. To explore the issues involved 42 teams of final year undergraduate marketing students completed online self-report questionnaires during the completion of a competitive marketing strategy simulation game. The research findings reveal team performance as a dynamic construct that is predicted by prior period performance and team resilience, but not emotional intelligence which is negatively related to team performance. It is hoped that future examinations of this model will highlight the need for management to be cognizant of these outcomes when designing training and intervention programmes to enable them to cope better with complex tasks and uncertainty. Keywords: emotional intelligence; team performance; team resilience; SPI; share price index and Markstrat. Reference to this paper should be made as follows: Gray, D. (2012) ‘The influence of complexity and uncertainty on self-directed team learning’, Int. J. Learning and Change, Vol. 6, Nos. 1/2, pp.79–96. Biographical notes: David Gray is a Senior Lecturer of Marketing at the Department Marketing and Management in Macquarie University. He holds BCom (Hons) and MCom (Hon) degrees from the University of New South Wales and a Doctor of Philosophy in Marketing from the University of New South Wales in Sydney. He has published in international journals and conferences. He has joint publications in the Journal of Nonprofit & Public Sector Marketing, The Journal of Applied Business Research, International Journal of Business and Management, Australasian Marketing Journal and The Marketing Review. Prior to his academic career, he worked extensively in business in the marketing and management consulting industries.
1
Introduction
Marketing decision-making teams are often self-managed and confronted with rising levels of complexity and uncertainty in both their intra-firm and inter-firm business relationships. Important interpersonal attributes such as emotional intelligence and resilience are thought to assist team decision-making in such difficult contexts. However, Copyright © 2012 Inderscience Enterprises Ltd.
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there is little empirical research to guide both scholarly and managerial thinking in relation to these attributes during the decision-making process, particularly when such decision-making could take many weeks, months or years to complete. This paper seeks to better understand the influence of these interpersonal attributes on team performance through the use of computer simulated marketing strategy game. The paper begins by explaining why self-managed marketing decision-making teams were chosen as the mechanism for studying complexity and uncertainty. Second, it briefly reviews the drivers of team performance and specifically focuses on the behavioural aspects of marketing performance relevant to this study. Third, it reviews the literature on emotional intelligence and resilience and their contribution to team performance. Fourth, informed by the literature review, a conceptual framework (i.e. see Figure 1) is proposed to show the linkages between emotional intelligence, resilience and team performance. This model defines both emotional intelligence and resilience as multidimensional constructs which act as positive enablers of team performance. The basis for establishing and measuring each of the variables defined in the conceptual model are then explained in the methodology section. Fifth, the hypotheses proposed in the conceptual framework are tested using a Partial Least Squares (PLS) modelling approach. Finally, the paper discusses the conclusions, implications and limitations of the study and provides recommendations for further research. Figure 1
Conceptual model Perseverance (P) Team Resilience (TR) H3
Emotional Intelligence (EI)
EP
Notes:
2
OE
H1 Team Performance (SPI)
H2
O
Personal and Task Commitment (C )
U
EP = Emotional Perception; MES = Managing Emotion in the Self; OE = Managing Others Emotions; UE = Utilisation of Emotions; EI = Emotional Intelligence; Team Performance = SPI (Share Price Index); TR = Team Resilience; P = Perseverance; C = Personal and Task Commitment.
Why study self-managed marketing decision-making teams?
Self-managed teams are a popular organisational vehicle used to pursue organisational goals. They can be defined in terms of team members being able to actively motivate one
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another, provide feedback on performance and direct the activities of the team (Perry et al., 1999). In more recent times, however, scholars have expressed some disquiet as to the ultimate benefit of enhanced team performance, which can appear relatively uncertain and elusive (Perry et al., 1999). This study focuses on the interpersonal attribute of emotional intelligence because of its often touted positive influence on individual performance. However, there is little hard evidence to support such propositions at the organisational team level. Likewise, there are many practical examples identified as to the importance of resilience as an enabler of individual performance particularly in the sporting arena. Whilst scholars have extensively studied the organisational role of commitment it is likely that commitment by itself is only part of the explanation in determining why both individuals and teams stay the course and achieve their objectives. Furthermore, there is little understanding of what happens to team performance when such teams undertake a task which is subject to conditions of multi-period complexity and uncertainty (Edmondson, 2003). This paper focuses on the marketing decision-making context because of its extensive use of small self-managed boundary spanning teams (Ancona, 1990; de Jong et al., 2004). These teams often focus on complex tasks such as strategic planning, customer account management, new product development, continuous improvement, specific marketing activities and inter-firm business partnerships (Stock, 2006). Self-managed teams are often inter-functionally interdependent and share joint responsibility for outcomes (Covert et al., 1995). Team members regulate their own behaviour (Cote, 2005), collect, synthesise and interpret information, decide strategies and make important decisions towards the achievement of team goals (Thoms et al., 2002). The advantages of such self-managed team structures compared to individual decision-making are claimed to be better problem-solving, closer employee cooperation and more efficient knowledge transfer (Hartline and Ferrell, 1996) Complex team tasks in marketing decision-making are an ideal area for study because they are often defined by their longer term temporal dimension, the unpredictable nature of competitor responses and the necessity of real-time dynamic decisions (Brehmer, 1992). Marketing decision-makers in such contexts are likely to face challenges such as information overload (Hahn et al., 1992), time pressure, stress, uncertain interpersonal group dynamics and uncertainty about the effect of competitive interaction (Aaker, 1998). Such decision-making relies heavily on goal achievement through indirect (versus direct control strategies) influence strategies such as cooperation and persuasion. Thus, it is likely that the performance of marketing decision-makers will depend, in part, on the quality of their interpersonal skills, their ability to understand and empathise with the needs of other functional areas (i.e. emotional intelligence and perspective taking), and their ability to adapt to the market conditions at hand and to persist with the task despite adverse conditions.
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The drivers of team and marketing team performance
During the last 25 years more than 800 empirical studies related to the antecedents of team performance have been published (Stock, 2004) and many researchers believe that organisational teams (as opposed to individuals) are the basic building blocks of
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successful organisational performance (e.g. O’Leary-Kelly et al., 1994; Perry et al., 1999). At the level of the individual the antecedents of team performance include functional expertise, extraversion, and involvement, self-consciousness and team orientation (Stock, 2004). At the level of the team there are a small number of objectively measured team performance outcomes which have identified the benefits of organisational teams including the speed of output, accuracy of output, productivity, costs, efficiency, firm growth, development time, profit, turnover, efficiency, product innovativeness and task-related performance (Stock, 2004). None of these outcome measures of team performance, however, have considered what happens to team performance when self-managed teams are subjected to decision-making under conditions of task complexity, market uncertainty over a protracted period of time. Whilst the literature on team performance is vast, for the purposes of this study the marketing literature identifies a number of likely variables which could be incorporated into a measure of marketing team performance including market share (Ambler and Putoni, 2003; Wong and Merrilees, 2008), innovation and sales growth (O’Cass and Ngo, 2007; Grinstein, 2008) and profitability (Lambe et al., 2002). These measures of marketing performance have been incorporated into a number of marketing simulation games including the Markstrat (Larreche et al., 2003) game selected in this research to test the relationship between team performance, emotional intelligence and resilience.
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The role of resilience
The ability to persevere is likely to be an important antecedent to both individual and team performance because it defines one’s ability to maintain action despite obstacles, distraction, difficulty or discouragement. Resilience has also been defined as “the individuals’ determination and willingness to perform a task before and during the performance on the task” (Yildir, 2005, p.113). Recent scholarly interest in resilience has focused on trait-level perseverance and passion for long-term goals as a predictor of stamina in areas such as completion of military academy training and high school GPA achievement (Duckworth et al., 2007). Another line of research has moved towards the development of a ‘grit’ scale “gritty individual approaches achievement as a marathon and his or her advantage is stamina. Whereas disappointment or boredom signals to others that it is time to change trajectory and cut losses, the gritty individual stays the course” (Duckworth et al., 2007, p.1088). In addition to perseverance organisational management and marketing scholars have focused on commitment as a mediator of firm and inter-firm relational performance (e.g. Morgan and Hunt, 1994; Lambe et al., 2002). Relationship commitment within the context of an organisational team includes a desire to develop a stable relationship, a willingness to sacrifice individual needs to maintain the relationship/team, and a confidence in the continuing stability of the team. The committed parties believe the relationship is worth working on to ensure that it endures (Morgan and Hunt, 1994; Gray, 2006). More committed team members will exert effort and balance short-term problems with long-term goal achievement (Mohr and Spekman, 1994). The positive relationship between task and group commitment to team performance is identified in one of the relatively few studies of Yildir (2005) using a game simulation context. However, little
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research has been undertaken involving team-based organisational decision-making in a complex context constrained by overload, uncertainty and time pressure. In such a context, it is likely that the construct of commitment requires augmentation. Yes, one can be committed to a goal/task but that commitment can be degraded over time by a range of both endogenous (i.e. experience, expertise, interpersonal skills) and exogenous factors (i.e. overload, environmental uncertainty and externally generated time pressure constraints). Augmentation of the commitment construct into resilience means that not only resilience reflects individual, group and task commitment, but it also perseverance of effort. High performers ought to also possess the stamina to persist at a task when obstacles are encountered. In this respect consistency of interest and perseverance of effort are likely positive contributors to resilience (Duckworth and Quinn, 2009) and it is the emphasis on perseverance which provides evidence of resilience. An important issue considered in this study is how individual team member resilience is transferred to the team. Here, we rely on the concept of ‘contagion’ (Hatfield et al., 1994). That is, individual team members with high levels of resilience can produce a corresponding change in resilience in other members of the team. Thus, in a general sense, teams that incline more versus less to stay-the-course are more likely to succeed. Teams possessing high levels of determination and willingness to perform a task (i.e. a high level of team resilience) are more likely to meet performance expectations than teams with lower levels of resilience. H1: Team resilience influences team performance positively.
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The role of emotional intelligence
The ability to connect emotionally is likely to be an important antecedent to both individual and team performance. Goleman (1995, 1998a, 1998b) popularised the concept of individual EI and adapted the original Salovey and Mayer’s (1990) model. Goleman’s competency based model is composed of five emotional and social competencies: self-awareness, self-regulation, motivation, empathy and social skills (Luca and Tarricone, 2001). At the team level emotional intelligence can lead to improved team effectiveness (Caruso and Salovey, 2004; Gantt and Agazarian, 2004). Several marketing scholars have also found a positive association between emotional intelligence and the firm marketing effectiveness (Nwokah and Ahiauzu, 2009) and financial performance (Heffernan et al., 2008). Whilst there are a number of alternative scales, which measure emotional intelligence, for example a reasonably well-accepted relatively short emotional intelligence scale is the 33 items developed by Schutte et al. (1998) and later modified by Petrides and Furnham (2000). This scale is based on the model developed by Salovey and Mayer (1990) and identifies a four-factor solution for the 33 items. The four factors were labelled as follows: emotional perception, managing emotions in the self, social skills or managing others’ emotions and utilising emotions. The first dimension of ‘emotional perception’ is defined as the ability to discern one’s own and other’s emotions based on situational and expressive cues that have some degree of cultural consensus as to their emotional meaning (Saarni, 1999). The second dimension of ‘managing emotions in the
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self’ involves the capacity for adaptively coping with aversive or distressing emotions by using self-regulatory strategies that ameliorate the intensity or duration of such emotional states (Saarni, 1999). The third dimension of ‘managing others emotions’ in many respects encompasses social skills (Petrides and Furnham, 2000) and includes the ability to arrange events that others enjoy, know when to speak about personal problems to others, engender trust in other people confiding in them and makes others feel better when they are down. These are all actions that would tend to maintain or increase others’ positive moods. The fourth and final dimension of ‘utilisation of emotion’ includes the components of flexible planning, creative thinking, redirected attention and motivation. These skills are designed to harness emotions to solve problems. Emotional intelligence is usually measured at the level of the individual team member. To move from individual to team emotional intelligence requires a bridging mechanism such as emotional contagion. The emotional contagion (Hatfield et al., 1994) model refers to the tendency for team members to catch (experience/express) other team member’s emotions. The contagion process helps to strengthen the ties between team members so that each team member now identifies with the goals of the team over and above the goals of the individual. Therefore, it is possible to theorise that teams with high levels of emotional intelligence are likely to be better able to effectively establish and maintain mutually satisfying relationships compared to an individual team member, thereby creating successful interpersonal relationship outcomes and therefore increased team performance. H2: Team emotional intelligence positively influences team performance. Furthermore, it is possible to theorise that teams who are better able to appraise, regulate and utilise emotions well are likely to be better able to control how they feel and react to others than compared to those with low levels of emotional intelligence. Such teams will also be more likely to find it easier to adapt to information overload, time pressure, stress, uncertain interpersonal group dynamics and uncertainty about the effect of competitive interaction. This means better communication and respect for each others’ opinions; greater ability to resolve conflict and an improved ability to meet deadlines and make decisions. In this situation it is possible to theorise that emotional intelligence facilitates the building of trust and provides a mechanism that can promote the adaptation of organisational forms such as intra-organisational teams and also inter-firm alliances and networks (Miles and Snow, 1992; Ford et al., 1998). Thus, organisational and marketing decision-making teams will be better able to adapt to the situation and to others, and in doing so will facilitate team resilience. H3: Emotional intelligence positively influences team resilience.
6
The effect of time on team performance
Just as inter-organisational relationships evolve over time through learning and adaptations (Day, 1995; Doz and Hamel, 1998; Iyer, 2002) intra-organisational teams also evolve and adapt over time. For example, an intra-organisational team could be a cross-functional team who meets regularly to discuss delivery schedule problems. In the kind of complex competitive team decision-making considered by this paper there is
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considerable pressure to make decisions quickly and having to deal with uncertainty about the effect of competitive interaction. In such a context it is possible to theorise that team resilience will increase with increasing team member task familiarity and goal commitment. Thus, H4: The strength of the relationship between team resilience and team performance increases over time during the decision-making cycle. It is also possible to theorise that the strength of the relationship between emotional intelligence and team performance would decrease over time as the impact of information overload, time pressure, stress, uncertain interpersonal group dynamics and competitive uncertainty takes hold. Thus, H5: The strength of the relationship between emotional intelligence and team performance reduces over time during the decision-making cycle. Given the nature of the competitive context being studied many teams will experience more disagreements and conflict as they struggle to understand how they can improve their performance as their performance deteriorates. In other words, these teams are more likely to give up on goal achievement. While emotional intelligence will remain relatively stable because of its near trait like features the strength of team resilience will decline in relation to emotional intelligence as individual teams realise that they cannot achieve their goals. Furthermore, even though teams might have clear goals, the right mix of experience and skills, adequate resources and a task that calls for teamwork yet still suffer a devastating breakdown in coordination due to miscommunication, interpersonal conflict or poor judgement in the heat of the moment (Edmondson, 2003, p.1420). Thus, support for H6. H6: The strength of the relationship between emotional intelligence and team resilience diminishes over time during the decision-making cycle. It is also possible to theorise that teams which experience the taste of success in the performance of their complex task are more likely to be successful in future periods. That is, teams which are more inclined to ‘stay the course’ will be more likely to succeed. It is likely that team resilience develops in an evolutionary pattern of interaction (Ford et al., 1986) as does any long-term relationship between members in an intra-organisational or inter-organisational team. This means that the state of resilience at a particular point in time can be defined in terms of the existing and previous pattern of interaction. This means that it is the strength of resilience and performance in the period prior to the completion of a round of the Markstrat simulation game that will have the greatest impact on team performance. However, Gersick (1989) also points out that if the group is not performing successfully then the best place to intervene is at the midpoint of the task performance. Interventions at other times might be resisted. Gersick (1989, p.304) notes that when “the midpoint (or other milestone) has passed. The current evidence suggests that if a milestone passes without the occurrence of enough perceived progress, a team will experience the passing as a failure, and their shared sense of opportunity will probably be lost until the next temporal milestone”. With this caveat in mind it is hypothesised that:
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H7: Team resilience will positively influence SPI in subsequent periods. H8: Team performance will positively influence team performance in subsequent periods.
7
Research method
7.1 Data collection and analysis procedures A convenience sample was selected comprising 42 teams composed of 150 undergraduate final year commerce students at a major Australian university who were required to compete in a complex competitive marketing strategy simulation. This research method was chosen because it allowed for the elimination of confounding factors including age, education, income and task experience. The objective of the research focuses on examining the influence of emotional intelligence and resilience on team performance rather than being complicated by these other confounding factors. The Markstrat simulation was used and is considered to be among the most realistic business simulations used in management education (Glazer et al., 1987; Malter and Dickson, 2001). The firms competed with one another over seven periods and the performance measure used to gauge team performance was a combinatory Share Price Index (SPI). To encourage task motivation 30% of each student’s final course grade was based on the simulation. Online self-report questionnaires using SurveyMonkey for emotional intelligence and team resilience were collected prior to beginning the simulation. Team resilience scores were collected at the end of round six (i.e. the second last round) and team performance scores were collected at the end of the simulation game. The survey data were then cleaned and coded and then analysed using XLSTATPro (Fahmy and Aubry, 2008) for PLS analysis. PLS was utilised as the method of analysis as it has minimal demands on measurement scales and sample size. Given the relatively small sample size comprised in this study (i.e. N = 42) PLS was deemed the most appropriate modelling technique. PLS can be used for theory confirmation, as well as for suggesting whether specific variables are related (Addinsoft, 2008). Owing to sample size and stringent distributional assumptions required by the well-known methods such as AMOS or LISREL, PLS was preferred as the estimation procedure to evaluate the theoretical hypotheses (Wold, 1981; Fornell and Cha, 1994). A PLS model is formally specified by two sets of linear relations: first, a measurement model is validated which specifies the relationship between the items that comprise each latent variable; second, a structural model is used to specify the relationships between the variables.
7.2 Construct measurement and reliability analysis The team performance measure was operationalised as the SPI. Consistent with marketing literature the variables used to comprise the SPI represent a combinatory index of firm profitability, growth in revenues, market share and the level of R&D activities. The SPI has a long history of being used to simulate the performance outcome of marketing decision-making (Malter and Dickson, 2001; Larreche et al., 2003). All teams start with an SPI of 1000 and are ranked according to their SPI at the end of the simulation. The index is calculated as the average of the growth rates of team
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profitability, growth in revenues, market share and the level of new R&D activities at the end of each round of the game. Each team is required to make a range of decisions including the selection of the target market, positioning, research and development, launching/improving/maintaining products and marketing mix decisions. The small sample size (N = 42) requires a multi-stage variable measurement process to reduce the number of variables down to the number which could be validly processed by the structural model. The emotional intelligence (EI) scale is developed by Schutte et al. (1998). This 33 items scale was adapted by Ciarrochi et al. (2001) to a fourdimensional 7-point Likert-type scale ranging from 1 to 7 (1 = strongly disagree; 7 = strongly agree). The dimensions consisted of emotional perception, managing one’s own emotions, managing others’ emotions and utilisation. The variable reduction process consisted of: first, calculating an average individual team member score (i.e. n = 150) for each item in the scale. Second, calculating an average team score for that item (i.e. n = 42 and see Table 1). Third, an average team score for each of the four dimensions was calculated. Justification for using a team measure of emotional intelligence as opposed to an individual measure is based on the work of Pate et al. (1998) who identified that the decision-making ability of a group is generally a better indicator of performance than the best decision-maker in the group. Table 1
Measurement model results – team emotional intelligence dimensions
Construct Item
M
SD
SFL
CR
AVE
α
D.G. rho
Emotional Intelligence (EI)
5.33
0.46
0.76
12.56
0.67
0.83
0.89
Emotional Perception (EP)
5.24
0.48
0.58
0.82
0.87
Managing Emotions-Self (OE)
5.44
0.40
0.88
27.40
0.50
0.74
0.82
Manage Other’s Emotions (O)
5.30
0.51
0.87
40.79
0.55
0.83
0.88
Utilisation of Emotions (U)
5.42
0.45
0.74
14.37
0.56
0.73
0.83
Notes:
AVE = Average Variance Extracted; α = Cronbach’s Alpha; M = Mean; SD = Standard Deviation; SFL = Standardised Factor Loading.
The resilience scale as shown in Table 2 was measured at the end of both the first and the sixth periods of the simulation game using a 15 items 7-point Likert-type scale ranging from 1 to 7 (1 = strongly disagree; 7 = ‘strongly agree). The scale consisted of two reflective dimensions: the first dimension represents personal and task commitment and is composed of nine items adapted from Yildir (2005) and Malter and Dickson (2001). The second dimension reflects the element of perseverance and consists of six items adapted from the EQMap Online perseverance dimension by Essi Systems (2011). The same averaging process as applied in relation to EI above was used to reduce the number of items to a two-dimensional scale. The measurement of the ‘time’ variable identified through Hypotheses H4–H6 was achieved through a comparison of the differences in the strength of the path weights between period 1 and period 7 of the marketing strategy simulation game. For example, the significance of the differences in the strength of the relationship between SPI and team resilience between period 1 (i.e. the opening round) and period 7 (i.e. the closing round) is tested by using a t-test comparison of the path weights.
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Table 2
Measurement model – Team Resilience (TR) and team performance (i.e. SPI)
Construct Item
M
SD
Team Resilience (TR) AVE = 0.74; Cronbach α = 0.87; D.G. rho = 0.94
5.64
0.71
Personal and Task Commitment – AVE=0.74; Cronbach α = 0.94; D.G. rho = 0.95
5.62
0.71
SFL
0.94
I am strongly committed to perform well in this game
5.74
0.76
0.89
I think the objectives in this game are a good thing to shoot for
5.51
0.77
0.58
I will not give up easily on Markstrat
5.65
0.93
0.71
My group will be strongly committed to performing well in this game
5.58
0.88
0.88
I am highly motivated to help my group to succeed
5.73
0.78
0.88
I am very committed to my group
5.85
0.76
0.87
My group is very committed to each other
5.64
0.90
0.86
Quite frankly, I don’t care if my group performs well or not R*
5.20
1.26
0.70
It is important to me to outperform the other teams in our Markstrat industry
5.71
0.66
0.86
5.57
0.66
0.94
Team members can finish most things that they start
5.73
0.68
0.92
Team members can completely focus on a task when they need to
5.62
0.76
0.93
Team members can easily shut out distractions when they need to concentrate
5.33
0.73
0.91
Team members can decide certain problems are not worth worrying about
5.35
0.79
0.90
Team members are likely to try to come up with an alternative plan when something is not working
5.72
0.61
0.88
When faced with a problem, team members can deal with it as soon as possible
5.71
0.71
0.91
Share Price Index (SPI Period 7)
1616
665
Share Price Index (SPI Period 1)
1144
132
TR (Period 0)
6.44
0.54
TR (Period 6)
5.64
0.71
Perseverance – AVE = 0.85; Cronbach α = 0.96; D.G. rho = 0.97
Notes:
AVE = Average Variance Extracted; M = Mean; SD = Standard Deviation; SFL = Standardised Factor Loading; * = reverse coded.
7.3 Reliability analysis and measurement model results The team EI scale was tested for both convergent and discriminant reliability. The measurement model for each dimension of the EI scale was tested for convergent reliability. The results of the reliability tests, means, standard deviations and Average Variance Extracted (AVE) are shown in Table 1. Table 1 also shows that the team EI
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scale achieves satisfactory convergent reliability with Cronbach alpha and the DillonGoldstein’s ρ for each of the modified four branches of team EI exceeding 0.7 (Chin, 1998; Addinsoft, 2008). Analysis of the team resilience scale identified 15 items with satisfactory standardised factor loadings. The results of the convergent reliability analysis in Table 2 showed a satisfactory Cronbach alpha and Dillon-Goldstein’s ρ larger than 0.9. Discriminant validity of the four components of EI and the TR scale was also tested for ensuring that the AVE satisfied such that R2 < mean communalities (Fornell and Larcker, 1981). This tests that the variables in the study are substantially different from one another and do not overlap. All variables were found to pass the discriminant validity test.
8
Results
8.1 Sample characteristics Team member participants consisted of students belong a variety of social and cultural backgrounds. The sample consisted of 50% male and 50% female. The age ranged from 19 to 30+, with 20% of individuals reporting their ages 19–20, 50% between the ages of 21 and 22, 26% between the ages of 23 and 24, and 4% greater than 24 years. The highest proportion of students was in the age range of 21–22, with very few reporting their age over 30, i.e. a young sample. The majority of students were studying for a Bachelor of Commerce Degree. Initial analysis included basic descriptive statistics of team emotional intelligence, team resilience and the SPI, in order to gain a basic understanding of the data and to determine if there were any unusual observations. Table 3 shows the descriptive statistics and the correlation matrix for the seven variables. The variable means for team EI range are centred tightly between 5.24 and 5.44 (i.e. the range is 1–7). The team resilience mean is a little higher at 5.64. The standard deviations for the variables range from 0.40 to 0.71, indicating a relatively small amount of variance in the responses. The correlations in Table 3 provide an initial test of the hypotheses and support H1 and H3 but fail to support H2 as the sign of each of the dimensions of team EI is in the wrong direction. Table 3
Team EI, TR and SPI correlation matrix (final round 7)
Variables Commitment (C) Perseverance (P) Performance (SPI) Perception (EP) Own Emotions (OE) Others Emotions (O) Utilise Emotions (U) Notes:
M
SD
C
5.62 5.57 1616 5.24 5.44 5.30 5.42
0.71 0.66 665 0.48 0.40 0.51 0.45
1.00 0.78** 0.50** 0.26** 0.21** 0.41** 0.27**
P
SPI
P
OE
O
0.78** 0.50** 0.26** 0.21** 0.41** 1.00 0.23** 0.43** 0.43** 0.57** 0.23** 1.00 –0.21* –0.19* –0.14 0.43** –0.21** 1.00 0.63** 0.50** 0.43** –0.19* 0.63** 1.00 0.72** 0.57** –0.14 0.50** 0.72** 1.00 0.33** –0.15 0.39** 0.57** 0.51**
M = Mean; SD = Standard Deviation; P = Emotional Perception; OE = Managing Emotion in the Self; O = Managing Others Emotions; U = Utilisation of Emotions; C = Team Commitment; SPI = Team Performance (i.e. Share Price Index as a measure of team performance at period 7 of the simulation); ** Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level.
U 0.27** 0.33** –0.15 0.39** 0.57** 0.51** 1.00
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8.2 Testing of hypotheses – results The overall structural model of the hypothesised relationships between EI, TR and SPI in the final period of the simulation was then tested and the results are shown in Table 4. The path weights and the critical ratio results were statistically significant thus supporting H1 and H3 but failing to support H2. In particular, whilst H2 is statistically significant, the sign is in the wrong direction, indicating that there is a negative relationship between team performance (SPI) and EI. The overall Goodness of Fit (GoF) index for the structural (inner) model is adequate at 0.86 and the associated critical ratio is statistically significant at 26.4. Table 4
Structural model results: team EI, TR and team performance (final round 7)
Predicted Variables
H
Hypothesis Support
Path
Path Variance
R2
CR
Team resilience
H1
Yes
0.65
0.32
0.43
12.13
SPI-P7
Emotional intelligence
H2
No
–0.46
0.11
TR-P6
Emotional Intelligence
H3
Yes
0.35
0.12
SPI-P7
Predictor Variables
Average Notes:
–6.57 0.12
2.54
0.27 CR = Critical Ratio; H = Hypothesis.
To test H4, H5, and H6 for significant changes in the strength of the relationships over time we ran the PLS model in period 1 (the opening round) of the simulation as shown in Table 5. This result was then compared with the results reported in period 7 (i.e. the closing round) by testing for differences between the path coefficients in TR, EI and SPI. Table 5 Predicted Variables
Structural model results: team EI, TR & team performance (round 1) Predictor Variables
H
Hypothesis Support
Path
Path Variance
R2
CR
0.01
–0.88
SPI-P1
Team Resilience
H1
No
–0.12
0.01
SPI-P1
Emotional intelligence
H2
No
0.07
0.00
TR-P1
Emotional Intelligence
H3
Yes
0.70
0.49
Average
0.63 0.49
21.27
0.25
Table 6 presents the results of a t-test of the difference between coefficients at two different time periods. The t-test comparison shows a statistically significant difference between the path coefficients at the end of the simulation as compared to the beginning of the simulation and thus finds support for H4. That is, the strength of the relationship between team resilience and team performance increases over time during the decisionmaking cycle. Likewise H5 is supported and the results show a decline in the strength of the relationship between emotional intelligence and team performance over time during the decision-making cycle. Third, H6 is also supported with the results showing a decline in the strength of the relationship between emotional intelligence and team resilience over time during the decision-making cycle.
The influence of complexity and uncertainty Table 6
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Comparisons of results between P1 and P7 re team EI, TR and SPI
Predicted Variables
Predictor Variables
Hyp
Hyp Support
Path P1
Path P7
t-statistic
SPI
Team Resilience
H4
Yes
–0.12 (0.13)
0.65 (0.05)
5.38
SPI
Emotional intelligence
H5
Yes
0.07 (0.11)
–0.46 (0.07)
3.03
TR
Emotional Intelligence
H6
Yes
0.70 (0.03)
0.35 (0.07)
4.63
Notes:
Hyp = Hypothesis; Path = the path weight. The standard errors are shown in brackets.
To test H7 and H8 for interdependences between team resilience over time we ran the PLS structural model regressing team resilience in period 6 of the game with team resilience at the start of the game. The findings shown in Table 7 imply that initial resilience is a prerequisite for resilience throughout the completion of the task. Thus, support for H7 is established. Likewise we ran the PLS regressing team performance in period 7 of the game with team performance at the end of the game. The findings shown in Table 7 imply that initial team performance success is a prerequisite for team performance success throughout the completion of the task. Thus, support for H8 is established. Table 7 Predicted Variables
Alternative model – longitudinal PLS structural model Predictor Variables
Hyp
Hypothesis Support
Path
Path Variance
R2
Critical Ratio
0.61
0.30
0.62
15.56
SPI-P7
Resilience-P6
H1
Yes
SPI-P7
SPI-P1
H8
Yes
0.44
0.22
10.44
SPI-P7
Emotional Intelligence
H2
No
–0.43
0.10
–5.56
TR-P6
Resilience-P0
H7
Yes
0.58
0.31
TR-P6
Emotional Intelligence
H3
No
–0.07
–00.02
0.29
6.79 –0.71
TR-P0
Emotional Intelligence
H3
Yes
0.67
0.49
0.49
19.92
SPI-P1
Emotional Intelligence
H2
No
–0.01
0.00
0.00
–0.18
Average
9
0.35
Discussion, conclusions, limitations and future research
9.1 Discussions and conclusions – coping with complexity and uncertainty To help increase the effectiveness of self-directed teams, this paper studied the attitudes and behaviour of self-directed team members during the course of a computer simulated marketing strategy game. The conditions in the game emulated complex team tasks in marketing decision-making that are defined by their longer term temporal dimension and the unpredictable nature of competitor responses. The results indicate that team performance is a dynamic construct that is predicted by prior period performance and
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team resilience, but not emotional intelligence which was found to be negatively related to team performance. The negative relationship between emotional intelligence and team performance whilst not directly comparable to prior research send a warning message about the efficacy of emotional intelligence training programmes. That is, this research send a message to managers and educational professionals that the facilitation of team cohesion through the use of emotional intelligence building programmes could easily be disrupted during conditions of task complexity and uncertainty. Empathy by itself is insufficient to ensure good team outcomes. The study does however underlie the importance of team resilience as a positive influence on team performance. Whether in academia such as undertaking a PhD and in business such as developing a new product or in sport becoming an elite sporting team, there is a certain level of determination and stamina required to stay the course. Team resilience is more than just commitment or motivation; rather it reflects an over-arching long-term driver of performance that assists both the individual and the team to overcome the barriers that team members face as complex tasks progress.
9.2 Implications for managers Managers and educators could improve the success of their self-directed teams by focusing on the development of programmes which assist managers to better cope with the kinds of complexity and uncertainty experienced during the simulation game conducted within the context of this research. For example, measures which could be used to build team member resilience include conducting extensive practice decision routine exercises, building self-esteem and self-efficacy. This is pretty much the same as teaching a pilot to fly through a storm or landing an aircraft in difficult conditions. This is the kind of training managers need to better survive in these kinds of challenging environments. It is important not to be assuming that the building of team member empathy through emotional intelligence will be sufficient to overcome poor decisionmaking. Specific strategies for building business resiliency should ensure that resiliency is directly incorporated into the formulation of strategy itself. That is, corporate strategies should incorporate clear goals and objectives; careful timing of any corrective intervention (i.e. midpoint as emphasised by Gersick, 1989); examine market vulnerabilities and risks that could negatively impact business performance and build organisation structures that clearly define communication protocols and skills that are critical to the business task being undertaken. Seligman (2011) also identified from an interpersonal perspective that people who do not give up have a habit of interpreting setbacks as temporary, local and changeable. That is, Seligman argues that people should be taught to think like optimists. However, we should not assume that it is just commitment or motivation which is a key determinant to resiliency. If it was so easy, then building team success would be a relatively straight forward endeavour. It is clear that more work needs to be done to discover the underlying driving ingredients of resilience so that we can build appropriate support mechanisms into the team development process to improve the odds of success.
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9.3 Limitations and future research As with all academic research, however, there are some limitations with this study. First, this study utilised a non-random quota sampling methodology comprising undergraduate students. As a result, it is not clear whether or not the reaction of the participants to the simulation game is reflective of the wider managerial population. These limitations were necessitated by the practicalities of the study and do not detract from the significance of the findings presented. Second, only a small number of variables were examined in this research. Therefore, the dynamic nature of team performance may vary according to the type of environmental stimulus used in the simulation game. Third, this study does not take into account the role of the team leader which has been shown in previous research to be an important determinant of team performance. Some potential research questions oriented towards the better understanding of team performance in dynamic contexts include: What are the effects of differences in the level of information, motivational support and feedback on team performance? What are the effects of differences in the level of time pressure, vis-a-via information overload on team performance? Are there varying effects in team performance based on the level of team member expertise? Does a team member’s critical thinking skill correlate with team performance? From a managerial perspective management should be also thinking about what kinds of activities or training initiatives could be used to build employee resilience. It is clear that there is much to discover in this important area.
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