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The influence of team functional processes on investment team performance.

INTRODUCTION

Virtual teams are groups/teams of people that work together, but may never actually meet in person. They conduct their work by means of computer mediated communication, e-mail, virtual work spaces, the internet, fax machines, telephones and the like. Fact to face teams are groups/teams of people that work together face to face. They meet and work together, at least part of the time, in the same space and time. Teams are often preferred to an individual when it comes to making important decisions. While there is evidence that teams may be better than individuals at some decision making tasks (Kerr, MacCoun, & Kramer, 1996), there is also evidence indicating that may not always be the case (Branson, Sung, Decker, & He, 2005). As teams form and function, they develop characteristics that often make a difference in the performance of the team (Branson, Moe, & Sung, 2005; Branson, Steele, & Sung, 2010a; Lievens, Conway, & DeCorte, 2008). In addition, individual differences between members of a team influence team performance. Face to face (F2F) teams and virtual (VT) (computer mediated) teams have systematic differences in how they form, and how they function (Branson, Clausen, & Sung, 2008). This study looks at the impact of team type (VT or F2F), team social processes as measured by the GSI, and the characteristics of individual members on the performance of investment teams. Performance is measured by return on investment portfolios made by each team and by using the risk adjusted returns of the team portfolios as measured by Sharpe's measure.

The majority of Cook and Szumal's research was based on F2F teams. Potter and Balthazard (2002) expanded Cook and Szumal's work by investigating virtual teams using group styles inventory techniques to determine whether factors that drive conventional team performance exist in virtual teams. The authors examined the decision process and performance styles of 42 virtual teams and reached the conclusion that virtual teams exhibit the distinctive group interactive styles found in traditional F2F teams. However, few studies have examined the differences in group styles found in virtual teams and F2F teams. This study examines the group style differences between F2F and virtual teams by using the Cooke and Lafferty Group Styles Inventory (GSI) to measure and compare the structure and social processes of virtual and F2F teams and to see if these differences impact the performance of investment teams. In addition, this study examines the impact of the GSI scores of individual team members on investment team performance.

LITERATURE REVIEW

Kerr, MacCoun and Kramer (1996) found that group decision-making sometimes mitigates the biases and limitations of individuals. Frey, Luthgens, and Schulz-Hardt (2000) maintain that group discussions allow for the correction of biases, and clarify issues and misunderstandings through the exchange of information that do not occur when an individual processes information alone. However, there are important social and relationship problems that can occur in a team that limit these benefits. Branson, Sung, Decker, and He (2005) found that VT teams process less information than F2F teams, that individuals process more information than virtual teams, and that F2F teams process more information than individuals or virtual teams in a performance evaluation decision. Branson and Sung (2004) found that team decision making, and type of information can help mitigate the impact of stereotyping on performance evaluations. To more clearly understand these relationships Branson, Steele and Sung (2010a) found that all types of teams do not benefit equally from group discussion and decision making. For example, in their research aggressive/defensive teams were not able to benefit from effective team decision making, and actually use more stereotyping information in a performance evaluation than the individual members of the team.

Researchers have found that computer mediated (CM) communication can lead to decreases in team effectiveness (Baltes, Dickson, Sherman, Bauer, & LaGanke, 2002). Benbunan-Fich, Hiltz, & Turoff (2002) found that anonymity in CM teams increases hostile behavior and extreme decision making. These researchers and others (Branson, Moe, & Sung, 2005; Branson & Sung, 2004) have found that asynchronous CM groups spend more time and energy on solving their general disagreements than solving their problem tasks. Branson, Clausen, and Sung (2008) found that F2F teams tend to form more constructive styled teams than virtual teams. Virtual teams tend to form less constructive styled teams, which results in teams that are less capable of sharing information, surfacing and including minority/diverse opinions and less able to conduct a discussion and reach a conclusion that incorporates all the information. (Branson, Clausen, & Sung, 2008). The result of using the virtual teaming process may lead to lower quality decisions because of the negative impact of the virtual teaming process (Baltes, Dickson, Sherman, Bauer, & LaGanke, 2002).

Diverse teams may be able to integrate more information into a decision, and therefore make better decisions (Kerr & Tindale, 2004). The emergence of virtual teams has made it possible for companies to form teams with optimum membership, rather than teams based on geography. The consequence is the formation of teams that have members with the optimum qualifications, who are able to see more alternatives, and to integrate more information and viewpoints into a decision--therefore making better decisions. However, the result of using the virtual teaming process may lead to lower quality decisions because of the negative impact of the virtual teaming process (Baltes, Dickson, Sherman, Bauer, & LaGanke, 2002; Branson, Clausen, & Sung, 2008). Benbunan-Fich, Hiltz, & Turoff (2002) found anonymity increases uninhibited, hostile behavior and extreme decision making. Branson, Steele and Sung (2010b) report finding an interaction between team type (VT or F2F), and constructive group style, and the use of stereotype information in a performance appraisal. Some of the negative effects on virtual teams are due to the absence of interpersonal cues, resulting in a state of deindividuation, reduced evaluation concern, and a more impersonal and task-oriented attentional focus. Branson, Clausen, Sung (2008) found that asynchronous computer-mediated groups spend valuable time and energy on solving their general disagreements. It appears the virtual team process sometimes results in a team that experiences net process loss. At the same time, when virtual teams spend their time on managing the team processes, and not performing the decision task, they are processing less relevant and less irrelevant information in the decision. The decision is more dependent on the social processes of the team than the information available to the team.

Successful teaming requires effective socialization processes. In order to assess the socialization processes, Cooke and Szumal (1994) developed the Group Style Inventory and categorized group styles as constructive, passive/defensive, or aggressive/defensive. Constructive styles exist when team members are trying to satisfy their higher order needs (need for affiliation and need for achievement). The constructive style taps the full potential of group members and produces effective solutions. The constructive style enables group members to fulfill both needs for personal achievement as well as needs for affiliation allowing the full potential of group members to be realized while facilitating effective solutions by group consensus. Passive/defensive groups behave in ways that fulfill their security needs by placing greater emphasis on fulfillment of affiliation goals only. They are interested in maintaining harmony in the group, and accept limited information sharing, questioning and impartiality. The passive/defensive style team will accept less than optimal solutions. Team members will accept decisions which have not benefited from constructive differing, creative thinking and individual initiative. Aggressive/defensive groups are characterized by competition, criticism, interruptions and overt impatience (Cooke & Lafferty, 2003). The aggressive/defensive style emerges when members approach the problem in ways intended to help them maintain their status/position and fulfill their need for security by task related activities. Aggressive/defensive groups are concerned with need for power and need for control.

In investment decisions the evidence on whether individuals or groups perform better is mixed (Rockenbach, Sadrieh, & Mathauschek, 2007). Barber and Odean (2000) found that investor clubs under-performed individuals. Rockenbach Sadrieh, and Mathauschek (2007) found that team decisions were not in compliance with expected utility theory more than individuals, but they did find that team decisions were more consistent with portfolio selection theory. Branson Sung Decker and He (2005) found that F2F teams used more information in making a decision than virtual teams so that F2F teams may be able to make better decisions than virtual teams.

METHODOLOGY

Procedure:

Senior and graduate students in investment classes at a Midwestern university were randomly placed on investment teams. Each student was placed on both a virtual and a F2F team, with different team members on the VT and F2F teams. Team size ranged from three to six. The teams performed investment decisions throughout a semester by creating and monitoring investment portfolios using Stock Track. The investment performance for each team was measured and monitored throughout the semester using the return on their investment portfolios and Sharpe's measure was used to adjust returns for different risk levels. The GSI was administered to all team members, both F2F and virtual. The GSI measures individual team members' perception of their team's social processes on twelve dimensions. Individual team member scores were averaged to calculate the overall team score on each of the twelve dimensions which are aggregated into the three group styles.

Sample:

The virtual and F2F teams in this study were teams of students in senior or graduate level investment classes in a Midwestern university. Sixty-three students were assigned to both a virtual and a F2F investment team. Since the same students populated the VT and F2F teams, there were no demographic differences between types of teams. The average age of the participants was 29, with an average of 11 years of work experience. The participants were all working adults pursuing professional and personal development.

Data Analysis:

We conducted our first-level analysis using SAS t-test procedures. Subsequent post hoc analysis of the various relationships was conducted using SAS GLM (regression) analysis as indicated in the results section below.

HYPOTHESES

Based on the literature review above we hypothesized that the F2F teams would generally outperform the virtual teams, due to their ability to incorporate more information in the decisions, and due to their superior group processes. F2F teams are able to work through member differences in a positive way that result in better decisions, more positive relationships, and higher order need fulfillment. Our overall hypothesis was:

[H.sub.o1]: There will be no difference in the portfolio returns earned by virtual and F2F teams.

RESULTS

Based on our analysis using SAS t-test procedures our overall results indicate there is no significant difference (p-value 0.3812) between the F2F teams' average return and the virtual teams' average return and no significant difference (p-value 0.3257) in the average Sharpe's measure for VT and F2F teams. The GSI methodology requires that the team scores for each of the twelve dimensions and three group styles be calculated by averaging the individual's team member's scores. In a post hoc analysis using SAS GLM (regression) this study looked at the maximum and minimum team member scores on each team for each dimension, and the range of scores for each dimension. The post hoc analysis examined the impact of maximum and minimum scores and the range of scores of each dimension on the performance of the team as measured by the return on investment portfolios and Sharpe's measure. Our post hoc analysis also looked at the impact of the risk tolerance of individual team members on team performance. The post hoc analysis discovered that the use of the average score camouflaged the importance of individual difference scores. The following differences were noted in our post hoc analysis. All presented differences are significant at the .05 level.

Branson, Clausen and Sung (2008) reported a significant difference in group styles between virtual and F2F teams. Face-to-face teams tend to form constructively styled teams, while VTs are less likely to form constructively styled teams, and more likely to form passive/defensive or aggressive/defensive styled teams. The results in table one and graph one align well with the lack of social interactions on a virtual team. While F2F teams benefit from having constructively styled members, VTs do not enjoy the same benefits from constructive members because they cannot get as much traction, or have as much influence in that environment. As graph one indicates, F2F teams' portfolio returns increase as minimum team member's constructive scores increase (regardless of average team constructive scores). At the same time VTs portfolio returns are not affected by the minimum team members increasing constructive score. The social processes in virtual teams apparently neutralize any advantage a team would have from having all team members with high constructive scores.

Generally speaking, teams with high aggressive/defensive scores do not perform well as teams. Members spend too much time trying to maintain control and power, and less time trying to achieve the objective--higher investment performance on the team's portfolio. Table two and graph two supports the conclusion that a highly aggressive/defensive member can be extremely harmful to a F2F team's interactions and performance. Aggressive team members have lower social traction in VTs, so have less influence on virtual team's interactions and performance

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Table three and graph three indicate that for passive/aggressive teams it was the range of scores on each team that resulted in a significant impact on portfolio returns. For F2F teams the wider the range of scores, the lower the returns. Passive/defensive teams are known for their need for approval and need for acceptance. Apparently, as the difference in team members passive/defensive scores increased, it became more difficult for the F2F teams to interact effectively, and to achieve higher performance levels. This is in line with prior research (Branson, Clausen, & Sung, 2008) that indicates that these types of teams spend more time managing the teaming process, and less time focused on the team's objectives. For VTs on the other hand, the range of passive/ aggressive scores of its team members did not affect the team's performance. While Branson, Clausen and Sung (2008) found that VTs tend to form passive/defensive and aggressive/defensive teams more than F2F teams, it appears the consequences of forming these types of teams is muted by the difficulty of individual team members to gain significant traction in affecting their team members.

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Impact of Team Member Risk Tolerance on Portfolio Returns:

Based on the basic risk-return relationship, one would expect that groups with higher risk tolerance would construct more risky portfolios, and receive higher non-risk adjusted portfolio returns. As indicated in table four and graph four, in the case of the F2F teams it is the team member with the maximum risk tolerance score that significantly affects portfolio returns. With VTs it is the average team members risk tolerance score that significantly affects portfolio returns. While both relationships are significant, the slope of the risk tolerance/return line is much steeper for the F2F teams, indicating that the impact on returns is greater for F2F teams than for VTs. An interesting implication for risk shift in teams is that F2F groups may be more likely to polarize around a highly risky member, while a VT would not, due to the paucity of cues on which to polarize (need to conform).

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Results for Sharpe's Measure:

Teams could improve their portfolio returns and their performance by simply constructing portfolios with more risk. In order to control for the level of risk in the portfolio, Sharpe's Measure was used to adjust the portfolio returns by the portfolio risk. Bodine, Kane and Marcus (2005) define Sharp's Measure as M = E([r.sub.p])-r/[[sigma].sub.p] Sharpe's measure is frequently used as a portfolio performance measure because it provides a risk adjusted investment portfolio performance measure instead of simple portfolio returns.

The only type of team with a factor that significantly affected Sharpe's measure was aggressive/defensive styled virtual teams. As table five and graph five indicate, the highest scoring aggressive team member on a virtual team has a significant influence on the team's portfolio performance as indicated by Sharpe's measure. On the other hand, the maximum F2F team member's aggressive style score had no impact on the team's portfolio performance as measured by Sharp's measure. No other team member's maximum or minimum score on any team style had an impact on Sharpe's measure. This result is difficult to interpret given our current level of understanding of group processes in virtual teams.

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LIMITATIONS OF THIS STUDY

The most important limitation of this study is the use of students as participants, which brings into question the external validity of the study. Whether or not the conclusions of this study can be generalized to the "real world" is the main concern. While our participants were not professional investors or investment advisors, they were mature adults with significant real world professional work experience and high levels of education, all of which should mitigate some of the limitation. Both a limitation and strength of this study is that the noise and "political" processes of real organizations did not exist in this study. Organizational processes can completely overwhelm, or camouflage the important underlying group and decision making processes which were measured in this study.

RECOMMENDATIONS FOR FURTHER STUDY

This study points to a problem with the use of the GSI technique of averaging the scores of the individuals on the team in order to get the "team" scores on the twelve dimensions of the Group Style Inventory. Ladbury and Hinsz (2009) found there is an overestimation of the influence of the majority influence in a group decision process. Similarly, this study found a systematic effect on returns resulting from the minimum individual's score, the maximum individual's score or the range in scores on a team, depending on the team type. Kerr and Tindale (2004) also point out there are problems with using means to represent a group. Future studies should look not only at the mean scores, but also the minimum, maximum and range. Additional studies need to be conducted to search for the systematic effect on team performance of these minimum, maximum and range scores. Having one person on a team with extreme scores in any of the twelve GSI dimensions may cause problems with team performance, especially F2F team performance where individual team members can gain more traction. It is not just the average scores that matter.

Other recommendations for future studies include investigating different types of decisions to see if the results from this study are the same with other types of decisions. There are numerous individual differences, such as emotional/social competence, self-monitoring scores and the like that may significantly and systematically impact both the teaming and decision making processes that should be studied in conjunction with team style, team type, and decision type.

CONCLUSION

In this study the type of team, F2F or VT, did not systematically affect the performance of the investment teams as measured by return on portfolio investment or Sharpe's measure, but there were other factors which did. While team type did not affect the performance measures, team type certainly could have affected the variables which did. There are interactions between team type and characteristics of group style that result in differences in investment team performance between VT and F2F teams. For example, virtual teams tend to create an environment more conducive to passive/defensive, and aggressive/defensive behaviors and F2F team environments tend to nurture more constructive styles (Branson, Clausen, & Sung 2008). In line with Leivens, Conway and DeCorte (2008) this study found that team based culture does matter. The differences in team type (F2F or VT) appear to create a social environment (team culture) in which individuals who score either high or low on certain group characteristics (as measured by the GSI) have more influence in a F2F team than in a VT.

REFERENCES

Baltes B. B., Dickson, M. W., Sherman, M. P., Bauer, C. C., & LaGanke, J. S. (2002). Computer-mediated communication and group decision making: A meta-analysis. Organizational Behavior and Human Decision Processes, 87, 156-179.

Barber, B., & Odean, T. (2000). Too many cooks spoil the profits: The performance of investment clubs. Financial Analyst Journal, 56, 17-25.

Benbunun-Fich, R., Hiltz, S., & Turoff, M. (2002). A comparative content analysis of face-to-face vs. asynchronous group decision making. Decision Support Systems, 34, 457-469.

Bodine, Z., Kane, A., & Marcus, A., (2005). Investments 6e. New York: McGraw-Hill Irwin.

Branson, L., Clausen, T., & Sung, C. (2008). Group style differences between virtual and F2F teams. The American Journal of Business, 23(1): 65-70.

Branson, L., Moe, B., & Sung, C. (2005). An empirical investigation of information processing in individuals, and virtual teams. Journal of Business and Behavioral Sciences, 13(1): 234-242.

Branson, L., Steele, N.L., & Sung, C. (2010a). When two heads are worse than one: Impact of group style and information type on performance evaluation. Journal of Business and Behavioral Sciences, 22(1): 75-84.

Branson, L., Steele, N.L., & Sung, C. (2010b). Differences in the decision making of constructively styled virtual and face-to-face teams regarding performance appraisal. International Journal of Business and Public Administration, 7(2): 11-22.

Branson, L., Sung, C., Decker, J., & He, F. (2005). An empirical investigation of the role of accounting outcome information and collocated and virtual team processes on decision making. Proceedings of American Academy of Accounting and Finance, St. Pete Beach, Florida, December 10, 2005.

Branson, L., & Sung, C. (2004). An empirical investigation of the role of accounting outcome information and group decision making in mitigating the affect of stereotyping on performance appraisal. Proceedings of the American Society of Business and Behavioral Sciences, Las Vegas, NV, 2004.

Cooke, R., & Lafferty. J. C. (2003). Group Style Inventory. Chicago: Human Synergistics, Inc.

Cooke, R. A., & Szumal, J. L. (1994). The impact of group interactions styles on problem-solving effectiveness. Journal of Applied Behavioral Science, 30, 415-437.

Frey, D., Luthgens, C., & Schulz-Hardt, S. (2000). Biased information search in group decision making. Journal of Personality and Social Psychology, 78(4): 655-669.

Kerr, N. L., MacCoun, R. J., & Kramer, G. P. (1996). Bias in judgment: Comparing individuals and groups. Psychological Review, 103, 687-719.

Kerr,N. L., & Tindale, R. S. (2004). Group performance and decision making. Annual Review of Psychology, 55, 623-655.

Ladbury, J. L., & Hinsz, V. B. (2009). Individual expectations for group decision processes: Evidence for overestimation of majority influence. Group Dynamics: Theory, Research and Practice, 4(4): 235-254.

Lievens, F., Conway, J. M., & DeCorte, W. (2008). The relative importance of task, citizenship and counterproductive performance on job performance ratings: Do rater source and team-based culture matter? Journal of Occupational and Organizational Psychology, 81, 11-27.

Potter, R. E, & Balthazard, P. A. (2002). Virtual team interaction styles: Assessment and effects. International Journal of Human-Computer Studies, 56, 1-21.

Rockenbach, B., Sadrieh, A., & Mathauschek, B. (2007). Teams take the better risks. Journal of Economic Behavior and Organization, 63, 412-422.

Leonard Branson

Feng-Shun Bin

Chung-Hsien Sung

University of Illinois Springfield

Fang He

Washington University

About the Authors:

Leonard Branson is Chair of the Department of Accountancy, and Professor of Accountancy at University of Illinois Springfield. His research interests include the role of financial and non-financial information in individual and group decision processes; and in the influence of cognitive and social (group) processes on decision making.

Feng-Shun Bin is Associate Professor of Finance at the University of Illinois Springfield. His research interests include the influence of financial information on an array of financial decisions, including investment decisions.

Chung-Hsien Sung is Chairman of the Mathematical Sciences Department at the University of Illinois Springfield. As a mathematical statistician his research interests include the application of mathematical statistics to the discovery of important patterns and relationships.

Fang He, whose doctorate is in Management Information Systems, has research interests which include the impact of technology on human decision processes and other human and organizational relationships.
Table 1
Returns and Minimum Constructive Style Scores

Model         Parameter        Estimate     t statistic   P-value

F2F Team      Intercept       2.298364420      2.15       0.0426
           [(ConMin).sup.3]   0.000030011      2.83       0.0097

Virtual       Intercept       3.178333333      3.87       0.0008
Team

Table 2
Returns and Maximum Aggressive Style Scores

Model      Parameter    Estimate     t statistic   P-value

F2F Team   Intercept   10.26760890      3.53       0.0019
             ADMax     -0.12531531      -2.13      0.0449

Virtual    Intercept   3.178333333      3.64       0.0014
Team

Table 3
Returns and Range of Passive Scores

Model      Parameter     Estimate     t statistic   P-value

F2F Team   Intercept   9.275046941       4.09       0.0005
            PDRange    -0.183855617      -2.34      0.0291

Virtual    Intercept   3.178333333       3.70       0.0012
Team

Table 4
Returns and Risk Tolerance

Model         Parameter         Estimate      t statistic   P-value

F2F Team      Intercept      -6.5917945414       -3.04      0.0062
           [(RTMax).sup.3]    0.0011671300       5.24       0.0001

Virtual       Intercept      -28.1794541400      -1.79      0.0885
Team        [(RTAverage)      7.4458317700       1.99       0.0598
              .sup.1/2]

Table 5
Sharpe's Measure, Team Style, and Team Type

Model          Parameter     Estimate         t       P-value
                                          statistic

F2F Team       Intercept   1.591538462      4.83      0.0001
Virtual Team   Intercept   -3.695776328     -1.84     0.0787
                 ADMax     0.088466433      2.41      0.0247
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Author:Branson, Leonard; Bin, Feng-Shun; Sung, Chung-Hsien; He, Fang
Publication:International Journal of Business, Marketing, and Decision Sciences (IJBMDS)
Geographic Code:1USA
Date:Mar 22, 2011
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