A methodology for controlled empirical investigation of membership continuity and change in GDSS groups

Mathiyalakan, Sathasivam

doi:10.1016/s0167-9236(01)00115-4

Cited by 10 publications

(4 citation statements)

References 32 publications

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“…Investigating creative design teams, Choi and Thompson (2005) point out that most studies (Rogers et al 1961, Goodman and Leyden 1991, Pisano et al 2001, Mathiyalakan 2002) report negative effects of membership change on team performance because they focus on task co-ordination. Building on Ziller's (Ziller et al 1961, 1962, Ziller 1965 research, Choi and Thompson (2005) investigate the effects of membership change on the performance of creative teams, and report that the reduction in level of team familiarity may have a positive impact on the team's performance.…”

Section: Discussionmentioning

confidence: 99%

Computational studies to understand the role of social learning in team familiarity and its effects on team performance

2012

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This paper concerns social learning modes and their effects on team performance. Social learning, such as by observing others' actions and their outcomes, allows members of a team to learn what other members know. Knowing what other members know can reduce task communication and co-ordination overhead, which helps the team to perform faster since members can devote their attention to their tasks. This paper describes agent-based simulation studies using a computational model that implements different social learning modes as parameters that can be controlled in the simulations. The results show that social learning from both direct and indirect observations positively contributes to learning about what others know, but the value of social learning is sensitive to prior familiarity such that minimum thresholds of team familiarity are needed to realise the benefits of social learning. This threshold increases with task complexity. These findings clarify the level of influence that sociality has on social learning and sets up a formal framework by which to conduct studies on how social context influences learning and group performance.

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Section: Discussionmentioning

confidence: 99%

Computational studies to understand the role of social learning in team familiarity and its effects on team performance

2012

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“…Vote on the MAS member is a game. Agents don't know each other contents of the voting, independent with the last game, their condition and other information known to the whole system, to make a favorable judgment [15,16].…”

Section: Mas Decision Rules For Members In the Algorithmmentioning

confidence: 99%

The Research on Mass-MAS Parallel Decision in Integrated Infrastructure for Agile Enterprise

Su¹

2015

TOCSJ

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The Information Economics and its platform can input the vigor into manufacturing industry, i.e. garment industry, and help to increase its ability for good return, can promote the level of technology and management of Garments from a labor-intensive industry, and can move it toward the accurate management. The platform is designed as Comprehensive Information Platform by our research team. This paper is one of the series of papers about the design of Agile Infrastructure for Collaborative Manufacturing as well as Agile Supply Chain. This one lays emphasis upon the design of the optimal production planning algorithm to keep the manufacture financing stability in product cycle. We mainly concern and research into the research on Mass-MAS parallel decision in integrated infrastructure for agile enterprise, including the analysis of information exchange rules for MAS members, individual behavior rules and its correction by environment driven, MAS decision rules for members in the Algorithm, the solution to the problem & algorithm design in the decision-making operation burden for IIAE. Their logical relationship and flow chart designs are elaborated in turn. The paper tries to introduce the research of Agile Infrastructure and its key technologies, showing how well they work in some traditional manual industries.

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“…Experimental work on the effects of finite membership changes in teams has been mixed. Some studies have indicated that changes in membership are disruptive, decrease productivity and increase the time it takes to solve problems (Ancona & Caldwell, 1998; Goodman & Leyden, 1991; Mathiyalakan, 2002). Conversely, others have suggested that under some circumstances, membership changes could be advantageous (Choi & Thompson, 1995; Guzzo & Dickson, 1996).…”

Section: Studying Changes In Group Composition Over Timementioning

confidence: 99%

“…In several areas of research, most notably in industrial/organizational psychology (IOP) and in addictions treatment research, there have been calls for advances in modeling the impact of turnover in membership among dynamic groups. In IOP, group dynamics researchers have theorized about the impact of turnover, but the empirical work centers primarily on finite (i.e., one-time changes in group membership) manipulations of group changes (Ancona & Caldwell, 1998; Goodman & Leyden, 1991; Mathiyalakan, 2002). Among group therapy researchers, the dearth of approaches to modeling turnover in treatment group membership under “ecologically valid” treatment protocols (i.e., continual addition and removal of treatment group members) has forced investigators to conduct research on treatment protocols that are inconsistent with the way treatment is conducted in community settings (i.e., individual therapies, closed enrollment groups; Morgan-Lopez & Fals-Stewart, 2006).…”

Section: Motivation For the Present Studymentioning

confidence: 99%

Capturing the impact of membership turnover in small groups via latent class growth analysis: Modeling the rise of the New York Knicks of the 1960s and 1970s.

Morgan‐López¹,

Cluff²,

Fals‐Stewart³

2009

Group Dynamics: Theory, Research, and Practice

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In several areas of research, most notably in industrial/organizational psychology and in addictions treatment research, there have been calls for advances in modeling the impact of turnover in membership within dynamic small N groups. The present study examines the utility of latent class growth analysis (LGCA; in combination with other approaches) in modeling turnover in group membership within a dynamic work group from popular culture: the National Basketball Association's (NBA) New York Knickerbockers (Knicks) of the 1960s and 1970s. Changes over time in the proportions of 5 longitudinal player efficiency classes accounted for variability in regular season wins, number of NBA All-Star Game appearances and the proportion of (eventual) basketball Hall-of-Fame members across a 20-year span. Using LCGA, there was a clear match between the empirical model and the "historical reality" with respect to identifying the players that were responsible for the success of the Knicks, particularly in the early 1970s. This study provides a practical example of how LCGA may be used to capture the impact of turnover in group membership on group-level outcomes across multiple areas in which groups with dynamic membership are studied.

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A methodology for controlled empirical investigation of membership continuity and change in GDSS groups

Cited by 10 publications

References 32 publications

Computational studies to understand the role of social learning in team familiarity and its effects on team performance

Computational studies to understand the role of social learning in team familiarity and its effects on team performance

The Research on Mass-MAS Parallel Decision in Integrated Infrastructure for Agile Enterprise

Capturing the impact of membership turnover in small groups via latent class growth analysis: Modeling the rise of the New York Knicks of the 1960s and 1970s.

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