robmed (version 0.6.0)

BSG2014: Business simulation game data

Description

The data were collected from 354 senior business administration students during a business simulation game at a Western European University.

The game was played for a total of 12 rounds (i.e., two separate games of 6 rounds) as part of the capstone strategy class. Students were randomly assigned to teams of four, and surveyed in three waves: prior to, during, and after the simulation game (with different variables being surveyed in the different waves).

The 354 students formed 92 teams, and the responses of individual students were aggregated to the team level. Leaving out teams with less than 50 percent response rate yields \(n = 89\) teams. Only a small subset of the collected variables are included here.

Usage

data("BSG2014")

Format

A data frame with 89 observations on the following 7 variables.

ProcessConflict

Process conflict was measured with the three item scale of Jehn (1995) and responses were aggregated.

SharedExperience

Teams were randomly formed, no prior shared group experience is expected and shared group experience and training is developed during the first game for the second game. Team performance in the first game, which was determined by objective performance measures, is a good proxy for the level of shared group experience and training.

TaskConflict

Task conflict was operationalized with the intra-group conflict scale of Jehn (1995). Five items on the presence of conflict were rated on a 5-point Likert scale (1 = none, 5 = a lot) and aggregated.

TeamCommitment

Team commitment was measured by four items based on Mowday, Steers & Porter (1979) and responses were aggregated.

TeamPerformance

Team performance in the second game was measured subjectively by the team members<U+2019> perceptions of the team<U+2019>s functioning. Hackman<U+2019>s (1986) Likert scale items were thereby used to operationalize team performance.

TMS

Transactive memory systems (TMS) are defined as shared systems that people in relationships develop for encoding, storing, and retrieving information about different substantive domains. TMS was operationalized with Lewis<U+2019> (2003) 15-item scale that measures the three sub-dimensions of TMS (credibility, specialization and coordination). Team members responded on a 5-point scale (1 = strongly disagree, 5 = strongly agree). Following Lewis (2003), the three sub dimensions were aggregated to form the TMS construct.

ValueDiversity

Value diversity was operationalized with the short version of Schwartz<U+2019>s Value Survey (SVS) to measure team members<U+2019> individual values (Lindeman & Verkasalo, 2005). The responses were aggregated with the average of the coefficient of variations of each value dimension among team members.

References

Hackman, J.R. (1986) The Psychology of Self-Management in Organizations. In Pallack, M.S and Perloff, R.O. (Eds.), Psychology and Work: Productivity, Change, and Employment, 89--136. Washington, DC: American Psychological Association.

Jehn, K.A. (1995) A Multi-Method Examination of the Benefits and Detriments of Intra-Group Conflict. Administrative Science Quarterly, 40(2), 256--285.

Lewis, K. (2003) Measuring Transactive Memory Systems in the Field: Scale Development and Validation. Journal of Applied Psychology, 88(4), 587--604.

Lindeman, M. and Verkasalo, M. (2005) Measuring Values With the Short Schwartz's Value Survey. Journal of Personality Assessment, 85(2), 170--178.

Mowday, R.T., Steers, R.M. and Porter, L.W. (1979) The Measurement of Organizational Commitment. Journal of Vocational Behavior, 14(2), 224--47.

Examples

# NOT RUN {
data("BSG2014")
summary(BSG2014)

## scatterplot matrices for three illustrative mediation analyses

# empirical case 1
x <- "SharedExperience"
y <- "TeamPerformance"
m <- "TMS"
plot(BSG2014[, c(x, y, m)], pch = 21, bg = "black")

# empirical case 2
x <- "ValueDiversity"
y <- "TeamCommitment"
m <- "TaskConflict"
plot(BSG2014[, c(x, y, m)], pch = 21, bg = "black")

# empirical case 3
x <- "ValueDiversity"
y <- "TeamPerformance"
m <- "ProcessConflict"
plot(BSG2014[, c(x, y, m)], pch = 21, bg = "black")
# }