ArticleBMC psychology2023
An empirical link between motivation gain and NBA statistics: applying hierarchical linear modelling.
Article in BMC psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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2 authors.
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Abstract
backgroundThis study tested whether the motivation gain in groups is the result of social compensation or the Köhler effect by examining scaled individual salaries of National Basketball Association (NBA) players. Both factors explain the positive effects of a group, unlike social loafing. However, differing causes in motivation gain relate to whether players are low or high performers and the Köhler effect or social compensation.
methodsTo test motivation gain, this study used 11-year NBA statistical data of 3247 players by applying hierarchical linear modelling (HLM) and HLM 7.0 was used for analysis. The players' individual statistics and annual salaries were collected from the NBA and ESPN websites, respectively. Whereas previous studies have looked at motivation gain through track-and-field and swimming relay records, this study verified motivation gain through salary variations among NBA players and their affiliated teams.
resultsThe high performers, while selecting teams with larger performance gaps among team members, earned a higher salary than while selecting teams with lower performance gaps among team members. This study found that motivation gain existed in high performers, which can be interpreted as support for social compensation rather than the Köhler effect.
conclusionsWe used our result to elucidate the basis for play-by-play decisions made by individuals and team behaviour. Our results are applicable for the enhancement of coaching strategies, ultimately improving team morale and performance. It can be interpreted that the motivation gains of high performers in the NBA are driven by the Cost Component of the Team member Effort Expenditure Model (TEEM), rather than the Expectancy and Value Components.
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