Influence Of Workforce Diversity On Team Cohesion Of Employees In Kenyan Private Universities: Evidence From Kabarak University

Workforce diversity provides an environment for employees to learn more from each other thus making an institution stronger in terms of tolerance, ideas, innovation and creativity. The changing demographics in workforce composition in Kenya not only increases the amount of diversity that institutions need to manage and integrate but also affects business operations and productivity that inevitably affects the overall well-being of the country. This study sought to determine the influence of workforce diversity on team cohesion of employees in Kenyan private universities: evidence from Kabarak University, and was anchored on four objectives; to examine the influence of gender diversity on team cohesion among employees at Kabarak University, to assess the influence of ethnic diversity on team cohesion among employees at Kabarak University, to determine the influence of age diversity on team cohesion among employees at Kabarak University and to assess the influence of religion diversity on team cohesion among employees at Kabarak University. The study was guided by the Resource Based Theory of Diversity Management (RBT) and the Team Development Model of team development and cohesion. The study adopted a descriptive research design to give an understanding of workforce diversity and team cohesion. The researcher adopted a case study model. Stratified random sampling was used to draw a sample size of 149 from the target population of 167 teaching staff and 274 non-teaching staff. The Nassiuma (2000) equation was applied to calculate the appropriate sample. Primary data was collected using a structured questionnaire and was analyzed using both descriptive statistics and inferential statistics. Descriptive statistics, mainly, frequencies and percentages were used in this study and the findings were presented using tables and graphs while the inferential statistics was analyzed in the form of correlation and multiple linear regression.