Predicting Barriers to Talent Management Implementation in Saudi Universities: A Stochastic Modeling Approach

Talent management (TM) has become vital to higher education institutions (HEIs) in Saudi Arabia as they endeavor to attain the goals of Vision 2030. However, there are several barriers to effective implementation of TM that impact on organizations and competitive advantage. The study seeks to predict and estimate the barriers to implementation of the TM practices in Saudi Universities using the stochastic modeling procedure, and provide a new statistical way to analyze the temporal dynamics of TM barriers. This study adopted a cross-sectional, exploratory design with a total of 450 faculty and administrators from different Saudi Universities, with a response rate of 425 faculty and administrators (N=425). A content validated questionnaire tool with 26 items representing the TM barriers was adapted and applied to the five parameters: Identifying Critical Job Positions, Talent Acquisition Practices, Competency Training, Talent Development and Rewards Management. The progression of barriers with time has been assessed using the Modified Weibull Distribution and the predictive models have been developed. The influence of this category on the other categories was shown by the results of the stochastic model, which gave a high value for the ICJP barrier of ($\alpha_{1}=3.421$). The TAP barrier parameter ($\alpha_{2} = 1.783$) was much smaller than the CT barrier parameter ($\beta_{1} = 2.568$) and TD barrier parameters ($\beta_{2}= 2.847$). The lowest influencing parameter found is the RM barrier parameter ($\mu = 1.500$). Systemic barriers must be removed, with a strategy around the allocation of resources, policy adjustment and culture change. By using the stochastic modeling approach, university leaders can have a quantitative forecasting tool ahead of time to proactively manage potential barriers, helping to develop an evidence-based TM strategy.