Predictive data analytics for contract renewals: a decision support tool for managerial decision-making. Issue 2 (30th September 2020)

• Record Type: Journal Article
• Authors: Simsek, Serhat; Albizri, Abdullah; Johnson, Marina; Custis, Tyler; Weikert, Stephan
• Is Part Of: Journal of enterprise information management. Volume 34:Issue 2(2021)
• Page Start: 718
• Page End: 732
• Publication Date: 2020-09-30
• Subjects: Machine learning -- Sports analytics -- Design science -- Cognitive analytics management; Management information systems -- Periodicals; Business logistics -- Periodicals; Business -- Data processing -- Periodicals; Management -- Data processing -- Periodicals; 658.05
• DOI: 10.1108/JEIM-12-2019-0375
• Languages: English
• ISSNs: 1741-0398

Abstract

Abstract : Purpose: Predictive analytics and artificial intelligence are perceived as significant drivers to improve organizational performance and managerial decision-making. Hiring employees and contract renewals are instances of managerial decision-making problems that can incur high financial costs and long-term impacts on organizational performance. The primary goal of this study is to identify the Major League Baseball (MLB) free agents who are likely to receive a contract. Design/methodology/approach: This study used the design science research paradigm and the cognitive analytics management (CAM) theory to develop the research framework. A dataset on MLB's free agents between 2013 and 2017 was collected. A decision support tool was built using artificial neural networks. Findings: There are clear links between a player's statistical performance and the decision of the player to sign a new offered contract. "Age, " "Wins above Replacement" and "the team on which a player last played" are the most significant factors in determining if a player signs a new contract. Originality/value: This paper applied analytical modeling to personnel decision-making using the design science paradigm and guided by CAM as the kernel theory. The study employed machine learning techniques, producing a model that predicts the probability of free agents signing a new contract. Also, a web-based tool was developed to help decision-makers in baseball front offices so they can determine which available free agents to offer contracts.