Predictive data analytics for contract renewals: a decision support tool for managerial decision-making
| Autor: | Serhat Simsek, Tyler Custis, Marina Johnson, Stephan Weikert, Abdullah Albizri |
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| Rok vydání: | 2020 |
| Předmět: |
Decision support system
Knowledge management business.industry Computer science 05 social sciences General Decision Sciences Cognition 02 engineering and technology Predictive analytics Design science Organizational performance Conceptual framework Analytics Management of Technology and Innovation 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Design science research business 050203 business & management Information Systems |
| Zdroj: | Journal of Enterprise Information Management. 34:718-732 |
| ISSN: | 1741-0398 |
| DOI: | 10.1108/jeim-12-2019-0375 |
| Popis: | PurposePredictive 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/approachThis 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.FindingsThere 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/valueThis 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. |
| Databáze: | OpenAIRE |
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