Utilizing Text Mining and Multi-Criteria Decision Making to Construct an Evaluation Model for CEO Selection and Sustainable Development

Abstract

The selection of a chief executive officer (CEO) has a crucial and decisive impact on the operational sustainability, organizational performance, and even the survival of a company. To ensure organizational success, board members and selection committees must ensure that the outcome of the CEO selection process can satisfy the needs for current and future development. Unfortunately, systematic research-based evaluation models to assist boards or selection committees in making informed CEO selection decisions are lacking. This study extracts data from 220 journal articles and 18 other sources related to the essential competencies required for CEO selection. Big data text mining techniques are employed such as CKIP segmentation, text matrix analysis, feature word extraction, and Term Frequency-Inverse Document Frequency (TF-IDF) term weighting calculations to identify the critical factors for CEO selection from which construct an evaluation framework. Finally, the Multi-Criteria Decision Making (MCDM) methods are applied to analyze the CEO selection decision-making process. It is hoped that the results will contribute to the formulation of strategies for sustainable development of business strategies and the essential process of selecting a CEO for sustainable business operations aligned with the decision-making environment and strategic objectives of the company.