Unpacking associations between positive-negative valence and ambidexterity of big data. Implications for firm performance: Positive-negative valence and ambidexterity of big data

Motivated by the importance of big data utilization and its impact on firm performance, the current study examines the influence of the perceived valence factors of the top management team (TMT) on the ambidexterity of big data utilization and firm performance. Despite the growing recognition of the significance of ambidexterity and the role of TMTs in leveraging big data, there remains a lack of empirical research that comprehensively examines the associations between TMT valence factors, the ambidexterity of big data utilization, and firm performance. By integrating valence theory and ambidexterity theory, this study fills this research gap and provides valuable insights into the relationship between TMT valence factors, big data utilization, and firm performance outcomes. Data were collected from 357 respondents, and the findings indicate that positive TMT valence factors – such as data proficiency, industry expertise, and knowledge diversity – as well as negative valence factors – such as data compatibility, complexity, and benefit disconfirmation – are negatively associated with ambidexterity. Furthermore, the findings of our study have important implications for organizations seeking to enhance their operational and financial performance through the effective utilization of big data. Notably, our results highlight that promoting ambidexterity in handling big data within firms results in improved operational and financial outcomes. These findings provide valuable insights into the relatively unexplored area of TMT valence factors and their impact on driving ambidexterity in big data utilization, ultimately leading to enhanced organizational performance.