Understanding the determinants of company data trading intention: a supply-side perspective

Purpose Data trading offers companies a chance to generate value from their data, yet many companies remain reluctant to sell data. This study aims to explore the determinants influencing companies' intentions to trade data from a supply-side perspective. Design/methodology/approach Drawing upon the fit-viability model (FVM) and the dual value model, we propose a comprehensive theoretical framework. We define three core dimensions of fit–viability: technology fit, environment fit and viability. Additionally, we examine both functional and symbolic benefits as key considerations in the decision-making process. The moderating effects of company size and industry background are also assessed. Employing partial least squares structural equation modeling, we analyze survey data from 221 managers involved in data-related roles. Findings Functional and symbolic benefits significantly increase willingness to trade data. Technology fit, environment fit and viability all enhance functional benefit; environment fit and viability also boost symbolic benefit. Both benefits mediate between fit-viability and data trading intention. In non-ICT companies, technology fit notably strengthens symbolic benefit; in information and communication technology (ICT) companies, this effect is negligible. Environment fit has a stronger effect on symbolic than functional benefit. Larger companies display a weaker link between environment fit and symbolic benefit. Originality/value This study advances the literature on data monetization and trading by illuminating new determinants, mechanisms and boundary conditions. Its findings offer actionable insights for business managers and data trading platforms to foster data trading.