From analytics personnel knowledge to user satisfaction: the mediating role of perceived value for money in Big Data Marketing Analytics (BDMA)

Purpose This research aims to understand how analytics personnel's technical and technology management knowledge contributes to user-related measures from the viewpoint of marketing managers using Big Data Marketing Analytics (BDMA) systems, drawing on value theory and the information systems success model. Design/methodology/approach A cross-sectional survey was conducted to collect data from marketing professionals (N = 236) employed by companies that are at least at the limited deployment stage of BDMA. The sampling method is classified as purposive, as specific criteria were used to select participants for feedback (Qualtrics, 2025). Data on satisfaction, perceived value for money, and personnel's technical and technology management knowledge were gathered. PLS-SEM and necessary condition analysis (NCA) were used as statistical tools. Findings Both technical and technology management knowledge are positively related to perceived value for money, which in turn is positively related to user satisfaction. This confirms that investing in personnel's technical and technology management knowledge is critical to enhancing the relevant user-centric measures. NCA indicates that both technical and technology management knowledge are necessary, must-have conditions for improving the value of money. Originality/value This study advances BDMA research by highlighting the critical, yet underexplored, role of analytics personnel's technical and technology management knowledge as key human capital capabilities shaping user-centric outcomes. Drawing on value theory and the information systems success model, it introduces a mediating framework linking personnel expertise to user satisfaction through perceived value for money. The study also contributes methodologically by combining PLS-SEM and NCA, enabling the distinction between sufficient and necessary conditions. This dual approach provides a more nuanced understanding of how human capital capabilities influence BDMA effectiveness beyond traditional system- and data-centric perspectives.