Personnel capabilities and the quality of big data marketing analytics (BDMA)

Purpose This paper examines the impact of personnel knowledge on the quality of big data marketing analytics (BDMA) against the backdrop of the knowledge-based theoretical framework. Design/methodology/approach This study employs a cross-sectional survey conducted among marketing professionals in companies that have reached at least the limited deployment level of BDMA. The sample (N = 236) comprised respondents from Canada or the United States. The data were analyzed using PLS-SEM. Findings Business and marketing knowledge emerged as the most crucial factor contributing to the quality of the BDMA, followed by technology management and relational knowledge. Technical knowledge was deemed unrelated to the quality of the BDMA. However, all knowledge constructs were necessary conditions for the quality of marketing analytics to manifest. The research model also indicated that the quality of BDMA was effectively measured through information quality (i.e. completeness, currency, format, and accuracy) and technology quality (i.e. reliability, adaptability, integration, and privacy). Originality/value This paper examines the quality of marketing analytics (MA) as a multi-dimensional higher-order construct and employs PLS-SEM to identify the essential components contributing to a system for high-quality BDMA based on the knowledge constructs of personnel in business/marketing, technical fields, technology management, and relational aspects. Furthermore, the sampling frame included marketing professionals rather than solely IT personnel, highlighting the differences in the provider/user context and potential perceptual variations. All identified knowledge constructs were found to be necessary conditions.