This study aims to examine the factors influencing professional users’ intention to use artificial intelligence (AI)-enabled chatbots in business-to-business (B2B) customer service within the industrial electrical equipment sector. It also evaluates the added explanatory value of incorporating trust in AI and perceived privacy risk in AI into an extended unified theory of acceptance and use of technology 2 (UTAUT2) model, addressing a relevant gap in B2B and industrial marketing literature where chatbot adoption remains underexplored.
A quantitative study was conducted using data from 105 professional users in Spanish small and medium-sized enterprises and B2B firms operating in the electrical equipment industry. Partial least squares structural equation modelling was used to assess the measurement and structural models, supported by out-of-sample predictive validation (PLSpredict) to test predictive accuracy.
Research results show that performance expectancy, effort expectancy, social influence and habit significantly influence behavioural intention, with performance expectancy emerging as the most influential predictor. In contrast, facilitating conditions, trust in AI and perceived privacy risk in AI show no significant effects. These null effects suggest that individual professionals may place less emphasis on individual trust and privacy evaluations, potentially relying instead on organizational safeguards and established digital infrastructures. The final model demonstrates substantial explanatory power and high predictive validity.
This research provides a contextualized refinement of UTAUT2 for industrial B2B environments, explaining why certain adoption drivers behave differently than in consumer settings. It advances theory by clarifying the boundary conditions under which trust in AI and privacy risk in AI matter in professional chatbot use. It also offers novel empirical evidence from a highly technical industrial context, highlighting how standardized service processes shape chatbot acceptance and how conversational AI becomes embedded in organizational tasks. This study contributes to B2B marketing by integrating behavioural, organizational and technological perspectives to explain early-stage AI adoption in industrial customer service.
