Environmental air pollution management system: Predicting user adoption behavior of big data analytics

Abstract This study has a dual purpose: to explore the novel phenomenon of a big data analytics-environmental air pollution (BDA-EAP) management system, and to propose a research model of factors influencing adoption of such a system. The research model is based on task-technology fit (TTF) and unified theory of acceptance and use of technology (UTAUT) concepts. A comprehensive BDA-EAP management system is proposed and the potential adoption speed of such a system evaluated by sending structured questionnaires to the employees of relevant environmental agencies, yielding 412 valid responses, using the structural equation modeling approach. The results of the study predict that factors of TTF including task characteristics and technology characteristics are strong influencers of TTF, and TTF is a strong predictor of the behavioral intention of users to adopt a BDA-EAP management system. The results demonstrated that the combination of TTF and UTAUT is a stronger predictor of behavioral intention than either TTF or UTAUT alone. Furthermore, resistance to change negatively moderates and extrinsic motivation positively moderates the significant positive relationship between behavioral intention and adoption of a BDA-EAP management system. Meanwhile, behavioral intention, resistance to change, and extrinsic motivation have a significant three-way interaction impact on adoption of a BDA-EAP management system such that an increase in users’ extrinsic motivation will decrease the negative impact of resistance to change during the process of adoption. The study findings contribute to the literature regarding the use of BDA to manage EAP, and provide a basis for future research in this area.