Recent headlines predict that artificial intelligence, machine learning, predictive analytics and other aspects of cognitive computing will be the next fundamental drivers of economic growth (Brynjolfsson & McAfee, 2017). We have evidenced several success stories in the recent years, such as those of Google and Facebook, wherein novel business opportunities have evolved based on data-driven business innovations. Our directional poll among companies, however, reveals that at present, only few companies have the keys to successfully harness these possibilities. Even fever companies seem to be successful in running profitable business based on data-driven business innovations. Company’s capability to create data-driven business relates to company’s overall capability to innovate. Therefore, this research builds a conceptual model of barriers to data-driven business innovations and proposes that a deeper understanding of innovation barriers can assist companies in becoming closer to the possibilities that data-driven business innovations can enable. As Hadjimanolis (2003) suggests, the first step in overcoming innovation barriers is to understand such barriers. Consequently, we identify technology-related, organizational, environmental and people-related i.e. attitudinal barriers and examine how these relate to company’s capability to create data-driven business innovations. Specifically, technology-related barriers may originate from the company’s existing practices and predominant technological standards. Organizational barriers reflect the company’s inability to integrate new patterns of behavior into the established routines and practices (Sheth & Ram, 1987). Environmental barriers refer to various types of hampering factors that are external to a company. Environmental barriers are caused by the company’s external environment and thus company has relatively limited possibilities to influence and overcome such factors. Attitudinal barriers are people-related perceptual barriers that can be studied at the individual level, and if necessary, separately for managers and employees (Hadjimanolis, 2003). Future research will pursue to build an empirical model to examine how these different barriers are related to company’s capability to create business based on data-driven innovations.

The objective of this study is to test how five theory-driven adoption barriers and three key consumer demographics influence consumer adoption versus rejection decisions in two seemingly similar service innovations. The earlier literature on innovation diffusion recognizes two streams of research: one focusing on innovation adoption and acceptance of innovations, and the other stream, though less traveled, calling attention to innovation resistance. All innovations face a certain degree of resistance among consumers depending on consumer characteristics and the innovation itself. The literature argues that consumers can simultaneously express views that are both favorable and unfavorable towards the innovations (Ferreira, da Rocha, & da Silva, 2014) and thus both resistance and adoption can coexist during the lifetime of an innovation (Ram, 1987). Thus it is reasonable to explore how innovation resistance influences consumer decisions in different service innovations. Initially scholars explained resistance to innovations through two constructs, habit or satisfaction with an existing behavior and perceived risks associated with innovation adoption (Sheth, 1981). Ram and Sheth (1989) provide a more comprehensive view to the phenomenon by explaining consumer resistance through functional and psychological barriers that they further divide into five distinct barriers, namely usage, value, risk, tradition and image. This study tests how these five adoption barriers as well as three consumer demographics, gender, age, and income, influence consumer adoption versus rejection decisions in Internet and mobile banking. An effective total sample size of 1,736 consumer responses were collected from Finland. Logistic regression analysis finds that the value barrier is the strongest inhibitor of Internet and mobile banking adoption. In addition, while the image barrier slows down mobile banking adoption, the tradition barrier explains the rejection of Internet banking. In addition, age greatly explains this behavior and the results show that younger segments have a significantly greater likelihood of Internet banking adoption than their older counterparts. Contrary to Internet banking, it appears that gender significantly contributes to mobile banking adoption and the intention to use it. The results predict that males have nearly two times greater likelihood towards adoption compared to females.