Marketing and information systems research has a long history of studying the customer’s acceptance and adoption process of new technologies (Venkatesh et al., 2003). These studies are often motivated by the potential of new technologies such as smart home systems to improve our everyday life. To benefit from these technologies, individuals have to accept them in the first place. To better understand the acceptance process, scholars regularly employ various theories in their studies such as technology acceptance model (Davis, 1989), innovation diffusion theory (Rogers 2003), and perceived risk theory (Featherman and Pavlou, 2003). Although each of these theories provides valuable insights, only recently scholars started combining them in larger framework and examining their interrelationships. These studies argue that each theory has the potential to make a unique contribution to understanding of technology acceptance, but the combination of theories leads to novel insights. The purpose of this study is the development of a comprehensive adoption model combining constructs from various theories and testing these theories against each other to provide new insights. The study develops this model in a smart home applications context which is a complex technological system. The study is based on an online survey consisting of 409 participants; the data is analyzed using structural equation modelling.

Worldwide, more than 1/3 of all e-commerce transactions in business-to-consumer industries are nowadays executed via mobile devices (Criteo, 2015). Despite its increasing importance, it can be noted that mobile commerce does not seem to “take-off” equally across diverse goods and services contexts. We observe, for instance, that mobile commerce is quite common in service industries for purchasing tickets (e.g., for flights, public transportation, and sport events), while it is less common for services such as financial products. Balasubramanian et al. (2002) addressed this issue and proposed that contexts of m-commerce differ from each other with regard to several characteristics. For example, they propose that location sensitivity (among other characteristics) differs for various m-commerce applications, and that this characteristic may impact acceptance of m-commerce across industries. It may be that acceptance of m-commerce is higher in some industries since use of location sensitive data is appreciated by customers, while it is not valued in other purchasing contexts. Against this background, we propose that examining (1) risk perception related to mobile commerce and (2) different types of mobile commerce applications are essential for gaining a deeper understanding of the phenomenon of differing relevance of m-commerce across industries. In particular, our study acknowledges the differential roles of the financial, performance, and security facets of risk. In addition, we assume that the role of value and risk dimensions differs subject to three mobile commerce application characteristics which are location sensitivity, time criticality, and extent of control. Based on a dataset of 800 respondents, results of our models demonstrate that especially security risk can act as a critical inhibitor of acceptance. The extent to which performance risk and financial risk impact perceived usefulness was found to be moderated by the three contextual characteristics. From a managerial perspective, results show which factors should deliberately be considered in the development of m-commerce applications, and in which different application contexts they matter.