The IoT potential economic impact is expected to fluctuate between $3.9 and $11 trillion a year by 2025 (Manyika et al. 2015). Although the clear potential of IoT market, its growth doesn't reflect the expectations. In this moment, after a decade of growth, the consumer technology market is flat (Björnsjö, Lovati, Viglino, 2016). For this reason, we will analyze why consumers do not buy smart objects, considering also the consumers¿ individual traits. In other words, the aim of this research is to identify consumers¿ barriers to the adoption and consumption of smart objects in order to understand how to remove or reduce them, and so improving the purchase intention of these products. More in details, starting from a review of both the literature about consumers¿ barriers to innovation in general, that we could call non-topic specific, and the literature about consumers¿ barriers to smart objects, that we could call topic-specific, we will identify the relevant barriers to our analysis. Then, we will identify clusters of consumers on the basis of their individual traits in order to understand if there are some barriers that ¿cut cross¿ all clusters. Finally, we will investigate more deeply these barriers because discovering how to remove them will exponentially increase the consumers'purchase intention and, consequently, the firms' profits.
Given the enormous potential of the IoT market, the slow adoption process of these products represents both a failure and a risk for the industry. Companies will likely find a high number of consumers who will be against their smart objects. Therefore, to the diffusion and the success of these products a detailed analysis about barriers to the adoption needs to be conducted. The advantages from this study are multiple. First, businesses will easily reduce odds of failure of and modify the attributes of new product to reduce or to bypass barriers, with a consequent increasing of adoption rate. Second, to date only few empirical studies have been conducted about consumers¿ adoption barriers to the innovations in general and the number of those dedicated to smart objects are even lower. Previous researches identify two main categories of barriers: functional barriers and psychological ones (Laukkanen, 2016). Some of latest studies (Hsu and Lin, 2016; Inman et al., 2017; Johnson et al., 2018; Mani and Chouk, 2016) have introduced new barriers as privacy concerns and intrusiveness. It means that on this field there is still much to investigate. Third, very few studies have been specifically dedicated to smart objects and those conducted to date have been focused on certain aspects only, for example on the variables that influence the purchase intention (Chang et al. 2014), the continue intention to use, the intention to use and the adoption (Hsu and Lin, 2016; Johnson et al., 2018), and the resistance to smart objects (Mani and Chouk, 2016); or they have been focused only on a specific kind of smart objects, for example the smartwatch (Mani and Chouk, 2016).
References abridged due to space constraints
Acquity Group (2014). Acquity Group 2014 Internet of Things Study, part of Accenture Interactive https://acquitygroup.wordpress.com/2014/08/21/acquity-group-2014-interne...
Broll, G., Paolucci, M., Wagner, M., Rukzio, E., Schmidt, A., Hussmann, H. (2009). Perci: pervasive service interaction with the Internet of Things. IEEE Internet Computing, 13(6), 74¿81.
Chang, Y. P., Dong, X. B., Sun, W. (2014). Influence of characteristics of the Internet of Things on consumer purchase intention. Social Behavior and Personality: An International Journal, 42(2), 321¿330.
Ellen, P. S., Bearden, W. O., & Sharma, S. (1991). Resistance to technological innovations: An examination of the role of self-efficacy and performance satisfaction. Journal of the Academy of Marketing Science, 19(4), 297¿307. 11
Garrido, P. C., Miraz, G. M., Ruiz, I. L., Gómez-Nieto, M. A. (2010). A model for the development of NFC context-awareness applications on Internet of Things. Second International Workshop on Near Field Communication (NFC).
Greenough, J. (2016). How the 'Internet of Things' will impact consumers, businesses, and governments in 2016 and beyond. Business insider.
Heidenreich, S., Kraemer, T., Handrich, M. (2016). Satisfied and unwilling: Exploring cognitive and situational resistance to innovations. Journal of Business Research, 69(7), 2440¿2447.
Hernández, G. (2016). The Internet of Things, Seizing the benefit and addressing the challenges, 2016 Ministerial Meeting the Digital Economy, Background Report. OECD Digital Economy Papers N. 252.
Hoffman, D. L., Novak, T. P. (2015). Emergent experience and the connected consumer in the smart home assemblage and the internet of things. The center for the connected consumer, the George Washington University School of Business.
Hsu, C. L., Lin, C. C. (2016). An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives. Computers in Human Behavior, 62, 516¿527.
Kim, K. J., Shin, D.-H. (2015). An acceptance model for smart watches: Implications for the adoption of future wearable technology. Internet Research, 25(4), 527¿541.
Marketing Management. Pearson Italia-Milano, Torino.
Kleijnen, M., Lee, N. J., & Wetzels, M. (2009). An exploration of consumer resistance to innovation and its antecedents. Journal of Economic Psychology, 30(3), 344¿357.
Laukkanen, T. (2016). Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of the Internet and mobile banking. Journal of Business Research, 69, 2432¿2439.
Mani, Z. & Chouk, I. (2017). Drivers of consumers¿ resistance to smart products. Journal of Marketing Management, 33 (1-2), 76-97.
Olson, J.M. (1992). Psychological barriers to behavior change. Canadian Family Physician, Vol. 38.
Page, A., Rosenbaum, H. (1992). Developing an effective concept testing program for consumer durables. Journal of Product Innovation Management, 9 (4), 267¿277.
Whitmore, A., Agarwal, A., Da Xu, L. (2014). The internet of Things ¿ A survey of topics and trends. Information Systems Frontiers, 17 (2), 261¿274.