The Role of User Resistance and Social Influences on the Adoption of Smartphone: Moderating Effect of Age

The Role of User Resistance and Social Influences on the Adoption of Smartphone: Moderating Effect of Age

Jaeheung Yoo (Software Policy and Research Institute, South Korea), Saesol Choi (Electronics and Telecommunications Research Institute, South Korea), Yujong Hwang (DePaul University, USA & Kyung Hee University, South Korea) and Mun Y. Yi (Korea Advanced Institute of Science and Technology, South Korea)
Copyright: © 2021 |Pages: 23
DOI: 10.4018/JOEUC.20210301.oa3
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Abstract

This study examines the factors affecting users' adoption of the smartphone as an innovative device. Prior studies on the acceptance of computing devices have primarily focused on the impact of the technological benefits and characteristics. Meanwhile, there is a lack of research approaching user resistance, which hinders the diffusion of an innovation. In particular, the smartphone is a highly communication-oriented device that people's attitude and evaluation critically influence its further diffusion. However, few studies have validated this link in the smartphone adoption context. Therefore, this study has attempted to build a research model that explains factors affecting user's resistance to smartphone adoption by integrating technological and social antecedents forming the resistance, and empirically analyzes the data obtained through a survey. As a result, the relative complexity and relative advantages presented in the theory of innovation diffusion had a direct impact on the user's resistance.
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Introduction

After the introduction of Apple’s iPhone in 2007, the mobile phone market has been rapidly replaced by the smartphone. What kind of factors affected the rapid diffusion of innovation and why did the previous version of smartphone largely led by Nokia and Blackberry failed to take off in the market? According to the Diffusion of Innovation Theory(DIT) suggested by Rogers (1995), key factors such as relative advantage, compatibility, complexity, trialability, and observability play a significant role in the diffusion of innovation. Dramatic technological enhancement in user interface, full browsing, aesthetic design, and wireless network connection have given the iPhone relative advantages compared to previous smartphone models. The graphical user interfaces of icons on the smartphone and the full browsing internet are highly compatible with the ways people use a personal computer. The dialing technique is also exactly the same in feature phones, except for the touch-screen (Oulasvirta et al. 2012). The innovative user interfaces enable the users to operate the smartphone in a more convenient way by overcoming the limitations of feature phones such as a small-sized keypad, small display, and low computing performance, as well as limited features of wireless communications.

Despite the enhancements in the technological and economic values of an innovation, the speed of diffusion depends on the user’s resistance towards the innovation (Ram and Sheth 1989). User resistance can be cognitive, affective, or opposing behaviors toward an innovation (Kim and Kankanhalli 2009). These kinds of behavior come from the users’ motivation to keep the status quo or unwillingness to change from the current position to another. User resistance increases when the expected loss is higher than the expected benefits from adopting the innovation or switching to the alternative. High uncertainties and risks regarding the performance or values of the innovation also exist (Bagozzi and Lee 1999). In order to diffuse an innovation, it is inevitable to overcome the non-adopters’ user resistance. According to Rogers’ theory, the potential users are classified as innovators, early adopters, early majority, late majority, and laggard (Martinez et al. 1998). Rogers categorized the groups by their innovativeness, in other words, an individual’s willingness to use an innovation. In general, the more innovative people show less resistance in adopting a new technology or product. People with high resistance are likely to remain non-adopters or laggards (Szmigin 1998). Therefore, it is crucial for innovation makers to reduce the innovation resistance of non-adopters and to make these people their customers. In order to do this, it is necessary to identify the impact of user resistance in the pre-adoption stage, and to figure out a way to reduce it with other factors including technological and social factors.

The smartphone is an innovative device that is highly communications-oriented and socially connected. To understand the impact of user resistance on smartphone adoption, it is not enough to investigate the influence of technological improvements. Rather, it is more important to unveil the impact of social influence and the interacting effects between technological and social influences on user resistance (Okazaki 2009). Indeed, the impacts of social factors are large, particularly in which the diffusion of innovative technologies relies on network effect or the size of the user base. Since the social influence can reinforce an individual’s resistance in either a positive or negative way, the perceived risk and uncertainty inherent in an innovation can be increased or decreased by the social influence (Schierz et al. 2010). Although it is generally known that older people are relatively unfavorable with new technologies compared to younger people (Karim and Oyefolahan 2009; Kumar and Lim 2008a), it is still unclear how the social influences on user resistance varies across the user’s age. The impact of social influences, which varies by age, should be empirically examined.

Hence, we proposed a research model that includes (1) the relationships between the technological attributes of an innovation such as relative advantage, compatibility, complexity, and user resistance, (2) the relationship between social influence and user resistance, and (3) the relationship between user resistance and adoption intention. In particular, the social influences are divided into two different types: normative social influence and informational social influence, according to Deutsch and Gerard (1995) (Deutsch and Gerard 1955). To examine the model empirically, we collected data from 250 non-adopters of smartphones in Korea. The partial least square method was used to analyze the research model using SmartPLS 2.0.

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