Impact of privacy concerns on resistance to smart services: does the ‘Big Brother effect’ matter?

Abstract

The aim of this study is to provide a better understanding of the factors that explain consumer resistance to smart services from a privacy perspective. To this end, an exploratory qualitative study and a quantitative study were carried out. 653 French consumers answered an online questionnaire regarding smart services in the banking sector. Structural equation modeling was used to test the conceptual model. The findings show that information privacy, the unauthorized secondary use of personal information and perceived intrusion have an impact on consumer resistance to smart services. Moreover, our research highlights the major role of the "Big Brother effect" as an antecedent to these various privacy concerns.

Consumers’ Resistance to Digital Innovations: A Systematic Review and Framework Development

“…Perceived price, complexity, perceived enjoyment, social influence, perceived usefulness, economic benefit, perceived novelty and intrusiveness are examples of innovation characteristics (Abbas et al, 2017; Achadinha et al, 2014; Antón et al, 2013). In comparison, emotions, innovativeness, motivation, self-congruity, self-efficacy and dependence are examples of consumer characteristics (Abbas et al, 2017; Cha, 2011; Chouk and Mani, 2019; Mani and Chouk, 2017; Matsuo et al, 2018).…”

“…Chemingui and Ben Iallouna, 2013) and structural equation modelling, both the parametric covariance-based (CB) (e.g. Matsuo et al, 2018) and non-parametric, partial least square (PLS) (e.g. Chang et al, 2019; Nel and Boshoff, 2019) forms.…”

“…Notably, most of the empirical studies evaluated the direct path, with only a limited number of studies (about 30%) considering the third variables in terms of moderating, mediating or controlling influences. Some key moderating influences considered by prior scholars include consumer innovativeness (Abbas et al, 2017), satisfaction with offline service (Chen et al, 2019), variety seeking (Kim et al, 2017), gender (Lian and Yen, 2014), experience (Matsuo et al, 2018), stickiness to cash payment (Sivathanu, 2019), attitude (Siyal et al, 2019), mobile shopping-service experience (Nel and Boshoff, 2019), voluntariness of use (Soh et al, 2020), task situations (Sun, 2016) and e-lifestyle (Yu et al, 2015).…”

“…The key mediating variables examined by the selected studies are attitude (Antón et al, 2013), innovation resistance (Hong, 2020; Im et al, 2014), perceived value (Kim et al, 2017), complexity barriers, performance risk barriers and existing usage patterns (Matsuo et al, 2018), scepticism (Mani and Chouk, 2018), perceived usefulness and perceived ease of use (Siyal et al, 2019). On the whole, the studies of utilised moderators focus on user characteristics and experience and mediators that capture barriers and resistance.…”

The role of smart technologies in decision making: developing, supporting and training smart consumers

“…Similarly, in the fourth paper, Mani and Chouk (2019) evaluate the drivers of consumers' resistance to smart services, by focusing on a privacy perspective. Results figure out the role of information privacy, the unauthorised secondary use of personal information and perceived intrusion have a causal impact on consumers resistance, in a sort of 'big brother effect'.…”