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Introduction
Most of the literature on consumers’ adoption of new products focuses on successful innovations (Heidenreich and Spieth, 2013), based on the belief that innovativeness is one of the main drivers of consumer behavior (Rogers, 2003). Nonetheless, the incidence of unsuccessful market introductions is very high, with failures of around 40 per cent (Castellion and Markham, 2013), which suggests that consumers are not always eager to adopt innovation. Whether consumers reject innovation permanently or temporarily, there is clearly some degree of resistance even before eventual adoption (Kuisma et al., 2007). Resisted innovations cannot generate future revenues or strengthen a firm’s competitive position, but despite its importance, research into consumer resistance to innovation is scarce (Claudy et al., 2015; Talke and Heidenreich, 2014).
Researchers tend to agree that such resistance may be product- (Bagozzi and Lee, 1999; Kleijnen et al., 2009; Laukkanen et al., 2007), consumer- (Heidenreich and Spieth, 2013; Oreg and Goldenberg, 2015) and situation-related (Heidenreich and Kraemer, 2016; Ram and Sheth, 1989). These factors are associated with two different types of innovation resistance:
active innovation resistance (AIR), described as a negative attitude stemming from new product evaluation (Heidenreich and Handrich, 2015); and
passive innovation resistance (PIR), described as a predisposition to resist innovations due to an individual’s inclination to resist changes and status-quo satisfaction that is formed somewhat unconsciously prior to new product evaluation (Heidenreich and Handrich, 2015).
Although both types of resistance are important, this paper focuses on AIR and extends current knowledge by incorporating an emotional dimension into the construct. It has been suggested and empirically confirmed that affect, a general term that encompasses mood and emotion (Bagozzi et al., 1999), plays an important role in consumer decisions. For instance, the level of consumer happiness affects product choices (Mogilner et al., 2012). Adding emotions to the technology acceptance model (TAM) increases its explanation power (Ferreira et al., 2014; Kulviwat et al., 2007).
In line with So et al.’s (2015, p. 11) call to explore “the nature of the decision-making contexts and investigate how emotional appraisals might interact with the decision-making contexts to shape judgments”, and Bagozzi and Lee’s (1999) proposition that emotions are part of the information taken into consideration by the consumer...