The paradox of technology: Negativity bias in consumer adoption of innovative technologies

Abstract

Innovative technologies often feature inherently conflicting properties. This poses a challenge for marketers because negative properties not only weigh heavily on consumers' technology adoption decisions but potentially do more so than positive ones. To shed light on the paradox of technology and its underlying processes, the present research develops a conceptual model drawing on technology adoption and valence perception theories about the prevalence of negativity bias in consumers' technology adoption decisions with its unique effect through the serial chain of consumers' perceptions of risk and trust regarding the technology. Results of three studies (N = 1309) demonstrate that the effect of negative valence consistently outperforms that of positive valence in consumers' technology adoption intentions (Studies 1–2) and decisions (Study 3). Furthermore, results show that the disproportionate effect of negative (vs. positive) valence can be explained by the proposed serial causal chain through consumers' perception of risk of the technology and trust in the technology (Studies 2–3) while ruling out company trust and consumer knowledge as alternative drivers of the effects (Study 3). These findings contribute to the pertinent literature on consumer psychology in decisions to adopt novel technologies in that they quantify and explain the potential outcome stemming from the ambiguous properties of novel technology. Moreover, this study finds negativity bias to be an often overlooked consumer bias with implications for marketing practice and useful for understanding and lowering resistance towards artificial intelligence technology.

In companies we trust: consumer adoption of artificial intelligence services and the role of trust in companies and AI autonomy

“…The reasoning for this relationship between trust and AI adoption is that consumers who trust a company are more committed to engaging with freshly introduced services by that company. This approach to understanding AI adoption is fundamentally different from earlier research on trust in the technology as a determinant of technology AI, autonomy and trust in companies adoption decisions (Frank et al, 2022;Gefen et al, 2003;McKnight et al, 2011), because it does not rely on customers' perceptions of the technology. Accordingly, the current approach (i.e.…”

“…When consumers face novelties as in the case of AI services, trust typically acts as a key determinant of consumers' adoption decisions (e.g. Frank et al. , 2022; Gefen et al.…”

Implicit Negativity Bias Leads to Greater Loss Aversion and Learning during Decision-Making

“…This may also fit with our results, which showed that only the implicit negativity bias was significantly associated with loss aversion. Nevertheless, to clarify these issues, it would be necessary to address, through instruments such as an eye-tracker [ 42 ], if the negativity bias measure used in this study could be also related to the heightened focus on losses reported by Sheng et al [ 25 ], as well as whether it is behaviorally meaningful, influencing other complex decisions as reflected in recent studies, where even the predisposition to adopt innovative technologies would depend on the level of negativity bias [ 13 ].…”

“…It is widely accepted there is the existence of negativity bias in human beings [ 4 , 5 , 6 , 7 ], referring to the greater sensitivity to negative stimuli compared with positive stimuli [ 4 , 8 ], and to the higher predisposition to consider ambiguous emotional stimuli as negative than positive [ 9 ]. The origin of this bias has been addressed from multiple perspectives (see Kanouse [ 10 ] for a review) and it is still an open question, however, its existence has been evidenced in both verbal and non-verbal stimuli [ 1 , 11 ]; in affective judgments [ 9 ], social information processing [ 12 ], during the child development [ 6 ], and on consumer behavior [ 13 ]; also when using event-related potentials [ 14 ], and peripherical physiological measures [ 15 , 16 ]. However, this generalizability was recently challenged.…”

Residential mobility boosts new product adoption

“…Extant research has revealed certain factors that affect consumer adoption decisions, such as product characteristics (Castaño et al, 2008;Frank et al, 2022;Lehr et al, 2020;Ostlund, 1974;Rogers, 1983Rogers, , 1995 and consumer characteristics (Cowart et al, 2008;Forlani et al, 2002;Gregan-Paxton et al, 2002;Hong et al, 2017;Im et al, 2003;Moorman et al, 2004). Unlike prior research on new product adoption, this paper employs the socio-ecological perspective and examines how a largely ignored but important social factorresidential mobility-influences consumers' adoption of new offerings.…”