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The Influence of Consumers’ Cognitive and Psychographic Traits on Perceived Deception: A Comparison Between Online and Offline Retailing Contexts

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Abstract

In this article, we examine the role of several consumers’ cognitive and psychographic traits in their perceptions of retailers’ deceptive practices (perceived deception) and the different effects on perceived deception associated with online vis-à-vis in-store shopping. Building on theoretical models of persuasion in consumer behavior, we hypothesize that the antecedents of perceived deception in traditional settings are the same as those on the Internet, while the intensity of the impact of these antecedents differs between the online and the offline environment. Results suggest that the effects of individual’s cognitive traits (Internet-based information search and perceived Internet usefulness) and risk aversion on perceived deception are more relevant when consumers shop online than when they purchase from traditional stores. Conversely, psychographic traits (shopping enjoyment and materialism) play a more important role in explaining perceived deception in the traditional shopping context as compared to the online channel. Several theoretical and managerial implications are derived from these findings.

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Notes

  1. Minor adaptations (i.e., replacing the term “web site” for “store”) were needed to suit Román (2010) perceived online deception scale to the traditional channel. Also, three items from the original Richins and Dawson’s (1992) materialism scale were eliminated, as they were found redundant.

  2. The first model tested whether the base model structure (i.e., the pattern of fixed and nonfixed parameters) is invariant across groups (Teo et al. 2009). The creation of this baseline model involved testing all of the hypothesized relationships in the theoretical model (Fig. 1) using our two distinct samples (online and offline) to calculate a joint structural equation model. This model is also known as the configural model and is evaluated based on its goodness-of-fit indices to determine if the model is a good representation of the hypothesized relationships (Steenkamp and Baumgartner 1998; Teo et al. 2009). Our results, reported in Table 5, indicate that the baseline model (M1) has 350 degrees of freedom (175 degrees of freedom for each individual baseline model) and showed reasonable fit indices (χ 2(350) = 652.10 (p < 0.001), χ 2/df = 1.86, GFI = .87, CFI = .96, NNFI = .95, RMSEA = .07). The results show that full configural invariance was established.

  3. Steenkamp and Baumgartner (1998) and Teo et al. (2009) recommend that a full metric invariance test is not a necessary requirement for further tests of invariance and substantive analysis if at least one item (other than the one fixed at unity to define the scale of each latent construct) is invariant, which is the case in our study.

  4. Consistent to prior research (Steenkamp and Baumgartner 1998; Byrne 2001; Teo et al. 2009), when performing the invariance tests of the structural relationships, these factor loadings were set free (partial measurement invariance).

  5. Additionally, measurement invariance was also investigated from a practical approach. Due to Chi square’s extreme sensitivity to sample size and model complexity, several researchers have proposed a more practical approach by investigating the worsening of the fit indices by constraining parameters to be equal across contexts (Little 1997; Steenkamp and Baumgartner 1998). Little (1997) proposed that the equality of factor loadings is upheld when the NNFI decreases less than .05 after imposing equality constraints on all factor loadings. This happened to be our case, as the NNFI did not drop more than .01 after imposing the equality constraints.

  6. In addition, an effort was made to control for the influence of previous purchase experience in the online channel on consumers’ perceptions of deception practices in the offline channel. Accordingly, the structural model was estimated again including the control variable “consumers’ online purchases in the last year” as a direct antecedent of perceived deception in the offline channel. Not only this relationship was not significant (β = 0.08; t = 1.21), but also all the other effects remained the same.

  7. We also checked for differences in perceived deception due to consumers’ demographic variables. Interestingly, these differences only occurred in the online sample. In particular, online perceived deception was significantly higher in less educated people, as compared to more educated people, and among consumers who were retired, homemakers, and unemployed, as compared to students and employed people. No significant gender differences were found on perceived deception in either of the samples.

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Acknowledgments

The authors thank the anonymous reviewers for their valuable and supportive suggestions and critical feedback. This research was supported by the grant ECO2012-35766 from the Spanish Ministry of Science and Innovation and by the Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia (Spain), under the II PCTRM 2007-2010. Authors also thank the support provided by Fundación Cajamurcia.

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  1. Marketing Department, Facultad de Economía y Empresa, Universidad de Murcia, 30.100 Espinardo, Murcia, Spain

    Isabel P. Riquelme & Sergio Román

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Correspondence to Sergio Román.

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Riquelme, I.P., Román, S. The Influence of Consumers’ Cognitive and Psychographic Traits on Perceived Deception: A Comparison Between Online and Offline Retailing Contexts. J Bus Ethics 119, 405–422 (2014). https://doi.org/10.1007/s10551-013-1628-z

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