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Exploring the cognitive and affective bases of online purchase intentions: a hierarchical test across product types

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Abstract

Whereas there is ample e-commerce research on how online store beliefs and consumer online affective states may influence online purchase intentions, no research so far has examined whether the hierarchy of effects between these concepts differs across product types. In this study, we fill this research gap by examining the explanatory power of the think-feel-do hierarchy versus the feel-think-do hierarchy in predicting online purchase intentions towards search versus experience products and high involvement versus low involvement products. Hypotheses are formulated and tested using a quasi-field experiment (n = 198) design. The results show the robustness of the think-feel-do hierarchy for three out of four product types (experience, low involvement, high involvement). Remarkably, the results also demonstrate that the formation of online purchase intentions for search products may occur via a more experiential form of online purchase decision-making. Implications of our findings for theory and online store practitioners are discussed.

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Notes

  1. The attitude concept in theories such as TRA, TPB, and TAM represents affect for or against an object. Both terms (attitude and affect) have been used interchangeably by psychologists [43] and have been used accordingly in the literature on online consumer behavior [7, 151].

  2. Following the predicted influence of intentions on behavior in social psychological theories such as TRA, TPB, and Triandis’s [141] attitude-behavior theory, many scholars have conceptualized and used the behavioral intention as proxy for overt behavior [155].

  3. The applicability of the χ2 goodness-of-fit measure has been questioned due to its relatively sensitivity to sample size and the number of measurement items used [58]. Also its focus on a perfect fit (or not) has been criticized given that theoretical models can only approach real-world data and never fit this data exactly. Therefore, other more realistic fit indices are recommended [20].

  4. The means of the research constructs, as well as their intercorrelations, were also computed and can be found in “Appendix 1”.

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  1. Centre for Market Insights, Amsterdam University of Applied Sciences, Wibautstraat 3b, Room 06C-42, 1091 GH, Amsterdam, The Netherlands

    Tibert Verhagen & Daniel Bloemers

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  1. Tibert Verhagen
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Appendix 1: Descriptive statistics research constructs

Appendix 1: Descriptive statistics research constructs

See Tables 5 and 6.

Table 5 Means and standard deviations (in parentheses) of the constructs
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Table 6 Inter-construct correlations (bivariate)
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Verhagen, T., Bloemers, D. Exploring the cognitive and affective bases of online purchase intentions: a hierarchical test across product types. Electron Commer Res 18, 537–561 (2018). https://doi.org/10.1007/s10660-017-9270-y

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