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Constraint-based and dedication-based mechanisms for encouraging online self-disclosure: Is personalization the only thing that matters?

  • Empirical Research
  • Published:
European Journal of Information Systems

Abstract

Consumer-generated self-disclosure is better than firm-generated advertising and sales reports in increasing contact opportunities and also more credible for firms to foster alignment with future market expectations. Previous research mostly assesses online self-disclosure from the rational approach of anticipated benefits and privacy risks without considering the “privacy paradox” phenomenon (users behave contrarily to privacy concern) in social networking sites (SNSs). We develop a theoretical model, grounded in constraint-based (lock-in) and dedication-based (trust-building) mechanisms and social identity theory, to predict online self-disclosure. We test the proposed theoretical model by surveying 395 consumers with participation experience in an online SNS. Different from the rational approach behind personalization, we advance knowledge on how to apply social identity, as well as constraint-based and dedication-based mechanisms, to motivate online self-disclosure induced by consumers. We provide theoretical and practical insights based on our research findings for managing the motivational mechanisms of online self-disclosure.

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Acknowledgements

We are grateful to the AE and referees for their helpful comments on earlier versions of our paper. Cheng was supported in part by The Hong Kong Polytechnic University under the Fung Yiu King - Wing Hang Bank Endowed Professorship in Business Administration.

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Authors and Affiliations

  1. Department of Information Management, Hsuan Chuang University, Hsinchu, Taiwan

    Hung-pin Shih

  2. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong, People’s Republic of China

    Kee-hung Lai & T. C. E. Cheng

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  1. Hung-pin Shih
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  2. Kee-hung Lai
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Correspondence to Hung-pin Shih.

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Appendices

Appendix A: Measurement items

Table A1.

Table A1 .
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Appendix B: The literature of self-disclosure and the design and test of the measurement instrument

Table B1 Theories for predicting self-disclosure
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The measurement instrument development

To survey those users of Yahoo (Taiwan) e-auction forum, we performed the translation of the measurement instrument in the form of a survey questionnaire from English to traditional Chinese. According to Brislin et al (1973), back-translation, bilingual techniques, commitment approach, and pretest were four techniques for enhancing instrument translation in cross-cultural research. Maneesriwongul and Dixon (2004) recommended that minimum standards for developing an instrument should at least include back-translation and monolingual testing among target language subjects. We followed the recommendation by using three appropriate techniques except for bilingual ones. This study did not follow the bilingual techniques because of the complexity in assessing cultural relevance and how it uniquely influences semantic equivalence between the source and target language versions. In the first step, we recruited bilingual experts in marketing and MIS to perform the translation from the source (English) to the target language (Chinese). One expert conducted forward translation, whereas the other expert conducted back-translation. In the back-translation, the target language version was translated back into the source language version for verifying the semantic equivalence. In the second step, following the committee approach, the expert panel cooperatively and mutually examined the original and back-translated versions. This expert panel iteratively conducted the back-translation until they reached a consensus on the instrument translation. In the third step, a pilot test was conducted to examine the clarity of the target language version among its monolingual subjects after completing the instrument translation. From the pilot test, discrepancies between the source and the target versions were identified, and the target version was also improved for formal survey. This survey did not focus on particular participants, and all volunteers understood that this academic survey would not apply for business-use profits. In sum, the instrument was developed by following the recommended techniques and thereby should possess face validity.

Regarding the scale for self-disclosure intention, this study developed new measures because the scale from the literature was not appropriate for this study. Previous studies often combine credibility and benevolence to assess the trust construct (Doney & Cannon, 1997; Ba & Pavlou, 2002). In this study, we consider cognitive trust and affective trust as distinct but correlated constructs. Hence, we measured cognitive trust in terms of the credibility dimension and affective trust in terms of the benevolence dimension. We dropped the integrity dimension because of the research goal on the comparison between cognition and affect in the trust building. Early scales for switching cost (e.g. money, time, and effort) are difficult to meet the current survey that focuses on the loss of loyalty benefits. We thus measured switching cost using new scales that refer to the cost of terminating the current relationship. This study measured dependency using new scales that refer to psychological costs or burdens incurred when participants seek better alternatives either for substitution or for more choices. Most measurement items were developed from prior studies and revised by experts for assessing the true properties of theoretical constructs, suggesting the instrument achieves content validity (Straub, 1989).

Table B2 CFA results: factor loadings and cross-loadings of measurement items
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CMV analysis

We took necessary steps to avoid the effects of CMV in the administration of questionnaire survey, including guarantee of respondent anonymity, counterbalance of question order, and improvement of scale items as recommended by Podsakoff et al (2003). Moreover, most constructs were measured in terms of previously validated measures and examined via pretest in order to increase the validity of the theoretical model (Sharma et al, 2009). We also examined CMV using a post hoc approach with two tests on the self-reported data. First, the test of inter-construct correlations (Table 2) shows that the highest correlation between the research constructs (0.594 for ASI-ESI) is far below the threshold of 0.90 (Bagozzi et al, 1991), indicating the effect of CMV is small. Second, we applied the partial correlation approach recommended by Lindell and Whitney (2001) to test CMV. In this study, we partial out the lowest correlation among the variables to test the effect of a method variance. The correlations among non-partial out variables did not cause a significant inflation, suggesting that our empirical results were unlikely to be explained by CMV. Overall, the effect of CMV is small in the survey.

Model-fit indices

The seven model-fit indices and corresponding cut-off values are (Wheaton et al, 1977; Bagozzi and Yi, 1988): Chi-square/degree of freedom (χ2/df ≤ 5.00), standardized root-mean-square residual (RMR ≤ 0.05), root-mean-square error of approximation (RMSEA ≤ 0.08), goodness-of-fit index (GFI ≥ 0.90), adjusted goodness-of-fit index (AGFI ≥ 0.80), normed fit index (NFI ≥ 0.90), and comparative fit index (CFI ≥ 0.90). Definitions of absolute fit indices (χ2/df, GFI, AGFI, RMR, and RMSEA) and relative fit indices (NFI and CFI) can be found in the AMOS manuals.

Measurement model

We employ the structural equation modelling (SEM) package (AMOS) to perform confirmatory factor analysis (CFA) to examine the psychometric properties of the measurement items. The test of seven fitness indices (χ2/df = 2.303; RMR = 0.023; RMSEA = 0.057; GFI = 0.91; AGFI = 0.87; NFI = 0.94; CFI = 0.96) shows an acceptable fit of the measurement model according to the recommended cut-off values (“Appendix B”). In sum, the tests of convergent and discriminant validity, and goodness-of-fit of the measurement model establish factorial validity of this measurement instrument (Straub et al, 2004).

Mediation effect analysis

We follow the analytic procedures to examine three structural models with partial mediation, full mediation, and no mediation (Baron and Kenny, 1986). The no-mediation model has poor model-fit indices (χ2/df = 14.873; RMR = 0.149; RMSEA = 0.188; GFI = 0.83; AGFI = 0.71; NFI = 0.71; CFI = 0.72), which should be dropped in further tests. The full-mediation model has an appropriate model fit (χ2/df = 3.861; RMR = 0.04; RMSEA = 0.08; GFI = 0.94; AGFI = 0.89; NFI = 0.94; CFI = 0.95). The partial-mediation model has a good model fit (χ2/df = 1.940; RMR = 0.03; RMSEA = 0.05; GFI = 0.97; AGFI = 0.94; NFI = 0.97; CFI = 0.98). Following the recommended procedures of Preacher and Hayes (2004), we run SPSS Macro (e.g. PROCESS.spd) programmed by Hayes to test indirect effects on SD. Taking the theoretical lens and mediation models into account (Zhao et al, 2010), we use the full-mediation model to conduct the hypothesis testing.

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Shih, Hp., Lai, Kh. & Cheng, T.C.E. Constraint-based and dedication-based mechanisms for encouraging online self-disclosure: Is personalization the only thing that matters?. Eur J Inf Syst 26, 432–450 (2017). https://doi.org/10.1057/s41303-016-0031-0

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