Mediating role of brand app trust in the relationship between antecedents and purchase intentions-Iranian B2C mobile apps

Abstract

This study investigates the mediating role of brand app trust in the relationship between brand app antecedents and purchase intentions via a brand mobile app. It explores the mediating potential of brand and platform trust in this relationship between antecedents and purchase intentions and also highlights which brand antecedents are useful for directly or indirectly establishing trust towards purchasing via a mobile app in the Iranian mobile commerce market. This study uses survey data collected through an online questionnaire form and applies the partial least squares method to extract its findings. All of the estimation results are found to be unbiased and robust. The results show that brand app trust plays a mediating role in the relationship between the brand app antecedents of word-of-mouth recommendation, subjective norms, perceived image (social antecedents), mobile computing self-efficacy (consumer-based antecedent), and perceived ease of use (system antecedent) and purchase intentions via a brand mobile app in Iran.

Keywords:

• brand app antecedents
• brand trust
• brand app trust
• platform trust
• purchasing intention via mobile app
• social commerce
• Iran

Acknowledgment

We are thankful to all peers and faculty of Antai College of Economics and Management for their fruitful discussions and guidance.

Declaration of interest

We would like to submit the enclosed manuscript entitled “Role of Brand App Mediation in Relationship of Antecedents and Purchase Intention: Case of Iranian Business to Consumer Mobile Commerce,” which we wish to be considered for publication in “Journal of Management Analytics.” No conflict of interest in the submission of this manuscript and the manuscript is approved by all authors for publication. I would like to declare on behalf of my co-author that the work describes was original research that has not been published previously, and not under consideration of publication elsewhere, in whole or in part.

Supplemental data

Supplemental data for this article can be accessed 10.1080/23270012.2019.1699874.

ORCID

Badiea Shaukat http://orcid.org/0000-0002-1360-2997

Notes

1 In this study, we consider these four antecedents as antecedents of b-apps.

2 In this study, we consider the trust-related antecedent as a mediator.

3 In this study, we do not consider non-utilitarian factors as antecedents, as too few previous studies support the notion that non-utilitarian factors lead to b-app trust in purchasing a product.

4 According to a literature review, these two factors are the most complete and precise antecedents that serve as good representatives of consumer-based antecedents (Keith et al., 2015; Okazaki, 2010).

5 Tables 1a and 1b in the supplementary appendix provide a brief overview of the recent literature exploring the dimensions of these antecedents based on b-apps.

6 Poel, Renda, and Ballon (2007) define the platform related to m-apps as an “operating system (i.e. Apple iOS, Android, Windows Mobile) running on the mobile devices and business relationships between the entities involved in the mobile-app ecosystem”

7 Moreover, Wozniak’s (2013) method, which is supported by Okazaki (2004, 2007) and Zhang and Mao (2008), is focused on trust in the b-app. It exclusively divides this trust into two main parts, brand trust and platform trust, which is much closer to the Iranian m-commerce model.

8 This study’s framework is based on the trust and TAM Model, which is the best model that can be chosen to investigate building trust in a b-app toward IP (Awad & Ragowsky, 2008; Gefen et al., 2003).

9 Table 2 in the supplementary appendix shows the definition of each variable supported by existing studies.

10 See the supplementary appendix for the questionnaire.

11 Email addresses were obtained with the help of friends, and respondents received no incentives for completing the questionnaire. Additionally, links were sent to around 2,000 participants, and 270 participants responded. However, Google form services do not make it possible to identify how many people responded via email or social media.

12 These tests include composite reliability, Cronbach’s alpha, average variance extracted, the Fornell–Larcker discriminant validity criterion, the standardized root mean square residual, the heterotrait-monotrait ratio, multicollinearity in reflective models, cross-loading, and redundancy.

13 In the PLS method, to check the discriminant validity of the model, three criteria should be checked: HTMT, the Fornell-Larcker criterion, and cross-loading (Garson, 2016).

14 In the PLS method, to check the goodness of fit of the model, two criteria should be checked: HTMT and SRMR (Garson, 2016).

15 In the PLS method, to check the multicollinearity of the model, one criterion should be checked: the inner VIF values (Garson, 2016).

16 For more discussion on the hypotheses, please see section 6.1, which provides an in-depth interpretation.

17 This practical suggestion will increase WOM recommendations and SN, which create brand trust and, ultimately, purchases via the b-app.

18 This practical suggestion will increase WOM recommendations, SN, PI, and, indirectly, MCSE, which create b-app trust and, ultimately, purchases via the brand-app.