Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63553
Volume:
10 |
5 |
May 2026
Pages:
1-9
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Management Studies, School of Management & Commerce
Babasaheb Bhimrao Ambedkar University, Lucknow
Department of Management Studies, School of Management & Commerce
Babasaheb Bhimrao Ambedkar University, Lucknow
There is a significant structural transformation of the consumer-retailer relationships caused by the intense growth of omnichannel retailing and the formation of digital and physical channels into service ecosystems. Concurrently, retailers have invested more in In-house (private-label) brands to enhance profitability and competitive distinction. Existing literature has discussed channel integration, trust, perceived risk and convenience separately with limited literature that incorporates theoretical role of omnichannel transformation and its effects on consumer adoption of In-house brands in various retail settings.Based on this, the study attempts to come up with a multi-contextual conceptual framework that fills this gap. With the Stimulus-Organism-Response (S-O-R) framework as the key theoretical framework, this theoretical study integrates the following concepts: the Commitment-Trust Theory, the Technology Acceptance Model (TAM) and the Perceived Risk Theory. Channel integration, perceived quality and perceived risk are conceptualized as the environmental stimuli that shape two mediating organismic states (i.e. trust and perceived convenience), which influence purchase intentions towards the private-label brands. The framework explicitly develops a comparative differentiation between the online and offline retail contexts and integrates consumer characteristics as boundary conditions. The model developed shows that trust and perceived convenience are two intermediaries between omnichannel retail transformation and the introduction of In-House brands with contextual differences being displayed in both digital and physical retailing contexts. The model demonstrates that omnichannel transformation has dual psychological mechanisms on the adoption of an in-house label, with contextual differences in trust formation and risk salience. The framework is noteworthy as it combines structural retail change with consumer-level processes, as well as provides strategic direction to retailers in the digital-physical convergence.
KEYWORDS
The paper develops a conceptualized multi context in which the purchase intention vis-a-vis In-House brands through trust and perceived convenience is illustrated considering channel integration, perceived quality as well as perceived risk.Complementary psychological processes of omnichannel consumer behavior have been incorporated in the framework based on S-O-R theory (Mehrabian and Russell, 1974), Commitment-Trust Theory (Morgan and Hunt, 1994), TAM (Davis, 1989) and Perceived Risk Theory (Bauer, 1960). Perceived convenience and trust play an important role in reducing relational risk and reducing the mental processing required for consumers to complete the purchase process. The online to offline divide is a theoretically distinct concept and recognising this enhances the conceptual relevance and rigour of the proposed framework. To address the gap created by digital transformation in these two environments, retailers need to build consumer trust and prioritise convenience in their business strategy. In doing this, a greater insight into the psychological factors in play will continue to be critical in facilitating the advancement and evolution of the omnichannel retail environment.
Ailawadi, K. L., & Keller, K. L. (2004). Understanding retail branding: Conceptual insights and research priorities. Journal of Retailing, 80(4), 331–342. https://doi.org/10.1016/j.jretai.2004.10.008 Bauer, R. A. (1960). Consumer behavior as risk taking. In R. S. Hancock (Ed.), Dynamic marketing for a changing world (pp. 389–398). American Marketing Association. Beck, N., & Rygl, D. (2015). Categorization of multiple channel retailing in multi-, cross- and omni-channel retailing for retailers and retailing. Journal of Retailing and Consumer Services, 27, 170–178. https://doi.org/10.1016/j.jretconser.2015.08.001 Beneke, J., Flynn, R., Greig, T., & Mukaiwa, M. (2013). The influence of perceived product quality, relative price and risk on customer value and willingness to buy: A study of private label merchandise. Journal of Product & Brand Management, 22(3), 218–228. https://doi.org/10.1108/JPBM-02-2013-0262 Berry, L. L., Seiders, K., & Grewal, D. (2002). Understanding service convenience. Journal of Marketing, 66(3), 1–17. https://doi.org/10.1509/jmkg.66.3.1.18505 Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 Diallo, M. F. (2012). Effects of store image and store brand price-image on store brand purchase intention: Application to an emerging market. Journal of Retailing and Consumer Services, 19(3), 360–367. https://doi.org/10.1016/j.jretconser.2012.03.010 Donovan, R. J., & Rossiter, J. R. (1982). Store atmosphere: An environmental psychology approach. Journal of Retailing, 58(1), 34–57. Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2001). Atmospheric qualities of online retailing: A conceptual model and implications. Journal of Business Research, 54(2), 177–184. https://doi.org/10.1016/S0148-2963(99)00087-9 Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519 Gielens, K., & Steenkamp, J.-B. E. M. (2019). Branding in the era of digital (dis)intermediation. International Journal of Research in Marketing, 36(3), 367–384. https://doi.org/10.1016/j.ijresmar.2019.01.005 Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. Herhausen, D., Binder, J., Schoegel, M., & Herrmann, A. (2015). Integrating bricks with clicks: Retailer-level and channel-level outcomes of online–offline channel integration. Journal of Retailing, 91(2), 309–325. https://doi.org/10.1016/j.jretai.2014.12.009
Publication Date
5/20/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
2
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...