The DOI matches its Crossref record; 135 of the 135 references it cites were found in a registry or an index.
Marketing mix, customer value, and customer loyalty in social commerce: A stimulus-organism-response perspective
Abstract
Purpose: Based on stimulus-organism-response model, the purpose of this paper is to develop an integrated model to explore the effects of six marketing-mix components (stimuli) on consumer loyalty (response) through consumer value (organism) in social commerce (SC). Design/methodology/approach: In order to target online social buyers, a web-based survey was employed. Structural equation modeling with partial least squares (PLS) is used to analyze valid data from 599 consumers who have repurchase experience via Facebook. Findings: The results from PLS analysis show that all components of SC marketing mix (SCMM) have significant effects on SC consumer value. Moreover, SC customer value positively influences SC customer loyalty (CL). Research limitations/implications: The data for this study are collected from Facebook only and the sample size is limited; thus, replication studies are needed to improve generalizability and data representativeness of the study. Moreover, longitudinal studies are needed to verify the causality among the constructs in the proposed research model. Practical implications: SC sellers should implement more effective SCMM strategies to foster SC CL through better SCMM decisions. Social implications: The SCMM components represent the collective benefits of social interaction, exemplifying the importance of effective communication and interaction among SC customers. Originality/value: This study develops a parsimonious model to explain the over-arching effects of SCMM components on CL in SC mediated by customer value. It confirms that utilitarian, hedonic, and social values can be applied to online SC and that SCMM can be leveraged to achieve these values.
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Metadata
- Authors
- Wu, Y.-L., & Li, E. Y.
- Venue
- Internet Research
- Publisher
- Emerald
- Published
- 2018-02-06
- Volume
- 28
- Issue
- 1
- Pages
- 74-104
- ISSN
- 1066-2243 (ISSN-L)
- OpenAlex
- W2772233004
- Cited by
- 293
- References
- 135
- FWCI
- 44.03
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.