Graduation Semester and Year
2017
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Marketing
Department
Marketing
First Advisor
B. Lawrence Chonko
Second Advisor
Fernando Jaramillo
Abstract
Customer identification defined as ‘customer’s perceived oneness with a company/brand’ is receiving increased attention in marketing. Despite the critical role customer identification plays, there is a lack of comprehensive work explicating the antecedents, outcomes, and possible boundary conditions of customer identification. The current study fills this gap in the literature by conducting a meta-analysis that synthesizes studies conducted over the past 25 years. The meta-analysis includes 167 independent samples (N = 87,538 customers) from which 24 antecedents and 7 outcomes of customer identification are identified and tested. Significant antecedents are grouped into two categories: company/brand antecedents and customer antecedents. Findings provide support to the critical role customer identification plays in driving outcomes such as loyalty, willingness-to-pay, word-of-mouth, resilience, and company financial performance, which all are important for marketer’s success. Various research context and measurement method moderators are studied to test the robustness of antecedents-customer identification and customer identification-outcomes relationships. In addition to the bivariate analysis conducted, a meta-analytic structural equation model is proposed for the purpose of testing a causal model of customer identification. The meta-analytic model demonstrates a relational-based path that is complementary to the conventional identity-based path of customer identification. Findings provide several theoretical and practical contributions.
Keywords
Customer identification, Relationship marketing, Meta-analysis
Disciplines
Business | Marketing
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Itani, Omar S., "WHO AM I? CUSTOMER IDENTIFICATION: A QUANTITATIVE SYNTHESIS" (2017). Marketing Dissertations. 52.
https://mavmatrix.uta.edu/marketing_dissertations/52
Comments
Degree granted by The University of Texas at Arlington