Exploring Dimensions and Abstractions of Customer Value: A Comprehensive Literature Review and Conceptual Analysis

Description

Page: 541-547

Huzina Saheal, Shabraiz Malik, and Mushtaq Ahmad Bhat (Department of Commerce, University of Kashmir (J & K))

The increasing significance of the concept of customer value has captured the attention of scholars and industry practitioners alike. Recognizing and establishing customer value is considered crucial for the success of companies, representing a fundamental goal in market exchanges for both producers and consumers. Despite its widespread application in marketing literature, there has been a surprising lack of effort in consolidating diverse views on its dimensionality, abstraction, and model taxonomy. Consequently, this article, through a comprehensive literature review, endeavours to elucidate the concept of value by examining and analysing its definitions, approaches, and dimensions. Additionally, it addresses questions pertaining to the abstraction level (whether 1st order or higher-order constructs) and the type of model taxonomy (reflective or formative). The objective is to provide a robust foundation for future empirical evaluations of the concept while suggesting potential avenues for further research. The key findings of the research are as follows: (i) unidimensional and multidimensional value models each serve important purposes-offering either simplified or more detailed interpretations of the concept, and (ii) the choice of conceptual framework is ultimately influenced by the intended meaning and context. As a result, both formative and reflective approaches to modeling value are considered appropriate. Additionally, the decision to employ higher-order or first-order models rests with the researcher, since both represent the same underlying construct. Nonetheless, if customer value is the central focus, higher-order models are more suitable for analyzing the influence of its dimensions. Conversely, when examining how customer value affects or relates to other variables, a first-order model may be more appropriate.