Guest article by Sara Leroi-Werelds, Assistant Professor at Hasselt University
Approximately 30 years ago, Zeithaml (1988) laid the foundations of one of the most universally accepted definitions of customer value as a trade-off between benefits and costs. Six years later, Holbrook (1994) proposed one of the most often used value typologies. Although their notions of value may still be valid, our perspective on customer value needs an update since the context of marketing and service research has changed as a result of advances in academic research (e.g. Service-Dominant Logic, Transformative Service Research) as well as business practice (e.g. technological advances, collaborative consumption).
The aim of this article (forthcoming in the Journal of Service Management) is to update our understanding of customer value, which can be considered one of the most fundamental concepts in marketing and service research. This update includes:
- the foundational characteristics of customer value
- an updated typology of customer value
- guidelines for measuring and modeling customer value
- a research agenda
Foundational characteristics of customer value
Based on an extensive investigation of the value literature, seven foundational characteristics can be derived. These foundational characteristics are intended to establish a unified view of the concept.
- implies an interaction between a subject (the customer) and an object (e.g. a product, service, store, technology, activity, …)
- involves a trade-off between the benefits and costs of an object.
- is not inherent in an object, but in the customer’s experiences derived from the object.
- is personal since it is subjectively determined by the customer.
- is situation-specific.
- is multidimensional and consists of multiple value types.
- is (co-)created by the customer by means of resource integration.
Additionally, this paper presents an updated typology which (1) starts from Holbrook’s typology; (2) revises Holbrook’s value types whenever needed; (3) includes new positive value types; (4) includes negative value types (which is in line with Zeithaml’s definition). The additional positive and negative value types are based on recent advances in theory and practice. For instance, prior research shows that technologies such as service robots can result in positive value types such as convenience, personalization, and excellence, but are also related to negative value types such as privacy and security risks (Wirtz et al. 2018).
The updated typology consists of 14 positive and 10 negative value types. However, it should be noted that not all value types are relevant/applicable in every context. Hence, it is important to consider this typology as a list of potential value types and every value researcher should check which of these value types are relevant for his/her study. This typology can thus be considered ‘a menu card’ researchers can use to tick off relevant value types for their own study. However, given fast and continuous evolutions in business practice and academic research, this ‘menu card’ should be updated frequently.
Guidelines for measuring and modeling
Several attempts have been made to develop a scale for measuring customer value but a universally accepted scale could not be proposed. However, this should not come as a surprise since “in view of the construct’s complexity and richness, operationalizing customer value in its entirety and developing one standard scale to capture all of these nuances may pose a challenge” (Parasuraman 1997, p. 160).
In light of the above, the use of a formative measurement index is recommended. Hence, this paper proposes the ‘Customer Value Index’ or CVI to measure customer value. The CVI is a weighted composite of the relevant positive and negative value types related to a particular object. To determine which value types are relevant, exploratory qualitative research is advisable. Furthermore, it is desirable to include the value types that are part of the value proposition of the object. This is especially relevant for new objects (such as a new technology), because customers don’t yet understand all benefits and costs.
Empirical value studies often use Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate customer value and its relationships with key outcomes (e.g. purchase intention, satisfaction). Although this is a suitable method, the way customer value is modeled differs significantly across studies. Based on the proposed CVI and recommendations provided by recent PLS-SEM literature (Hair et al. 2018), this paper provides guidelines for future research. Specifically, three potential models are proposed: an aggregate model, a disaggregated model, and a simplified model:
- The aggregate model specifies customer value as a formative second-order construct with the value types as first-order constructs.
- The disaggregate model specifies the value types as first-order constructs, without a higher-order overall value construct. This thus implies that the value types themselves are part of the structural model instead of an overall value construct.
- The simplified model specifies customer value as a formative construct where each relevant value type is covered with one item.
The paper includes a flowchart that guides researchers when measuring and modeling customer value.
To stimulate future value research, this paper concludes with a research agenda organized around six themes:
Overall, this update on customer value was needed to obtain a more unified and evolved perspective on the concept which can help researchers as well as practitioners to better understand one of the most fundamental concepts in our domain. To find support for this updated perspective on customer value even before the publication of this manuscript, Morris B. Holbrook – one of the founding fathers of value research – was asked to provide his opinion on this paper. I am delighted to read his statement:
“Sara has done a magnificent job of organizing, reviewing, and extending the research on consumer value that has begun to appear in proportions resembling an onslaught in recent years. Her command of the literature inspires admiration and a touch of envy from those of us who have been laboring in this area of inquiry. More importantly, her ability to structure this material in meaningful ways and to develop hitherto unrecognized insights makes her article a major gift to the field. I am flattered that Sara has used my own typology as a point of departure. And I greatly appreciate the ways in which she has extended my formulation so as to take account of recent developments in services marketing that have appeared since I first addressed these issues many years ago. I applaud her efforts to bring these topics up to date. And I predict that her work will make a powerful and lasting contribution.” Morris B. Holbrook
The link to the full article is: https://www.emerald.com/insight/content/doi/10.1108/JOSM-03-2019-0074/full/html
Hair, J.F., Sarstedt, M., Ringle, C.M., and Gudergan, S.P. (2018), Advanced Issues in Partial Least Squares Structural Equation Modeling, SAGE Publications, Thousand Oaks, CA.
Holbrook, M.B. (1994), “The nature of customer value: An axiology of services in the consumption experience,” in Rust, R.T. and Oliver, R.L. (Eds.), Service Quality: New Directions in Theory and Practice, Sage Publications, Thousand Oaks, CA, pp. 21-71.
Parasuraman, A. (1997), “Reflections on gaining competitive advantage through customer value”, Journal of the Academy of Marketing Science, Vol. 25, pp. 154-161.
Wirtz, J., Patterson, P.G., Kunz, W.H., Gruber, T., Lu, V.N., Paluch, S., and Martins, A. (2018), “Brave new world: Service robots in the frontline”, Journal of Service Management, Vol. 29 No. 5, pp. 907-931.
Zeithaml, V.A. (1988), “Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence”, Journal of Marketing, Vol. 52, No. 3, pp. 2-22.