Antecedents of Customers' Desired Value Change in a Business-to-Business Context: Theoretical Model and Empirical Assessment

ABSTRACT

Anticipating the value that key customers will want in future becomes a matter of survival in an increasingly dynamic environment. An essential part of predicting customers' desired value change (CDVC) involves identifying the reasons behind the phenomenon. In the present study, we examine CDVC antecedents by developing and empirically testing a conceptual model in a business-to-business context involving professional services in the field of information and communication technology (ICT). Grounded on a “top-down” view of the customer value hierarchy (Woodruff 1997), our results show that changes in customers' desired end-states are concomitant with changes in both desired consequences and desired attributes. We further observe that end-state desires shift even more as customer firms make more organizational changes and their financial performance declines. Lastly, our results show that organizational changes are a mediating variable between changes in the external environment (i.e. dynamism) and changes in desired end-states. This study is meant as further theoretical and empirical support for the outcomes of previous exploratory research on CDVC.

KEYWORDS:

• Customer desired value change
• information and communication technologies
• professional services

Notes

^1. All items are measured on a 7-point Likert-type scale ranging from “no change” (1) to “major change” (7).

^2. Probability p ≤ .001 for all t-values (> 3.09); “—” indicates a fixed parameter. (N): New item.

^1. 7-point Likert-type scale: 1 = “no change”, 7 = “major change.”

^2. 7-point Likert-type scale: 1 = 1 = “much below expectations,” 7 = “much above expectations.”

^3. Probability p ≤ .001 for all t-values (> 3.09); “—” indicates a fixed parameter.

^1. Each indicator represents change in the importance the customer attaches to the value component in question. See “Measurement” section for more about how the measure was developed.

^2. Probability p ≤ .001 for all t-values (> 3.09); “—” indicates a fixed parameter.

^1. The t-tests of significance are two-tailed; ∗ = p ≤ .1, ∗∗ = p ≤ .05, ∗∗∗ = p ≤ .01, ∗∗∗∗ = p ≤ .001.

^1. The t-tests of significance are one-tailed; ∗ = p ≤ .1, ∗∗ = p ≤ .05, ∗∗∗ = p ≤ .01, ∗∗∗∗ = p ≤ .001.

Additional information

Notes on contributors

Jozée Lapierre

Professor at the École Polytechnique de Montréal and holds the JVR Cyr – Bell Canada Chair in Value Creation and Commercialization.

Jimmy Skelling

PhD candidate in the Department of Mathematics and Industrial Engineering