Dyadic Operationalization in Business Relationships: The Empirical Example of Marketing-Purchasing Collaboration

Purpose: The purpose of this paper is to explore whether dyadic operationalization within business relationships is feasible and sensible in a rigorous way. It aims to introduce quantitative operationalizations of business relationship characteristics from both monadic and dyadic datasets, and to introduce aggregation techniques for utilizing the richness of dyadic data. It compares and contrasts the effectiveness of different techniques in terms of explaining business relationship phenomena, using an empirical exemplification.

Methodology/Approach: The paper reviews the relevant literature and summarizes various dyadic operationalization and aggregation approaches. It furthermore illustrates such operationalization and aggregation by utilizing an empirical example. A nomological model of marketing-purchasing collaboration is developed and tested based upon internal dyadic data. Using alternative model comparisons, we contrast several different ways of operationalizing dyadic data (combined, dyadic, and dyadic with asymmetry), and compare the outcomes utilizing structural equation modeling.

Findings: The study of business relationships typically makes use of a variety of data types, ranging from simple monadic to perceived dyadic, through to rigorous dyadic data. The various aggregation methods include value, asymmetry, and directional asymmetry approaches. Pertinent sub-constructs are developed based on these aggregation methods and relevant hypotheses incorporating and reflecting on the role of the sub-constructs are suggested to develop a more meaningful and rich quantitative analysis of business relationship phenomena.

Research Implications: This paper explores the different ways in which data assessing the relationship between two interacting parties can be operationalized. Dyadic operationalization within the context of business relationships is sensible and recommended. Researchers can adopt approaches to conduct dyadic data operationalization including data collection methods such as perceived dyadic and rigorous dyadic. They should benefit from rich dyadic aggregation approaches such as value, asymmetry, and directional asymmetry, noting the strengths and weaknesses of each approach discussed in this paper.

Practical Implications: Businesses are recommended to increase customer orientation and marketing-purchasing interaction to improve collaboration between marketing and purchasing departments and thus their overall performance. Businesses should also develop an alignment between the collaboration perceptions of the involved departments, and note that perceptual symmetry improves collaboration. Perception matching in a dyadic relationship plays a role in enhancing the overall firm performance. Managers should note that all involved parties’ perspectives are to be included to ensure a positive and collaborative liaison. An all-encompassing attitude and perspective (as opposed to an asymmetric, unbalanced one) ensures an effective relationship.

Originality/Value/Contribution of the paper: The contribution of the research lies in outlining different ways to accomplish more insightful analytics regarding data operationalization, and their different strengths and weaknesses in terms of explaining relationship characteristics, and therefore enriches research on business relationships by making better sense of quantitative dyadic data.