Relationships in business markets: Exchange episodes,value creation,and their empirical assessment

Conclusion David Wilson (1995) has provided us with much grist for thought with his integrated framework for customer-supplier relationship development. In focusing on which constructs are “active” and therefore most meaningful at each stage, he has opened a new vista for research in this area. Our models and empirical research ought to reflect this, but to date they largely have not. I have suggested qualitative, longitudinal research as a preferred next step in gaining the knowledge that we will need to make field-survey, longitudinal research worthwhile. Wilson also is to be lauded for drawing greater attention to value creation as a central undertaking in customer-supplier relationships. Understanding and actualizing value creation (and value sharing) are critical aspects of the market-sensing and customer-linking capabilities in market-driven organizations (Day 1994), yet the mechanisms underlying them and the methodologies for accurately assessing them remain largely unknown. Here, particularly for tool development research, it would seem to be an opportune time for business marketing academics and practitioners to form their own collaborative relationships for mutual gain. His research interests are in working relationships between firms in business markets and measurement techniques. His articles have appeared inHarvard Business Review, Journal of Marketing, Journal of Marketing Research, Management Science, Psychological Bulletin, andPsychometrika, among others. He has been vice president of the Business Marketing Division of the American Marketing Association and is a fellow of the American Psychological Association.     

Strategy in research design and hypothesis testing

The efficiency of a research design may be measured in terms of the degree to which knowledge is enhanced within given resource constraints. Thus, two different types of research design, even though they contain the same number of expected observations, may differ considerably in the amount of information provided. An example is the number N of 32 observations obtained with an analysis of variance witheither 2 factors, 2 levels per factor and a replication of 8or 4 factors, 4 levels per factor and a replication of 2. We analyze and compare the relative efficiencies of regression and variance analysis models and their implications to research strategy development. Three major considerations are evaluated: (1) short versus long time horizon (interval until effects of a decision are realized), (2) small versus large cost of erroneous rejection of the Null Hypothesis and (3) gross versus refined stage of development of the research study. A set of general guidelines towards improved designs is developed.