Gibb Dyer Jr. And W. Justin Dyer.

Gibb Dyer Jr. And W. Justin Dyer. "Putting the Family into Family Business

Research." Family Business Review, Vol. 22, No. 3 (September 2009): 216-219.

Retrieved October 13, 2012:

http://fbr.sagepub.com/content/22/3/216.full.pdf+html

This article explains some of the issues that arise in connection with measuring the effects of family relationships and family systems on family-owned or family-operated businesses. It regards families as complex systems representing numerous potential variables that could potentially impact the manner in which family-owned businesses are run. Using the metaphor of the iceberg, the authors suggest that most of the variables within family systems that are relevant to understanding the connection between family systems and businesses are unseen on the surface of a cursory examination and require a much more in-depth understanding of the family. According to the authors, it is important to have a basic understanding of family systems theory first, as a necessary element of understanding how family dynamics affect or shape the business.

One of the reasons that this kind of research is complex is that the family systems and processes can be regarded either as independent variables or as dependent variables, because the causal relationship between family dynamics and family business operations is bidirectional, since family dynamics can be both the source of the character of the business as well as results of the process of working together in the business. In that regard, family dynamics and business characteristics are more likely to exhibit a reciprocal relationship. In principle, the authors suggest that the first step to an effective research design requires longitudinal data relating family variables and business variables. The next step should incorporate controlled outcome variables to increase confidence levels in the initial data purporting to demonstrate causal relationships. More specifically, the authors suggest using a cross-lagged model to measure reciprocal relationships of cause and effect. Another suggested advantage supporting this approach is that it also permits measurement of the entire system based on latent variables and longitudinal studies instead of cross-sectional studies.