Jon C. Carr

Bio: Jon C. Carr is an academic researcher from North Carolina State University. The author has contributed to research in topics: Entrepreneurship & Social capital. The author has an hindex of 26, co-authored 62 publications receiving 4124 citations. Previous affiliations of Jon C. Carr include Texas Christian University & Babson College.

Toward a Theory of Familiness: A Social Capital Perspective

Abstract: In the search for ways in which the family firm context is unique to organizational science, the construct of “familiness” has been identified and defined as resources and capabilities that are unique to the family’s involvement and interactions in the business. While identification and isolation of a construct unique to family firms is both groundbreaking and important for family firm research, it is also important that the development of the construct continues to be examined from complementing theoretical viewpoints. As such, we set out to review the development of the familiness construct and identify its dimensions. We also explore the nomological relationships of the construct based on a social capital theory perspective and offer a theory of familiness.

Abusive Supervision, Upward Maintenance Communication, and Subordinates' Psychological Distress

Abstract: This study reanalyzes data from Tepper's (2000) two-wave study regarding the effects of subordinates' perceptions of supervisory abuse to assess previously unexamined relationships. As predicted, we found that subordinates who more rather than less strongly perceived that they had been abused by supervisors tended to use regulative maintenance tactics with higher frequency. Further, the positive relationship between abusive supervision and subordinates' psychological distress was exacerbated by subordinates' use of regulative maintenance communications, and that relationship was reduced by subordinates' use of direct maintenance communication. Theoretical and practical implications are discussed.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

Abstract: What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

On making causal claims : A review and recommendations

Abstract: Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity – which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error – renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.