This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Business and Management. Please check back later for the full article.
Since the dawn of artistic pursuits by human beings, the artist has been thought of as having a special sphere of influence for representing feelings, emotions, and human conditions through their art. Fast forward to the early days of arts apprenticeship and education, and we can generally conclude that the domain of arts education prepares artists for such representation of feelings and emotions. But what is missing from arts education are skill sets needed to manage the economic realities of artistic pursuits. This skill gap perhaps gave birth to the starving-artist myth, a notion that has endured since the early 1600s. Passion and desire for artistic expression are considered superior to business and economic considerations. Throw into this situation concern for social justice, ethics, and political invective, and a mix of dichotomies emerges. Also, consider that entrepreneurship is primarily an economic behavior. Some suggest that arts entrepreneurship lacks empirical studies, and thus lacks legitimacy. The concepts presented in this discussion include observations in preparing arts entrepreneurs for success as defined by themselves.
As one of the early developers of arts entrepreneurship curriculum, I was expected to define the domain of arts entrepreneurship. Added to this expectation are my duties as director of the Coleman Fellows Program. This task includes the need for developing effective pedagogical constructs that can cultivate arts entrepreneurship modules and lesson plans across the Coleman Fellows Program. Based on my own entrepreneurial experiences, my non-academic approach to this work is viewed by artists as “commercializing the arts” and seen as a polluter of the purist methodology to arts. My colleagues who teach entrepreneurship label this differently. Some say the approach is creative; others think it pollutes entrepreneurship education. Because of these unexpected but different and sometimes passionate reactions from groups of educators and artists, I started investigating the revenue models of arts-based industries with the hope of bridging these dichotomies. The age-old adage “follow the money” seemed to be a good approach to better understand such reactions.
The key sources of information for this discussion include the Coleman Fellows Program, a nationwide program initiated and supported by the Coleman Foundation, located in Chicago, Illinois. I also rely on 30 interviews with arts faculty and 32 interviews with student artists. Added to these sources of data is my work with the Arts Entrepreneurship Special Interest Group, which I helped create and led for a few years with the United States Association for Small Business and Entrepreneurship (USASBE), a member of International Council of Small Business (ICSB). I also include contributions to this field by Linda Essig, publisher of Artivate, the very first journal dedicated to entreprenuership in the arts, and Gary Beckman’s doctoral thesis and his subsequent writings.
Family business is a multidisciplinary subject area of critical importance to practitioners. The global volume of family business owners and managers is enormous. The firms are significant components of national economies. Yet they are often underappreciated and have been under-represented in business and economic research. Scholars have the potential for contributing to the survival and prosperity of these firms. The boundaries of the field are ill-defined. Family business scholars are seeking recognition from their colleagues. Opportunities for future research are unlimited.
Entrepreneurship is a critical driver of economic health, industrial rejuvenation, social change, and technological progress. In an attempt to determine how to best support such an important component of society, researchers and practitioners alike continue to ask why some countries, regions, and cities have more entrepreneurship than others. Unfortunately, the answer is not clear. This question is addressed by focusing on location-based support or infrastructure for entrepreneurship. A framework based on a social systems perspective guides this examination by concentrating on three main categories of infrastructure: resource endowments, institutional arrangements, and proprietary functions. Work from the knowledge-based perspective of entrepreneurship, systems of innovation, entrepreneurial ecosystems, and resource dependence literatures is integrated into this framework.
Heather A. Haveman and Gillian Gualtieri
Research on institutional logics surveys systems of cultural elements (values, beliefs, and normative expectations) by which people, groups, and organizations make sense of and evaluate their everyday activities, and organize those activities in time and space. Although there were scattered mentions of this concept before 1990, this literature really began with the 1991 publication of a theory piece by Roger Friedland and Robert Alford. Since that time, it has become a large and diverse area of organizational research. Several books and thousands of papers and book chapters have been published on this topic, addressing institutional logics in sites as different as climate change proceedings of the United Nations, local banks in the United States, and business groups in Taiwan. Several intellectual precursors to institutional logics provide a detailed explanation of the concept and the theory surrounding it. These literatures developed over time within the broader framework of theory and empirical work in sociology, political science, and anthropology. Papers published in ten major sociology and management journals in the United States and Europe (between 1990 and 2015) provide analysis and help to identify trends in theoretical development and empirical findings. Evaluting these trends suggest three gentle corrections and potentially useful extensions to the literature help to guide future research: (1) limiting the definition of institutional logic to cultural-cognitive phenomena, rather than including material phenomena; (2) recognizing both “cold” (purely rational) cognition and “hot” (emotion-laden) cognition; and (3) developing and testing a theory (or multiple related theories), meaning a logically interconnected set of propositions concerning a delimited set of social phenomena, derived from assumptions about essential facts (axioms), that details causal mechanisms and yields empirically testable (falsifiable) hypotheses, by being more consistent about how we use concepts in theoretical statements; assessing the reliability and validity of our empirical measures; and conducting meta-analyses of the many inductive studies that have been published, to develop deductive theories.
Tracking the Entrepreneurial Process with the Panel Study of Entrepreneurial Dynamics (PSED) Protocol
Paul D. Reynolds
In the early 1990s business creation was receiving a great deal of attention after it was clear that new firms were a major source of job creation. There was not, however, reliable data on the prevalence of persons participating in firm creation, what they would do to implement new ventures, or the proportion of start-up efforts that became profitable businesses. This hiatus led to the development of longitudinal studies of the entrepreneurial process; 14 projects have now been implemented in 12 countries.
The Panel Study of Entrepreneurial Dynamics (PSED) protocol was designed to provide estimates of the prevalence of individuals involved in business creation and the presence of pre-profit, start-up ventures; data on the major activities undertaken to implement a new firm; and track the proportion that completed the transition from start-up to profitable new firm. A number of challenges were involved in implementing the research program, including the development of efficient procedures for identifying representative samples of nascent entrepreneurs and criteria for determining the dates of entry into the start-up process, the transition to a profitable business, and disengagement from the initiative.
Data collection is a three-stage process. The initial stage is identifying nascent entrepreneurs in a representative sample of adults. The second are detailed interviews on the start-up team and activity related to creating a new venture. The third stage is follow-up interviews completed to determine the outcome of the start-up efforts. A large number of scholars have been involved in development of the interviews and the PSED data sets have considerable information on the perspectives, activities, and strategies of those involved in the start-up process.
Since the initial data sets were made available 15 years ago, there has been considerable research utilizing PSED data sets. One major finding, however, is that the firm creation process is much more diverse and complicated than had been expected. There are substantial research opportunities to be explored. A review of the major features of the PSED protocol and a summary of the existing data sets provides background that will facilitate additional analysis of the firm creation process. Four data sets (Australia, Sweden, and U.S. PSED I & II) are now in the public domain. Critical features of the start-up process have been consolidated and harmonized in a five-cohort, four country data set which is also available.
Kathleen R. Allen
For decades researchers have studied various aspects of the technology transfer and commercialization process in universities in hopes of discovering effective methods for enabling more research to leave the university as technologies that benefit society. However, this effort has fallen short, as only a very small percentage of applied research finds its way to the marketplace through licenses to large companies or to new ventures. Furthermore, the reasons for this failure have yet to be completely explained.
In some respects, this appears to be an ontological problem. In their effort to understand the phenomenon of university commercialization, researchers tend to reduce the process into its component parts and study each part in isolation. The result is conclusions that ignore a host of variables that interact with the part being studied and frameworks that describe a linear process from invention to market rather than a complex system. To understand how individuals in the technology commercialization system make strategic choices around outcomes, studies have been successful in identifying some units of analysis (the tech transfer office, the laboratory, the investment community, the entrepreneurship community); but they have been less effective at integrating the commercialization process, contexts, behaviors, and potential outcomes to explain the forces and reciprocal interactions that might alter those outcomes.
The technology commercialization process that leads to new technology products and entrepreneurial ventures needs to be viewed as a complex adaptive system that operates under conditions of risk and uncertainty with nonlinear inputs and outputs such that the system is in a constant state of change and reorganization. There is no overall project manager managing tasks and relationships; therefore, the individuals in the system act independently and codependently. No single individual is aware of what is going on in any other part of the system at any point in time, and each individual has a different agenda with different metrics on which their performance is judged. What this means is that a small number of decision makers in the university commercialization system can have a disproportionate impact on the effectiveness and success of the entire system and its research outcomes.
Critics of reductionist research propose that understanding complex adaptive systems, such as university technology commercialization, requires a different mode of thinking—systems thinking—which looks at the interrelationships and dependencies among all the parts of the system. Combined with real options reasoning, which enables resilience in the system to mitigate uncertainty and improve decision-making, it may hold the key to better understanding the complexity of the university technology commercialization process and why it has not been as effective as it could be.