Erik E. Lehmann
Corporate governance is a recent concept that encompasses the costs caused by managerial misbehavior. It is concerned with how organizations in general, and corporations in particular, produce value and how that value is distributed among the members of the corporation, its stakeholders. The interrelation of value production and value distribution links the ubiquitous technological aspect (the production of value) with the moral and ethical dimension (the distribution of value). Corporate governance is concerned with this link in general, but more specifically with the moral and ethical dimensions of distributing the generated value among the stakeholders. Value in firms is created by firm-specific investments, and the motivation and coordination of value-enhancing activities and investment is protected by the power concentrated at the pyramidal top of the organization. In modern companies, it is the CEO and the top management who decide how to create value and how to distribute it among the relevant stakeholders. Due to asymmetric information and the imperfect nature of markets and contracts, adverse selection and moral hazard problems occur, where delegated (selected) managers could act in their own interest at the costs of other relevant stakeholders.
Corporate governance can be understood as a two-tailed concept. The first aspect is about identifying the (most) relevant stakeholder(s), separating theory and practice into two different and conflicting streams: the stakeholder value approach and the shareholder value approach. The second aspect of the concept is about providing and analyzing different mechanisms, reducing the costs induced by moral hazard and adverse selection effects, and balancing out the motivation and coordination problems of the relevant stakeholders. Corporate governance is an interdisciplinary concept encompassing academic fields such as finance, economics, accounting, law, taxation, and psychology, among others.
As countries differ according to their institutions (i.e., legal and political systems, norms, and rules), firms differ according to their size, age, dominant shareholders, or industries. Thus, concepts in corporate governance differ along these dimensions as well. And while the underlying characteristics vary in time, continuously or as a result of an exogenous shock, concepts in corporate governance are dynamic and static, offering a challenging field of interest for academics, policymakers, and firm managers.
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.
Paul D. Reynolds
In the late 1990s, there was considerable interest in national differences in entrepreneurial activity. The Global Entrepreneurship Monitor (GEM) research program was developed to provide harmonized, cross-national measures of participation in business creation; business creation was considered a critical aspect of entrepreneurship. This information was considered important for understanding the national characteristics associated with business creation and its subsequent impact on economic growth. The initial effort involved 10 countries in 1999. By 2014 Adult Population Surveys (APS) had been completed 705 times in 104 countries and with six special samples; this involved 2.3 million individual interviews. While there have been changes in the administrative structure and the focus of the annual global reports, the most significant data collection procedures have been stable since 2002. The GEM APS data sets are currently the only harmonized cross-national comparisons of business creation and business ownership. Designed to provide estimates of the prevalence of both business creation and existing firms, they also allow estimates of the total number of business ventures. GEM data sets are publically available three years after completion, providing a unique resource for assessing factors affecting business creation and its subsequent role in economic growth. Systematic assessments by national experts in participating countries provide measures of the national entrepreneurial framework conditions, complementing a variety of established measures of national economic and political characteristics.
There are three distinct features that characterize the GEM initiative: the unique organizational structure, the global reports summarizing annual assessments of entrepreneurial activity, and data sets assembled and made available for public use. The initial organizational structure, a collaborative arrangement among national teams, was replaced by membership in the Global Entrepreneurship Research Association (GERA) in 2004. The annual global reports emphasize comparisons among member countries, the annual national reports the country-specific situations. Both are designed to facilitate reality-based public policy.
Data collection for the APS provides harmonized comparisons of business creation across countries and within-country time series. The APS data has made clear the substantial variation among countries, by a factor of 10; that national levels of participation are very stable over time; that business creation is much more prevalent in poorer countries; that all segments of society are active in business creation; and that business creation is an important catalyst for the processes that lead to economic growth. The National Expert Survey (NES) questionnaire data provides information about the nature of the entrepreneurial framework in the GEN countries.
There is much to be learned about the relationships between national context, entrepreneurship, and economic growth. The unique information in the GEM data sets should continue to facilitate improved understanding of this important phenomenon.
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.
Nydia MacGregor and Tammy L. Madsen
Regulatory shocks, either by imposing regulations or easing them (deregulation), yield abrupt and fundamental changes to the institutional rules governing competition and, in turn, the opportunity sets available to firms. Formally, a regulatory shock occurs when jurisdictions replace one regulatory system for another. General forms of regulation include economic and social regulation but recent work offers a more fine-grained classification based on the content of regulations: regulation for competition, regulation of cap and trade, regulation by information, and soft law or experimental governance. These categories shed light on the types of rules and policies that change at the moment of a regulatory shock. As a result, they advance our understanding of the nature, scope, magnitude, and consequences of transformative shifts in rules systems governing industries. In addition to differences in the content of reforms, the assorted forms of regulatory change vary in the extent to which they disrupt an industry’s state of equilibrium or semi-equilibrium. These differences contribute to diverse temporal patterns or dynamics, an area ripe for further study. For example, a regulatory shock to an industry may be followed by rapid adjustment and, in turn, a new equilibrium state. Alternatively, the effects of a regulatory shock may be more enduring, contributing to ongoing dynamics and prolonging an industry’s convergence to new equilibrium state. As such, regulatory shocks can both stimulate ongoing heterogeneity or promote coherence within and among industries, sectors, organizational fields, and nation states. It follows that examining the content, scope, and magnitude of regulatory shocks is key to understanding their impact.
Since conforming to industry regulation (deregulation) increases economic returns, firms attempt to align their policies and behaviors with the institutional rules governing an industry. Thus, regulatory shocks stimulate the evaluation of strategic choices and, in turn, impact the competitive positions of firms and the composition of industries. Following a shock, at least two generic cohorts of firms emerge: incumbents, which are firms that operated in the industry before the change, and entrants, which start up after the change. To sustain a position, entrants must build capabilities from scratch whereas incumbents must replace or modify the practices they developed in the prior regulatory era. Not surprisingly, the ensuing competitive dynamics strongly influence the distribution of profits observed in an industry and the duration of firms’ profit advantages.
Our review highlights some of the prominent areas of research inquiry regarding regulatory shocks but many areas remain underexplored. Future work may benefit by considering regulatory shocks as embedded in a self-reinforcing system rather than simply an exogenous inflection in an industry’s evolutionary trajectory. Opportunities also exist for studying how the interplay of industry actors with actors external to an industry (political, social) affects the temporal and competitive consequences of regulatory shocks.
Alexander Bolinger and Mark Bolinger
There is currently great enthusiasm for entrepreneurship education and the economic benefits that entrepreneurial activity can generate for individuals, organizations, and communities. Beyond economic outcomes, however, there is a variety of social and emotional costs and benefits of engaging in entrepreneurship that may not be evident to students nor emphasized in entrepreneurship courses. The socioemotional costs of entrepreneurship are consequential: on the one hand, entrepreneurs who pour their time and energy into new ventures can incur costs (e.g., ruptured personal and professional relationships, decreased life satisfaction and well-being, or strong negative reactions such as grief) that can often be as or more personally disruptive and enduring than economic costs. On the other hand, the social and emotional benefits of an entrepreneurial lifestyle are often cited as intrinsically satisfying and as primary motivations for initiating and sustaining entrepreneurial activity.
The socioemotional aspects of entrepreneurship are often poorly understood by students, but highlighting these hidden dimensions of entrepreneurial activity can inform their understanding and actions as prospective entrepreneurs. For instance, entrepreneurial passion, the experience of positive emotions as a function of engaging in activities that fulfill one’s entrepreneurial identity, and social capital, whereby entrepreneurs build meaningful relationships with co-owners, customers, suppliers, and other stakeholders, are two specific socioemotional benefits of entrepreneurship. There are also several potential socioemotional costs of entrepreneurial activity. For instance, entrepreneurship can involve negative emotional responses such as grief and lost identity from failure. Even when an entrepreneur does not fail, the stress of entrepreneurial activity can lead to sleep deprivation and disruptions to both personal and professional connections. Then, entrepreneurs can identify so closely and feel so invested that they experience counterproductive forms of obsessive passion that consume their identities and impair their well-being.
Briance Mascarenhas and Megan Mascarenhas
A strategic group is defined as a set of firms within an industry pursuing a similar strategy. The strategic group concept emerged with much promise over 40 years ago. Research on strategic groups over time in a broad variety of settings has sought to clarify their theoretical and empirical properties. These research findings are gradually being translated into practical managerial guidance, so that the strategic group concept can be understood, operationalized, and used productively by managers.
Two main approaches exist for identifying strategic groups—a ground-up approach, using disaggregated data, and a top-down, using cognition. Once identified, managerial insights can be derived from clarifying a strategic group’s profile. Firm membership in a group helps to uncover immediate and more distant types of competitors. Group profitability differences reveal the more rewarding and less attractive areas within an industry, as well as identify the lower-return groups from where firm exits are likely to occur. Group dynamics reflect competitive and cooperative behavior within and between groups. Several promising areas for future research on strategic groups to improve understanding and practice of strategy.
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.