## 121-140 of 269 Results

### Article

Hypothesis testing is an approach to statistical inference that is routinely taught and used. It is based on a simple idea: develop some relevant speculation about the population of individuals or things under study and determine whether data provide reasonably strong empirical evidence that the hypothesis is wrong. Consider, for example, two approaches to advertising a product. A study might be conducted to determine whether it is reasonable to assume that both approaches are equally effective. A Type I error is rejecting this speculation when in fact it is true. A Type II error is failing to reject when the speculation is false. A common practice is to test hypotheses with the type I error probability set to 0.05 and to declare that there is a statistically significant result if the hypothesis is rejected. There are various concerns about, limitations to, and criticisms of this approach. One criticism is the use of the term significant. Consider the goal of comparing the means of two populations of individuals. Saying that a result is significant suggests that the difference between the means is large and important. But in the context of hypothesis testing it merely means that there is empirical evidence that the means are not equal. Situations can and do arise where a result is declared significant, but the difference between the means is trivial and unimportant. Indeed, the goal of testing the hypothesis that two means are equal has been criticized based on the argument that surely the means differ at some decimal place. A simple way of dealing with this issue is to reformulate the goal. Rather than testing for equality, determine whether it is reasonable to make a decision about which group has the larger mean. The components of hypothesis-testing techniques can be used to address this issue with the understanding that the goal of testing some hypothesis has been replaced by the goal of determining whether a decision can be made about which group has the larger mean. Another aspect of hypothesis testing that has seen considerable criticism is the notion of a p-value. Suppose some hypothesis is rejected with the Type I error probability set to 0.05. This leaves open the issue of whether the hypothesis would be rejected with Type I error probability set to 0.025 or 0.01. A p-value is the smallest Type I error probability for which the hypothesis is rejected. When comparing means, a p-value reflects the strength of the empirical evidence that a decision can be made about which has the larger mean. A concern about p-values is that they are often misinterpreted. For example, a small p-value does not necessarily mean that a large or important difference exists. Another common mistake is to conclude that if the p-value is close to zero, there is a high probability of rejecting the hypothesis again if the study is replicated. The probability of rejecting again is a function of the extent that the hypothesis is not true, among other things. Because a p-value does not directly reflect the extent the hypothesis is false, it does not provide a good indication of whether a second study will provide evidence to reject it. Confidence intervals are closely related to hypothesis-testing methods. Basically, they are intervals that contain unknown quantities with some specified probability. For example, a goal might be to compute an interval that contains the difference between two population means with probability 0.95. Confidence intervals can be used to determine whether some hypothesis should be rejected. Clearly, confidence intervals provide useful information not provided by testing hypotheses and computing a p-value. But an argument for a p-value is that it provides a perspective on the strength of the empirical evidence that a decision can be made about the relative magnitude of the parameters of interest. For example, to what extent is it reasonable to decide whether the first of two groups has the larger mean? Even if a compelling argument can be made that p-values should be completely abandoned in favor of confidence intervals, there are situations where p-values provide a convenient way of developing reasonably accurate confidence intervals. Another argument against p-values is that because they are misinterpreted by some, they should not be used. But if this argument is accepted, it follows that confidence intervals should be abandoned because they are often misinterpreted as well. Classic hypothesis-testing methods for comparing means and studying associations assume sampling is from a normal distribution. A fundamental issue is whether nonnormality can be a source of practical concern. Based on hundreds of papers published during the last 50 years, the answer is an unequivocal Yes. Granted, there are situations where nonnormality is not a practical concern, but nonnormality can have a substantial negative impact on both Type I and Type II errors. Fortunately, there is a vast literature describing how to deal with known concerns. Results based solely on some hypothesis-testing approach have clear implications about methods aimed at computing confidence intervals. Nonnormal distributions that tend to generate outliers are one source for concern. There are effective methods for dealing with outliers, but technically sound techniques are not obvious based on standard training. Skewed distributions are another concern. The combination of what are called bootstrap methods and robust estimators provides techniques that are particularly effective for dealing with nonnormality and outliers. Classic methods for comparing means and studying associations also assume homoscedasticity. When comparing means, this means that groups are assumed to have the same amount of variance even when the means of the groups differ. Violating this assumption can have serious negative consequences in terms of both Type I and Type II errors, particularly when the normality assumption is violated as well. There is vast literature describing how to deal with this issue in a technically sound manner.

## An Identity Lens to Understand Teams in International Business

In international business, teams can take on a variety of forms, including domestic collocated teams, multinational collocated teams, global virtual teams, and multicultural teams. All of these types of teams offer the potential for developing innovative products and services, but they also may face substantial challenges with respect to collaboration and coordination. Team members are likely to identify with a variety of affiliations, based on dimensions such as gender, family roles, ethnicity, culture, nation, profession, organization, and team. Identification with each of these social groups brings with it the opportunity for diverse insights and perspectives, skill breadth, and broad social connections. However, this can lead to both benefits and challenges for teams. As a result, the ability to negotiate identities has become critical in international business. Drawing upon concepts of social identity, an identity lens can be used to document the promise and problems of teams in international business. An understanding of how multiple identity interactions within an individual can affect processes and outcomes for the team has the potential to create a more nuanced comprehension of international teams.

## Immigrant Entrepreneurship: A Typology Based on Historical and Contemporary Evidence

Immigrant entrepreneurs are different, and they are everywhere. They can be unambiguously distinguished from entrepreneurs without a migration background. They operate under distinct conditions and respond to unique opportunities and challenges. They have specific motivational, economic, and social resources at their disposal, for example, ethnic solidarity and international networks. Their knowledge of languages and cultures, as well as the high pressure to integrate themselves into a new society, can be factors that stimulate entrepreneurship and innovation. It is hard to find countries with no immigrant entrepreneurs. In many places like the United States, Canada, or South East Asia, they play a substantial economic role. The ubiquity, dynamism, and significance of immigrant entrepreneurs has led to a spate of research projects since the 1990s, especially by economic sociologists and ethnologists, but also by management scholars and historians. On the basis of their work, the article distinguishes six different ideal types of immigrant entrepreneurs, even though these categories are neither clear-cut nor mutually exclusive. Necessity entrepreneurs react to blocked careers in other areas and often set up small, precarious businesses, out of which in exceptional cases more viable companies emerge. Diaspora merchants are part of commercial networks of people with the same ethnic background who live in foreign countries and trade with each other. Transnational entrepreneurs are not necessarily part of networks and do not always engage in mercantile activities. This category also encompasses individual actors and industrial activities. They are characterized by the ability to mobilize resources in several countries and facilitate activities between different countries. Middleman minorities stand between the majority society and third parties, often minorities. They fill niches that are left by indigenous businesses, which consider these areas as unattractive. Entrepreneurs in ethnic enclave economies live and work with their co-ethnics in neighborhoods defined by their group. Their main function is to cater to their own communities, often with ethnic products such as food or publications from their countries of origin. Refugee entrepreneurs leave their home country involuntarily, often driven out by violence and expropriation. In most cases their emigration is unprepared. Starting conditions in the country of destination are unfavorable. Conversely, the pressure for social integration is pronounced and can act as an impulse for self-employment. There are, however, cases in which refugees are consciously patronized or even summoned by the governments of the receiving countries, turning them into a highly privileged group.

## The Impact of Corporate Governance on Firms’ International Strategies

The structure and characteristics of firms’ corporate governance influence the internationalization choices of companies, impacting different and heterogeneous features. The international business literature focuses on two fundamental characteristics of corporate governance: ownership and board of directors. The features of different shareholders and the level of ownership shares result in different global strategies and objectives for multinational companies. Considering the executive level, the characteristics of the different directors involved in the governance may influence investment choices and relations with different stakeholders in different countries. Corporate governance is therefore a fundamental dimension to be taken into account in international business research, with special reference to two particular types of companies: family- and state-owned firms. Ownership and the board of directors of these companies present specific corporate governance features and dynamics that expand the classical theory of internationalization. The focus on these two types of firms helps to understand and describe the current global context and the set of decisions and different policies that influence the different choices related to firms’ internationalization strategies.

## The Impact of Diversity Training Programs in the Workplace and Alternative Bias Reduction Mechanisms

Corporations often start their diversity journey by providing their managers or all workers with diversity training. These trainings were first offered as race-relations sessions in the 1960s and are among the most popular tools for diversity managers. Diversity training programs have changed their content during the decades, but they usually include live or online explanations about unlawful discrimination and bias, often supplemented by discussions of cultural differences and business needs for diversity. Despite their popularity and often high costs, a large body of research conducted over decades shows that most diversity training programs do not lead to long-term improvements in participants’ bias, attitudes, behavior, or workforce diversity. Some studies also show that training has negative effects on bias and diversity. Factors that impede the success of diversity training or make them backfire include the hardwiring of cognitive biases and people’s complex reactions to direct attempts to change their biases, as well as the broader systemic biases rooted in everyday organizational routines. These suggest that common diversity training simply may not be the right tool for reducing bias and generating the changes needed for increasing workplace diversity. Some studies suggest that trainings’ effects could be improved by carefully designing them. This includes: avoiding training features that increase participants’ alienation, such as mandatory attendance, quizzes, and legalistic content; designing long-term training such that meaningful learning can be achieved; calibrating training to specific organizational challenges rather than using off-the-shelf content; and including ongoing collaborative contact with members of underrepresented groups and integrating training as part of broader diversity and accountability efforts. More research is needed to determine whether these types of training indeed produce sustained improvements in bias and diversity. Alternative bias reduction mechanisms can be found in popular management models that increase collaboration between workers, such as cross-functional teams and cross-training. Such collaborative teams and training improve corporate performance and, as a byproduct, also reduce bias. Cognitive biases are affected by the work contexts in which individuals operate. Highly segregated workplaces, where White people and men meet women and people of color (or other underrepresented groups) primarily in marginalized jobs, deepen group boundaries and strengthen stereotypes. When organizations create cross-functional collaborations using self-directed teams and cross-training, workers from different groups have more opportunities to collaborate and, as studies show, biases and group boundaries are reduced, and leadership diversity increases.

## Individualism-Collectivism: A Review of Conceptualization and Measurement

The concept of individualism-collectivism (I-C) has been a prominent construct in philosophy, political science, sociology, psychology, and organization and management. Its meaning may vary greatly in scope, content, and levels of analysis, depending on the fields of inquiry and the phenomenon of interest. We focus on I-C as it relates to values, identities, motives, and behaviors in the context of organization and management. At its core, I-C is about self-collective relationships and the impact they have on the relational dynamics and outcomes at various levels of analysis. Theory and research have identified patterns of contrasts between individualism and collectivism. While the individualist orientation emphasizes individual self-identity, personal agency, and values that tend to prioritize individuals over collectives, the collectivist orientation emphasizes individuals’ collective identity, collective agency, and values that tend to prioritize collectives over individuals. Various I-C conceptions have been critically evaluated with the focus on basic assumptions regarding the nature of individualism and collectivism as unidimensional, bidimensional, or multidimensional constructs, and whether or not individualism and collectivism are conceived as inherently oppositional or complementary to form a high-order construct. Specifically, previous reviews of culture and value studies in general, and of I-C studies in particular, acknowledge the possibility that individualist and collectivist orientations may coexist within a diverse society, organization, or group, and that those orientations may change over time or evolve to tackle emergent survival challenges. However, most previous reviews continue to focus on the unitary construct of I-C composed of two entities as polar opposites of each other, the high of one meaning the low of the other. Over time, instead of or in addition to the initial unidimensional conception of I-C, research has adopted the bidimensional or multidimensional conceptions. Furthermore, more of bi- or multidimensional conceptions have adopted the unipolar approach. That is, maintaining I-C as a high-order construct, individualism and collectivism are conceived as independent dimensions of I-C, each varies on a separate continuum, making it possible that individuals, groups and societies may be categorized on the various combinations of individualism and collectivism. The advantages of the multidimensional approach have been emphasized, but issues of conceptual muddiness have also been raised, together with the challenges of theory-based research. It is recommended that I-C researchers be mindful of conceptual equivalence in developing I-C constructs and measurements and consider the optimal distinctiveness theory and the dialectic perspective as two potential overarching perspectives for comparative research on I-C. Finally, areas of future research have been identified as fertile fields for generating knowledge and understanding of I-C.

## Inferential Statistics

Inferential statistical methods stem from the distinction between a sample and a population. A sample refers to the data at hand. For example, 100 adults may be asked which of two olive oils they prefer. Imagine that 60 say brand A. But of interest is the proportion of all adults who would prefer brand A if they could be asked. To what extent does 60% reflect the true proportion of adults who prefer brand A? There are several components to inferential methods. They include assumptions about how to model the probabilities of all possible outcomes. Another is how to model outcomes of interest. Imagine, for example, that there is interest in understanding the overall satisfaction with a particular automobile given an individual’s age. One strategy is to assume that the typical response Y , given an individuals age, X , is given by Y = β 0 + β 1 X , where the slope, β 1 , and intercept, β 0 , are unknown constants, in which case a sample would be used to make inferences about their values. Assumptions are also made about how the data were obtained. Was this done in a manner for which random sampling can be assumed? There is even an issue related to the very notion of what is meant by probability. Let μ denote the population mean of Y . The frequentist approach views probabilities in terms of relative frequencies and μ is viewed as a fixed, unknown constant. In contrast, the Bayesian approach views μ as having some distribution that is specified by the investigator. For example, it may be assumed that μ has a normal distribution. The point is that the probabilities associated with μ are not based on the notion of relative frequencies and they are not based on the data at hand. Rather, the probabilities associated with μ stem from judgments made by the investigator. Inferential methods can be classified into three types: distribution free, parametric, and non-parametric. The meaning of the term “non-parametric” depends on the situation as will be explained. The choice between parametric and non-parametric methods can be crucial for reasons that will be outlined. To complicate matters, the number of inferential methods has grown tremendously during the last 50 years. Even for goals that may seem relatively simple, such as comparing two independent groups of individuals, there are numerous methods that may be used. Expert guidance can be crucial in terms of understanding what inferences are reasonable in a given situation.

## Information Technology Project Risk as a Dynamic Phenomenon

Information systems (IS) research provides strong evidence for the effect of information technology (IT) project risk and on-project failure. However, no consensus has yet been reached on what constitutes risk and how it should be specified. Existing definitions of the risk construct are diverse leading to fragmented scientific knowledge. This article specifies IT project residual risk as an aggregate multidimensional construct comprised of four dimensions: project sources, undesirable events, risk management mechanisms, and expected outcomes. The construct accentuates the dynamic nature of IT project risk and can help reorganize the abundant risk factors found in the IS literature under its four dimensions while exposing their interactions.

## Infrastructure for Entrepreneurship

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.

## Innovation Challenges

Starting from early 21st century, companies increasingly use open innovation challenges to generate creative solutions to business problems. This revolution in business models and management strategy reflects the evolution supported by new technology. Employing this new strategic model, companies seek to innovate in a wide variety of areas, such as clothes designs, photography solutions, business plans, and film production. Contrary to closed innovation through which companies develop creative ideas internally, innovation challenges are catalyzed by socioeconomic changes such as the rapid advancement of information technologies, increased labor division, as well as ever-expanding globalization. Going hand in hand are trends such as outsourcing, occurring in parallel in the management area, which makes companies more agile and flexible. Multifaceted and multidimensional, open innovation challenges consist of various activities such as inbound innovation (acquiring and sourcing), outbound innovation (selling and revealing), or a compound mix of these two forms. It also pertains to complementary assets, absorptive capacity, organizational exploration, and exploitation. In an attempt to determine how to best support such an important component of society, scholars and practitioners continue to pursue effective innovation challenge architecture (the art or practice that guides participants’ interactions and exchange) that allows open collaboration among the crowd, as well as an approach for incorporating such architecture into technological platforms in order to improve the crowd’s creativity. This issue is addressed by focusing on existing research that delineates various types of effective architecture of innovation challenges. A theory-based framework guides this examination, and work from various scholarly perspectives of innovation challenges, knowledge management, motivated knowledge sharing, and crowdsourcing are integrated into this framework.

## Innovation Ecosystems in Management: An Organizing Typology

The concept of an “ecosystem” is increasingly used in management and business to describe collectives of heterogeneous, yet complementary organizations who jointly create some kind of system-level output, analogous to an “ecosystem service” delivered by natural ecosystems, which extends beyond the outputs and activities of any individual participant of the ecosystem. Due to its attractiveness and elasticity, the ecosystem concept has been applied to a wide range of phenomena by a variety of scholarly perspectives and under varying monikers such as “innovation ecosystems,” “business ecosystems,” “technology ecosystems,” “platform ecosystems,” “entrepreneurial ecosystems,” and “knowledge ecosystems.” This conceptual and application heterogeneity has contributed to conceptual and terminological confusion, which threatens to undermine the utility of the concept in supporting cumulative insight. In this article, we seek to reintroduce some order into this conceptual heterogeneity by reviewing how the ecosystem concept has been applied to variably overlapping phenomena and by highlighting key terminological and conceptual inconsistencies and their sources. We find that conceptual inconsistency in the ecosystem terminology relates to two key dimensions: the “unit” of analysis and the type of “ecosystem service”—that is the ecosystem output collectively generated. We then argue that although there is considerable heterogeneity in application, the concept nevertheless offers promise in its potential to support insights that are distinctive relative to other concepts describing collectives of organizations, such as those of “industry,” “supply chain,” “cluster,” and “network.” We also find that despite such proliferation, the concept nevertheless describes collectives that are distinctive in that they uniquely combine participant heterogeneity, coherence of ecosystem outputs, participant interdependence, and nonhierarchical governance. Based on our identified dimensions of conceptual heterogeneity, we offer a typology of the different ecosystem concepts, thereby helping reorganize this proliferating domain. The typology is based upon three distinct ecosystem outputs—ecosystem-level value offering for a defined audience, the collective generation of business model innovation, and the collective generation of research-based knowledge—and three research emphases that resonate with alternative “units” of analysis—community dynamics, output cogeneration, and interdependence management. Together, these allow us to clearly differentiate between the concepts of innovation ecosystems, business ecosystems, platform ecosystems, technology ecosystems, entrepreneurial ecosystems, and knowledge ecosystems. Based on the three distinct types of ecosystem outputs, our typology identifies three major types of ecosystems: innovation ecosystems, entrepreneurial ecosystems, and knowledge ecosystems. Under the rubric of “innovation ecosystems,” we further distinguish between business ecosystems, modular ecosystems, and platform ecosystems. We conclude by considering innovation ecosystem dynamics, highlighting the important role of digitalization, and reviewing the implications of our model for ecosystem emergence, competition, coevolution, and resilience.

## Innovation for Society

At a macro level, innovation for society refers to innovation of societal institutions. At a micro level, it refers to innovations undertaken by social entrepreneurs as start-ups with a social and/or environmental mission and innovations undertaken by firms in products/services, processes, operations, technologies, and business models to address social and environmental challenges while achieving core economic objectives. The focus here is on firm-level innovations and the drivers for such innovations. Exogenous drivers include institutional-level influences such as regulations, societal norms, and industry best practices (mimetic forces) and stakeholder-level influences including shareholders, investors, customers, regulators, nongovernmental organizations, media, and others that have power, legitimacy, and urgency of their claims directly or indirectly via other stakeholders. The endogenous drivers include institutional ownership, activist shareholders, boards of directors, ownership, and competitive strategy focused on developing profitable businesses that address societal challenges. Even when the firm is motivated due to exogenous and endogenous drivers to undertake investments in innovating for society, it needs the capacity to generate and implement such innovations. Innovations for society require motivated managers, managerial capacity, and organizational capabilities that go beyond routine innovations that firms undertake to improve products and processes and enter new markets. This capacity enables firms to reconcile their performance on economic, social, and environmental metrics to address societal challenges while achieving core economic objectives. Managerial capacity requires firms to overcome cognitive biases and create opportunity frames that convert negative loss bias, where managers perceive lack of control over outcomes, to a positive opportunity bias, where managers perceive the ability to control their decisions and actions. Opportunity framing involves legitimization of innovation for society in the corporate identity, integration of sustainability metrics into performance evaluation, creation of discretionary slack, and empowerment of managers with a relevant and ongoing information flow. Innovating for society also requires major changes in a firm’s decision-making processes and investments in new organizational capabilities of engaging stakeholders and integration of external learning, processes of continuous improvement of operations, higher order or double-loop organizational learning by integrating external learning with internal knowledge, cross-functional integration, technology portfolios, and strategic proactivity, all leading to processes of continuous innovation. Knowledge about the role of firms in addressing societal challenges has grown over the past three decades as scholars in multiple disciplines have explained the motivations of firms to undertake innovations for society, processes to build organizational capabilities to adopt and implement sustainability strategies, and linkages of such strategies to financial performance. Nevertheless, such innovations and strategies are far from a universal norm.

## Innovation in Artificial Intelligence: Illustrations in Academia, Apparel, and the Arts

Artificial intelligence (AI), commonly defined as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation,” can be classified into analytical, human-inspired, and humanized AI depending upon its application of cognitive, emotional, and social intelligence. AI’s foundations took place in the 1950s. A sequence of vicissitudes of funding, interest in, and support for AI followed subsequently. In 2015 AlphaGo, Google’s AI-driven system, won against the human grandmaster in the highly complex board game Go. This is considered one of the most significant milestones in the development of AI and marks the starting of a new period, enabling several AI innovations in a variety of sectors and industries. Higher education, the fashion industry, and the arts serve as illustrations of areas wherein ample innovation based on AI occurs. Using these domains, various angles of innovation in AI can be presented and decrypted. AI innovation in higher education, for example, indicates that at some point, AI-powered robots might take over the role of human teachers. For the moment, however, AI in academia is solely used to support human beings, not to replace them. The apparel industry, specifically fast fashion—one of the planet’s biggest polluters—shows how innovation in AI can help the sector move toward sustainability and eco-responsibility through, among other ways, improved forecasting, increased customer satisfaction, and more efficient supply chain management. An analysis of AI-driven novelty in the arts, notably in museums, shows that developing highly innovative, AI-based solutions might be a necessity for the survival of a strongly declining cultural sector. These examples all show the role AI already plays in these sectors and its likely importance in their respective futures. While AI applications imply many improvements for academia, the apparel industry, and the arts, it should come as no surprise that it also has several drawbacks. Enforcing laws and regulations concerning AI is critical in order to avoid its adverse effects. Ethics and the ethical behavior of managers and leaders in various sectors and industries is likewise crucial. Education will play an additional significant role in helping AI positively influence economies and societies worldwide. Finally, international entente (i.e., the cooperation of the world’s biggest economies and nations) must take place to ensure AI’s benefit to humanity and civilization. Therefore, these challenges and areas (i.e., enforcement, ethics, education, and entente) can be summarized as the four summons of AI.

## Innovation Indicators

Innovation indicators support research on innovation and the development of innovation policy. Once a policy has been implemented, innovation indicators can be used to monitor and evaluate the result, leading to policy learning. Producing innovation indicators requires an understanding of what innovation is. There are many definitions in the literature, but innovation indicators are based on statistical measurement guided by international standard definitions of innovation and of innovation activities. Policymakers are not just interested in the occurrence of innovation but in the outcome. Does it result in more jobs and economic growth? Is it expected to reduce carbon emissions, to advance renewable energy production and energy storage? How does innovation support the Sustainable Development Goals? From the innovation indicator perspective, innovation can be identified in surveys, but that only shows that there is, or there is not, innovation. To meet specific policy needs, a restriction can be imposed on the measurement of innovation. The population of innovators can be divided into those meeting the restriction, such as environmental improvements, and those that do not. In the case of innovation indicators that show a change over time, such as “inclusive innovation,” there may have to be a baseline measurement followed by a later measurement to see if inclusiveness is present, or growing, or not. This may involve social as well as institutional surveys. Once the innovation indicators are produced, they can be made available to potential users through databases, indexes, and scoreboards. Not all of these are based on the statistical measurement of innovation. Some use proxies, such as the allocation of financial and human resources to research and development, or the use of patents and academic publications. The importance of the databases, indexes, and scoreboards is that the findings may be used for the ranking of “innovation” in participating countries, influencing their behavior. While innovation indicators have always been influential, they have the potential to become more so. For decades, innovation indicators have focused on innovation in the business sector, while there have been experiments on measuring innovation in the public (general government sector and public institutions) and the household sectors. Historically, there has been no standard definition of innovation applicable in all sectors of the economy (business, public, household, and non-profit organizations serving households sectors). This changed with the Oslo Manual in 2018, which published a general definition of innovation applicable in all economic sectors. Applying a general definition of innovation has implications for innovation indicators and for the decisions that they influence. If the general definition is applied to the business sector, it includes product innovations that are made available to potential users rather than being introduced on the market. The product innovation can be made available at zero price, which has influence on innovation indicators that are used to describe the digital transformation of the economy. The general definition of innovation, the digital transformation of the economy, and the growing importance of zero price products influence innovation indicators.

### Article

The involvement of families in firms’ ownership, management, and governance is a key driver of organizational attitudes, behaviors, and performances, especially those related to innovation. Starting from the beginning of the 21st century, the academic interest toward family firm innovation has bloomed. This body of research has mostly emerged from family firm scholars, while mainstream innovation scholars have often overlooked family variables in their studies. Indeed, innovation is one of the main areas in family firm research, integrating family and business aspects, leading to a plethora of sometimes contradictory findings. Initially, research compared innovation between family and nonfamily firms. While this approach has been beneficial to the rise of this stream of research and underlined the idiosyncratic characteristics of family firms on this matter, it soon emerged that within family firms there is a high degree of heterogeneity, especially in their attributes and the way they relate to innovation. Therefore, scholars have delved deeper into the heterogeneous influence that different types and degrees of family involvement in the firm can exert on innovation. This vast body of literature can be reconciled according to an antecedents–activities–outcomes framework allowing to attune current understanding of family firm innovation and recommend directions for future research. While most of current research has examined the antecedents of family business innovation, further examination of the activity of innovating in family firms is needed. Fostering accessibility to this literature allows students, practitioners, and scholars to grasp and digest this insightful area of family business research. It also encourages an extension of the range of perspectives adopted to examine innovation in family firms, contributing to advance current knowledge.

## Institutional Logics

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.

## An Institutional Perspective on Corporate Governance

Concerned with the structure of rights and responsibilities among corporate actors, corporate governance focuses primarily on the monitoring of executive boards, the protection of minority shareholders, corporate reporting and disclosure, and the improvement of employee participation in the corporate decision-making process. An institutional theory–driven approach helps position corporate governance as a social construct that reflects formal institutional rules as well as the informal practices that prevail when formal rules are absent, weak, or ambiguously defined. The institutional context thus constitutes a framework for corporate governance that captures not only the internal structures of corporations but also the institutional arrangements and national business systems in which these corporations are embedded. The actor-centered institutional perspective provides a comprehensive, in-depth, and nuanced picture not only of current governance structures but also of the characteristics and practices that prevail within and across different corporate governance models. Overall, adopting an institutional perspective underscores the importance of recognizing that corporate governance at the national level remains a key unit of analysis for explaining its diversity because it highlights the role of national institutions and their powerful institutional actors.

## Institutional Theory in Organization Studies

Institutional theory is a prominent perspective in contemporary organizational research. It encompasses a large, diverse body of theoretical and empirical work connected by a common emphasis on cultural understandings and shared expectations. Institutional theory is often used to explain the adoption and spread of formal organizational structures, including written policies, standard practices, and new forms of organization. Tracing its roots to the writings of Max Weber on legitimacy and authority, the perspective originated in the 1950s and 1960s with the work of Talcott Parsons, Philip Selznick, and Alvin Gouldner on organization–environment relations. It subsequently underwent a “cognitive turn” in the 1970s, with an emphasis on taken-for-granted habits and assumptions, and became commonly known as “neo-institutionalism” in organizational studies. Recently, work based on the perspective has shifted from a focus on processes involved in producing isomorphism to a focus on institutional change, exemplified by studies of the emergence of new laws and regulations, products, services, and occupations. The expansion of the theoretical framework has contributed to its long-term vitality, though a number of challenges to its development remain, including resolving inconsistencies in the different models of decision-making and action (homo economicus vs. homo sociologicus) that underpin institutional analysis and improving our understanding of the intersection of socio-cultural forces and entrepreneurial agency.

## Inter-Firm and Intra-Firm Managerial Mobility

Inter- and intrafirm managerial mobility has emerged as a topic of growing interest among management and organizational scholars. The movement of managers within and between organizations is one of the fundamental processes that links organizations and labor markets and has been the focus of research in organizational behavior, strategy, organization theory, and entrepreneurship for more than 50 years. Managerial mobility affects career opportunities and labor market outcomes for individual managers; influences the structure, strategy, routines, and processes of organizations; and shapes the environments within which organizations operate. Thus, managerial mobility research is a key to unlocking our understanding of a wide range of organizational behaviors and outcomes at several different analytical levels. Readers are introduced to the topic of managerial mobility and the vast body of existing research is summarized here. To help researchers understand the phenomenon, “managerial mobility” is distinguished from the more general topic of “employee mobility,” various terms that researchers have used to characterize managerial mobility processes are defined, and a distinction is made between intra- and interorganizational mobility. Next, because managerial mobility is a complex process, relevant research on the antecedents of managerial mobility is identified, categorizing some of the most important predictors into individual-, organizational-, and environmental-level antecedents. To demonstrate to researchers the importance of studying managerial mobility, some of the significant consequences of managerial mobility are highlighted, again distinguishing between consequences for individuals, organizations, and the environments in which they reside. To conclude, four potential directions for research to guide scholars and help set a research agenda on lines of inquiry on intra- and interorganizational managerial mobility are offered.