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Article

Board Processes and Performance: The Impact of Directors’ Social and Human Capital  

Morten Huse

What do we know about actual board behavior and board performance? How can we develop our knowledge about board processes and board members’ capabilities? As a research field grows into maturity, we learn to see nuances, and the vocabulary used becomes richer and more detailed. However, the development of a consistent and nuanced language in research about board processes and performance is lagging behind. How have research streams and individual scholars influenced how we do research today, and why are these stories not included in most of the published literature reviews on this topic? What distinguishes research about boards and governance from various disciplines? How do we find research about board processes and board capital, and how has groundbreaking research on the human side of corporate governance developed? Groundbreaking research of Myles Mace was conducted more than half a decade ago, and we need to understand what has taken place after the seminal 1989 contribution of Zahra and Pearce. Research about actual board behavior and processes were not for decades published in leading management and strategy journals. Most published research about board processes and board capital is formulaic, leans on proxies rather than direct observation, and has only incremental if any practical contributions. A message is thus that we should strive for more groundbreaking studies that challenge existing knowledge and practice, including our research practice. A research agenda about board processes and board capital should be influenced by some of the following suggestions: • It should go beyond formulaic and incremental studies. We should challenge existing wisdom and practice and search for alternative ways of doing research. • It should include more processual studies rather than archival data studies using proxies. • We should learn from the scholars doing groundbreaking research before us. • We should learn by comparing experiences from various types of organizations. • We must include lessons and publications not found in leading English-language journals. • We should apply a sharing philosophy and a programmatic approach in which we as researchers contribute to developing future generations of scholars.

Article

Constructs and Measures in Stakeholder Management Research  

James Mattingly and Nicholas Bailey

Stakeholder strategies, or firms’ approaches to stakeholder management, may have a significant impact on firms’ long-term prosperity and, thereby, on their life chances, as established in the stakeholder view of the firm. A systematic literature review surveyed the contemporary body of quantitative empirical research that has examined firm-level activities relevant to stakeholder management, corporate social responsibility, and corporate social performance, because these three constructs are often conflated in literature. A search uncovered 99 articles published in 22 journals during the 10-year period from 2010 to 2019. Most studies employed databases reporting environmental, social, and governance (ESG) ratings, originally created for use in socially responsible investing and corporate risk assessment, but others employed content analysis of texts and primary surveys. Examination revealed a key difference in the scoring of data, in that some studies aggregated numerous indicators into a single composite index to indicate levels of stakeholder management, and other studies scored more articulated constructs. Articulated constructs provided richer observations, including governance and structural arrangements most likely to provide both stakeholder benefits and protections. Also observed were constraining influences of managerial and market myopia, sustaining influences from resilience and complexity frameworks, and recognition that contextual variables are contingencies having impact in recognizing the efficacy of stakeholder management strategies.

Article

Content and Text Analysis Methods for Organizational Research  

Rhonda K. Reger and Paula A. Kincaid

Content analysis is to words (and other unstructured data) as statistics is to numbers (also called structured data)—an umbrella term encompassing a range of analytic techniques. Content analyses range from purely qualitative analyses, often used in grounded theorizing and case-based research to reduce interview data into theoretically meaningful categories, to highly quantitative analyses that use concept dictionaries to convert words and phrases into numerical tables for further quantitative analysis. Common specialized types of qualitative content analysis include methods associated with grounded theorizing, narrative analysis, discourse analysis, rhetorical analysis, semiotic analysis, interpretative phenomenological analysis, and conversation analysis. Major quantitative content analyses include dictionary-based approaches, topic modeling, and natural language processing. Though specific steps for specific types of content analysis vary, a prototypical content analysis requires eight steps beginning with defining coding units and ending with assessing the trustworthiness, reliability, and validity of the overall coding. Furthermore, while most content analysis evaluates textual data, some studies also analyze visual data such as gestures, videos and pictures, and verbal data such as tone. Content analysis has several advantages over other data collection and analysis methods. Content analysis provides a flexible set of tools that are suitable for many research questions where quantitative data are unavailable. Many forms of content analysis provide a replicable methodology to access individual and collective structures and processes. Moreover, content analysis of documents and videos that organizational actors produce in the normal course of their work provides unobtrusive ways to study sociocognitive concepts and processes in context, and thus avoids some of the most serious concerns associated with other commonly used methods. Content analysis requires significant researcher judgment such that inadvertent biasing of results is a common concern. On balance, content analysis is a promising activity for the rigorous exploration of many important but difficult-to-study issues that are not easily studied via other methods. For these reasons, content analysis is burgeoning in business and management research as researchers seek to study complex and subtle phenomena.

Article

Experiments in Organization and Management Research  

Alex Bitektine, Jeff Lucas, Oliver Schilke, and Brad Aeon

Experiments randomly assign actors (e.g., people, groups, and organizations) to different conditions and assess the effects on a dependent variable. Random assignment allows for the control of extraneous factors and the isolation of causal effects, making experiments especially valuable for testing theorized processes. Although experiments have long remained underused in organizational theory and management research, the popularity of experimental methods has seen rapid growth in the 21st century. Gatekeepers sometimes criticize experiments for lacking generalizability, citing their artificial settings or non-representative samples. To address this criticism, a distinction is drawn between an applied research logic and a fundamental research logic. In an applied research logic, experimentalists design a study with the goal of generalizing findings to specific settings or populations. In a fundamental research logic, by contrast, experimentalists seek to design studies relevant to a theory or a fundamental mechanism rather than to specific contexts. Accordingly, the issue of generalizability does not so much boil down to whether an experiment is generalizable, but rather whether the research design matches the research logic of the study. If the goal is to test theory (i.e., a fundamental research logic), then asking the question of whether the experiment generalizes to certain settings and populations is largely irrelevant.

Article

Innovation Indicators  

Fred Gault and Luc Soete

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

Organizational Neuroscience  

Sebastiano Massaro and Dorotea Baljević

Organizational neuroscience—a novel scholarly domain using neuroscience to inform management and organizational research, and vice versa—is flourishing. Still missing, however, is a comprehensive coverage of organizational neuroscience as a self-standing scientific field. A foundational account of the potential that neuroscience holds to advance management and organizational research is currently a gap. The gap can be addressed with a review of the main methods, systematizing the existing scholarly literature in the field including entrepreneurship, strategic management, and organizational behavior, among others.

Article

Qualitative Comparative Analysis in Business and Management Research  

Johannes Meuer and Peer C. Fiss

During the last decade, qualitative comparative analysis (QCA) has become an increasingly popular research approach in the management and business literature. As an approach, QCA consists of both a set of analytical techniques and a conceptual perspective, and the origins of QCA as an analytical technique lie outside the management and business literature. In the 1980s, Charles Ragin, a sociologist and political scientist, developed a systematic, comparative methodology as an alternative to qualitative, case-oriented approaches and to quantitative, variable-oriented approaches. Whereas the analytical technique of QCA was developed outside the management literature, the conceptual perspective underlying QCA has a long history in the management literature, in particular in the form of contingency and configurational theory that have played an important role in management theories since the late 1960s. Until the 2000s, management researchers only sporadically used QCA as an analytical technique. Between 2007 and 2008, a series of seminal articles in leading management journals laid the conceptual, methodological, and empirical foundations for QCA as a promising research approach in business and management. These articles led to a “first” wave of QCA research in management. During the first wave—occurring between approximately 2008 and 2014—researchers successfully published QCA-based studies in leading management journals and triggered important methodological debates, ultimately leading to a revival of the configurational perspective in the management literature. Following the first wave, a “second” wave—between 2014 and 2018—saw a rapid increase in QCA publications across several subfields in management research, the development of methodological applications of QCA, and an expansion of scholarly debates around the nature, opportunities, and future of QCA as a research approach. The second wave of QCA research in business and management concluded with researchers’ taking stock of the plethora of empirical studies using QCA for identifying best practice guidelines and advocating for the rise of a “neo-configurational” perspective, a perspective drawing on set-theoretic logic, causal complexity, and counterfactual analysis. Nowadays, QCA is an established approach in some research areas (e.g., organization theory, strategic management) and is diffusing into several adjacent areas (e.g., entrepreneurship, marketing, and accounting), a situation that promises new opportunities for advancing the analytical technique of QCA as well as configurational thinking and theorizing in the business and management literature. To advance the analytical foundations of QCA, researchers may, for example, advance robustness tests for QCA or focus on issues of endogeneity and omitted variables in QCA. To advance the conceptual foundations of QCA, researchers may, for example, clarify the links between configurational theory and related theoretical perspectives, such as systems theory or complexity theory, or develop theories on the temporal dynamics of configurations and configurational change. Ultimately, after a decade of growing use and interest in QCA and given the unique strengths of this approach for addressing questions relevant to management research, QCA will continue to influence research in business and management.