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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

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.