Inductive Theorizing Research Articles

Making the most of AI and machine learning in organizations and strategy research: Supervised machine learning, causal inference, and matching models

AbstractResearch SummaryWe spotlight the use of machine learning in two‐stage matching models to deal with sample selection bias. Recent advances in machine learning have unlocked new empirical possibilities for inductive theorizing. In contrast, the opportunities to use machine learning in regression studies involving large‐scale data with many covariates and a causal claim are still less well understood. Our core contribution is to guide researchers in the use of machine learning approaches to choosing matching variables for enhanced causal inference in propensity score matching models. We use an analysis of real‐world technology invention data of public–private relationships to demonstrate the method and find that machine learning can provide an alternative approach to ad hoc matching. However, as with any method, it is also important to understand its limitations.Managerial SummaryThis article explores the use of machine learning to enhance decision‐making, particularly in addressing sample selection bias in large‐scale datasets. The rapid development of AI and machine learning offers new, powerful tools especially for digital ecosystems where complex data and causal relationships are complex to analyze. We offer managers and stakeholders insight into the effective integration of machine learning for selecting critical variables in propensity score matching models. Through a detailed examination of real‐world data on technology inventions within public–private relationships, we demonstrate the effectiveness of machine learning as a robust alternative to traditional matching methods.

Identifying key leadership competencies for digital transformation: evidence from a cross-sectoral Delphi study of global managers

PurposeEven with the recognized impact organizational leaders have on the outcome of digital transformation (DT), a comprehensive scholarly understanding of the competencies that leaders must possess to lead a DT to success is lacking.Design/methodology/approachTo derive and list the competencies considered by experts as necessary for managing DT, the authors recruited 18 international senior managers with relevant experience and applied the Delphi method to survey the managers. Upon the completion of three survey rounds and the authors modifying the response list until consensus was reached, 39 items were shortlisted as constituting key competencies for managing DT. Furthermore, the authors engaged in inductive theorizing to derive propositional statements using these findings.FindingsThe practitioners agreed on visionary thinking, agility, understanding the value of data, data-driven decision-making, knowledge of strategy and accepting change as the most important requirements for managing DT. Through inductive theorizing, the authors further derived that the seven discovered clusters fell into two broader competencies – behavioral and strategic – and that each behavioral competency would have varying importance depending on the country and industry that the organization operates in.Research limitations/implicationsAs is typical for Delphi studies that involve multiple survey rounds, the study participant response rate was moderate. The implications of this study, in finding that a variety of leadership competencies are needed to ensure successful DT, validate prior research that people, not technology, drive DT.Practical implicationsThis study helps mitigate assumptions that successful DT processes are only possible by hiring technological experts, as doing so highlights the importance of behavioral leadership competencies.Originality/valueThe study is one of the first to interlink digital leadership with DT by inductively theorizing behavioral and strategic competencies. The authors also establish that contexts are vital in determining which aspects of leadership competencies are deemed most important in driving DT.

Causal Inference in Strategy: How can Machine Learning Help?

Recent advances in machine learning methods have opened up a set of new possibilities for inductive theorizing, and for analysis of unstructured text data in strategy and organizations research. In contrast, the application of machine learning to improve causal explanation in deductive strategy research is less well understood. In this paper, we spotlight supervised machine learning and its application to matching methods to support stronger inference in deductive research. We use a simulation and an analysis of technology invention data to illustrate the method. A core contribution is to provide guidance to strategy researchers in the use of supervised machine learning.

Protect the protectors: a qualitative study on OSH in conjunction with unwanted side effects for the work performance of frontline police officers

Objectives. Occupational safety and health (OSH) measures for frontline police officers are developing rapidly in many western societies. Various organizations, such as police forces and unions, view this development as necessary in the face of increasing numbers of assaults, knife crime, rampages and terrorist attacks. Also, current research seems to promote a progressive aggregation of OSH focusing on frontline officers on the beat. This article aims to explore possible side effects of the current development, based on a recent qualitative small-scale research project with the participation of 12 highly experienced frontline police constables in Hampshire, England, all having around 24 years of service. Methods. As a clearly explorative study, semi-structured interviews were deployed. While using inductive theorizing and understanding the participating police constables as humans at work, the identification and collection of inner context components such as the participants’ perceptions, motivations, emotions, beliefs, values or attitudes have been intentionally put at front. Conclusions. Particularly, emphasizing individual risk assessment and risk perception, current research has identified that an organizational over-reliance on standardized risk management processes might trigger police officers’ risk averseness, overall leading to a habitual over-worrying about things and bringing safety culture and occupational cop culture into disagreement.

Putting food access in its topological place: thinking in terms of relational becomings when mapping space

This paper adopts a relational, also known as a topological, approach to food accessibility—the idea that food spaces are best understood as relational becomings rather than as voids filled exclusively with mass (immutable materiality) and address (geospatial coordinates). It is animated by an experimental spirit, in terms of the methods employed, the data collected, and by how those data are brought together, which together better enriches inductive theorizing. The project looks at the daily macro-mobilities—trips from one GPS coordinate to another—of 70 Coloradoans, triangulated with qualitative semi-structured interview data. Data collection occurred over three phases: baseline interviews, which lasted approximately 45 min; 30-day study period, which involved using a GPS tracking app. on their mobile phones; and follow-up interviews that lasted roughly two hours. The paper, through inductive theorizing and methodological experimentation, contributes to the critical food mapping scholarship in three ways: first, by looking at rural and urban food environments and livelihoods (as opposed to focusing exclusively on the latter); second, by focusing on mobilities as opposed to fixities (an example of the former: mapping projects that reduce food access to Euclidean coordinates); and, lastly, by its conceptual innovations attributed to phenomena such as life course and geographies of care.

Resolving Barriers to an Applied Science of the Human Condition: Rule Governance and the Verbal Behavior of Applied Scientists.

Rules / verbal behavior governing applied behavior scientists since Skinner have achieved great success resolving challenges experienced by individuals with severe developmental and intellectual disabilities. We extend prior work by Dixon, Belisle, Rehfeldt, and Root (2018, "Why We Are Still Not Acting to Save the World: The Upward Challenge of a Post-Skinnerian Behavior Science," Perspectives on Behavior Science, 41, 241-267) by suggesting that many of these rules, applied inflexibly, are unlikely to resolve significant problems experienced by humans without these same intellectual challenges (i.e., most humans). Particularly, methodological models of human behavior that ignore both private events and advances in relational frame theory and that favor a bottom-up inductive theorizing have not, and we argue cannot, address uniquely human challenges. Instead, we propose alternative rules developed in part within contextual behavior science that are more consistent with Skinner's radical behaviorism than are current approaches and that may expand the scope of applied behavior science. Only by adapting our own public and private verbal behavior as applied scientists can we move toward solving the wide range of challenges within the human condition.

Strategic human resource management: a power based critique

PurposeBy undertaking a detailed review of the Strategic Human Resource Management (SHRM) discourse, the purpose of this paper is to uncover and explicate the power differentials embedded in the social structure of organizations and suggests ways to reconcile them.Design/methodology/approachMethods used are thematic review, content analysis, and inductive theorizing, with Foucault’s archaeological and genealogical analysis style as the overarching framework.FindingsAt the methodological level, the authors demonstrate the application of Foucault’s twin methods: archaeological and genealogical analysis. At the substantive level, the authors have two contributions. First, the authors critique and analyze the various themes of power that emerge from the SHRM discourse as well as the hybridized overlaps of SHRM with other organization studies topics of interest such as organizational learning, network studies, control and postmodernism. Second, the authors propose a “Power” theory based nomothetic, typological synthesis for crafting the business-facing human resource (HR) function. The power lens manifests as the meta-theory to guide a much required streamlining of constructs and “value laden” synthesis of the literature.Research limitations/implicationsThe potential of critical theory in crafting situated and context-sensitive research propositions is demonstrated.Practical implicationsOrganizational strategists and HR managers can utilize the proposed typology to better understand their current ideological positions and decide future aspired images.Originality/valueThis is a conversation between two paradigms, SHRM and power theory, that are epistemologically at two opposite poles.

ACHIEVING FAMILY FARMERS' DEMANDS FOR MICROCREDIT THROUGH CREDIT UNIONS: A FRAMEWORK FOR DISCUSSION

ABSTRACT Purpose: This study aims to analyze a model of credit union, which allows meeting demands of microcredit of family farmers in a responsible way, identifying the real needs of clients and promoting financial access. Originality/value: In recent years, the microfinance community has witnessed unwanted consequences of microcredit misuse. We present an original approach about the way credit unions may achieve their target in a responsible way, offering appropriate products, services and training to the entire community involved. Design/methodology/approach: Through secondary data and interviews with decision makers and credit union members, we studied the case of Cresol Baser, a large credit union in Brazil. We analyzed data based on inductive theorizing. First and second-order themes were identified, resulting in a procedural model, representing relationships between responsible microfinance factors. Findings: The three main dimensions in the model of inclusive microfinance - conditions, proximity, and access - were integrated in order to promote a dynamic that respects the assumption of a responsible financial inclusion with regards to design, offer and access of products and services related to microfinance.

The Status of the Social Fact

The Status of the Social Fact

Using Market Basket Analysis in Management Research

Market basket analysis (MBA), also known as association rule mining or affinity analysis, is a data-mining technique that originated in the field of marketing and more recently has been used effectively in other fields, such as bioinformatics, nuclear science, pharmacoepidemiology, immunology, and geophysics. The goal of MBA is to identify relationships (i.e., association rules) between groups of products, items, or categories. We describe MBA and explain that it allows for inductive theorizing; can address contingency (i.e., moderated) relationships; does not rely on assumptions such as linearity, normality, and residual equal variance, which are often violated when using general linear model–based techniques; allows for the use of data often considered “unusable” and “messy” in management research (e.g., data not collected specifically for research purposes); can help build dynamic theories (i.e., theories that consider the role of time explicitly); is suited to examine relationships across levels of analysis; and is practitioner friendly. We explain how the adoption of MBA is likely to help bridge the much-lamented micro–macro and science–practice divides. We also illustrate that use of MBA can lead to insights in substantive management domains, such as human resource management (e.g., employee benefits), organizational behavior (e.g., dysfunctional employee behavior), entrepreneurship (e.g., entrepreneurs’ identities), and strategic management (e.g., corporate social responsibility). We hope our article will serve as a catalyst for the adoption of MBA as a novel methodological approach in management research.