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The purpose of this paper is to explore the role of qualitative research in causality, with particular emphasis on process causality. In one paper, it is not possible to discuss all the issues of causality, but the aim is to provide useful ways of thinking about causality and qualitative research. Specifically, a brief overview of the regularity theory of causation is provided, qualitative research characteristics and ontological and epistemological views that serve as a potential conceptual frame to resolve some tensions between quantitative and qualitative work are discussed and causal processes are explored. This paper offers a definition and a model of process causality and then presents findings from an exploratory study that advanced the discussion beyond the conceptual frame.

This paper first conceptually frames process causality within qualitative research and then discusses results from an exploratory study that involved reviewing literature and interviewing expert researchers. The exploratory study conducted involved analyzing multiple years of literature in two top human resource development (HRD) journals and also exploratory expert interviews. The study was guided by the research question: How might qualitative research inform causal inferences in HRD? This study used a basic qualitative approach that sought insight through inductive analysis within the focus of this study.

The exploratory study found that triangulation, context, thick description and process research questions are important elements of qualitative studies that can improve research that involves causal relationships. Specifically, qualitative studies provide both depth of data collection and descriptive write-up that provide clues to cause-and-effect relationships that support or refute theory.

A major conclusion of this study is that qualitative research plays a critical role in causal inference, albeit an understated one, when one takes an enlarged philosophical view of causality. Equating causality solely with variance theory associated with quantitative research leaves causal processes locked in a metaphoric black box between cause and effect, whereas qualitative research opens up the processes and mechanisms contained within the box.

This paper reframed the discussion about causality to include both the logic of quantitative studies and qualitative studies to demonstrate a more holistic view of causality and to demonstrate the value of qualitative research for causal inference. Process causality in qualitative research is added to the mix of techniques and theories found in the larger discussion of causality in HRD.

This study examines the roles of cross-sectional dependence, asymmetric structure and country-to-country policy variations in the inflation-poverty reduction causal nexus in selected sub-Saharan African (SSA) countries from 1981 to 2019.

To account for cross-sectional dependence, heterogeneity and policy variations across countries in the inflation-poverty reduction causal nexus, this study uses robust Hatemi-J data decomposition procedures and a battery of second-generation techniques. These techniques include cross-sectional dependency tests, panel unit root tests, slope homogeneity tests and the Dumitrescu-Hurlin panel Granger non-causality approach.

Unlike existing studies, the panel and country-specific findings exhibit several dimensions of asymmetric causality in the inflation-poverty nexus. Positive inflationary shocks Granger-causes poverty reduction through investment and employment opportunities that benefit the impoverished in SSA. These findings align with country-specific analyses of Botswana, Cameroon, Gabon, Mauritania, South Africa and Togo. Also, a decline in poverty causes inflation to increase in the Congo Republic, Madagascar, Nigeria, Senegal and Togo. All panel and country-specific analyses reveal at least one dimension of asymmetric causality or another.

All stakeholders and policymakers must pay adequate attention to issues of asymmetric structures, nonlinearities and country-to-country policy variations to address country-specific issues and the socioeconomic problems in the probable causal nexus between the high incidence of extreme poverty and double-digit inflation rates in most SSA countries.

Studies on the inflation-poverty nexus are not uncommon in economic literature. Most existing studies focus on inflation’s effect on poverty. Existing studies that examine the inflation-poverty causal relationship covertly assume no asymmetric structure and nonlinearity. Also, the issues of cross-sectional dependence and heterogeneity are unexplored in the causal link in existing studies. All panel studies covertly impose homogeneous policies on countries in the causality. This study relaxes this supposition by allowing policies to vary across countries in the panel framework. Thus, this study makes three-dimensional contributions to increasing understanding of the inflation-poverty nexus.

This chapter’s focus is comparative causal mapping (CCM) methods in MOC research. For a background, the chapter discusses first the conceptual (cognitive theoretic) basis in typical CCM studies and its implications for understanding the target phenomena and for CCM methods. Next, it presents the CMAP3 software and describes its operating logic and main functions. Third, the chapter describes how to use CMAP3 in three prototypical cases of CCM, each characterized by different research objectives, kinds of data, and methods of data acquisition but also by potential dilemmas. The chapter concludes by speculating about the future directions of causal mapping and suggesting some ideas for developing in particular large-N CCM methods.

The paper's first objective is to develop a new conceptual framework for categorizing and designing cognitive, specifically comparative, causal mapping (CCM) research by building upon the theory‐centred and participant‐centred perspectives. The second purpose is to enable the discerned study prototypes by introducing a new CCM software application, CMAP3.

Building upon the distinction between theory‐centred (etic) and participant‐centred (emic) perspectives in social research, we first construct and apply a conceptual framework for analysing and categorising extant CCM studies in terms of their objectives and basic design. Next, after noting the important role and basic tasks in computerising causal mapping studies, we present a new CCM software application.

The theory‐centred/participant‐centred perspectives define four causal mapping study prototypes, each with different goals, basic designs and methodological requirements. Noting the present lack of widely accessible software for qualitatively oriented CCM studies, we introduce CMAP3, a new non‐commercial Windows application, and summarise how it is used in related research.

The framework and the studies representing the prototypes demonstrate the versatility of CCM methods and that the proposed framework offers a new, systematic approach to categorising and designing CCM studies. Research technically, CMAP3 can support the defined CCM‐prototypes, based on a low‐structured (inductive/qualitative) or a structured (nomothetic/quantitative) methodological approach/stance, and having therefore different needs of data acquisition, processing, coding, aggregation/comparison, and analysis of the emerging aggregated cause maps’ contents or structure.

The purpose of this paper is to discuss the current status of experimental research within hospitality and tourism. This paper further aims to develop practical ideas for enhancing the adoption of a cause and effect mindset in researchers.

A mini-review of the level of experimental designs and best-practice ideas published by the top 12 journals in hospitality and tourism over a five-year period was conducted.

Although the absolute number of experimental studies is growing, the ratio of experimental studies to overall publications remains low at 6.4%. To increase the take-up of experimental design, a broader typology of field experiments is presented. Practical steps to increase causal reality are provided under the categories of purpose; scenario development; scenario testing; and sample characteristics.

The methodological advances suggested in this paper can contribute to more robust theory development and testing. The recommendations offer guidance to a new generation of researchers seeking to add causal value to their studies, researchers collaborating with scholars from other discipline areas and hospitality managers seeking stronger evidence of cause and effect.

This paper identifies key obstacles to the take-up of experimental design and the contemporary status of experimental design. A novel typology of five experimental designs that distinguish the difference between experimental and correlational designs in terms of explanatory power is presented, together with a comprehensive list of best practice suggestions to increase causal reality in scenario design.

The purpose of this study is to explain how the application of fuzzy-set qualitative comparative analysis (fsQCA) and experiments can advance theory development in the field of servitization by generating better causal explanations.

FsQCA and experiments are established research methods that are suited for developing causal explanations but are rarely utilized by servitization scholars. To support their application, we explain how fsQCA and experiments represent distinct ways of developing causal explanations, provide guidelines for their practical application and highlight potential application areas for a future research agenda in the servitization domain.

FsQCA enables specification of cause–effects relationships that result in equifinal paths to an intended outcome. Experiments have the highest explanatory power and enable the drawing of direct causal conclusions through reliance on an interventionist logic. Together, these methods provide complementary ways of developing and testing theory when the research objective is to understand the causal pathways that lead to observed outcomes.

Applications of fsQCA help to explain to managers why there are numerous causal routes to attaining an intended outcome from servitization. Experiments support managerial decision-making by providing definitive “yes/no” answers to key managerial questions that address clearly specified cause–effect relationships.

The main contribution of this study is to help advance theory development in servitization by encouraging greater methodological plurality in a field that relies primarily on the qualitative case study methodology.

The construction collapse is one of the most serious accidents since it has several attributes (e.g. accident type and consequence) and its occurrence involves various kinds of causal factors (e.g. human factors). The impact of causal factors on construction collapse accidents and the interrelationships among causal factors remain poorly explored. Thus, the purpose of this paper is to use association rule mining (ARM) for cause analysis of construction collapse accidents.

An accident analytic framework is developed to determine the accident attributes and causal factors, and then ARM is introduced as the method for data mining. The data are from 620 historical accident records on government websites of China from 2010 to 2020. Through the generated association rules, the impact of causal factors and the interrelationships among causal factors are explored.

Collapse accident is easily caused by human factors, material and machine condition and management factors. Furthermore, the results show a close interrelationship between many causal factors and construction scheme and organization. The earthwork collapse is greatly related to environmental condition and the scaffolding collapse is greatly related to material and machine condition.

This study found relevant knowledge about the key causes for different types of construction collapses. Besides, several suggestions are further provided for construction units to prevent construction collapse accidents.

This study uses data mining methods to extract knowledge about the causes of collapse accidents. The impact of causal factors on various types of construction collapse accidents and the interrelationships among causal factors are explained from historical accident data.

Although QCA was originally developed specifically for small-N settings, recent studies have shown its potential for large-N organization studies. In this chapter, we provide guidance to prospective researchers with the goal of opening up QCA’s potential for widespread use in organization studies involving large-N settings, both as an alternative and as a complement to conventional regression analyses. We compare small-N and large-N QCA with respect to theoretical assumptions and objectives, processes and decisions involved in building the causal model, selecting the sample, as well as analyzing the data and interpreting the results. Finally, we discuss the prospects for large-N configurational analysis in organization studies and related fields going forward.

This study addresses the confusion prevailing over the nature of the relationship between satisfaction and commitment in regard to employee turnover, and examines the causal pattern of relationships among stress, satisfaction, commitment, and turnover intentions by employing a structural equations analysis approach. The results indicate that there are strong causal links between stress and satisfaction (higher stress leads to lower satisfaction) and between satisfaction and commitment (lower satisfaction leads to lower commitment), and a reciprocal relationship between commitment and turnover intentions (lower commitment leads to greater intentions to quit which, in turn, further lowers commitment).

This study aims to explain the value of using critical realist case research in supply chain management (SCM). While positivist case research focuses on generalizable law-like rules, and interpretivist research explores social meaning, critical realist case research seeks to make objective explanations that are bound by the case context. This study demonstrates how a critical realist synthesis of causal reasoning and contextual complexity allows for stronger theorizing in SCM.

This study highlights the possibilities of conducting critical realist case research in SCM by investigating philosophical perspectives in existing literature.

Based on existing literature, this study identifies which parts of contemporary SCM research will benefit from the critical realist perspective. This study also contends that supply chain scholars can use critical realist case research to develop new types of contextualized middle-range theories.

This study proposes to complement the qualitative SCM toolbox with critical realist case research to further refine the development of novel theories. This will benefit not only researchers but also managers, as it opens the doors to new and inspiring research.

This study takes an important step toward establishing critical realist case studies as a key methodology in SCM. While other scholars have introduced critical realism as a paradigmatic approach in SCM, to the best of the authors’ knowledge, this is the first article that develops a qualitative critical realist case research approach.