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This paper attempts to demystify the technique of causal path modeling for the non‐specialists by presenting aspects of its value for social science and management research and by illustrating common misunderstandings about its attributes. Special emphasis is placed on the real world validity of causal relationships depicted in causal path models and on the information that the data‐fitting properties of causal path models provide regarding this issue. Causal path models that are based on research in antecedents of career success are used to illustrate the points that are made. It is stressed that the validity of causal relationships depicted in causal path models is subject to exactly the same methodological restrictions as the validity of causality claims that are made without the use of causal path modeling; and that the purpose of using quantitative techniques in causal path modeling is not to improve certainty on causality direction.

Several well‐known managerial accounting performance measurement models rely on causal assumptions. Whilst users of the models express satisfaction and link them with improved organizational performance, academic research, of the real‐world applications, shows few reliable statistical associations. This paper seeks to provide a discussion on the “problematic” of causality in a performance measurement setting.

This is a conceptual study based on an analysis and synthesis of the literature from managerial accounting, organizational theory, strategic management and social scientific causal modelling.

The analysis indicates that dynamic, complex and uncertain environments may challenge any reliance upon valid causal models. Due to cognitive limitations and judgmental biases, managers may fail to trace correct cause‐and‐effect understanding of the value creation in their organizations. However, even lacking this validity, causal models can support strategic learning and perform as organizational guides if they are able to mobilize managerial action.

Future research should highlight the characteristics necessary for elaboration of convincing and appealing causal models and the social process of their construction.

Managers of organizations using causal models should be clear on the purposes of their particular models and their limitations. In particular, difficulties are observed in specifying detailed cause and effect relations and their potential for communicating and directing attention. They should therefore construct their models to suit the particular purpose envisaged.

This paper provides an interdisciplinary and holistic view on the issue of causality in managerial accounting models.

Aims to facilitate the work of researchers studying problems/phenomena in social systems from a systemic point of view. Discusses the type of causal processes which exist in social systems and how patterns in social systems can be revealed. Presents four causal processes: historical, functional, cybernetic and pattern. Typologizes pattern processes into the four categories of empirical generalizations, models, theories and social laws. Elaborates on the relationship between models and theories relative to systemic research strategy.

The purpose of this paper is to provide a methodology by which qualitative analyses serve as rich source materials for discovery of theoretically cogent interrelations between latent variables.

In an illustrative case, qualitative data are collected from US franchisee managers from a single branded franchise of automotive repair outlets. Qualitative analysis of franchisee experiences and attitudes is critical for construction of a causal model used to predict conflict intensity between franchisee managers and franchisors.

The model is based on franchisees' normative expectations for resource allocation within the franchise; and their perceptions of franchisor normative violations, which are determinative of grievances, distrust, and hostility. This theoretical orientation serves to generate a system of interrelated empirically testable propositions.

In principle, the primary limitation of using qualitative analysis for the construction of causal models is the fruitfulness of the theoretical orientation shared by the qualitative analyst and the causal modeler.

The methodological approach advanced in this paper advances qualitative research and causal modeling beyond the individual contributions. Qualitative analysis infuses variables and process imagery into causal modeling. In turn, causal modeling elaborates the qualitative analysis and makes explicit logical connections between variables.

This paper advances a methodology by which qualitative analysis and causal model construction may be usefully integrated. Theory‐based qualitative analysis may be formalized to map latent concepts and their interrelations. Further, operational measures of these concepts may be adduced from the analysis of textual data.

Mediating effects are often tested using hierarchical multiple regression (HMR) procedures. Typical of the HMR-based strategies is the very frequently cited and widely used procedure described by Baron and Kenny (1986). Unfortunately, there are several important problems with it. More specifically, as we demonstrate below, it: (a) is of virtually no value for buttressing claims of mediating effects for data from non-experimental research; (b) produces erroneous inferences about the existence of mediating effects for misspecified mediation models; and (c) is incapable of providing credible evidence of such effects in a large proportion of cases, even for properly specified mediation models. We detail a number of important implications of our analyses.

Research has highlighted the cognitive nature of the business model intended as a cognitive representation describing a business’ value creation and value capture activities. Although the content of the business model has been extensively investigated from this perspective, less attention has been paid to the business model’s causal structure – that is the pattern of cause-effect relations that, in top managers’ or entrepreneurs’ understandings, link value creation and value capture activities. Building on the strategic cognition literature, this paper argues that conceptualizing and analysing business models as cognitive maps can shed light on four important properties of a business model’s causal structure: the levels of complexity, focus and clustering that characterize the causal structure and the mechanisms underlying the causal links featured in that structure. I use examples of business models drawn from the literature as illustrations to describe these four properties. Finally, I discuss the value of a cognitive mapping approach for augmenting extant theories and practices of business model design.

In order to explain a phenomenon/problem, some of the mechanisms which elicit the phenomenon/problem must be clarified, since: “a goal of scientific research is to uncover reality beneath appearance”. The purpose of this paper is to investigate the following issue: how can social mechanisms be examined from a systemic point of view?

The paper investigates, at an abstract level, what is meant by social mechanisms in social systems in Part 1. Social mechanisms and various explanation models are investigated in Part 2, using the systemic approach.

However well‐functioning the models developed, this procedure will not have developed a theory of the phenomenon. For that purpose, explanations at a more basic level than the model is able to disclose, will be necessary. The empirical causal model says something about the strength in the relation between the variables and can be used in practice in order to change certain variables to facilitate the desired change in the system.

The paper usefully shows that, if possible, explanations at a more basic level would be desirable; but not necessary for the application of insights in practical contexts. By this, the paper has stated that a theory can be desirable, but not necessary, in order to develop, e.g. innovative organisations. Models and social mechanisms, on the other hand, are necessary to organise knowledge for the purpose of use in practical contexts.

The purpose of this paper is to show how non-linear causal modelling knowledge, already accumulated by other disciplines, is central to unravelling wicked problem scoping and definition in social marketing.

The paper is an illustrative case study approach, highlighting three real-world exemplars of causal modelling for wicked problem definition.

The findings show how the traditional linear research methods of social marketing are not sensitive enough to the dynamics and complexities of wicked problems. A shift to non-linear causal modelling techniques and methods, using interaction as the unit of analysis, provides insight and understanding into the chains of causal dependencies underlying social marketing problems.

This research extends the application of systems thinking in social marketing through the illustration of three non-linear causal modelling techniques, namely, collective intelligence, fuzzy cognitive mapping and system dynamics modelling. Each technique has the capacity to visualise structural and behavioural properties of complex systems and identify the central interactions driving behaviour.

Non-linear causal modelling methods provide a robust platform for practical manifestations of collaborative-based strategic projects in social marketing, when used with participatory research, suitable for micro, meso, macro or systems wide interventions.

The paper identifies non-linear causality as central to wicked problem scoping identification, documentation and analysis in social marketing. This paper advances multi-causal knowledge in the social marketing paradigm by using fuzzy, collective and interpretative methods as a bridge between linear and non-linear causality in wicked problem research.

The purpose of this paper is to shed light on improving value creation from intellectual capital (IC) through reducing causal ambiguity and finding effective IC interventions.

First, several guiding rules demonstrating the contribution of system dynamics (SD) to the field of IC management are introduced. Second, evidence for modelling resource dynamics is provided across a knowledge-based industry, insurance. Third, a management problem of an insurance company is modelled and then simulated using SD tools to monitor and improve the alignment of key resources with the firm’s market growth strategy.

The modelling and further simulation practice demonstrated the advantages of applying SD for analysing resource management problems to identify the critical IC components, intervention points and decision rules that may stimulate value-creating loops. Specifically for the case of an insurance company’s failure in market growth, it led to recognising the critical role of agency sales productivity as a key component of company’s relational capital and the intellectual liabilities that can lead to value destruction.

Reducing causal ambiguity in IC value creation through modelling and simulating firm resource dynamics is the main contribution of this paper. It enables finding the best intervention points for developing IC-based initiatives to stimulate value-creation mechanisms, as well identifying possible points of value destruction.

The purpose of this paper is to conduct a critical review of the mediation studies published in the field of Human Resource Development (HRD) to discern if the study designs, the nature of data collection and the choice of statistical methods justify the causal claims made in those studies.

This paper conducts a critical review of published refereed articles that examined mediation in Human Resource Development Quarterly, Human Resource Development International, Advances in Developing Human Resources and European Journal of Training and Development. Mediation studies published in these journals from 2000 to 2015 were identified and coded. The four journals sampled were chosen to provide breadth of coverage of the different types of empirical studies published in the field of HRD.

The review findings imply that HRD scholars are not employing experimental or longitudinal designs in their studies when randomized experiments and longitudinal studies with at least three waves of data collection are regarded as the golden standards of causal research. Further, the findings indicate that sophisticated statistical modeling approaches like structural equation modeling are widely used to examine mediation in cross-sectional studies and most importantly, a large number of such studies do not acknowledge that cross-sectional data does not allow definite causal claims.

Although the findings urge us to rethink the inferences of mediation effects reported over the past 15 years in the field of HRD, this study also serves as a guide in thinking about framing and testing causal mediation models in future HRD research and even argues for a paradigm shift from a positivist orientation to critical and postmodern perspectives that can accommodate mixed methods designs for mediation research in HRD.

This paper presents a critical review of the trends in examining mediation models in the HRD discipline, suggests best practices for researchers examining the causal process of mediation and directs readers to recent methodological articles that have discussed causal issues in mediation studies.