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The implications and influence of different cognitive map structures on decision-making, reasoning, predictions about future events, affect, and behavior remain poorly understood. To-date, we have not had the mechanisms to determine whether any measure of cognitive map structure picks up anything more than would be detected on a purely random basis. We report a Monte Carlo method of simulation used to empirically estimate parameterized probability outcomes as a means to better understand the behavior of cognitive map. Using worked examples, we demonstrate how the results of our simulation permit the use of exact statistics which can be applied by hand to an individual map or groups of maps, providing maximum utility for the collective and cumulative process of theory building and testing.

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.

Our primary objective is to provide method-related broad guidelines to researchers on the entire spectrum of issues involved in cause mapping and to encourage researchers to use causal mapping techniques in strategy research. We challenge strategists to open the black box and investigate the mental models that depict the cause and effect beliefs of managers, “walk” readers through the causal mapping process by discussing the “nuts and bolts” of cause mapping, provide an illustration, and outline “key issues to consider.” We conclude with a discussion of some promising research directions.

This chapter aims to enhance understanding of the main drivers of internal auditors' moral courage to speak up about sensitive information and their cause-and-effect relationships. We use cognitive mapping method to analyze 20 chief audit executives' cognitive maps in Tunisia. A collective map was grounded through assembling the full individual maps. Using the Decision Explorer software for our analysis, we find that the state hope, whistle-blowing policy, self-efficacy, perceived supervisor support and independence of internal audit function are the main drivers for internal auditors' moral courage. Our findings are also supplemented by semi-structured interviews. Our chapter offers a novel methodological contribution to auditing literature as well as new empirical evidence (contribution to knowledge) on the drivers of internal auditors' moral courage.

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.

At the level of a cognitive schema, a business model is a mental map of a firm’s value-creating, value-delivering, and value-capturing activities and the linkages between them. An important question in the study of business models as cognitive schemas is whether and how schemas differ across industry actors and whether the differences are connected to the variation observed in actual business models in the industry. This chapter examines, in particular, the ways in which business model schemas of industry insiders differ from those of industry outsiders. Using data from interviews with chief executive officers (CEOs) of 30 legal-tech firms, we graphically construct and analyze the CEOs’ schemas of important causal interdependencies between their firms’ activities. The analysis shows systematic differences between insiders and outsider CEOs’ schemas. We theorize that these differences underlie insider and outsider CEOs’ distinct approaches to opportunity recognition, expertise perception, and value framing, and have consequences for actual business model evolution in the industry.

The purpose of this study is to illustrate the use of the ad hoc methodology of causal mapping to support the process of quantifying the financial returns related to sustainability investments. The present study uses two methods to build causal maps, that is aggregate and congregate mapping, in order to capture managerial cognition and derive a model that reflects companies’ competitive advantages. The resulting causal map is a prerequisite and serves as a building block for the design of the organisation’s performance management systems for sustainability.

This study relies on qualitative, deductive research undertaken at the leading international pharmaceutical company Novo Nordisk. This chapter presents the results of a longitudinal study developed through an action research approach conducted at the Company over a four-year period.

This study illustrates how the described approach for developing causal maps can facilitate the elicitation of managerial tacit knowledge and the consequent identification of indicators to quantify the investments in sustainability.

This chapter proves the relevance of causal mapping as a comprehensive, articulated basis for developing and improving organisations’ strategic performance measurement systems (SPMSs).

This study’s main contribution is the triangulation of multiple qualitative methods to enhance the reliability of causal maps. This innovative approach supports the use of causal mapping to extract managerial tacit knowledge in order to identify indicators for the evaluation of investments in sustainability.

In this chapter, the authors discuss how visual artifacts may support the analysis and interpretation of qualitative data in organization studies. They draws on their own experience as well as other scholars’ published work to explore the distinctive affordances of visual forms. In particular, the authors identify four roles – namely “mapping,” “analyzing,” “conceptualizing,” and “communicating” – that visual artifacts play to help us move from raw qualitative data to a compelling conceptual product.

Specifically, the use of visuals for “mapping” involves directly coding data into visual forms such as cognitive maps, flow charts or relational diagrams, an approach that may offer a useful complement to traditional verbal coding. Using visuals for “analyzing” implies either comparing, aggregating or decomposing previously constructed visual maps, or drawing directly on verbal data to develop visuals such as analytical flow charts, process replication maps, and trend charts. Using visuals for “conceptualizing” involves rising above the data to develop more abstract representations of concepts and relationships, while maintaining recognizable connections to empirical phenomena. While conceptual models can take a wide variety of forms, the authors illustrate, in particular, the use of visuals to represent linear, dialectic and multi-level process theories. Finally, the authors consider the importance of visualizations for “communicating” insights as well as for developing them, and the inextricable linkages between the two.

The authors conclude by discussing some of the strengths and weaknesses of visualization and by considering how new technologies may offer further possibilities for useful and insightful visual representations of qualitative data that can enhance theory-building.