Search results

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.

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 unorganised manufacturing sector contributes one third share of overall manufacturing employment and one fifth share of gross value added of the manufacturing sector. Despite its important role in large-scale employment generation, this sector is neglected by the researchers as well as by the policy makers as compared to the focus given on the organised manufacturing sector. The issues of energy intensity, environment emissions and growth of unorganised manufacturing enterprises (UMEs) remain unexplored. The present chapter attempts to estimate the CO₂ emission and emission intensity (EI) across UMEs on the basis of NSSO Unit Level data of 62nd, 67th and 73rd rounds. It also analyses the growth of UMEs in relation to CO₂ emission and EI. The nature of the sector is very much dispersed. Our study reveals that a portion of unorganised enterprises did not use any energy in their production activities and used manually operated instruments like – handlooms, weaving machines, hand-operated oil and rice mills, etc. The main energy inputs of UMEs are electricity and fuel & lubricants. The CO₂ emission is relatively less in UMEs compared to organised manufacturing enterprises. Across the unorganised manufacturing industries, the higher CO₂ emission are observed in manufacturing of food product industry and other non-metallic mineral industry. The study found that CO₂ EI of UMEs depends on firm-level characteristics like perennial nature, establishment type, urban location and expanding growth status. However, capital intensive UMEs are more polluting.

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.

This book comprises the second volume in the recently launched New Horizons in Managerial and Organizational Cognition book series. Volume 1 (Sund, Galavan, & Huff, 2016), addressed the topic of strategic uncertainty. This second volume comprises a collection of contributions that variously report new methodological developments in managerial and organizational cognition, reflect critically on those developments, and consider the challenges that have yet to be confronted in order to further advance this exciting and dynamic interdisciplinary field. Contextualizing within an overarching framework the various contributions selected for inclusion in the present volume, in this opening chapter we reflect more broadly on what we consider the most significant developments that have occurred over recent years and the most significant challenges that lie ahead.

This study empirically examines the relationship between institutional ownership and innovation activity in the unique setting of the clinical trials for US biopharmaceutical companies. We used multiple statistical techniques in the period from 1990 through 2006 for firms in the biopharmaceutical industry to examine this relationship. Contrary to the widely believed relationship discussed in the literature, our findings suggest that institutional investors vary in their reactions to innovative progress. Specifically, we find that institutional investors with a long-term investment horizon (i.e., dedicated owners) increase their holdings of a firm’s equity as the number of the firm’s products increases in phases I and II of FDA clinical trials. These findings are robust for heteroskedasticity and autocorrelation as well as for different operationalizations of the change of institutional ownership.

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.

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.