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This paper aims to examine the relationship between the characteristics of strategic decision-making team’s mental model and its performance. The authors propose that the relationship between mental models and performance is two-way, rather than one-way. Thus, performance feedback should, in turn, influence strategic behavior and future performance by either triggering or hindering the learning process.

The authors conduct the research in the setting of a simulation experiment. A longitudinal data set was collected from 36 teams functioning as strategic decision makers over three periods.

This study provides support for the positive impacts of both the complexity and centrality of a team’s mental model on its performance. The authors also find that positive performance feedback reduces changes in complexity and centrality of team mental models due to cognitive inertia.

The study contributes to the literature by investigating the specific mechanisms that underlie mental model evolution. Different from the existing studies on team mental models that mainly focus on similarity of these shared cognitive structures, this study examines another two characteristics of team mental model, complexity and centrality, that are more relevant to the strategic decision-making process but has not been extensively studied in the team literature. In addition, this study reveals that performance feedback has different effects on team mental models depending on the referents – past performance or social comparison – which advances the understanding of the learning effects of performance feedback.

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 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.

This paper introduces a conceptual framework to assess the foreign market entry behavior of emerging market multinationals (EMMs). By introducing strategic cognition as the underlying theoretical perspective, this paper postulates that different levels of institutional voids in home markets shape the strategic cognition of EMMs, influencing their market entry behavior due to the prevalence of organizational imprinting in the early stages of internationalization. The paper aims to contribute to the strategic cognition literature by introducing emerging markets as a relevant context in which to apply and extend current thinking. Additionally, it aims to contribute to the institutional voids literature by providing a cognitive framework of behavioral patterns that is rationalized by institutional voids. Finally, the paper contributes to the entry mode literature by proposing strategic cognition as a relevant moderator for foreign entry mode choices, particularly those of EMMs.

Article explains that institutional knowledge is crucial to the effectiveness of an organization because it enables it to reduce the time and effort needed to explore a novel challenge.

Article tells how to access institutional knowledge and how to foster a culture that respects it.

A supportive culture makes the sharing of institutional knowledge a normal facet of organizational functioning, thereby enabling managers to be highly effective when they have to deal with challenges and opportunities outside their normal routine.

When an organization is threatened by unexpected crises, senior personnel who have gone through previous disasters, can be tapped for some valuable insights into ways of handling the matter quickly and appropriately.

A useful “how-to” guide for integrating institutional knowledge into project management, crisis management, and novel innovation and marketing initiatives.

Sustaining product innovation in an established company – increasingly the key to a company’s economic success, and perhaps its survival – is a challenging task The author describes a and the model often referred to as an “Idea lab” that has emerged as a necessary organizational feature to accomplish this goal.

The author explains how to manage Idea labs as deliberately established locations, where individuals and teams with new product ideas can work together for concentrated bursts of time, sharpening and focusing their product concept, embedding the voice of the customer in product design and charting alternative progression paths for their ideas to be developed into potentially profitable offerings by units of the business that will nurture them.

In today’s organizations, the managerial prime directive is fundamentally being redefined as one of addressing the challenge of sustained, profitable innovation that opens new markets or reinvigorates existing ones. Idea labs should be considered a vital process in fostering sustaining innovation.

A critical success factor is the interplay between idea originators, technology specialists and product managers with a keen awareness of customer needs, competitor initiatives and genuine product differentiation.

A comprehensive guide for top managers and innovators, the article details the four key facets of Idea labs: Positioning in the firm’s innovation value chain. Tasks. Processes. Structure.

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.