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Online privacy research has seen a focus on user behavior over the last decade, partly to understand and explain user decision-making and seeming inconsistencies regarding users' stated preferences. This article investigates the level of modeling that contemporary approaches rely on to explain said inconsistencies and whether drawn conclusions are justified by the applied modeling methodology. Additionally, it provides resources for researchers interested in using computational modeling.

The article uses data from a pre-existing literature review on the privacy paradox (N = 179 articles) to identify three characteristics of prior research: (1) the frequency of references to computational-level theories of human decision-making and perception in the literature, (2) the frequency of interpretations of human decision-making based on computational-level theories, and (3) the frequency of actual computational-level modeling implementations.

After excluding unrelated articles, 44.1 percent of investigated articles reference at least one theory that has been traditionally interpreted on a computational level. 33.1 percent of all relevant articles make statements regarding computational properties of human cognition in online privacy scenarios. Meanwhile, 5.1 percent of all relevant articles apply formalized computational-level modeling to substantiate their claims.

The findings highlight the importance of formal, computational-level modeling in online privacy research, which has so far drawn computational-level conclusions without utilizing appropriate modeling techniques. Furthermore, this article provides an overview of said modeling techniques and their benefits to researchers, as well as references for model theories and resources for practical implementation.

In this chapter we examine some possibilities of using computer simulation methods to model the interaction of affect and cognition in organizations, with a particular focus on agent-based modeling (ABM) techniques. Our chapter has two main aims. First, we take stock of methodological progress in this area, highlighting important developments in the modeling of affect and cognition in other fields, including psychology and economics. Second, we outline how ABM in particular can help to advance managerial and organizational cognition by building and testing theoretical models predicated on the interaction of affect and cognition. We argue that using ABM for this purpose can improve the level of specificity of cognitive and affective concepts and their interrelationships in organizational theories, yield more behaviorally plausible models of behavior in and of organizations, and deepen understanding of the generative behavioral mechanisms of multi-level organizational phenomena. We highlight possibilities for using ABM to model affect–cognition interactions in studies of mental models, collective cognition, diversity in work groups and teams, and organizational decision-making.

Pew and Mavor (1998) called for an integrative representation of human behavior for use in models of individual combatants and organizations. Models with integrated representation of behavior have only been achieved at rudimentary levels according to those performing the studies (e.g. Pew & Mavor, 1998; Tulving, 2002) and those building the models (e.g. Warwick et al., 2002). This chapter will address aspects of cognitive performance that are important to incorporate into models of combat based on acceptance of theory, strength of empirical data, or for other reasons such as to bridge gaps where incomplete knowledge exists about cognitive behavior and performance. As a starting point, this chapter will assess which of Pew and Mavor’s recommendations are still appropriate as determined by a review of selected literature on cognition and its representation. We will also provide some review and extensions of key literature on cognition and modeling and suggest a way ahead to close the remaining gaps. Different aspects of cognition are described with recent findings, and most are followed by an example of how they have been represented in computer models or a discussion of challenges to their representation in modeling.

This chapter wants to understand under which circumstances and conditions non-traditional aids are effective in the strategic process. This study builds an agent-based computational simulation model – the S-uFUNK 2.1.0 – to explore the research question. The model features a group of managers that seeks to interpret environmental cues using both traditional and non-traditional tools. When interpretations converge, the group then settles on different focus areas to define a business strategy for their organization. The process is set in a way such that 11 parameters can be manipulated to explore the different conditions under which non-traditional aids are of use. Results suggest that non-traditional aids differ from traditional aids only in limited circumstances and that social dynamics and dispositions within the group are crucial. In general, the simulation helps us reflect on the way in which we consider traditional aids to strategy. In fact, if they are no different than non-traditional aids, their effectiveness is directly challenged.

There has been a lack of focus on multi-level issues within leadership research. Dionne and Dionne (2009) address this gap in the research by presenting a Monte Carlo simulation examining leadership at four levels of analysis within a group decision-making context. While their work makes a strong contribution to the sciences of leadership, group decision making, and team complexity, many aspects of the research demonstrate potential for great expansion and improvement. Toward this purpose, this commentary discusses and provides suggestions regarding the topics of computer simulation in team research, group decision-making theory, and the modeling of team complexity. It is intended to stimulate continued critical thinking and more innovative, practical, and carefully designed research efforts.

The contributions proposed in this paper are motivated by principles of incompatibility, and non‐equilibrium states, existing between the continuous growth in the level of environmental complexity and the insufficient cognitive capacity of the organization. From such a view, the purpose of this paper is to ask: what are the core competencies of the new industrial organization in the twenty‐first century?

First, the paper examines the characteristics and limitations of past and current industrial organizations; second, it contributes by extending their frontiers and by proposing technological, managerial and organizational core competencies of the new enterprise.

From such analyses, this paper introduces the features of customer‐centric systems (CCS) which represent new industrial organizations in the pursuit of high degrees of organizational cognition, intelligence and autonomy, and consequently, high degrees of agility and flexibility, in order to manage high levels of environmental complexity and uncertainty, to operate through intensive mass customization, and to provide customers with immersiveness.

For further research, this paper suggests the investigation of practical implementation of the features of the new enterprise of CCS. In such a direction, it recommends additional reading on the concept and design of computational organizational management networks.

This paper emphasizes that CCS are firm types which strategically organize their resources and competencies around customers' values and needs, in order to involve customers into their business. By involving customers into their task environments and business, CCS‐based firms have the chance to understand their clients' real needs and to produce the appropriate goods and services.

The uniqueness of this paper lies in its attempt to master, analyze and integrate technological, managerial and organizational perspectives of past and current manufacturing organizations, which contribute to illuminate features and to identify core competencies of future industrial firms, which are in the pursuit of innovation and sustainable competitive advantage in the twenty‐first century.