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Different forms of strategic flexibility allow for reactive adaptation to different changing environments and the proactive driving of change. It is therefore becoming increasingly important for decision makers to not only possess marketing capabilities, but also the capabilities for strategic flexibility in its various forms. However, our knowledge of the relationships between decision makers' different ways of thinking and their capabilities for strategic flexibility is limited. This limitation is constraining research and understanding. In this article we develop a theoretical cognitive content framework that postulates relationships between different ways of thinking about strategy and different information‐processing demands. We then outline how the contrasting beliefs of decision makers may influence their capabilities to generate different hybrid forms of strategic flexibility at the cognitive level. Theoretically, the framework is embedded in resource‐based theory, personal construct theory and schema theory. The implications for research and theory are discussed.

This paper proposes an integrated knowledge visualization and digital twin system for supporting strategic management decisions. The concepts and applications of strategic architecture have been illustrated with a concrete real-world case study and decision rules of using the strategic digital twin management decision system (SDMDS) as a more visualized, adaptive and effective model for decision-making.

This paper integrates the concepts of mental and computer models and examines a real case's business operations by applying system dynamics modelling and digital technologies. The enterprise digital twin system with displaying real-world data and simulations for future scenarios demonstrates an improved process of strategic decision-making in the digital age.

The findings reveal that data analytics and the visualized enterprise digital twin system offer better practices for strategic management decisions in the dynamic and constantly changing business world by providing a constant and frequent adjustment on every decision that affects how the business performs over both operational and strategic timescales.

In the digital age and dynamic business environment, the proposed strategic architecture and managerial digital twin system converts the existing conceptual models into an advanced operational model. It can facilitate the development of knowledge visualization and become a more adaptive and effective model for supporting real-time management decision-making by dealing with the complicated dependence of constant flow of data input, output and the feedback loop across business units and boundaries.

This study attempts to discover effective strategies for mobile commerce applications (apps) to grow their consumer base by releasing app strategic updates. Drawing on the landscape search model from strategy research, this study conceptualizes mobile app update strategy as three interdependent decisions, i.e. what business elements are changed in an app strategic update, how substantial the changes are and when strategic updates are released relative to the competitive environment.

Using a field data set of 1,500 strategic updates of seven rival apps in the mobile travel market, this study integrated fuzzy set qualitative comparative analysis (fsQCA) with econometric analysis to analyze how app strategic update decisions interdependently influence app performance.

This study identified three effective and one ineffective mobile app update strategies from the mixed-method analysis, which verified the complex interdependency of app strategic update decisions. A general takeaway from these strategies is that a complex strategy problem on the mobile platform must be solved with respect to the constraints and capabilities of mobile technology.

This study moves beyond a linear view of the relationship between app update frequency and app performance and provides a holistic view of how and why app strategic update decisions mutually influence one another in their impact on app performance. This work makes contributions by identifying interdependency as a conceptual bridge between strategy and mobile app literature and developing an empirically testable version of the landscape search model.

Despite the extensive research on the determinants and consequences of firm growth, research focusing on how the actual process unfolds is still evolving. An important part of firm growth process research is entrepreneurial cognition. The purpose of this chapter is to explore the relationship between entrepreneurial cognition and firm growth intentions. Specifically, we propose a theoretical model of entrepreneurial cognitive interpretation and categorization of market information as it relates to firm growth intentions. Drawing from the strategic cognition literature in general and strategic issue interpretation literature in particular, we propose that entrepreneurs’ interpretation of market information as opportunity or threat, gain or loss, and controllable or uncontrollable influences their firm growth intentions. Furthermore, our theoretical model discusses the condition under which favorable interpretation of market information leads to higher growth intentions by incorporating insights from the Entrepreneurial Orientation (EO) construct. This chapter extends our understanding of firm growth processes by highlighting the important role cognitive interpretation and categorization play in facilitating or hindering entrepreneurial firm growth.

The purpose of this paper is to propose a comprehensive methodology and a problem-specific model for the configuration of the optimal strategic supplier portfolio in terms of traditional, performance-related objectives and sustainability targets.

To bridge the research gap, i.e., to align strategic supplier portfolio selection with corporate sustainability targets, a hybrid model of the analytic network process (ANP) and goal programming (GP) is developed. To validate the model, a case example is presented and managerial feedback is collected.

By enabling the integration of sustainability targets into strategic supplier portfolio configuration, the hybrid ANP-GP model contributes to research in the area of sustainable supply chain management. Results indicate that simplifying the model by omitting one or more details may lead to unfortunate actions.

The model has been applied using a case example in the automotive industry. To strengthen the findings, it should be examined under other terms as well.

Integrating economic, environmental, and social targets into strategic supplier portfolio configuration reduces supply risks and promotes the achievement of the sustainability goals of the purchasing company.

Strategic supplier selection counts among the decisions that have an impact on the environment and society for several years. Configuring economically rational, environmentally friendly, and socially responsible supplier bases supports worldwide efforts towards sustainable development.

Although sustainable supplier selection has gained importance in recent years, this is the first time that a comprehensive model for the determination of the optimal strategic supplier portfolio in terms of performance-related objectives and sustainability targets has been proposed.

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.

The purpose of this paper is to develop a conceptual model that uses dialectical inquiry (DI) to create cognitive conflict in strategic decision‐makers for the purpose of improving strategic decisions. Activation of the dialectical learning process using DI requires strategic decision‐makers to integrate conflicting information causing cognitive conflict. Cognitive conflict is the catalyst that stimulates the creation of new knowledge in strategic decision‐makers resulting in improved organizational performance.

A conceptual model is developed that explicitly links DI to the dialectical learning process of strategic decision‐makers. This model extends previous research on DI by identifying cognitive conflict as the critical component that links DI as a learning method to the process of dialectical learning in strategic decision‐making.

The major finding of the model of dialectical learning is that the model is an important resource that can be applied to create cognitive conflict in strategic decision‐makers for the purpose of expanding the strategic options of organizations.

Empirical research on DI that focuses on the role of cognitive conflict in the dialectical learning process is lacking. It is hoped that this conceptual paper will stimulate further interest on the topic and a greater appreciation of this method of learning. Strategic decision‐makers must consider alternative ways of generating new knowledge that is crucial for organizational performance.

It is important that the benefits of creating cognitive conflict in the dialectical learning process are understood by strategic decision‐makers. Training for participants in a DI learning intervention is essential to help minimize any dysfunctional behaviors that could result from affective conflict.

This conceptual model identifies the importance of cognitive conflict in the dialectical learning process of strategic decision‐makers and the critical role of cognitive conflict rather than affective conflict in the use of this learning method.

Although there has been increasing interest in the role of affect in work settings, the impact of moods and emotions in strategic decision making remains largely unexplored. In this essay, we address this shortcoming by proposing a conceptual model of strategic decision making that incorporates, at its core, the impact of affective states on cognitive processes that are integral to the decision outcome. The model is based on the principles of Affective Events Theory, which holds that environmental exigencies generate “affective events” that cause emotional reactions in organizational members which, in turn, determine members’ attitudes and behaviors. We extend this model to include the effect of the extra-organizational environment, and propose that emotions “infuse” those cognitive processes that are critical to the strategic decision making process. We conclude that strategic decision making in organizations is not always a controlled, deliberate, purely cognitive process, as it is often described. Rather, we contend that the moods and emotions that managers experience in response to positive and negative workplace events have a significant affect on strategic decision-making processes and ultimately, organizational-level outcomes. We discuss the implications of our model for theory, research, and practice.

A computer environment to support the strategic decision‐making process in construction firms is presented. The system implements modelling concepts originally developed to evaluate project execution strategies, extending and generalizing the modelling methodology to a broader range of strategic decisions. An application to a construction firm's strategic planning is used in this paper to illustrate the modelling process. The computer system is designed to help the users in building a conceptual model for the decision problem, this model is a simplified structure of the variables and interactions that influence the decisions being analysed, including internal as well as external factors. An analytical model is then designed to predict the impact of these strategies, integrating expert knowledge and assessments of the strategic planning team into a mathematical model. The mathematical component uses concepts of cross‐impact analysis and probabilistic inference to capture uncertainties and interactions among project variables. The system provides multiple analysis capabilities, including sensitivity analysis, selected outcome prediction, isolated or combined effect of strategies and changes in performance due to changes in the external environment. The system allows management to test different combinations of long‐term strategies and predict expected sales, market share or other measures of performance.

The paper aims to examine a strategic decision model and its potential value for assisting academic institutional leaders in addressing climate change by making better informed sustainability investment decisions.

Although the vast majority of engagements have focused on IT project selection and prioritization, it will be demonstrated that there is direct applicability to capital investment decisions related to sustainability. Examples of the results are provided with instructions on how to implement a similar approach for organizations challenged with selection and prioritization of sustainability projects. Finally, the model is assessed critically in terms of challenges and lessons learned from client projects.

The strategic decision model has been found to provide a broader framework for decisions, i.e. both qualitative and quantitative. At the same time, the methodology used with the model engages more stakeholders in the process to help build consensus regarding investment and project decisions. As a portfolio-based model, relative value and risk can be viewed easily to enable focus on what the stakeholders believe to be key projects. This perspective and the functionality of the application enable action to be taken by the decision maker to possibly reduce project risk and/or increase potential value of specific projects.

It was determined that additional research, data collection and application development would be necessary if the model were to be used for benchmarking for sustainability planning investment decisions.

The paper proposes that colleges and universities consider the use of the portfolio decision model and, where appropriate, implement the model as part of their sustainability program. The anticipated benefits of this action would include: greater involvement of key stakeholders in support of required organizational change, improvement in the quality of investment decisions, increased success of sustainability projects and data which will enable future benchmarking.

Given this emergent leading role of universities in championing sustainability efforts, the purpose of this paper is to provide college and university leaders a tool to address the challenges related to investment decisions targeted at sustainability projects, such as environmental footprint reduction in support of achieving sustainability for their respective institutions.