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The purpose of this study is to investigate the relationship between artificial intelligence (AI) and decision making in the development of AI-related capabilities. We investigate if and how AI-driven decision making has an impact on firm performance. We also investigate the role played by environmental dynamism in the development of AI capabilities and AI-driven decision making.

We surveyed 346 managers in the United States using established scales from the literature and leveraged p modelling to analyse the data.

Results indicate that AI-driven decision making is positively related to firm performance and that big data-powered AI positively influences AI-driven decision making. Moreover, there is a positive relationship between big data-powered AI and the development of AI capability within a firm. It is also found that the control variables of firm size and age do not significantly affect firm performance. Finally, environmental dynamism does not have a positive and significant moderating effect on the path connecting big data-powered AI and AI-driven decision making, while it exerts a positive moderating effect on the development of AI capability to strengthen AI-driven decision making.

These findings extend the resource-based view by highlighting the capabilities developed within the firm to manage big data-powered AI. This research also provides theoretically grounded guidance to managers wanting to align their AI-driven decision making with superior firm performance.

As the scale of mega transportation infrastructure projects (MTIs) continues to expand, the complexity of engineering construction sharply increases and decision-making sustainability faces severe challenges. Decision-making for mega transportation infrastructure projects unveils the knowledge-intensive characteristic, requiring collaborative decisions by cross-domain decision-makers. However, the exploration of heterogeneous knowledge fusion-driven decision-making problems is limited. This study aims to improve the deficiencies of existing decision-making by constructing a knowledge fusion-driven multi-attribute group decision model under fuzzy context to improve the sustainability of MTIs decision-making.

This study utilizes intuitionistic fuzzy information to handle uncertain information; calculates decision-makers and indicators weights by hesitation, fuzziness and intuitionistic fuzzy entropy; applies the intuitionistic fuzzy weighted averaging (IFWA) operator to fuse knowledge and uses consensus to measure the level of knowledge fusion. Finally, a calculation example is given to verify the rationality and effectiveness of the model.

This research finally constructs a two-level decision model driven by knowledge fusion, which alleviates the uncertainty and fuzziness of decision knowledge, promotes knowledge fusion among cross-domain decision-makers and can be effectively applied in practical applications.

This study provides an effective decision-making model for mega transportation infrastructure projects and guides policymakers.

In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.

This study investigates the profound impact of artificial intelligence (AI) capabilities on decision-making processes and organizational performance, addressing a crucial gap in the literature by exploring the mediating role of decision-making speed and quality.

Drawing upon resource-based theory and prior research, this study constructs a comprehensive model and hypotheses to illuminate the influence of AI capabilities within organizations on decision-making speed, decision quality, and, ultimately, organizational performance. A dataset comprising 230 responses from diverse organizations forms the basis of the analysis, with the study employing a partial least squares structural equation model (PLS-SEM) for robust data examination.

The results demonstrate the pivotal role of AI capabilities in shaping organizational decision-making processes and performance. AI capability significantly and positively affects decision-making speed, decision quality, and overall organizational performance. Notably, decision-making speed is a critical factor contributing significantly to enhanced organizational performance. The study further uncovered partial mediation effects, suggesting that decision-making processes partially mediate the relationship between AI capabilities and organizational performance through decision-making speed.

This study contributes to the existing body of literature by providing empirical evidence of the multifaceted impact of AI capabilities on organizational decision-making and performance. Elucidating the mediating role of decision-making processes advances our understanding of the complex mechanisms through which AI capabilities drive organizational success.

This exploratory study aimed to investigate law enforcement officers’ decision-making processes in high-stress scenarios, exploring variations based on experience and how cognitive demands influence officer decision-making processes.

Employing a naturalistic decision-making approach and macrocognitive framework, the study utilizes scenario-based virtual reality simulations and qualitative interviews to examine the decision-making processes of law enforcement officers in high-stress policing contexts.

Thematic coding of interview data from twelve LEOs immediately following a high-stress virtual reality task revealed differences in decision-making processes and cognitive demands between novice and expert officers. Findings also revealed differences in the type of cognitive demands experienced by officers at key points in the simulated scenario.

The authors offer theoretical and practical contributions toward an increased understanding of officer decision-making, factors and conditions that impact LEO decisions and potential mitigation strategies that law enforcement organizations may leverage to improve officer decision-making in high-stress contexts.

The purpose of this study is to explore the intersection of mindfulness practices and ethical decision-making within organisational leadership. Drawing from ancient Buddhist principles and contemporary neuroscience, this study aims to illuminate how mindfulness can enhance cognitive and emotional regulation, thereby fostering ethical behaviour and improved decision-making among leaders and employees. By examining the theoretical and practical implications of mindfulness in the context of organisational behaviour, this research seeks to contribute to the development of more compassionate, ethical and effective leadership practices, ultimately promoting a more mindful and sustainable business environment.

This concept paper explores the integration of mindfulness meditation practices with decision-making, particularly its influence on ethical choices, through a comparative study of modern techniques and the ancient teachings of the Tripitaka. Using a methodology that spans literature review in organisational behaviour and leadership, alongside in-depth analysis of the Tripitaka and contributions from scholars like Bhikkhu Bodhi, the paper examines the potential of mindfulness in enhancing ethical decision-making. It incorporates a range of sources, including peer-reviewed journals and seminal books across various disciplines, to underscore the transformative potential of mindfulness in addressing contemporary challenges and guiding leadership practices.

This discussion explores how mindfulness, rooted in ancient Buddhist philosophy and aligned with modern neuroscience, can significantly enhance managerial decision-making by fostering a balance between cognitive and emotional factors. It delves into the transformative potential of mindfulness in refining thought processes, promoting ethical decision-making and mitigating cognitive biases. By bridging traditional wisdom with contemporary scientific insights, the analysis underscores mindfulness as an active, dynamic process crucial for personal growth and effective leadership in complex environments.

One limitation of this research is its reliance on theoretical frameworks and literature reviews, which may not capture the full range of practical challenges in implementing mindfulness practices within organisations. Additionally, the diversity in mindfulness methodologies and the subjective nature of mindfulness experiences may affect the generalisability of the findings. Future research should include empirical studies to validate the proposed benefits of mindfulness in organisational settings and explore the most effective strategies for integrating mindfulness practices into leadership and decision-making processes. This would help in understanding how mindfulness can be tailored to suit different organisational cultures and individual preferences.

The practical implications of applying mindfulness in organisational settings include enhanced decision-making abilities, improved leadership effectiveness and increased employee well-being. Mindfulness training can equip leaders and employees with the skills to manage stress, navigate complex ethical decisions and maintain focus amidst distractions, leading to more thoughtful and responsible business practices. Organisations might see a reduction in conflict, enhanced creativity and better teamwork, contributing to a more harmonious and productive workplace. Implementing mindfulness programmes could also support talent retention and attraction by promoting a workplace culture that values mental health and ethical behaviour.

The social implications of integrating mindfulness into organisational decision-making and leadership, as suggested by the document, include promoting ethical behaviour, enhancing emotional regulation and improving team dynamics. Mindfulness practices can lead to more informed and conscious decision-making, reducing cognitive biases and fostering a culture of ethical awareness within organisations. This shift towards mindful leadership could potentially transform organisational cultures, encouraging greater compassion, ethical responsibility and collective well-being, thereby contributing positively to broader societal values and norms.

The originality and value of this research lie in its novel integration of mindfulness concepts derived from ancient Buddhist teachings with contemporary neuroscience and organisational behaviour studies. By exploring the deep-rooted philosophical underpinnings of mindfulness and their applicability to modern ethical decision-making and leadership practices, this work offers a unique perspective that bridges historical wisdom with current scientific understanding. It provides a comprehensive framework for understanding the transformative potential of mindfulness in organisational settings, highlighting its capacity to foster ethical leadership, enhance decision-making processes and contribute to a more mindful, compassionate and sustainable business environment.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

Research has shown that business model innovation can facilitate most ventures to innovate and remain competitive, yet there has been limited work on how digital leadership capabilities influence business model innovation. Building on the dynamic capabilities view, we address this gap by linking digital leadership capabilities with business model innovation via managerial decision-making through provision of grants received by new ventures.

The study is cross-sectional research. Data have been collected utilizing purposive sampling from 313 founding members of new ventures in high-velocity markets, i.e. from Pakistan. SPSS has been used to conduct the moderated mediation analysis.

Digital leadership capabilities foster the business model innovation of the new ventures because they enable new ventures to capitalize on digital technologies and create new ways of generating value for the customers and themselves. Moreover, managerial decision-making mediates digital leadership capabilities and business model innovation relationship, whereas, grants moderate the indirect positive effect of digital leadership capabilities on business model innovation via managerial decision-making. The study generates initial evidence on the impact of digital leadership capabilities on business model innovation via managerial decision-making for new ventures. We advance knowledge on new ventures’ business model innovation by deep-diving into dynamic capabilities view and emphasizing digital leadership capabilities as a significant driver for business model innovation.

With the help of dynamic capabilities theory, this study analyzes how new ventures make use of digital leadership capabilities to promote business model innovation.

The purpose of this study is to investigate Koreans' preferences for FCDM versus SDM and explored the influence of Asian cultural values on decision-making in the context of managing chronic illnesses, such as type 2 diabetes mellitus (T2DM). Shared decision-making (SDM) emphasizes collaboration between providers and service recipients to decide on the best treatment options. However, it may not fully account for the role of families in managing chronic illness, particularly for people from Eastern cultural backgrounds who value active participation from their families in decisions. In response, family-centered decision-making (FCDM) has been proposed as an alternative approach. Using a vignette experiment design, data (n = 316) were collected from Koreans in the US and in Korea who were randomly presented with either SDM or FCDM processes for reaching T2DM treatment decisions. In addition to demographic information, participants reported on three dimensions of their decision-making experience: satisfaction, perceived effectiveness, and perspective-taking. They also rated their Asian cultural values and familiarity with T2DM. Results show better satisfaction, perceived effectiveness, and perspective taking for FCDM compared to SDM when examined in context of treatment types and perceived illness severity. Moderation effects were found for familiarity of illness, with familiarity effects varying by perceived severity. Study findings provided some evidence in favor of FCDM in Asian communities addressing the disabilities and chronic illness of a family member. Although the current study investigated treatment decisions for T2DM during doctor's visits, FCDM has shown potential to be applied in other service settings.

Prior research has revealed that a large share of managers is reluctant towards the use of artificial intelligence (AI) in decision-making. This aversion can be caused by several factors, including individual drivers. The purpose of this paper is to better understand the extent to which individual factors influence managers’ attitudes towards the use of AI and, based on these findings, to propose solutions for increasing AI adoption.

The paper builds on prior research, especially on the factors driving the adoption of AI in companies. In addition, data was collected by means of 16 expert interviews using a semi-structured interview guideline.

The study concludes on four groups of individual factors ranked according to their importance: demographics, familiarity, psychology and personality. Moreover, the findings emphasized the importance of communication and training, explainability and transparency and participation in the process to foster the adoption of AI in decision-making.

The paper identifies four ways to foster AI integration for organizational decision-making as areas for further empirical analysis by business researchers.

This paper offers four ways to foster AI adoption for organizational decision-making: explaining the benefits and training the more adverse categories, explaining how the algorithms work and being transparent about the shortcomings, striking a good balance between automated and human-made decisions, and involving users in the design process.

The study concludes on four groups of individual factors ranked according to their importance: demographics, familiarity, psychology and personality. Moreover, the findings emphasized the importance of communication and training, explainability and transparency and participation in the process to foster the adoption of AI in decision-making.

This study is one of few to conduct qualitative research into the individual factors driving usage intention among managers; hence, providing more in-depth insights about managers’ attitudes towards algorithmic decision-making. This research could serve as guidance for developers developing algorithms and for managers implementing and using algorithms in organizational decision-making.