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How can managers reduce information asymmetry in dyadic manager-external stakeholder relationships in a complex and evolving environment? Addressing this question has significant implications for firm survival, growth, and competitive advantage.

We have adopted a multiparadigm approach to theory building, known as metatriangulation. We integrate the dynamic capabilities, sensemaking, and evolutionary theory literatures to theorize how managers can relate to stakeholders in a complex and evolving environment.

We propose, via a conceptual framework and three propositions, “evolutionary sensemaking” as the managerial metacognitive dynamic capability that helps managers hone their understanding based on the evolutionary changes in the stakeholder’s interpretations of information quality preferences. The framework unfolds across three evolutionary stages: sensing preferences' variation of the stakeholder, seizing preferences, and transforming for complexity alignment and retention. The propositions focus on managing complexity in stakeholder information quality preference, employing cognitive capabilities to simplify, interpret, and align interpretations for effective information asymmetry reduction.

To develop the metacognitive dynamic capability of evolutionary sensemaking, managers need to train for and foster the underlying complex cognitive capabilities by enhancing their (1) perception and attention skills, (2) problem-solving and reasoning skills, and (3) language, communication, and social cognition skills, focusing specifically on reducing the complexity embedded in stakeholder cognition and diverse stakeholder preferences for information quality. Contrary to the current advice to “keep things simple” and provide “more” information to the stakeholders for opportunism reduction, trust-building, and superior governance, our framework suggests that managers hone their cognitive capabilities by learning to deal with the underlying complexity.

The proposed framework and propositions address research gaps in reducing information asymmetry. It enriches the dynamic capabilities literature by recognizing complexity (as opposed to opportunism) as an alternative source of information asymmetry, which needs to be addressed in this stream of research. It extends the sensemaking literature by identifying the complexity sources – i.e. stakeholder preferences for diverse information quality attributes and the associated cognitive preference interpretation processes. The article enhances evolutionary theory by delving into microprocesses related to information asymmetry reduction, which the existing literature does not thoroughly investigate.

This research examines how responsible deployment of ChatGPT in the UAE’s government sector, guided by New Public Management principles, can enhance customer journeys by integrating services across government bodies. Through semi-structured interviews with UAE government officers, the study investigates this approach’s benefits, challenges, and applications for achieving efficient and integrated public service delivery.

This research adopts a qualitative approach, purposive sampling strategy, and semi-structured interviews to explore the subjective viewpoints of 20 high-level UAE government authorities. The thematic analysis uncovers ChatGPT’s benefits, challenges, and applications, aligning with New Public Management principles.

Thematic analysis reveals four themes: Benefits and Applications of ChatGPT, Challenges, Strategies to Overcome Challenges, and Steps for Customer Journey Enhancement through ChatGPT.

The analysis is based on participant responses provided during the interviews, which may be subject to biases or incomplete information. Secondly, the study focuses solely on the provided applications and participant responses, limiting the generalizability of the conclusions to other contexts.

The implementation of ChatGPT in the government sector has practical implications for transforming its operations and enhancing communication, efficiency, decision-making, and service offerings: citizen engagement, streamlined processes, and informed governance.

This study uniquely examines ChatGPT’s role in government, offering insights into communication, efficiency, decision-making, and service offerings. Identifying hurdles enriches understanding of ChatGPT’s practical integration in government.

Extant research supports the importance of dynamic managerial capabilities in capturing managers’ individual roles in organisations’ adjustments to change. This paper develops a multidimensional scale for measuring dynamic managerial capabilities consisting of sensing, seizing and reconfiguration capacities that mediate between managers’ affective states and their firms’ performance.

The scale is validated in a survey-based study among 204 managers in companies in the United States of America (USA). We applied a multiple regression model (a triple mediation) using each of DMCs’ three dimensions to test the effects of managers’ affective states on their firms’ performance.

The multidimensional construct of DMCs adds about 15 % of variance explained to a firm’s performance, as perceived by its managers. So managers’ affective states do have an impact on DMCs and, later, on their firms’ performance.

We show the impact of negative and positive affect on DMCs. We also show that DMCs’ three dimensions should be treated in a formative manner that advances discussion on DMCs and their role in a firm’s performance.

Understanding managers’ affective states helps incorporate “hot cognition” into firms’ strategising processes. Although both positive and negative emotions can be helpful, depending on the situation, positive affect is generally more valuable than negative affect as it relates to a firm’s performance.

Our work proposes measuring DMCs based on Teece’s (2007) disaggregation of DMCs into sensing, seizing and reconfiguration capacities. We approach each of these dimensions separately and show that managers’ affective states influence each dimension differently.

Accurate diagnosis of brain tumors is crucial for effective treatment and improved patient outcomes. Magnetic resonance imaging (MRI) is a common method for detecting brain malignancies, but interpreting MRI data can be challenging and time-consuming for healthcare professionals.

An innovative method is presented that combines deep learning (DL) models with natural language processing (NLP) from ChatGPT to enhance the accuracy of brain tumor detection in MRI scans. The method generates textual descriptions of brain tumor regions, providing clinicians with valuable insights into tumor characteristics for informed decision-making and personalized treatment planning.

The evaluation of this approach demonstrates promising outcomes, achieving a notable Dice coefficient score of 0.93 for tumor segmentation, outperforming current state-of-the-art methods. Human validation of the generated descriptions confirms their precision and conciseness.

While the method showcased advancements in accuracy and understandability, ongoing research is essential for refining the model and addressing limitations in segmenting smaller or atypical tumors.

These results emphasized the potential of this innovative method in advancing neuroimaging practices and contributing to the effective detection and management of brain tumors.

With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

This paper analyzes the ethics of integrating Artificial Intelligence (AI), particularly regarding AI-generated educational content in academia. It attempts to explore how AI customization mimics human interaction and behavior in education, investigate ethical concerns in educational AI adoption, and assess ChatGPT’s ethical use for nurturing curiosity and maintaining academic integrity in education.

Fictional tales may help us think critically and creatively to uncover hidden truths. The narratives are analyzed to determine the affordances and drawbacks of Artificial Intelligence in Education (AIEd).

The study highlights the imperative for innovative, ethically grounded strategies in harnessing AI/GPT technology for education. AI can enhance learning, and human educators’ irreplaceable role is even more prominent, emphasizing the need to harmonize technology with pedagogical principles. However, ensuring the ethical integration of AI/GPT technology demands a delicate balance where the potential benefits of technology should not eclipse the essential role of human educators in the learning process.

This paper presents futuristic academic scenarios to explore critical dimensions and their impact on 21^st century learning. As AI assumes tasks once exclusive to human educators, it is essential to redefine the roles of both technology and human teachers, focusing on the future.

Artificial intelligence (AI) technologies have led to significant transformations across industries and society, including the field of education. The integration of AI in educational settings has the potential to improve students' learning experience and support their individual competencies when paired with non-AI methods. Despite the growing importance of AI in modern education, there remains a noticeable research gap regarding its use in entrepreneurship education and the effects of Chatbots on students' entrepreneurial competencies. To address this gap, an exploratory study was conducted on undergraduate students who were tasked with using ChatGPT to improve their business model canvas.

The chosen methodology aligned with the research purpose, aiming to explore the relationship between Generative AI and competencies. Due to the novel nature of the research problem, an exploratory study was conducted using a mixed methods approach. A survey with open- and closed-ended questions was designed, and statistical and text analyses were performed to interpret data and test identified propositions.

The findings of this study indicate that ChatGPT can enhance the types of students' entrepreneurial competencies considered in this study: spotting opportunities, creativity, vision, valuing ideas and ethical and sustainable thinking. The results show that ChatGPT can be particularly helpful to improve the ability of students of valuing ideas.

Overall, this study highlights the potential of adopting ChatGPT in experiential learning methodologies for enhancing students' entrepreneurial competencies and improving their learning outcomes.

Family businesses (FBs) are considered an essential type of entrepreneurship that impacts economic growth. However, statistics show that after a period of performance they ultimately fail, and comparatively little is known about the reasons for their failing when compared to the amount of research focusing on keys to success.

Through the implementation of an case study technique, which is widely used in research to address the complex phenomenon of failure, this paper aims to analyse the antecedents of failure in the case of four Catalan FBs. In doing so, this article develops propositions based on Institutional Economics Theory and Dynamic Capability Theory, with a focus on innovation and product diversification in family firms.

Using interviews as a means of obtaining a large amount of information, it is observed that problems related to governmental regulations and constantly changing social behaviour can lead to failure for FBs. Additionally, a link between R&D activities and new product development and FB failure is observed. More specifically, this research highlights that a lack of product diversification and innovation can become a hindrance for FB performance when the institutional environment is unstable. It reveals the importance of developing dynamic capabilities that can meet the demands of fast-changing consumer behaviour. From a practical perspective, these findings can be used by governments in developing regulations focused on the dynamic capabilities of FBs, and by managers in order to learn from these experiences and implement appropriate strategies for long-term development and crisis management.

This paper theoretically contributes to both the FB literature, as well as to institutional economics and dynamic capability theories by offering a combined perspective on how FB's dynamic capabilities change based on environmental factors and impact FB failure.

The purpose of this paper is to identify the differences between Hungarian family-owned businesses (FOBs) and non-family-owned businesses (NFOBs) concerning the elements of SME competitiveness and financial performance.

The research covers the Hungarian data set of the Global Competitiveness Project (GCP, www.sme-gcp.org) of 738 (data collection between 2018 and 2020) non-listed SMEs, of which 328 were FOBs. The study uses the comprehensive, multidimensional competitiveness measurement of the GCP built on the resource-based view (RBV) and the configuration theory. Financial performance was captured with two composite indicators: short-term and long-term financial performance (LTFP). The comparative analysis between FOBs and NFOBs was conducted using binary logistic regression.

The results show that FOBs are more prone to focusing on local niche markets with higher longevity and LTFP than NFOBs. However, FOBs have lower innovation intensity and less organised administrative procedures. The most contradicting finding is that the FOBs’ higher LTFP is accompanied by significantly lower competitiveness than in the case of NFOBs.

This study goes beyond other GCP studies by including composite financial performance measures among the variables examined. The combination of performance-causing (resources and capabilities) and performance-representing (financial performance) variables provides a better understanding of the non-listed SMEs in terms of family ownership. The results help academia to enrich the RBV-competitiveness, the non-listed SME management and finance literature, and policymakers to design business development and support schemes. They also show future entrepreneurs the impact of family ownership on entrepreneurial success.