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A better understanding of the characteristics and capabilities of women entrepreneurs can significantly improve their chances of success. Therefore, three studies were conducted for this exploratory paper. We have discovered the characteristics of entrepreneurial intelligence among female entrepreneurs through semi-structured interviews based on conventional content analysis. According to the second study, qualitative meta-synthesis was utilized to identify characteristics of women's entrepreneurial intelligence at the international level. As a third study, we examined the evolutionary relationships of entrepreneurs' intelligence components following the discovery and creation of opportunities.

The present paper was based on three studies. In the first study, 15 female entrepreneurs were interviewed using purposive sampling in the Guilan province of Iran to identify the characteristics of entrepreneurial intelligence at the national level. An inductive content analysis was performed on the data collected through interviews. Using Shannon entropy and qualitative validation, their validity was assessed. In the second study, using a qualitative meta-synthesis, the characteristics of women's entrepreneurial intelligence were identified. Then the results of these two studies were compared with each other. In the third study, according to the results obtained from the first and second studies, the emergence, priority and evolution of entrepreneurial intelligence components in two approaches to discovering and creating entrepreneurial opportunities were determined. For this purpose, interviews were conducted with 12 selected experts using the purposeful sampling method using the fuzzy total interpretive structural modeling (TISM) method.

In the first research, this article identified the components of entrepreneurial intelligence of women entrepreneurs in six categories: entrepreneurial insights, cognitive intelligence, social intelligence, intuitive intelligence, presumptuous intelligence and provocative intelligence. In the second study, the components of entrepreneurial intelligence were compared according to the study at the national level and international literature. Finally, in the third study, the evolution of the components of entrepreneurial intelligence was determined. In the first level, social intelligence, presumptuous intelligence and provocative intelligence are formed first and social intelligence and provocative intelligence have an interactive relationship. In the second level, entrepreneurial insight and cognitive intelligence appear, which, in addition to their interactive relationship, take precedence over the entrepreneur's intuitive intelligence in discovering entrepreneurial opportunities. With the evolution of the components of entrepreneurial intelligence in the opportunity creation approach, it is clear that intuitive intelligence is formed first at the first level and takes precedence. At the second level, there is cognitive intelligence is created. At the third level, motivational intelligence and finally, at the last level, entrepreneurial insight, social intelligence and bold intelligence.

This study has the potential to discover credible and robust approaches for further examining the contextualization of women's entrepreneurial intelligence at both national and international levels, thereby advancing new insights. By conceptualizing various components of entrepreneurial intelligence for the first time and exploring how contextual factors differ across nations and internationally for women's entrepreneurship, this paper challenges the assumption that the characteristics of women's entrepreneurial intelligence are uniform worldwide. It also depicts the evolution of the components of entrepreneurial intelligence.

The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

Studies have shown that within-domain compensatory consumption can successfully repair the damaged self, but other research indicates that it can undermine self-control because such consumption causes self-threat rumination that impairs self-regulatory resources. This paper aims to identify a boundary condition that reconciles and explains these contradictory findings.

The authors conducted three experiments to show that within-domain compensatory consumption undermines self-control, but only in some situations. They test a boundary condition (i.e. type of connections between within-domain products and self-threat domain) for the effects of such consumption on self-threat rumination and self-control.

This paper demonstrates that within-domain (but not across-domain) compensatory consumption induces rumination and reduces subsequent self-control, but only when the product’s connection to the self-threat domain is made explicit through brand names or slogans. When the connection is merely implicit, rumination and self-control deficits are not observed.

Consumers may seek certain products to bolster threatened aspects of their self-concept. Marketing tactics that explicitly highlight connections to such self-aspects can lower a consumer’s self-control resulting in stronger purchase intent, while at the same time hindering the possibility of self-concept repair. Managers need to be wary of ethical concerns.

This research qualifies the existing findings by presenting “type of product connection” as a key determinant of within-domain compensatory consumption’s impact on self-control. Researchers need to be conscious of the type of products (explicitly vs implicitly connected to the self-threat domain) they use in compensatory consumption studies, because this may influence their findings.

Emotional intelligence (EI) has a profound effect on the performance of tour guides, though past studies on the subject have neglected to examine the effects of EI on neither the overall nor specific pattern of tour guides' performance. This study examines the overall link between four dimensions of EI (self-emotion appraisal [SEA], others' emotion appraisal [OEA], use of emotions [UOEs] and regulation of emotions [ROEs]) and five dimensions of citizenship performance (helping, self-development, initiative, compliance and adaptability), ultimately identifying the relationship between EI factors and each dimension of tour guides' citizenship performance.

The data from 244 Jordanian tour guides were gathered through a quantitative survey. A confirmatory factor analysis (CFA) and multiple regression analysis were performed to analyze the data.

The results reveal that emotionally intelligent tour guides are more likely to have higher levels of citizenship performance. The most significant factors in determining extra-role behaviors by tour guides are “UOEs” and “others’ emotion appraisal (OEA)”.

This study equips different professional entities in the tour guide industry with useful, contextualized links between EI and citizenship performance. This assists in developing new accreditation policies and business practices to augment tour guides' citizenship professionalism, thereby maximizing their valuable contributions to tourism growth.

This study advances the tourism studies by establishing a new theoretical link between tour guides' EI and citizenship performance. The study also uncovers unique factors of EI that significantly determine tour guides' citizenship behaviors.

This study aims to assess the effectiveness of a scale measuring artificial intelligence capabilities by using the resource-based theory. It seeks to examine the impact of these capabilities on the organizational-level resources of dynamic capabilities and organizational creativity, ultimately influencing the overall performance of government organizations.

The calibration of artificial intelligence capabilities scale was conducted using a combination of qualitative and quantitative analysis tools. A set of 26 initial items was formed in the qualitative study. In the quantitative study, self-reported data obtained from 344 public managers was used for the purposes of refining and validating the scale. Hypothesis testing is carried out to examine the relationship between theoretical constructs for the purpose of nomological testing.

Results provide empirical evidence that the presence of artificial intelligence capabilities positively and significantly impacts dynamic capabilities, organizational creativity and performance. Dynamic capabilities also found to partially mediate artificial intelligence capabilities relationship with organizational creativity and performance, and organizational creativity partially mediates dynamic capabilities – organizational creativity link.

The application of artificial intelligence holds promise for improving decision-making and problem-solving processes, thereby increasing the perceived value of public service. This can be achieved through the implementation of regulatory frameworks that serve as a blueprint for enhancing value and performance.

There are a limited number of studies on artificial intelligence capabilities conducted in the government sector, and these studies often present conflicting and inconclusive findings. Moreover, these studies indicate literature has not adequately explored the significance of organizational-level complementarity resources in facilitating the development of unique capabilities within government organizations. This paper presents a framework that can be used by government organizations to assess their artificial intelligence capabilities-organizational performance relation, drawing on the resource-based theory.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

This paper aims to explore the opportunities and challenges associated with adopting artificial intelligence (AI) in libraries in Bangladesh and provide recommendations to guide the responsible integration of AI to enhance library services and accessibility.

The paper reviews relevant literature on the applications of AI in libraries, the current state of technology adoption in Bangladeshi libraries and the ethical considerations surrounding AI implementation. It analyzes the potential benefits of AI tools such as chatbots, intelligent search engines, text-to-speech and language translation for improving user services and inclusion. The challenges of infrastructure constraints, lack of resources and skills, data privacy issues and bias are also examined through the lens of the Bangladeshi context.

AI offers transformative opportunities to automate operations, strengthen user services through 24/7 virtual assistants and personalized recommendations and promote accessibility for diverse users in Bangladeshi libraries. However, significant challenges such as inadequate technology infrastructure, funding limitations, shortage of AI-skilled staff, data privacy risks and potential biases must be addressed. Strategically planning for sustainable implementation, building AI capacity, prioritizing ethical AI development and fostering collaborations are critical factors for successful AI adoption.

This paper provides an in-depth analysis of the prospects and obstacles in leveraging AI specifically for libraries in Bangladesh. It offers original insights and context-specific recommendations tailored to the needs and constraints of a developing nation working to harness AI’s potential to create dynamic, inclusive knowledge centers serving all communities.

Following the theory of emotional events, this paper aims to use the bright triad and the dark tetrad as representations to investigate the role of positive and negative personality patterns in achieving positive and negative innovation. The study also examines the mediating role of emotional intelligence and abusive supervision and the interactive role of emotional exhaustion in understanding the relationship between positive and negative personality patterns and positive and negative innovation.

To test the hypotheses of the study model, a set of questionnaires was distributed to a sample of 500 medical officers working in different departments of public hospitals in southern Iraq. The data were analysed using the structured equation model.

The results of the current study confirm previous studies on emotional intelligence because the bright triad negatively associates with negative innovation and positively associates with positive innovation. Meanwhile, the dark tetrad positively associates with negative innovation through abusive supervision, and that emotional exhaustion reinforces the negative side and weakens the positive side of the relationships.

This study contributes to the literature by emphasising that the values represented by the bright triad have a strong readiness to show positive innovation and immunity to negative influence caused by abusive supervision. Meanwhile, the negative emotions of the dark tetrad pattern result in negative patterns because they correlate with negative innovation and the avoidance of positive behaviour, which is escalated by abusive supervision.

The objective of this research is to investigate the barriers impacting the integration of Industry 5.0 (I5.0) in supply chain sustainability. By understanding these challenges, this study aims to provide valuable insights that can guide organizations in successfully implementing the transformative potential of I5.0. The ultimate aim is to improve operational efficiency and advocate for sustainable practices within supply chains.

Research has used industry expert interviews, a comprehensive literature review and the decision-making trial and evaluation laboratory approach for analysis. Industry expert interviews serve to capture first-hand insights from professionals well versed in the field, providing practical perspectives on the barriers to I5.0 adoption.

This study identifies technological challenges, organizational barriers, regulatory impediments and economic constraints as pivotal factors inhibiting the widespread adoption of I5.0 in supply chain sustainability.

This research serves as a foundation for future investigations into overcoming barriers to I5.0 adoption, guiding scholars and practitioners in refining strategies for successful implementation.

The findings offer practical insights for organizations aiming to adopt I5.0, informing decision-makers on key challenges and facilitating the development of targeted strategies to overcome them.

The social implications lie in fostering sustainable business practices through the adoption of I5.0, contributing to environmental responsibility and societal well-being.

This research contributes original insights from practitioners, policymakers and researchers in navigating the complex landscape of I5.0 adoption, ensuring meaningful contributions to both academia and industry.

This research study aims to inquire into the technostress phenomenon at an organizational level from machine learning (ML) and artificial intelligence (AI) deployment. The authors investigated the role of ML and AI automation-augmentation paradox and the socio-technical systems as coping mechanisms for technostress management amongst managers.

The authors applied an exploratory qualitative method and conducted in-depth interviews based on a semi-structured interview questionnaire. Data were collected from 26 subject matter experts. The data transcripts were analyzed using thematic content analysis.

The study results indicated that role ambiguity, job insecurity and the technology environment contributed to technostress because of ML and AI technologies deployment. Complexity, uncertainty, reliability and usefulness were primary technology environment-related stress. The novel integration of ML and AI automation-augmentation interdependence, along with socio-technical systems, could be effectively used for technostress management at the organizational level.

This research study contributed to theoretical discourse regarding the technostress in organizations because of increased ML and AI technologies deployment. This study identified the main techno stressors and contributed critical and novel insights regarding the theorization of coping mechanisms for technostress management in organizations from ML and AI deployment.

The phenomenon of technostress because of ML and AI technologies could have restricting effects on organizational performance. Executives could follow the simultaneous deployment of ML and AI technologies-based automation-augmentation strategy along with socio-technical measures to cope with technostress. Managers could support the technical up-skilling of employees, the realization of ML and AI value, the implementation of technology-driven change management and strategic planning of ML and AI technologies deployment.

This research study was among the first few studies providing critical insights regarding the technostress at the organizational level because of ML and AI deployment. This research study integrated the novel theoretical paradigm of ML and AI automation-augmentation paradox and the socio-technical systems as coping mechanisms for technostress management.