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This paper aims to investigate the firm performance of micro, small and medium enterprises (MSMEs) by using artificial intelligence-based supply chain resilience strategies. A theoretical framework shows the relationship between artificial intelligence, supply chain resilience strategy and firm performance.

A questionnaire is developed to survey the MSMEs of India. A sample size of 307 is considered for the survey. The employees working in MSMEs are targeted responses. The conceptual model developed is tested empirically.

The study found that eight hypotheses were accepted and two were rejected. There are five mediating variables in the current study. Artificial intelligence, the independent variable, positively affects all five mediators. Then, according to the survey and analysis of the final 307 responses from MSMEs, the mediating variables significantly impact the dependent variable, firm performance.

This study is limited to emerging markets only. Also this study used only cross sectional data collection methods.

This study is essential for supply chain managers and top management willing to adopt the latest technology in their organisation or firmfor a better efficient supply chain process.

This study investigated artificial intelligence-based supply chain resilience for improving firm performance in emerging countries like India. This study tried to fill the research gap in artificial intelligence and supply chain resilience.

Although digital transformation (DT) has emerged as an important phenomenon for both research and practices, the influences remain inconclusive and inadequate. The emerging artificial intelligence (AI) technologies further complicate the understanding and practices of DT while understudied yet. To address these concerns, this study takes a process perspective to empirically investigate when and how digital-intelligence transformation can improve firm performance, aiming to enrich the literature on digital-intelligence transformation and strategic information systems (IS) field.

Drawing on the dynamic capability view and business agility, we took a process perspective to conceptualize and empirically examine the influence of digital-intelligence transformation and the process characteristics. Taking a continuous panel dataset of listed Chinese firms covering 2007 to 2020, we investigated digital-intelligence transformation’s effect on firm performance and the moderating roles of three strategic aspects: pace, scope and rhythm.

This study found that digital-intelligence transformation positively affects firm performance and is moderated by the characteristics of transformation processes (i.e. pace, scope and rhythm). Specifically, the high-paced and rhythmic transformation processes facilitate the positive relationship, while the large scope undermines the benefits of transformation. These relationships hold across various endogeneity and heterogeneity analyses.

Our findings provide valuable implications for digital-intelligence transformation and strategic IS field. First, this study enriches existing literature on digital-intelligence transformation by empirically investigating the influence from a process perspective. Moreover, this study provides insights into a comprehensive understanding of the complexity of digital-intelligence transformation and the influences of AI. Finally, this study provides practical implications on how to make digital-intelligence transformation to benefit firm performance.

The purpose of this study is to understand how the level of readiness for artificial intelligence (AI) affects the overall performance of companies, determine the role of organizational flexibility in adapting to new technologies and business models and assess the importance of lean sustainability and value creation for technology-focused companies.

Technology companies working in technoparks in Istanbul were determined, and a questionnaire was applied to senior employees such as experts, engineers and managers working in these companies. The results were processed with a sample of 456 units. SmartPLS program was used for analysis.

As a result of the analyzes, it is supported by hypotheses that AI readiness and organizational flexibility have positive effects on lean sustainability and value creation.

When evaluated in terms of the limitations of the research, it would not be correct to evaluate the results of the analysis in general, since the data were collected from technology-focused companies in technoparks in Istanbul.

Examining the variables that make up the research model in technology-oriented companies helps to understand the critical factors for the future success of companies. At the same time, this research is important for companies to make more informed decisions in their strategic planning, technological transformation processes and value creation strategies.

This research topic offers a unique approach in terms of bringing together topics such as AI readiness, organizational flexibility, sustainability and value creation. These issues play an important role in the strategic planning of technology-focused companies, and when considered together, they are important in terms of examining the critical factors that affect the future success of companies.

Export market orientation can be broadly divided into intelligence (generation and dissemination) and responsiveness activities. Although previous studies assess intelligence and responsiveness activities, little is known about what type of international channel partner acts as an enabling condition for the impact of these activities on export venture performance. This study aims to examine the extent to which the selection of international channel partners through word-of-mouth referrals versus direct contacts affects the benefits of intelligence and responsiveness activities.

Data were collected from 246 exporting manufacturers in Japan. To test the hypotheses, we conducted regression analyses using a subjective performance measure at the venture level. We also performed a post hoc analysis using objective performance measure at the function level.

We find that the extent to which international channel partners are selected through word-of-mouth referrals has a moderating role in the export market-oriented activities–performance linkages. Specifically, it acts as an enabling condition for intelligence activity and a disenabling condition for responsiveness activity.

This study contributes to a better understanding of export market orientation by classifying it into intelligence and responsiveness activities and providing empirical evidence on their different interaction effects with partner selection. It also contributes to the elaboration of agency theory by offering insights into the fit between task characteristics and contract type. Our study is critical for business managers as it suggests guidelines for manufacturing exporters engaging in export market-oriented behaviors and export channel management.

Drawing on resource-orchestration theory (ROT), this study investigates the influence of market intelligence and entrepreneurial orientation (EO) on international performance of born global (BG) small and medium enterprises (SMEs) in emerging markets with the mediating role of global technological competence. Quality focus is used as a moderator between global technological competence and international performance.

Data were gathered through a survey, and PLS-SEM was employed for hypotheses testing with a sample of 256 BG SMEs.

The results showed that market intelligence, EO, global technological competence and quality focus positively relate to international performance. Moreover, market intelligence and EO are positively associated with global technological competence. Besides, global technological competence significantly mediates the relationship between market intelligence, EO and international performance. Finally, quality focus strengthens the positive relationship between global technological competence and international performance.

Our research demonstrates that if management utilizes or invests on market intelligence, EO, global technological competence and quality focus, then the BG SMEs will increase their international performance.

The paper contribution lies in its focus on exogenous constructs (i.e. market intelligence, EO, global technological competence and quality focus) to determine the international performance of born global SMEs in emerging markets.

This study builds on a conceptual model by integrating AI features – Perceived intelligence (PIN) and anthropomorphism (PAN) – while extending expectation confirmation theory (ECT) factors – interaction quality (IQU), confirmation (CON), and customer experience (CSE) – to evaluate the continued intention to use (CIU) of AI-enabled digital banking services.

Data were collected through an online questionnaire administered to 390 digital banking customers in India. The data were further analysed, and the presented hypotheses were evaluated using partial least squares structural equation modelling (PLS-SEM).

The research indicates that perceived intelligence and anthropomorphism predict interaction quality. Interaction quality significantly impacts expectation confirmation, consumer experience, and the continuous intention to use digital banking services powered by AI technology. AI design will become a fundamental factor; thus, all interactions should be user-friendly, efficient, and reliable, and the successful implementation of AI in digital banking will largely depend on AI features.

This study is the first to demonstrate the effectiveness of an AI-ECT model for AI-enabled Indian digital banks. The user continuance intention to use digital banking in the context of AI has not yet been studied. These findings further enrich the literature on AI, digital banking, and information systems by focusing on the AI's Intelligence and Anthropomorphism variables in digital banks.

The aim of this paper is the validation of a scale that has Emotional Intelligence (EI) and Internal Marketing (IM) as determinants of organizational success.

The survey (questionnaire) allows assessing the contribution of motivation and job satisfaction to organizational success and was disseminated on social networks and directly in some national institutions.

The results of this research suggest that the 58 items of the scale can be grouped into eight main dimensions and can be confidently applied to professionals from organizations and companies operating in Portugal.

Furthermore, this study can be considered as an innovative, effective and useful tool for entrepreneurs, managers and organizations, as it can help diagnose the perceptions of their employees and promote a healthy and appealing environment, moving towards an excellent organizational performance, greater profitability and corporate sustainability.

Artificial intelligence (AI) has the potential to address significant challenges in education, innovate learning and teaching practices and achieve SDG 4. However, existing literature often overlooks the behavioural aspects of students regarding AI in education, focusing predominantly on technical and pedagogical dimensions. Hence, this study aims to explore the significant relationships among AI literacy, AI usage, learning outcomes and academic performance of generation Z students in the Indian educational context.

The study used structural equation modelling (SEM) on Gen Z students born in the years 1997–2012 as a sample population for the research in the north Indian states like Punjab, Haryana, Himachal and regions like Chandigarh and N.C.R. Delhi.

The results established significant positive relationships between AI literacy, AI usage, AI learning outcomes and academic performance. Specifically, higher levels of AI literacy were associated with increased engagement with AI technologies and tools for learning purposes, leading to better learning outcomes and academic performance. The findings demonstrated that AI literacy plays a crucial role in providing effective learning experiences and fostering skills such as problem-solving and critical thinking among Gen Z students.

The implications of the study include the significance of integrating AI education initiatives into curricula, prioritising professional development programmes for educators and making sure that every student has equitable access to AI technologies.

The study introduces a novel perspective by examining variables such as AI literacy, AI usage, AI learning outcomes and academic performance and developing a model that has not been previously studied. It provides a new discourse and proposes a framework uniquely combining AI-infused curriculum design, educator empowerment, robust assessment mechanisms and sustainable practices.

This paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.

Drawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correlation coefficient tests.

All AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions. The number of days used to calculate the technical indicators enabled the development of models with better performance. Those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.

The study expands knowledge on the topic and provides robust evidence on the role of AI in financial analysis and decision-making, as well as in predicting the movements of the largest stock exchanges in the world. This brings theoretical, strategic and managerial contributions, enabling the discussion of efficient market hypothesis (EMH) in a complex economic reality – in which the use of automation and application of AI has been expanded, opening new avenues of future investigation and the extensive use of technical analysis as support for decisions and machine learning.

The AI algorithms' flexibility to determine their parameters and the window for measuring and estimating technical indicators provide contextually adjusted models that can entail the best possible performance. This expands the informational and decision-making capacity of investors, managers, controllers, market analysts and other economic agents while emphasizing the role of AI algorithms in improving resource allocation in the financial and capital markets.

The originality and value of the research come from the methodology and systematic testing of the EMH through the main indices of the world’s largest stock exchanges – something still unprecedented despite being widely expected by scholars and the market.

This chapter examines the effect of the Regenerative Tourism Movement on the global industry and the role of Artificial Intelligence (AI) in driving sustainability and innovation. The Regenerative Tourism Movement represents a paradigm shift in the tourism industry, focussing on the interconnectedness of economic, social, cultural, and environmental well-being. This approach aims to generate positive impacts on local systems by fostering partnerships, diversity in local economies, and transformative experiences for travelers. This chapter explores the key principles and nature-based solutions associated with regenerative tourism. Additionally, it delves into the role of AI in the tourism sector, highlighting its potential to enhance sustainability practices, deliver personalised experiences, and streamline operations. Various AI tools and technologies, such as data analytics, machine learning, natural language processing, computer vision, IoT integration, recommender systems, optimisation algorithms, blockchain technology, and AR/VR, are discussed in the context of regenerative tourism. This chapter concludes by outlining the benefits of AI in sustainable and regenerative tourism, emphasising reduced environmental impact, enhanced efficiency, and improved customer service. It also highlights the challenges and considerations associated with AI adoption in the tourism industry. Recommendations for the integration of AI-driven solutions and future directions for research in this field are provided, aiming to inspire further exploration and implementation of AI in regenerative tourism.