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PurposeChatGPT is a cutting-edge chatbot powered by artificial intelligence that could revolutionise and advance the teaching and learning process. Drawing on the technology acceptance model (TAM) and information system (IS) success model, this study aims to investigate determinants of students' intention to use ChatGPT for education purposes.Design/methodology/approachThe partial least squares technique was used to analyse 406 usable data collected from university students in Malaysia.FindingsThe results confirmed the relationships between perceived usefulness (PU), perceived ease of use (PEU), attitude and intention to use proposed by TAM. PU and PEU are influenced by system quality. Surprisingly, trust in information moderates negatively the influences of PEU and PU on attitude.Practical implicationsThe findings provide insight for higher education institutions, unit instructors and ChatGPT developers on what may promote the use of ChatGPT in higher education.Originality/valueThe study contributes to the literature by exploring the determinants of ChatGPT adoption, extending the TAM model by incorporating IS success factors and assessing the moderating effect of trust in information.

In the era of rapid technological advancement, generative artificial intelligence (AI) has emerged as a transformative force in various sectors, including environmental sustainability. This research investigates the factors and consequences of using generative AI to access environmental information and influence green purchasing behavior. It integrates theories such as the information adoption model, value-belief-norm theory, elaboration likelihood model, and cognitive dissonance theory to pinpoint and prioritize determinants of generative AI usage for environmental information and green purchasing behavior. Data from 467 participants were analyzed using a hybrid methodology that blends partial least squares (PLS) with artificial neural networks (ANN). The PLS outcomes indicate that interactivity, responsiveness, knowledge acquisition and application, environmental concern, and ascription of responsibility are key predictors of generative AI use for environmental information. Furthermore, environmental concerns, green values, personal norms, ascription of responsibility, individual impact, and generative AI use emerge as predictors of green purchasing behavior. The ANN analysis offers a unique perspective and discloses variations in the hierarchy of these predictors. This research provides valuable insights for stakeholders on harnessing generative AI to promote sustainable consumer behaviors and environmental sustainability.

Objectives:

This research aims to enhance the quality of hospital services by utilizing Quality Function Deployment (QFD) with a novel Multi-Dimensional House of Quality (MD-HOQ) approach. This method integrates Service Quality (SERVQUAL) analysis and considers resource constraints, such as financial and workforce limitations, to select and prioritize technical requirements effectively.

Methods:

The proposed MD-HOQ approach was applied to a private hospital in Tehran, Iran. Data were gathered from a sample of 8 experts and a sample of 386 patients, using 2 in-depth interviews and 4 questionnaires. The process included identifying hospital sections and determining their importance using the Analytic Hierarchy Process. Patients’ needs in each section were then identified and weighted through SERVQUAL analysis. Subsequently, technical requirements to meet these needs were listed and weighted using MD-HOQ. A mathematical model was employed to determine the optimal set of technical requirements under resource constraints.

Results:

Application of the MD-HOQ approach resulted in the identification of 50 patient needs across 5 hospital sections. Additionally, 40 technical requirements were identified. The highest implementation priorities were assigned to “training practitioners and nurses,” “improving the staff’s sense of responsibility,” and “using experienced specialists, physicians, and surgeons.”

Conclusions:

The integrated QFD approach, utilizing MD-HOQ and SERVQUAL analysis, provides a comprehensive framework for hospital managers to prioritize technical requirements effectively. By considering resource constraints and the gap between patient expectations and perceptions, this method ensures that resources are allocated to the most impactful technical requirements, leading to improved patient satisfaction and better overall hospital service quality. This approach not only enhances the quality of hospital services but also ensures efficient utilization of resources, ultimately benefiting patient satisfaction.

ChatGPT transforms the shopping experience by providing responses in human-like language about products, services, and brands to customers. This study investigated the influential drivers of intention to use ChatGPT to obtain shopping information. We extended the "extended unified theory of acceptance and use of technology" UTAUT2 by incorporating the direct and moderating effects of trust and technology anxiety. To test the model on data from 412 respondents, a hybrid Partial Least Squares-Artificial Neural Network (PLS-ANN) approach was employed. This approach combines the strengths of PLS for modeling complex variable relationships and ANN for capturing nonlinear dependencies and interactions. PLS analysis identified performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, and trust as significant drivers of ChatGPT usage. The associations between the intention to use ChatGPT and its predictors are negatively moderated by trust and technology anxiety. ANN analysis revealed that trust has the highest effect on the choice to use ChatGPT, followed by facilitating conditions, performance expectancy, hedonic motivation, and effort expectancy. By extending the UTAUT2 framework and applying the PLS-ANN method, this study advances the theoretical understanding of technology adoption and provides practical insights for marketers and developers of AI-driven text generators. It emphasizes the importance of building trust and alleviating technology anxiety to promote wider adoption of ChatGPT. The broader significance of this research lies in its contribution to shaping the future of retail and e-commerce strategies by encouraging a more informed and user-centric development of AI technologies in the shopping domain.

In the rapidly evolving landscape of hyperconnected digital manufacturing, known as Industry 4.0, achieving energy efficiency has become a critical priority. As manufacturers worldwide strive to meet sustainable development goals, enhancing energy efficiency is essential for reducing operational costs and minimizing environmental impact. In this context, line balancing is a pivotal strategy for optimizing energy consumption within manufacturing processes. This study presents a comprehensive literature review on the Line Balancing Problems (LBPs) focused on enhancing energy efficiency. The review aims to provide a holistic understanding of this domain by examining past, present, and future trends. A systematic literature review is conducted using the PRISMA method, incorporating both qualitative and quantitative analyses. The quantitative analysis identifies prevalent patterns and emerging trends in energy efficiency optimization within the LBP domain. Concurrently, the qualitative analysis explores various aspects of existing studies, including configurations of lines, managerial considerations, objectives, solution methodologies, and real-world applications. This review synthesizes current knowledge and highlights potential avenues for future research, underlining the importance of energy efficiency in driving sustainable practices in Industry 4.0 and the emerging Industry 5.0 paradigm.

Despite the rapid advancement and integration of Generative AI in various sectors, understanding its role in promoting social sustainability, particularly within the educational domain, remains unexplored. This research is propelled by the urgent need to explore this underexamined area, especially given the critical importance of social sustainability in ensuring equitable and inclusive educational outcomes. To address this gap, this study develops an integrated model that combines the insights of the Technology Threat Avoidance Theory (TTAT), the Technology-Environmental, Economic, and Social Sustainability Theory (T-EESST), and key knowledge management (KM) factors (knowledge acquisition and knowledge application). Using PLS-SEM, the proposed model was evaluated based on data collected through an online survey from 378 university students. The results showed that while perceived threats have a significant negative impact on Generative AI use, knowledge acquisition and knowledge application emerge as critical drivers for its effective use. Interestingly, using Generative AI was found to promote social sustainability significantly and positively. In addition to its theoretical contributions, this study underscores the need for a nuanced understanding of the barriers and enablers of Generative AI adoption, offering valuable insights for various stakeholders aiming to leverage AI tools for sustainable educational outcomes.

Big data analytics creates and consolidates competitive advantage by providing insights on data with enormous variety, velocity, and volume to firms. However, many companies' investments in big data analytics were unsuccessful, and they could not gain full advantage of these technologies. This study investigates the impacts of big data analytics capabilities on innovation quality and speed by considering organizational learning culture as a moderator. The study's data are obtained from a survey of 221 managers in the manufacturing industry. We integrate the Partial Least Squares (PLS) technique and fuzzy-set Qualitative Comparative Analysis (fsQCA) to perform the analysis. The findings of PLS indicated that big data analytics capabilities positively influence both innovation quality and speed. However, innovation quality influences both market performance and financial performance, and innovation speed only affects market performance. Organizational learning culture negatively moderates the impacts of big data analytics on innovation speed and quality. fsQCA uncovered four solutions with varied combinations of factors that predict the high market and financial performance. The theoretical and practical implications are explained at the end of the paper.

This study explores the determinants of intelligent automation implementation within organisations and their implications for strategic value and technological adoption. Grounded in diffusion of innovation theory, this study examines how digital competencies, technology absorptive capacity, and strategic value influence the adoption of intelligent automation. Using a quantitative approach, the findings reveal that digital competencies do not directly impact intelligent automation implementation, but exert an indirect influence through technology absorptive capacity and strategic value. Technology absorptive capacity emerges as a critical enabler facilitating the assimilation and application of the external knowledge necessary for intelligent automation integration, whereas strategic value plays a significant role in aligning intelligent automation adoption with organisational goals. These results emphasise the importance of absorptive and strategic capacities in bridging the gap between digital readiness and intelligent automation. This study highlights that successful intelligent automation adoption requires a multifaceted approach that integrates technological, organisational, and strategic considerations. Although intelligent automation offers substantial potential to improve operational efficiency and competitiveness, its adoption remains resource-intensive, necessitating investments in digital capabilities, training, and stakeholder engagement. This research also underscores the need for a human-centric approach to address employee concerns and align intelligent automation with broader organisational strategies. This study contributes to the literature on digital transformation and automation by providing empirical evidence of the determinants of intelligent automation implementation and their interplay. These findings offer insights for managers, policymakers, and researchers and pave the way for more effective and sustainable adoption strategies for intelligent automation. Future research should explore additional factors that influence the adoption of intelligent automation across diverse sectors and organisational contexts.

To remain competitive and make effective decisions in increasingly challenging markets, firms must integrate internal and external knowledge by embedding knowledge management strategies and technologies into their operations. This study aims to examine the roles of strategic foresight and knowledge management in promoting open innovation and driving new product development. Grounded in the knowledge-based view (KBV) of the firm, it investigates how strategic foresight influences open innovation processes and how knowledge management catalyzes innovation success. Using structural equation modelling (SEM) on data collected from 298 technology-based firms located in Lithuania (n = 142) and Slovakia (n = 156), the study demonstrates that strategic foresight directly impacts open innovation and significantly improves new product development through open innovation; in addition, knowledge exploration and exploitation are shown to play important roles in open innovation, with balanced effects on new product development outcomes. The study identifies open innovation as a critical mechanism that links strategic foresight and knowledge management to improve new product development, extending the KBV of the firm by highlighting the integration of external knowledge with internal processes, particularly in smaller, emerging economies. Practically, managers are recommended to prioritize foresight and balanced knowledge management practices while leveraging strategic alliances and networks to improve new product development outcomes. This integrated approach highlights the importance of collaborative innovation and external knowledge in achieving competitive advantage in dynamic business environments.

The performance of a production system is primarily evaluated by its throughput, which is constrained by throughput bottlenecks. Thus, bottleneck analysis (BA), encompassing bottleneck identification, diagnosis, prediction, and prescription, is a crucial analytical process contributing to the success of manufacturing industries. Nevertheless, BA requires a substantial quantity of information from the manufacturing system, making it a data-intensive task. Based on the dynamic nature of bottlenecks, the optimal strategy for BA entails making well-informed decisions in real-time and executing necessary modifications accordingly. The efficient implementation of BA requires gathering, storing, analyzing, and illustrating data from the shop floor. Utilizing Industry 4.0 technologies, such as cyber-physical systems and cloud technology, facilitates the execution of data-intensive operations for the successful management of BA in real-world settings. The main objective of this study is to establish a framework for BA through the utilization of Cloud-Based Cyber-Physical Systems (CB-CPSs). First, a literature review was conducted to identify relevant research and current applications of CB-CPSs in BA. Using the results of the review, a CB-CPSs framework was subsequently introduced for BA. The application of the framework was assessed via simulation in a real-world manufacturer of marine engines. The findings indicate that the implementation of CB-CPSs can contribute significantly to throughput improvement.