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This study aims to show the effectiveness and applicability of artificial intelligence applications in the measurement and evaluation of university services. Universities can gain competitive advantage through providing their students with quality services in various aspects, such as bookstores, dormitories, recreation centers as well as cafeterias. Among these facilities, university cafeterias are places where students spend a significant amount of time. Therefore, this study aims to integrate artificial intelligence application in the evaluation of university cafeteria services based on students' perceptions with two-stage structural equation modeling (SEM) and artificial neural network (ANN) approach.

An artificial intelligence based SEM-ANN hybrid approach was used to determine the factors that have significant influence on student satisfaction, sufficiency-of-services and likelihood-of-recommendation. Data were collected from 373 students through a face-to-face questionnaire. Initially, four service quality dimensions were attained through factor analysis. Then, hypotheses, which were determined via literature review, were tested through SEM-ANN hybrid approach.

Incorporating the results of SEM analysis into the ANN technique resulted in superior models with good prediction performance. Based on four ANN models created and ANN sensitivity analyses conducted, significant predictors of satisfaction, sufficiency, reliability and recommendation are determined and ranked.

Prior studies have assessed service quality using traditional techniques, whereas, this study integrates artificial intelligence in the assessment of higher-educational institutions' services quality. Also, as a distinction from previous studies, this study ranked importance levels of predictor variables through ANN sensitivity analysis.

This research aimed to analyze and prioritize the factors affecting sustainable marketing (SM) and sustainable operation (SO) of manufacturing small and medium-sized enterprises SMEs through big data adoption (BDA).

The technology-organization-environment (TOE) framework was used as a theoretical base and data were gathered from manufacturing SMEs in Malaysia. The 159 questionnaire replies of chief executive officer (CEO)/managers were analyzed using a hybrid approach of structural equation modeling-artificial neural network (SEM-ANN).

The findings of this study showed that perceived benefits (PB), technological complexity (TC), organization's resources (OR), organization's management support (OMS) and government legislation (GL) are the factors that influence BDA and promote SM and SO. The findings of ANN showed that a perceived benefit is the most important factor, followed by OMS.

The findings of this study can assist SMEs managers in making strategic decisions and improving sustainable performance and thus contribute to overall economic development.

The manufacturing industry is under immense pressure to integrate sustainable practices for long-term success. BDA can assist industries in aligning industries' operational capabilities. The majority of the current research have mainly emphasized on BDA in corporations. However, the associations between BDA and sustainable performance of manufacturing SMEs have been less explored. To address this issue, this study developed a theoretical model and examined the influence of BDA on SM and SO of manufacturing SMEs. Meanwhile, the hybrid methodological approach can help to uncover both linear and non-linear relationships better.

The purpose of this study is to propose and test a model to explain users’ intention to adopt m-health devices and divide the importance of antecedents for users to adopt m-health devices based on the stimulus-organism-response (S-O-R) framework.

This research conducted an online survey with m-health app users and collected 562 valid responses. A hybrid SEM-ANN approach was employed to evaluate the research model and hypotheses.

The results show that motivation (M), opportunity (O), and ability (A) affect users’ flow experience and loyalty and further affect their adoption intention of m-health technology. Opportunity plays a more critical role in m-health adoption intention than ability.

This study comprehensively examined the factors that affect users’ deep engagement and m-health adoption from the perspective of MOA. It used the hybrid SEM-ANN method to divide the critical role of motivation, opportunity and ability, providing a new analysis approach for studying information technology (IT) behavior.

The proliferation of smartwatches in the digital age has radically transformed health and fitness management, offering users a multitude of functionalities that extend beyond mere physical activity tracking. While these modern wearables have empowered users with real-time data and personalized health insights, their environmental implications remain relatively unexplored despite a growing emphasis on sustainability. To bridge this gap, this study extends the UTAUT2 model with smartwatch features (mobility and availability) and perceived security to understand the drivers of smartwatch usage and its consequent impact on environmental sustainability.

The proposed theoretical model is evaluated based on data collected from 303 smartwatch users using a hybrid structural equation modeling–artificial neural network (SEM-ANN) approach.

The PLS-SEM results supported smartwatch features’ effect on performance and effort expectancy. The results also supported the role of performance expectancy, social influence, price value, habit and perceived security in smartwatch usage. The use of smartwatches was found to influence environmental sustainability significantly. However, the results did not support the association between effort expectancy, facilitating conditions and hedonic motivation with smartwatch use. The ANN results further complement these outcomes by showing that habit with a normalized importance of 100% is the most significant factor influencing smartwatch use.

Theoretically, this research broadens the UTAUT2 by introducing smartwatch features as external variables and environmental sustainability as a new outcome of technology use. On a practical level, the study offers insights for various stakeholders interested in smartwatch use and their environmental implications.

While there is an abundant amount of literature studies on mobile payment adoption, there is a scarce of knowledge concerning the sustainable use of mobile payment contactless technologies. As those technologies are mainly concerned with security and users' trust, the question of how security factors and trust can influence the sustainable use of those technologies within and beyond the COVID-19 pandemic is still unanswered. This research thus develops a theoretical model based on integrating the protection motivation theory (PMT) and the expectation-confirmation model (ECM), extended with perceived trust (PT) to explore the sustainable use of mobile payment contactless technologies.

The developed model is evaluated based on data collected through a web-based survey from 523 users who used contactless payment technologies. Unlike the existing literature, the collected data were analyzed using a hybrid structural equation modeling-artificial neural network (SEM-ANN) technique.

The data analysis results reinforced all the proposed relationships in the developed model. The sensitivity analysis results showed that PT has the largest impact on the sustainable use of mobile payment contactless technologies with 97.2% normalized importance, followed by self-efficacy (SE) (77%), satisfaction (72.1%), perceived vulnerability (PV) (48.9%), perceived usefulness (PU) (48.2%), perceived severity (PS) (40.7%), response efficacy (RE) (28.7%) and response costs (RCs) (24.1%).

The originality of this research lies behind the development of an integrated model based on PMT and ECM to understand the sustainable use of mobile payment contactless technologies. The study provides several managerial implications for decision-makers, policy-makers and service providers to ensure the sustainability of those contactless technologies within and beyond the COVID-19 pandemic.

This study aims to explore the adoption of contact tracing apps through a hybrid analysis of the collected data using structural equation modelling (SEM) and artificial neural networks (ANN), leading to the identification of the critical determinants for the adoption of contact tracing apps in Australia.

A research model is developed within the background of the unified theory of acceptance and use of technology (UTAUT) and the privacy calculus theory (PCT) for investigating the adoption of contact tracing apps. This model is then tested and validated using a hybrid SEM-ANN analysis of the survey data.

The study shows that effort expectancy, perceived value of information disclosure and social influence are critical for adopting contact tracing apps. It reveals that performance expectancy and perceived privacy risks are indirectly significant on the adoption through the influence of perceived value of information disclosure. Furthermore, the study finds out that facilitating condition is insignificant to the adoption of contact tracing apps.

The findings of the study can lead to the formulation of targeted strategies and policies for promoting the adoption of contact tracing apps and inform future epidemic control for better emergency management.

This study is the first attempt in integrating UTAUT and PCT for exploring the adoption of contact tracing apps in Australia. It combines SEM and ANN for analysing the survey data, leading to better understanding of the critical determinants for the adoption of contact tracing apps.

This study aims to understand the impact of leadership styles on the sustainability of organizational energy, using the mediator role of organizational ambidexterity in family firms in Malaysia. To this end, dual-stage Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) were adopted to determine the leadership style of family firms in Malaysia.

An exploratory design (i.e. questionnaire) was used to collect data from 528 workers in the family firms in Malaysia.

According to the results, leadership styles and long-term organizational energy have a positive and significant relationship. Furthermore, organizational ambidexterity mediates the relationship between leadership styles and organizational energy sustainability. On the other hand, based on nonlinear and compensatory relationships, the ANN method predicted a bureaucratic leadership style typical in Malaysian family businesses. The results of this study indicate transformational, transactional and bureaucratic leadership styles affect sustainable organizational energy. Besides, organizational ambidexterity fully mediates the relationship between leadership styles and sustainable organizational energy. On the other hand, the results of non-compensatory relationships revealed organizational ambidexterity is the most determinant of sustainable organizational energy, followed by bureaucratic leadership. As a result, leadership styles encourage human resources to perform tasks with energy and vitality. In family businesses, bureaucratic leadership increases job immersion and positive motivations toward work challenges.

From a practitioner's perspective, leaders and practitioners must encourage creativity and idea generation to give members sufficient strength to work and focus on goals that support building sustainable organizational energy. A family business is a type of capitalism that significantly impacts employees. The family-owned businesses surveyed by first-generation families lack subsidiaries and are ingrained in a paternalistic culture that offers employees greater security at a lower wage. Although there are few details, the study sample size is small and has limitations. This study suggests that understanding the leadership styles on sustainable organizational energy and using the mediator role of organizational ambidexterity in the family business has immense value. Characteristics such as transformational, transactional and bureaucratic leadership styles have a significant role in sustainable organizational energy. Also, organizational ambidexterity is the mediator for the relationship between leadership styles and sustainable organizational energy.

This study sheds light on the effect of leadership styles on sustainable organizational energy through organizational ambidexterity in family firms. In this context, the novelty of this study includes two perceptions. The first explored the impact of exploration and exploitation on sustainable organizational energy. The second investigates linear and nonlinear relationships to predict sustainable organizational energy determinants.

This study looks to understand the opposing forces that would influence continuance intention. This is significant as users will take into account the positive and negative use experiences in determining their continuance intention. Therefore, this study looks to highlight the opposing forces of users’ continuance intention by proposing the Expectation-Confirmation-Resistance Model (ECRM).

Through an online survey, 411 responses were obtained from mobile payment users. Subsequently, a hybrid approach comprised of the Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) was utilized to analyze the data.

The results revealed that all hypotheses proposed in the ECRM are supported. More precisely, the facilitating and inhibiting variables were found to significantly affect continuance intention. In addition, the ECRM was revealed to possess superior explanatory power over the original model in predicting continuance intention.

This study successfully developed and validated the ECRM which captures both facilitators and inhibitors of continuance intention. Besides, the relevance and significance of users’ innovative resistance to continuance intention have been highlighted. Following this, effective business and research strategies can be developed by taking into account the opposing forces that affect users’ continuance intention.

This study explores the adoption of Industry 4.0 in developing countries' export industries, focusing on factors influencing this adoption, the moderating role of market pressure and prioritizing key factors for sustainable growth.

Based on the “TOE theory” this study has proposed a research framework to identify the factors influencing the adoption and sustainable implementation of Industry 4.0 in the export industry. This study has collected valid datasets from 387 export-oriented industries and applied SEM-ANN dual-stage hybrid model to capture linear and nonlinear interaction between variables.

Results revealed that Technical Capabilities, System Flexibility, Software Infrastructure, Human Resource Competency and Market pressure significantly influence the Adoption of Industry 4.0. Higher market pressure as a moderator also improves the Industry 4.0 adoption process. Results also pointed out that system flexibility is a gray area in Industry 4.0 adoption, which can be enhanced in the export industry to maintain a sustainable adoption and implementation of Industry 4.0.

Minute information is available on the factors influencing the adoption of Industry 4.0 in export-oriented industries. This study has empirically explored the role of influential factors in Industry 4.0 and ranked them based on their normalized importance.

This study aims to investigate Sri Lankan Government university students’ acceptance of Chat Generative Pretrained Transformer (ChatGPT) for educational purposes. Using the unified theory of acceptance and use of technology 2 (UTAUT2) model as the primary theoretical lens, this study incorporated personal innovativeness as both a dependent and moderating variable to understand students’ ChatGPT use behaviour.

This quantitative study used a questionnaire survey to collect data. A total of 500 legitimate undergraduates from 17 government universities in Sri Lanka were selected for this study. Items for the variables were adopted from previously validated instruments. Partial least squares structural equation modelling (PLS-SEM) using SmartPLS 4 was used to investigate latent constructs’ relationships. Furthermore, the variables’ relative relevance was ranked using a two-stage artificial neural network analysis with the SPSS 27 application.

The results of the analysis revealed that eight of the nine proposed hypotheses were confirmed. The most significant determinants of behavioural intention were habit and performance expectancy, closely followed by hedonic motivation and perceived ease of use. Use behaviour was highly influenced by both behavioural intention and personal inventiveness. Though personal innovativeness (PI) was suggested as a moderator, the relationship was not significant.

The research highlights the impact of habit, performance expectancy and perceived ease of use on students’ acceptance of AI applications such as ChatGPT, emphasising the need for efficient implementation techniques, individual variations in technology adoption and continuous support and training to improve students’ proficiency.

This study enhances the comprehension of how undergraduate students adopt ChatGPT in an educational setting. The study emphasises the significance of certain variables in the UTAUT2 model and the importance of PI in influencing the adoption of ChatGPT in educational environments.