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The present study aimed to explore the consequences of perceived authenticity in artificial intelligence (AI) assistants and develop a serial-mediation architecture specifying causation of loyalty in human–AI relationships. It intended to assess the predictive power of the developed model based on a training-holdout sample procedure. It further attempted to map and examine the predictors of loyalty, strengthening such relationship.

Partial least squares structural equation modeling (PLS-SEM) based on bootstrapping technique was employed to examine the higher-order effects pertaining to human–AI relational intricacies. The sample size of the study comprised of 412 AI assistant users belonging to millennial generation. PLS-Predict algorithm was used to assess the predictive power of the model, while importance-performance analysis was executed to assess the effectiveness of the predictor variables on a two-dimensional map.

A positive relationship was found between “Perceived Authenticity” and “Loyalty,” which was serially mediated by “Perceived-Quality” and “Animacy” in human–AI relational context. The construct “Loyalty” remained a significant predictor of “Emotional-Attachment” and “Word-of-Mouth.” The model possessed high predictive power. Mapping analysis delivered contradictory result, indicating “authenticity” as the most significant predictor of “loyalty,” but the least effective on performance dimension.

The findings of the study may assist marketers to understand the relevance of AI authenticity and examine the critical behavioral consequences underlying customer retention and extension strategies.

The study is pioneer to introduce a hybrid AI authenticity model and establish its predictive power in explaining the transactional and communal view of human reciprocation in human–AI relationship. It exclusively provided relative assessment of the predictors of loyalty on a two-dimensional map.

This study aims to examine factors influencing potential customers’ intention to patronize Islamic banking products in Cameroon.

To achieve this, a structured questionnaire was used with 318 respondents, and 300 were usable for analysis with a respondent rate of 94%. The study used SEM-PLS to analyze the data.

The findings suggested that attitude, religious motivation, awareness, subjective norm and relative advantage significantly affect potential customers intention toward Islamic banking products, while perceived regulatory and perceived innovation are insignificant. Furthermore, attitude substantially mediates the relationship between religious motivation, awareness, subjective norm, relative advantage and perceived innovation.

However, this study focused on potential customers living in Muslim zones; future research should compare users and nonusers of Islamic banking products in both Muslim and non-Muslim zones to capture a big picture about customers’ perceptions of Islamic banking products in Cameroon.

The results of this study contribute to the literature by providing a new framework that combines the theories of planned behavior and diffusion of innovation theory and provides managerial implications at the level of Islamic finance operators. Meanwhile, this research offers some policy recommendations that can help boost the development of Islamic finance in Cameroon and promote financial inclusion.

To the best of the authors’ knowledge, this is the first research about potential customers’ intention to use Islamic banking products in Cameroon.

Limited use of Internet of Things (IoT) technology on construction sites has restricted its value in the construction industry. To propel its widespread application, this paper explores the influencing factors and action paths of construction companies' IoT technology adoption behavior.

First, literature research, technology adoption theories, and semi-structured expert interviews were employed to build the adoption model. Second, a questionnaire survey was conducted among Chinese construction contractors to collect empirical data. Third, the structural equation model method and regression analysis were used to test the adoption model. Finally, the findings were further validated with interviews, case studies, and field observations.

External environmental pressure (EEP), perceived benefit (PB), top management support (TMS), company resource readiness (CRR), adoption intention (AI), and perceived compatibility (PCA) have a direct positive impact on adoption behavior (AB). In contrast, perceived cost (PC) and perceived complexity (PCL) exert a direct negative impact on AB. The EEP, PB, and PC are critical factors affecting AB, whereas AI is strongly affected by CRR and TMS. Besides, AI plays a part mediating role in the relationship between seven factors and AB. Company size and nature positively moderate AI's positive effect on AB.

This paper contributes to the knowledge of IoT technology adoption behavior in the construction sector by applying the technology adoption theories. Exploring the implementation barriers and drivers of IoT technology in construction sites from the perspective of organizational technology adoption behavior and introducing moderating variables to explain adoption behavior are innovations of this paper. The findings can help professionals better understand the IoT technology adoption barriers and enhance construction companies' adoption awareness, demand, and ability. This work also provides a reference for understanding the impact mechanism of the adoption behavior of other innovative technologies in construction.

Generative artificial intelligence (GAI) can disrupt how consumers search for information on retail products/services online by reducing information overload. However, the risk associated with GAI is high, and its widespread adoption for product/service information search purposes is uncertain. This study examined psychological drivers that impact consumer adoption of GAI platforms for retail information search.

We conducted 31 in-depth, semi-structured interviews with the lead GAI users regarding product/service information search. The data were analysed using a grounded theory paradigm and thematic analysis.

Results show that consumers experience uncertainty about GAI’s functioning. Their trust in GAI impacts the adoption and usage of this technology for information search. GAI provides unique settings to investigate potential additional factors, leveraging UTAUT as a theoretical basis. This study identified three overarching themes – technology characteristics, technology readiness and information characteristics – as possible drivers of adoption.

Consumers seek exhaustive and reliable information for purchase decisions. Due to the abundance of online information, they experience information overload. GAI platforms reduce information overload by providing synthesized and customized product/service search results. However, its reliability, trustworthiness and accuracy have been questioned. The functioning of GAI is opaque; the popular technology adoption model such as UTAUT is general and is unlikely to explain in totality the adoption and usage of GAI. Hence, this research provides the adoption drivers for this unique technology context. It identifies the determinants/antecedents of relevant UTAUT variables and develops an integrated conceptual model explaining GAI adoption for retail information search.

Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.

The purpose of this study is to learn how the incorporation and use of leanness, agility and innovation in Indian manufacturing micro, small and medium enterprises (MSMEs) affect their bottom lines and how much these factors contribute to the MSMEs’ ability to meet their long-term sustainability goals.

The suggested model was subjected to data validation and additional empirical validation using a sample of 411 Indian manufacturing MSMEs. The analysis of construct measures is conducted through the utilization of confirmatory factor analysis, a statistical technique that is grounded in the theoretical framework of structural equation modeling (SEM). In addition, path model analysis was applied for the purpose to validate the assumptions that were included in the structural models.

Consistent with the proposed model, the findings of this study demonstrate that leanness, agility and innovation have a substantial favorable impact on the sustainability of a company’s performance. These findings may be helpful in gaining professionals, academics and policymakers to acknowledge the significance of leanness, agility and innovation in enhancing the long-term sustainability of MSMEs and enhancing the overall performance of a particular company. This research excluded the service industries-based research papers.

Many research in the field of manufacturing industries that have adopted leanness, agility, innovativeness and sustainability as individual approaches or as a collective methodology of two or more were considered in the current study. This research excluded the service industries-based research papers.

This literature review has recognized and analyzed various dimensions and roles of leanness, agility, innovativeness and sustainability that are prevalent in manufacturing industries that include the positive and negative effects on the performance of the industries. The research enlightens the path and shows future directions for research to develop efficient, effective and sustainable manufacturing industries.

By promoting the concept of focusing on the “human factor”, namely, stakeholder perspectives, the MSME sector is propagating a strategy that moves away from an excessive focus on technology and toward a more humane one. Through the application of the three key concepts of leanness, agility and innovation, this work aims to create a framework for measuring the sustainability performance of micro-, small- and medium-sized enterprises (MSMEs), with the ultimate goal of assisting the country in achieving the Sustainable Development Goals in the fields of industry, innovation and infrastructure by supporting environmentally friendly and resource-conserving businesses that give back to society and the natural environment.

The objective of this research is to assess the importance and effectiveness of integrating various approaches such as leanness, agility, innovativeness and sustainability within the framework of manufacturing micro, small, and medium enterprises (MSMEs). The authors hope that by going further into these concepts, they will be able to broaden their understanding and get a more comprehensive insight into the role that these concepts play and how they might be successfully used within this environment.

This study aims to explore the intention to quit and its associations among ambulance personnel and to compare the intention to quit and its associations between paramedic and non-paramedic staff.

A cross-sectional study was conducted on 492 Australian ambulance personnel. Participants were selected by stratified random sampling. Data were collected using phone interview-administered questionnaires. Descriptive analyses, bivariate associations and structural equation modelling were performed for data analysis.

The study found that 70% of ambulance personnel intended to quit their jobs. Intention to quit was similar between paramedics and non-paramedic staff. In both staff groups, supervisors' and colleagues' support was associated with mental health symptoms; job satisfaction was associated with the intention to quit. Supervisors' and colleagues' support was indirectly associated with the intention to quit via increasing job satisfaction and reducing the experience of mental health symptoms among paramedics only. Mental health symptoms were directly associated with the intention to quit and indirectly associated with the intention to quit via reducing job satisfaction among paramedics only.

The study findings provide evidence for resource allocation in human resource management. The findings suggest that interventions to increase job satisfaction may reduce the intention to quit for all ambulance personnel. Interventions to improve supervisors' and colleagues' support and to manage depression, anxiety and stress symptoms may help to reduce the intention to quit for paramedics only.

This is the first study to model and compare the direct and indirect associations of intention to quit between paramedics and non-paramedic staff in ambulance personnel.

Based on the ability–motivation–opportunity (AMO) model, this research aims to examine the hierarchical impact of high-performance human resource practices (HPHRPs) on the work practices and service performance of hospitality organizations.

Through an extensive analysis of time-lagged, multilevel and multisource data encompassing 721 employees and 153 stores across 17 restaurant brands in Taiwan, this study illuminated the hierarchical impact of HPHRPs in fostering a service-oriented environment.

This study reveals that HPHRPs have a direct positive effect on service performance. It also highlights an exclusive indirect positive impact, indicating that HPHRPs contribute to elevated service performance through the multilevel mediating effect of team engagement. A distinctive aspect of this study is that it identifies service climate as a critical multilevel moderator, strengthening the positive relationship between HPHRPs and team engagement. Additionally, service climate is found to be a key factor that amplifies the indirect multilevel positive effect HPHRPs have on service performance by reinforcing team engagement.

Strategically implementing robust HPHRPs, fostering a stimulating work environment and emphasizing team interaction can help hospitality organizations cultivate workplaces that deliver unparalleled guest experiences.

This study offers a strategic roadmap for the hospitality industry with a comprehensive multilevel HPHRPs framework that is customized to the specific needs of the workforce, and focused on fostering a service climate to maximize the positive outcomes of service excellence.

This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining strategies, this study seeks to develop a technique that could assess and predict the onset of cardiac sickness in real time. The use of a triple algorithm, combining particle swarm optimization (PSO), artificial bee colony (ABC) and support vector machine (SVM), is proposed to enhance the accuracy of predictions. The purpose is to contribute to the existing body of knowledge on cardiac disease prognosis and improve overall performance in health care.

This research uses a knowledge-mining strategy to enhance the detection and quantification of cardiac issues. Decision trees are used to form predictions of cardiovascular disorders, and these predictions are evaluated using training data and test results. The study has also introduced a novel triple algorithm that combines three different combination processes: PSO, ABC and SVM to process and merge the data. A neural network is then used to classify the data based on these three approaches. Real data on various aspects of cardiac disease are incorporated into the simulation.

The results of this study suggest that the proposed triple algorithm, using the combination of PSO, ABC and SVM, significantly improves the accuracy of predictions for cardiac disease. By processing and merging data using the triple algorithm, the neural network was able to effectively classify the data. The incorporation of real data on various aspects of cardiac disease in the simulation further enhanced the findings. This research contributes to the existing knowledge on cardiac disease prognosis and highlights the potential of leveraging past data for strategic forecasting in the health-care sector.

The originality of this research lies in the development of the triple algorithm, which combines multiple data mining strategies to improve prognosis accuracy for cardiac diseases. This approach differs from existing methods by using a combination of PSO, ABC, SVM, information gain, genetic algorithms and bacterial foraging optimization with the Gray Wolf Optimizer. The proposed technique offers a novel and valuable contribution to the field, enhancing the competitive position and overall performance of businesses in the health-care sector.