Word of mouth (WOM) and electronic word of mouth (eWOM) are very effective and important communication tools to persuade consumers for purchasing the products/services. These become more significant with products that are difficult to assess before consumption, e.g., hospitality. The tourism industry is reviving, and the consumer is conscious when booking a particular destination. Thus, it is important to understand how WOM and eWOM are impacting the various factors in distinct ways while choosing the tourist destination. The seven factors identified, for the present study, are channel engagement, expertise, homophily, resource helpfulness, source credibility, tie-strength, and trustworthiness. The PLS-SEM was used to test the theoretical model of this study. The study shows that both WOM and eWOM impact an individual in different ways. The expertise of the reviewer is the most important factor in the case of WOM and channel engagement is the most significant factor for eWOM. Resource helpfulness is common for both WOM and eWOM.

The emergence of digital technology and the increasing prevalence of smartphones have promoted innovations in payment options available in finance and consumption markets. Banks providing mobile payment must ensure the information security. Inadequate security control leads to information leakage, which severely affects user rights and service providers' reputations. This study uses control objectives for Information and Related Technologies 4.1 as the mobile payment security control framework to examine the emergent field of mobile payment. A literature review is performed to compile studies on the safety risk, regulations, and operations of mobile payments. In addition, the Delphi questionnaire is distributed among experts to determine the practical perspectives, supplement research gaps in the literature, and revise the prototype framework. According to the experts' opinions, 59 control objectives from the four domains of COBIT 4.1 are selected. The plan and organise, acquire and implement, deliver and support, and monitor and evaluate four domains comprised 2, 5, 10, and 2 control objectives that had mean importance scores of > 4.50. Thus, these are considered the most important objectives by the experts, respectively. The results of this study can serve as a reference for banks to construct secure frameworks in mobile payment services.

The prevalence of social media transforms e-business into social commerce and facilitates consumers' engagement in cross-cultural social commerce. However, social commerce operations encounter unpredictable challenges in cross-cultural business environment. It is vital to further investigate how contextual elements affect consumers' trust and their engagement when they are exposed to the complexity of cross-cultural business environment. The stimuli-organism-response paradigm is employed to examine how the two dimensions of social media usage influence consumers' engagement in cross-cultural social commerce. The current study surveyed 2,058 samples from 135 countries, and the regression analysis results illustrate the mechanism whereby informational and socialising usage of social media positively influences consumers' engagement in social commerce through consumers' trust toward social commerce websites. Additionally, the associations between two aspects of social media usage and consumers' trust towards social commerce are negatively moderated by cultural distance. Both theoretical and practical implications are also discussed.

A metaverse is a virtual shared space connected to the real world, an alternative reality that enables economic activities, exchanges, and transactions as well as formation of relationships between user avatars and non-player characters (NPCs). Initial experiences of the metaverse were not very satisfactory; new virtual world metaverses may or may not survive as information services or platforms. The purpose of this empirical study is to identify the characteristics of a virtual world metaverse and their effects on intention to continue usage of the platform. Considering the metaverse as a new type of user experience and a powerful mode of communication, we examine the mediating role of these characteristics according to Pine and Gilmore's (1998) experience economy theory, which enriches our understanding of the factors affecting the success of a metaverse. In addition, since social interaction is important in metaverses, we extend Pine and Gilmore's experience economy model by including Schmitt's (2011) relate experience for better understanding.

Drawing on the MOA framework, this study establishes a research model that explains the usage intention of robo-advisors. In the model, three predictors that consist of technology relative advantage, technology herding, and technology familiarity influence usage intention of robo-advisors directly and indirectly via the partial mediation of trust. At the same time, the effects of the three predictors on trust are hypothetically moderated by learning goal orientation and perceived performance risk respectively. Statistical analyses are provided using the data of working professionals from the insurance industry in Taiwan. Based on its empirical findings, this study discusses important theoretical and practical implications.

It is inevitable for basketball players to have physical injury in sports. Reducing basketball injury is one of the main aims of the study of basketball. In view of this, this paper proposes a monocular vision and FMS injury prediction model for basketball players. Aiming at the limitations of traditional FMS testing methods, this study introduces intelligent machine learning methods. In this study, random forest algorithm was introduced into OpenPose network to improve model node occlusion, missed detection or false detection. In addition, to reduce the computational load of the network, the original OpenPose network was replaced by a lightweight OpenPose network. The experimental results show that the average processing time of the proposed model is about 90 ms, and the output video frame rate is 10 frames per second, which can meet the real-time requirements. This study analysed the students participating in the basketball league of the College of Sports Science of Nantong University, and the results confirmed the accuracy of the injury prediction of college basketball players based on FMS scores. It is hoped that this study can provide some reference for the research of injury prevention of basketball players.

Along with amazing advancements in the field of digital payments, this article seeks to provide a summary of research undertaken over the last four decades and to suggest areas in need of additional study. This study employs a two-pronged technique for analysing its data. The first is concerned with performance analysis, and the second with science mapping. The study uses the apps VOS viewer and R-studio to do bibliometric data analysis. From 1982 until May 2022, the most trustworthy database, Scopus, is used to compile a database of 923 publications The findings of this study identify the scope of current research interest, which is explored with critical contributions from a variety of authors, journals, countries, affiliations, keyword analysis, citation analysis, co-citation analysis, and bibliometric coupling, as well as a potential research direction for further investigation in this emerging field.

The banking sector has experienced a substantial technological shift that has opened up new and better opportunities for its customers. Based on their technological expenditures, the study assessed the two biggest public Indian banks and the two biggest private Indian banks. The most crucial statistical techniques used to demonstrate the aims are realistic are bivariate correlations and ordinary least squares. This work aims to establish a connection between research and a technology index that serves as a proxy for operational efficiency. The results show that for both public and private banks, the technology index positively influences operational efficiency metrics like IT costs, marketing costs, and compensation costs. This suggests that when the technology index increases, so do IT, marketing, and compensation costs, even though it has been shown that the technology index favourably improves operational efficiency measures like depreciation and printing. This means that the cost to banks is high despite greater investment in technology for these activities.

The proliferation of technology and communication has resulted in increased digitalisation that includes digital payments. This study is aimed at unravelling the relationship between awareness of individuals about the digital payment system and customer satisfaction with digital payments. Two models were developed in this study. First model considers awareness → usage pattern → customer satisfaction. Second model considers usage pattern → customer satisfaction → perception of digital payments. These two alternative models were tested by collecting data from 507 respondents in southern India was analysed using the structural equation modelling. The results indicate that usage pattern acted as a mediator between awareness and satisfaction, and satisfaction acted as a mediator between usage pattern and consumers' perception of digital payments. The implications for theory and practice are discussed.

In order to further improve the public health assurance system and the infectious diseases early warning system to give play to their positive roles and enhance their collaborative capacity, this paper, based on the big and thick data analytics technology, designs a 'rolling-type' data synergy model. This model covers districts and counties, municipalities, provinces, and the country. It forms a data blockchain for the public health assurance system and enables high sharing of data from existing system platforms such as the infectious diseases early warning system, the hospital medical record management system, the public health data management system, and the health big and thick data management system. Additionally, it realises prevention, control and early warning by utilising data mining and synergy technologies, and ideally solves problems of traditional public health assurance system platforms such as excessive pressure on the 'central node', poor data tamper-proofing capacity, low transmission efficiency of big and thick data, bad timeliness of emergency response, and so on. The realisation of this technology can greatly improve the application and analytics of big and thick data and further enhance the public health assurance capacity.

In the era of big data, enterprises have accumulated a large amount of research and development data. Effective management of their precipitated data and safe sharing of data can improve the collaboration efficiency of research and development personnel, which has become the top priority of enterprise development. This paper proposes to use blockchain technology to assist the collaboration efficiency of enterprise R&D personnel. Firstly, the multi-chain blockchain platform is used to realise the data sharing of internal data of enterprise R&D data department, project internal data and enterprise data centre, and then the process of construction of multi-chain structure and data sharing is analysed. Finally, searchable encryption was introduced to achieve data retrieval and secure sharing, improving the collaboration efficiency of enterprise research and development personnel and maximising the value of data assets. Through the experimental verification, the multi-chain structure improves the collaboration efficiency of researchers and data security sharing.

The utilisation of big data presents significant opportunities for businesses to create value and gain a competitive edge. This capability enables firms to anticipate and uncover information quickly and intelligently. The author introduces a human resource scheduling optimisation strategy using a parallel network fusion structure model. The author's approach involves designing a set of network structures based on parallel networks and streaming media, enabling the macro implementation of the enterprise parallel network fusion structure. Furthermore, the author proposes a human resource scheduling optimisation method based on a parallel deep learning network fusion structure. It combines convolutional neural networks and transformer networks to fuse streaming media features, thereby achieving comprehensive identification of the effectiveness of the current human resource scheduling in enterprises. The result shows that the macro and deep learning methods achieve a recognition rate of 87.53%, making it feasible to assess the current state of human resource scheduling in enterprises.

The intelligent financial sharing platform in the online realm is capable of collecting, storing, processing, analysing and sharing financial data through the integration of AI and big data processing technologies. However, as data volume grows exponentially, the cost of financial data storage and processing increases, and the asset accounting and financial profit data sharing analysis structure in financial sharing platforms is inadequate. To address the issue of data security sharing in the intelligent financial digital sharing platform, this paper proposes a data-sharing framework based on blockchain and edge computing. Building upon this framework, a non-separable task distribution algorithm based on data sharing is developed, which employs multiple nodes for cooperative data storage, reducing the pressure on the central server for data storage and solving the problem of non-separable task distribution. Multiple sets of comparative experiments confirm the proposed scheme has good feasibility in improving algorithm performance and reducing energy consumption and latency.

Emergency disaster management presents substantial risks and obstacles to shield tunnel building projects, particularly in the event of water leakage accidents. Contemporary water leak detection is critical for guaranteeing safety by reducing the likelihood of disasters and the severity of any resulting damages. However, it can be difficult. Deep learning models can analyse images taken inside the tunnel to look for signs of water damage. This study introduces a unique strategy that employs deep learning techniques, generative adversarial networks (GAN) with long short-term memory (LSTM) for water leakage detection i shield tunnel construction (WLD-STC) to conduct classification and prediction tasks on the massive image dataset. The results demonstrate that for identifying and analysing water leakage episodes during shield tunnel construction, the WLD-STC strategy using LSTM-based GAN networks outperformed other methods, particularly on huge data.

To achieve psychological and emotional analysis of massive internet chats, researchers have used statistical methods, machine learning, and neural networks to analyse the dynamic tendencies of texts dynamically. For long readers, the author first compares and explores the differences between the two psychoanalysis algorithms based on the emotion dictionary and machine learning for simple sentences, then studies the expansion algorithm of the emotion dictionary, and finally proposes an extended text psychoanalysis algorithm based on conditional random field. According to the experimental results, the mental dictionary's accuracy, recall, and F-score based on the cognitive understanding of each additional ten words were calculated. The optimisation decreased, and the memory and F-score improved. An <i>F</i>-value greater than 1, which is the most effective indicator for evaluating the effectiveness of a mental analysis problem, can better demonstrate that the algorithm is adaptive in the literature dictionary. It has been proven that this scheme can achieve good results in analysing emotional tendencies and has higher efficiency than ordinary weight-based psychological sentiment analysis algorithms.

With the expansion of business scope, the environment faced by enterprises has also changed, and competition is becoming increasingly fierce. Traditional financial systems are increasingly difficult to handle complex tasks and predict potential financial risks. In the context of the digital economy era, the booming financial sharing services have reduced labour costs and improved operational efficiency. This paper designs and implements an intelligent financial sharing platform, establishes a fund payment risk early warning model based on an improved support vector machine algorithm, and tests it on the Financial Distress Prediction dataset. The experimental results show that the effectiveness of using F2 score and AUC evaluation methods can reach 0.9484 and 0.9023, respectively. After using this system, the average financial processing time per order decreases by 43%, and the overall financial processing time decreases by 27%. Finally, this paper discusses the role of intelligent financial sharing platform in accounting transformation and optimisation of production.

The aim of the study is to examine the determinants of digitalisation in public sector. This research is particularly relevant as digital transformation has become a crucial factor in modernising public sector and enhancing service delivery to citizens. The method of the systematic literature review (SLR) was implemented by searching documents on the Scopus database. The initial research reached the 7902 documents and after specifying the keywords the authors found 207 relevant documents. Finally; after the careful read of their abstracts and the use of inclusion and exclusion criteria; the most cited and relevant 32 papers constituted the final sample. Findings highlighted the focus of the literature on technological factors such as the sense of trust and safety as well as the ease of use in the adoption of digital governance; emphasising the need for effective; trustworthy and user-friendly digital services. The most discussed internal factors were leadership and organisational culture. The study offers a deeper understanding of the factors that shape the successful implementation of digital governance initiatives.

This study investigates the well-being of healthcare professionals in India, with a specific focus on the detrimental effects of the pandemic on their mental and physical health, including stress, burnout, and fatigue. This research examines the roles played by calling, mindfulness, and compassionate love as essential resources in promoting the well-being of healthcare professionals. Utilising structural equation modelling (SEM), the results reveal a significant cause and effect relationship between calling, mindfulness, and compassionate love and their influence on overall well-being. Furthermore, the study identifies a noteworthy parallel mediation effect, demonstrating that mindfulness and compassionate love serve as mediators in the relationship between calling and well-being. This research offers practitioners invaluable insights into the effective utilisation of mindfulness and compassionate love practices to enhance the overall well-being of healthcare professionals.

The study investigated the influence of ebullient supervision on employee commitment in a Ghanaian public university through the mediating role of employee engagement. The simple random sampling technique was used to draw 302 administrative staff of the university to respond to the self-administered questionnaire on the constructs. Furthermore, the partial least square structural equation technique was deployed to test the research hypotheses in the study. The results showed that ebullient supervision had a significant positive relationship with employee commitment and employee engagement. The findings further revealed that employee engagement positively correlated with employee commitment. Finally, the study's findings established that employee engagement partially mediated the link between ebullient supervision and employee commitment. The study emphasised that various supervisors in a university's administration should create an environment that favours fun where subordinates can form ties with one another.

Physical and social aspects each make up a separate part of servicescape. Together, these make up the servicescape. Although previous research has frequently investigated these aspects separately, the purpose of this study is to simultaneously find out the impact of both aspects within the casual dining restaurants' context. In total, 462 customers in Delhi were polled for this study, and structural equation modelling was used to analyse the data. According to the results, both the social and physical parts of the servicescape have the ability to affect how satisfied customers are, which in turn can affect how they behave in the future.

This paper examines the growing aspect of perceive organisational politics (POPs) in organisations by understanding their employee engagement with mediating effect of emotional intelligence. This study is cross-sectional, wherein a survey is conducted on executives of different sectors holding strategic positions. The purposive sampling technique is applied to find the 117 most suitable executives for this survey. The survey is self-administered, and a questionnaire is used as an instrument with 43 measurement scale items adopted from previous similar studies. Construct's reliability and validity followed by PLS-SEM is performed using JASP statistical application. The result revealed that the dimensionality support and validation of POP based on a new set of measures centred on generalised beliefs of the application and abuse of power, infrastructure, credibility, choice making, and line-of-sight. In line with previous findings, the current findings also showed that POP works as a barrier to individual behavioural demand and can negatively affect work efficiency. Existence of perceive organisational politics due to the normative belief of the situation happing in the organisation, disengagement of employees, and also evaluates new empirical insight into the organisation by mediating emotional intelligence.

The research uses a machine learning approach to appraising the validity of customer aptness for a loan. Banks and non-banking financial companies (NBFC) face significant non-performing assets (NPAs) threats because of the non-payment of loans. In this study, the data is collected from Kaggle and tested using various machine learning models to determine if the borrower can repay its loan. In addition, we analysed the performance of the models [K-nearest neighbours (K-NN), logistic regression, support vector machines (SVM), decision tree, naive Bayes and neural networks]. The purpose is to support decisions that are based not on subjective aspects but objective data analysis. This work aims to analyse how objective factors influence borrowers to default loans, identify the leading causes contributing to a borrower's default loan. The results show that the decision tree classifier gives the best result, with a recall rate of 0.0885 and a false- negative rate of 5.4%.

The face of the hospitality sector in India is continuously changing and in times of career transitiveness, it is important to know the factors that support a successful career. The current research aims to explore the relationship between career planning, employee optimism, career adaptability and career satisfaction in the Indian hospitality sector. The study included 283 employees from Indian hospitality sector. Additionally, the study used SEM and bootstrap method to measure the dual mediating relationship between career planning, employee optimism dimensions, career adaptability dimensions, and career satisfaction in Indian setting. The results indicated that optimism dimensions and career adaptability dimensions partially mediate the relationship between career planning and career satisfaction in Indian hospitality sector. The study suggests useful implications for academia and industrial purpose. The limitations and future research avenues have been discussed. The study would contribute to the sparse literature on employee optimism, career planning, career adaptability and subjective career success. It would contribute to the social cognitive career theory (SCCT).

An online Moodle Workshop was evaluated for peer assessment effectiveness. A quasi-experiment was designed using a Seminar in Professionalism course taught in face-to-face mode to undergraduate students across two campuses. The first goal was to determine if Moodle Workshop awarded a fair peer grader grade. The second objective was to estimate if students were consistent and reliable in performing their peer assessments. Statistical techniques were used to answer the research hypotheses. Although Workshop Moodle did not have a built-in measure for peer assessment validity, t-tests and reliability estimates were calculated to demonstrate that the grades were consistent with what faculty expected. Implications were asserted to improve teaching and recommendations were provided to enhance Moodle.

Pair programming is a collaborative programming practice that places participants in dyads, working in tandem at one computer to complete programming assignments. Pair programming studies with Computer Science (CS) and Software Engineering (SE) majors have identified benefits such as technical productivity, program/design quality, academic performance, and increased satisfaction for their participants. In this paper, pair programming is studied with Management Information Systems (MIS) majors, who (unlike CS and SE majors taking several programming courses) typically take only one programming course and often struggle to develop advanced programming skills within that single course. The researchers conducted two pair programming experiments in an introductory software development course for MIS majors over three semesters to determine if pair programming could enhance learning for MIS students. The program results, researchers’ direct observations, and participants’ responses to a survey questionnaire were analyzed after each experiment. The results indicate that pair programming appears to be beneficial to MIS students’ technical productivity and program design quality, specifically the ability to create programs using high-level concepts. Additionally, results confirmed increased student satisfaction and reduced frustration, as the pairs worked collaboratively to produce a program while actively communicating and enjoying the process.