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Purpose - This study analyses the relationship between destination competitiveness and behavioral intentions of agritourists in Prachinburi, Thailand. It investigates which competitiveness dimensions most influence intentions to revisit and recommend. It also explores the effects of sociodemographic as control variables. Methodology/Design/Approach – Using models developed by Ritchie & Crouch (2010) and Dwyer & Kim (2003), data from on-site agritourists was analysed using Factor Analysis and Multiple Regression. Findings –Results indicate that four competitiveness dimensions (accessibility, marketing strategy, created resources, and endowed resources) positively influence revisit intentions. Five dimensions (adding quality of service) influence recommendation intentions. Generational cohorts and distance from destination affect competitiveness’s impact on revisit intentions, while sociodemographic variables don’t influence recommendation intentions. The study concludes that the role of supporting factors cannot be mitigated if a destination aims to retain customers. Furthermore, destinations having less distinctive natural resources can still be competitive by enhancing created resources and marketing strategy. Originality of the research – This research contributes to literature by focusing on Asia Pacific/ developing countries, emphasizing the demand side, and exploring the agritourism context.

In the digital age, digital collection and recording technology can handle various types of tangible and intangible cultural heritage. Virtual tourism technology for cultural heritage has great potential in providing users with personalized experiences, but it also faces the problem of ignoring the personalized needs of different users. To this end, a user behavior classification model for cultural heritage virtual tourism technology and a cultural heritage virtual tourism recommendation model based on matrix factorization and coordinated filtering were developed. In the classification task, this study used Virtual Reality scene action data collected from HTC VIVO devices. In the recommendation task, MovieLens, Amazon-charts, ciao, and Epinions datasets were used. The findings denoted that the accuracy of the raised user behavior classification model was 85.47%, 94.62%, and 80.17% in the controller, head mounted display, and button data, respectively. In the mixed data source, the classification accuracy of the proposed model was 98.42%, and the F1 value was 97.74%. The Recall@20 of virtual tour recommendation model in MovieLens and Amazon-charts Dataset were 72.36% and 72.84%, respectively, with diversity values ranging from 0.7 to 0.9. On the Ciao dataset and Epinions dataset, the Root Mean Squared Error and Mean Absolute Error of the proposed model were 0.937 and 0.701, 1.033 and 0.796, respectively. The experimental results demonstrated that the proposed model improved classification and recommendation performance by innovatively combining additive attention mechanism, contextual multi-arm slot machine algorithm, and deep analysis of user behavior, surpassing standard matrix factorization and collaborative filtering methods. The research results help improve the display and service quality of cultural heritage virtual exhibition halls, effectively protect and inherit intangible cultural heritage, and promote the digital development of cultural resources.

PurposeThe purpose of this paper is to identify the key facilitators and inhibitors of fitness wearable technology (FWT) adoption and the intention to recommend this technology.Design/methodology/approachAn innovative and integrated research model was developed by combining constructs from two well-established theoretical models, the extended unified theory of acceptance and use of technology (UTAUT2) and diffusion of innovation (DOI). The proposed research model was empirically validated using data collected from 392 respondents in China. The data was analyzed using the partial least squares method, a statistical analysis technique based on structural equation modeling.FindingsThe results indicate that performance expectancy, effort expectancy, social influence, habit, compatibility and innovativeness have significant direct and indirect effects on FWT adoption and the intention to recommend it. The significance of people’s intention to recommend FWT to others in social networking sites (e.g. Facebook, Weibo, and WeChat) is also confirmed.Practical implicationsThe findings may facilitate the design and implementation of FWT products, applications and functionalities that can achieve high consumer acceptance and positive recommendations in social networks.Originality/valueThis study is among the first to investigate FWT adoption from behavioral, social and environmental perspectives. It also highlights the importance of social marketing campaigns and suggests directions of future wearable technology adoption research.

The global spread and use of the internet and mobile phones has contributed to the development of digital payments. Despite its growth potential, until now there is a lack of research providing a comprehensive synthesis and analysis of factors affecting the use, adoption, and acceptance of digital payment methods. This study aims to address this gap by providing a comprehensive review of the related literature retrieved from Scopus and Web of Science databases. Following a systematic method, a final sample of 193 research articles was identified and analysed. The results highlight that a single theory has failed to comprehensively explain the complex nature of electronic payment adoption. The key limitation of the existing theories is their inability to consider the role of social and cultural facets in the adoption of new technology. While literature reviews are a widespread practice in business studies, there are scant reviews that use the systematic review methodology that aggregates knowledge using clearly defined processes and criteria. This is the first systematic review on electronic payment adoption, which structures the existing knowledge and provides directions for future research.

PurposeInternet banking services are proven to be much advantageous and convenient during COVID-19 pandemic. However, vibrant networking designs and dynamic changes in software development have made these services bit complex. Thus, the current study seeks to investigate Internet banking user continuance intention with factors underpinning self-determination theory (SDT) and expectation confirmation model (ECM) theory. The moderating role of image is studied between user intention to continue use of Internet banking and intention to recommend Internet banking service in social networks during COVID-19 pandemic.Design/methodology/approachThe quantitative research approach is applied and data collected through a research survey. For inferential analysis, 360 responses were collected from active Internet banking users. The integrated information system model was empirically tested using structural equation modeling (SEM) approach.FindingsFindings indicate that integrated IS research model has substantial explanatory power, i.e. 57.8% to predict continuance intention of Internet banking users. Within integrated research model, intrinsic regulation was found the most influential factor in order to determine Internet banking user continuance intention. Beside two theories integration, this study confirmed that the relationship between user continuance intention and intention to recommend Internet banking is moderated by image.Practical implicationsThe fundamental contribution of this study is the integration of technological and motivational factors in Internet banking user continuance intention context. Theoretically, integration of both theories ECM and SDT in technology continuance intention context will enrich the emerging e-commerce literature. Concerning with managerial implications, intrinsic regulation was identified as an important factor among other factors. Therefore, managers and software developers need to understand user’s intrinsic motivational factors in order to boost continuance intention of Internet banking users. It is also suggested that managers and marketing personnel should pay special attention to create a positive image of Internet banking services among Internet banking users.Originality/valueWithin information system literature the concept of user continuance intention has yet to be examined especially in Internet banking context. Thus, current research fills research gap and proposes an integrated technology motivational framework that combines motivational factors and technology factors altogether to investigate Internet banking user continuance behavior.

Background: Diabetes is a public health problem worldwide. Although diabetes is a chronic and incurable disease, measures and treatments can be taken to control it and keep the patient stable. Diabetes has been the subject of extensive research, ranging from disease prevention to the use of technologies for its diagnosis and control. Health institutions obtain information required for the diagnosis of diabetes through various tests, and appropriate treatment is provided according to the diagnosis. These institutions have databases with large volumes of information that can be analyzed and used in different applications such as pattern discovery and outcome prediction, which can help health personnel in making decisions about treatments or determining the appropriate prescriptions for diabetes management. Objective: The aim of this study was to develop a drug recommendation system for patients with diabetes based on collaborative filtering and clustering techniques as a complement to the treatments given by the treating doctor. Methods: The data set used contains information from patients with diabetes available in the University of California Irvine Machine Learning Repository. Data mining techniques were applied for processing and analysis of the data set. Unsupervised learning techniques were used for dimensionality reduction and patient clustering. Drug predictions were obtained with a user-based collaborative filtering approach, which enabled creating a patient profile that can be compared with the profiles of other patients with similar characteristics. Finally, recommendations were made considering the identified patient groups. The performance of the system was evaluated using metrics to assess the quality of the groups and the quality of the predictions and recommendations. Results: Principal component analysis to reduce the dimensionality of the data showed that eight components best explained the variability of the data. We identified six groups of patients using the clustering algorithm, which were evenly distributed. These groups were identified based on the available information of patients with diabetes, and then the variation between groups was examined to predict a suitable medication for a target patient. The recommender system achieved good results in the quality of predictions with a mean squared error metric of 0.51 and accuracy in the quality of recommendations of 0.61, which is acceptable. Conclusions: This work presents a recommendation system that suggests medications according to drug information and the characteristics of patients with diabetes. Some aspects related to this disease were analyzed based on the data set used from patients with diabetes. The experimental results with clustering and prediction techniques were found to be acceptable for the recommendation process. This system can provide a novel perspective for health institutions that require technologies to support health care personnel in the management of diabetes treatment and control.

The development of artificial intelligence technology has greatly helped social productivity and economic growth. At the same time, we have changed modern marketing methods, provided technical assistance for precision marketing, improved modern marketing efficiency, and effectively reduced marketing costs. Compared to traditional marketing, artificial intelligence technology is applied to accurate marketing activities. It will make the marketing effect more accurate and personalized. Faced with these technological advances, it is important to study the application of artificial intelligence technology for the precise new marketing model. The advancement of artificial intelligence technology not only changed the way of marketing activities, but also enabled marketers to attract consumers more effectively. The enormous amount of data provides new opportunities and challenges for marketers. AI technology can accurately identify customer needs in a huge database to locate potential customers, meet customer needs, and establish a good relationship between marketers and consumers.

Despite the rise of digital technologies and social media, mobile banking continues to see increased post-consumption engagement, yet faces challenges from fintech competitors and mobile wallets. Customer positive response, such as referrals and recommend is key to addressing these challenges. This study analysed 297,116 customer referrals, the key indicators of recommendation intentions by using text mining, topic modeling, and K-means clustering. The study uncovered latent themes by mapping Jobs-to-be-Done theory. Findings highlight functional factors (reliability, speed, security) and non-functional factors (user-centric design, technology ecosystem, reputation) that shape mobile banking recommendations. This study offers critical insights into factors influencing customer response and a robust framework for future research and practice.

This study examines the willingness of customers to accept, and their intention to recommend, the services provided by service robots in restaurants. A mixed-methods research approach was taken to evaluate a theoretical model based on behavioural reasoning theory (BRT). The results demonstrated the important influence of positive attitudes and objections to the use of service robots on consumers’ willingness to use service robots, as well as their intention to recommend restaurants that use them. Among the main aspects that affect attitudes and objections, we found hedonic perceptions, perceived safety, interaction quality perception and anthropomorphism.