Applied Artificial Intelligence and user satisfaction: Smartwatch usage for healthcare in Bangladesh during COVID-19

Psychometric Evaluation of the Chinese Version of the Mental Health System Responsiveness Questionnaire for Psychiatric Outpatients: Classical Test Theory and Item Response Theory Approaches

Background

The Health System Responsiveness Questionnaire has been an important tool for evaluating the quality of health services from non-clinical perspectives. However, there is a lack of reliable and valid tools to assess the responsiveness of the mental health system in China, despite the increasing attention to the quality of mental health services.

Purpose

This study aimed to adapt a Chinese version of the Mental Health System Responsiveness Questionnaire based on the WHO Health System Responsiveness Questionnaire and assess its psychometric properties among psychiatric outpatients.

Patients and Methods

We adapted the Health System Responsiveness Questionnaire into Chinese and used it on psychiatric outpatients through face-to-face interviews, resulting in 1067 valid responses. We conducted a reliability and validity analysis based on classical test theory (CTT) and applied the graded response model based on item response theory (IRT) to examine the performance of each item.

Results

From the CTT analysis, values of Cronbach's alpha, test-retest reliability, and split-half reliability of the questionnaire were all greater than 0.8. Confirmatory factor analysis exhibited a good fit; convergent and discriminant validity were achieved with average variance extracted > 0.5 and heterotrait-monotrait ratio < 0.9. Results from the IRT analysis showed that all items demonstrated acceptable discrimination (discrimination parameters ≥ 0.65) and appropriate difficulty parameters (ranging from -4 to 4). Additionally, all items provided sufficient information, except for two items in the prompt attention domain.

Conclusion

Our adapted Mental Health System Responsiveness Questionnaire possessed strong psychometric properties and could serve as an effective tool for assessing the responsiveness of mental health systems in China. However, improving two items in the domain of prompt attention may contribute to more optimal psychometric properties of the questionnaire.

Evaluation and examination of factors influencing the continued effective use of E-learning among faculty members of medical sciences universities

INTRODUCTION

E-learning facilitates the learning process for medical students by allowing them to enhance their knowledge, skills, and professional experiences, thereby improving quality and standards in education. The objective is to investigate the continuity of use of e-learning systems among academic faculty members of medical sciences universities.

METHODS

A researcher-constructed questionnaire was distributed to collect data from approximately 300 faculty members of medical sciences universities who have used the e-learning system for at least one year. Out of these, 120 questionnaires contained valid responses. The data were processed using statistical software and the statistical analysis technique based on (SEM).

RESULTS

The results indicated that among the demographic factors of the members, age has a significant impact on the continued use of the electronic learning system. Among the two most influential factors on the continued use of the system, perceived usefulness and satisfaction, perceived usefulness was not influential in our study. This means that the members are aware of the benefits of face-to-face instruction compared to system use because the system is simple and basic without features.

CONCLUSION

In our study, the incentive factor was not influential, partly due to the alignment of objectives between university management and faculty members and the use of the electronic system. Presenting more complex systems incorporating decision support and virtual training could increase perceived usefulness somewhat. Additionally, providing blended systems, combining face-to-face and virtual modalities, could compensate for the perceived usefulness.

CLINICAL TRAIL NUMBER

Not applicable.

Users' Perceived Service Quality of National Telemedicine Services During the COVID-19 Pandemic in Bangladesh: Cross-Sectional Study

Background

COVID-19 created an opportunity for using teleconsultation as an alternative way of accessing expert medical advice. Bangladesh has seen a 20-fold increase in the use of teleconsultation during the pandemic.

Objective

The aim of our study was to assess the influence of service quality and user satisfaction on the intention to use teleconsultation in the future among users of national teleconsultation services during the pandemic.

Methods

A cross-sectional survey was conducted in 2020 among users of the national teleconsultation service-Shastho Batayon for acute respiratory infection. A validated mobile health service quality model based on structural equation modeling and confirmatory factor analysis was used to analyze the data with SmartPLS (version 3.0).

Results

Among the 2097 study participants, 1646 (78.5%) were male, 1416 (67.5%) were aged 18-39 years, 1588 (75.7%) were urban residents, 1348 (64.2%) had more than 10 years of schooling, and 1657 (79%) were from middle-income households. From a consumer perspective, the quality of the service platform (β=.946), service interaction (β=.974), and outcome (β=.955) contributed to service quality. Service quality was positively associated with user satisfaction (β=.327; P<.001) and intention to use teleconsultation services (β=.102; P<.001). User satisfaction was positively associated with the intention to use teleconsultation services (β=.311; P<.001).

Conclusions

The increase in the use of teleconsultation during the pandemic indicated that such services were potentially used for emergencies. However, the future use of teleconsultation will be dependent on the quality of service and user satisfaction. Our findings are relevant for low-income contexts where teleconsultation services are used to address gaps in service delivery.

Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework

The rapid evolution of Artificial Intelligence (AI) and its widespread adoption have given rise to a critical need for understanding the underlying factors that shape users' behavioral intentions. Therefore, the main objective of this study is to explain user perceived behavioral intentions and use behavior of AI technologies for academic purposes in a developing country. This study has adopted the unified theory of acceptance and use of technology (UTAUT) model and extended it with two dimensions: trust and privacy. Data have been collected from 310 AI users including teachers, researchers, and students. This study finds that users' behavioral intention is positively and significantly associated with trust, social influence, effort expectancy, and performance expectancy. Privacy, on the other hand, has a negative yet significant relationship with behavioral intention unveiling that concerns over privacy can deter users from intending to use AI technologies which is a valuable insight for developers and educators. In determining use behavior, facilitating condition, behavioral intention, and privacy have significant positive impact. This study hasn't found any significant relationship between trust and use behavior elucidating that service providers should have unwavering focus on security measures, credible endorsements, and transparency to build user confidence. In an era dominated by the fourth industrial revolution, this research underscores the pivotal roles of trust and privacy in technology adoption. In addition, this study sheds light on users' perspective to effectively align AI-based technologies with the education system of developing countries. The practical implications encompass insights for service providers, educational institutions, and policymakers, facilitating the smooth adoption of AI technologies in developing countries while emphasizing the importance of trust, privacy, and ongoing refinement.

AI Meets the Shopper: Psychosocial Factors in Ease of Use and Their Effect on E-Commerce Purchase Intention

The evolution of e-retail and the contribution of artificial intelligence in improving algorithms for greater customer engagement highlight the potential of these technologies to develop e-commerce further, making it more accessible and personalized to meet individual needs. This study aims to explore the psychosocial factors (subjective norms; faith; consciousness; perceived control) that affect AI-enabled ease of use and their impact on purchase intention in online retail. We will also assess the mediating effect of AI-enabled ease of use between psychosocial factors and consumer purchase intention. A quantitative methodology was used, and 1438 responses were collected from Portuguese consumers on e-retail. Structural equation modeling was used for the statistical treatment. The findings indicate that subjective norms do not positively impact AI-enabled ease of use, whereas factors such as faith, consciousness, and perceived control do enhance it. Furthermore, AI-enabled ease of use itself boosts purchase intention. Additionally, the effects of subjective norms, faith, consciousness, and perceived control on purchase intention are significantly enhanced when mediated by AI-enabled ease of use, highlighting the crucial role of usability in shaping consumer purchase behavior. The contribution of this study has been made through the formulation model that provides a systematized perspective about the influencers of purchase intentions and extends the knowledge about the impact of artificial intelligence in e-retail. Furthermore, this study offers insights into the impact of artificial intelligence in e-commerce-artificial intelligence directly affects purchase intentions and plays an important mediator role in the interaction mechanisms between psychosocial factors and purchase intentions.

Analyzing ChatGPT adoption drivers with the TOEK framework

With the rapid advancements in AI technology and its growing impact on various aspects of daily life, understanding the factors that influence users' adoption intention becomes essential. This study focuses on the determinants affecting the adoption intention of ChatGPT, an AI-driven language model, among university students. The research extends the Technology-Organization-Environment (TOE) framework by integrating the concept of knowledge application. A cross-sectional research design was employed, gathering data through a survey conducted to university students. Structural equation modeling was used to analyze the data, aimed at examining the relationships between key determinants influencing adoption intention. The findings of this research indicate that factors such as network quality, accessibility, and system responsiveness contribute to satisfaction. Furthermore, satisfaction, organizational culture, social influence, and knowledge application significantly affect adoption intention. These findings offer both theoretical and practical implications.

Applied artificial intelligence: Acceptance-intention-purchase and satisfaction on smartwatch usage in a Ghanaian context

Technology and its continuous advancement facilitate human beings to get rid of their criticality and limitation. Applied artificial intelligence (AAI) is one of the latest forms that delimited the limitation of human beings. Smartwatch acts as an applied artificial intelligence to assist various patients to check medical care without going to hospital and physicians. This (three) multiple-study research focused on the intention to use, purchase, and their satisfaction and spread positive word of mouth among others in the Ghanaian. To investigate these issues two renowned theories were underpinned- TAM theory and the Stimulus-Organism-Response (S-O-R). Total 550, 320, and 170 respondents were interviewed with Google forms due to COVID-19 using social media. AI-enabled smartwatch considering Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Perceived Credibility (PC), Perceived Self-Efficacy (PSE), and Perceived Financial Cost (PFC) were significant on intention to adoption and adoption intention on actual purchase. The final study showed device quality, its service level, their usage experience, perceived value, and the extent to which the satisfied customers made positive word of mouth to their friends and family, colleagues and followers. This research is significant in understanding the usage of AI-enabled smartwatches as a device doctor or electronic doctor (e-doctor).

Pre-COVID-19 predictors of low-income women's COVID-19 appraisal, coping, and changes in mental health during the pandemic

Study examined predictors and mental health consequences of appraisal (threat, support satisfaction) and coping (active, avoidant) in a sample of low-income women during coronavirus disease 2019 (COVID-19). Pre-COVID-19 contextual risk and individual resources and COVID-19-specific risk factors were examined as predictors of COVID-19 appraisal and coping, which, in turn, were tested as predictors of changes in depression and anxiety across the pandemic. Pre-COVID-19 resilience predicted more active coping, whereas pre-COVID-19 anxiety and depression predicted more avoidant coping and lower support satisfaction, respectively. Increases in anxiety were predicted by lower pre-COVID-19 self-compassion and higher concurrent threat appraisal and avoidant coping. Increases in depression were related to lower pre-COVID-19 self-compassion, active coping and support satisfaction, and higher COVID-19 hardships and health risk. Findings highlight contextual and individual factors and processes that contribute to mental health problems in a vulnerable population during community-level stressors, with implications for prevention and intervention.

Resistance to innovation in low-income populations: The case of university students' resistance to using digital productivity applications

Innovation resistance research remains in its early stages. Efforts to define and comprehend consumer resistance to innovation necessitate in-depth studies that consider the contextual factors of resistance to innovation. To address this challenge, this research explored consumer resistance to innovation in a low-income population, namely, university students on financial support. The innovation under this study is the productivity applications provided for free by the University of Hail, Saudi Arabia, to all students. This study explores variables such as value barrier, risk barrier, tradition barrier, and image barrier and how they impact consumer resistance to innovation in a low-income population. We extend the theory by investigating the moderating roles of consumer characteristics (motivation, self-efficacy, emotion, and attitude toward existing products). The current study is based on an online survey of low-income students at a Saudi Arabian University; 258 cases were gathered. We found that all the direct effects of the variables (i.e., Value Barrier, Risk Barrier, Tradition Barrier, and Image Barrier) were positively related to consumer resistance to innovation. Importantly, consumer characteristics significantly moderate this relationship, as the relationship is stronger when the measurements for consumer characteristics are high.

Highlighting artificial intelligence roles in business area Amid the COVID-19 crisis

PURPOSE

The fast development of technology and data has fueled the use of artificial intelligence (AI) in the business area, but there has been no comprehensive review to guide and assess this evolution, especially in the context of Covid-19 crisis. Our objective is to highlight the nature and scale of AI research in the business area, during the COVID-19 Pandemic.

METHODS

We performed a scoping review and searched two literature databases (Scopus and MDPI) for terms related to AI and Covid-19 by focusing on scientific papers published in the field of business. We used multiple tools (Endnote, Covidence) for titles and abstracts selection, followed by full-text screening. The studies must include research on artificial intelligence and Covid-19, and then be published in English-language, between March 2020 and March 2022.

RESULTS

31 studies met eligibility criteria (of 391 studies selected). Most of the published articles refer to conceptual analysis or quantitative works, the rest of the articles used a literature review except 4 articles published using a qualitative method of analysis. In addition, we observe an evolution of the total number of publications for the 31 articles included in the analysis.

CONCLUSIONS

Studying AI in the business field amid the covid-19 crisis is at an early stage of maturity, especially with the use of new AI technologies.). For the field to progress, more studies are needed in the next few years.

Customer satisfaction with Restaurants Service Quality during COVID-19 outbreak: A two-stage methodology

Online reviews have been used effectively to understand customers' satisfaction and preferences. COVID-19 crisis has significantly impacted customers' satisfaction in several sectors such as tourism and hospitality. Although several research studies have been carried out to analyze consumers' satisfaction using survey-based methodologies, consumers' satisfaction has not been well explored in the event of the COVID-19 crisis, especially using available data in social network sites. In this research, we aim to explore consumers' satisfaction and preferences of restaurants' services during the COVID-19 crisis. Furthermore, we investigate the moderating impact of COVID-19 safety precautions on restaurants' quality dimensions and satisfaction. We applied a new approach to achieve the objectives of this research. We first developed a hybrid approach using clustering, supervised learning, and text mining techniques. Learning Vector Quantization (LVQ) was used to cluster customers' preferences. To predict travelers' preferences, decision trees were applied to each segment of LVQ. We used a text mining technique; Latent Dirichlet Allocation (LDA), for textual data analysis to discover the satisfaction criteria from online customers' reviews. After analyzing the data using machine learning techniques, a theoretical model was developed to inspect the relationships between the restaurants' quality factors and customers' satisfaction. In this stage, Partial Least Squares (PLS) technique was employed. We evaluated the proposed approach using a dataset collected from the TripAdvisor platform. The outcomes of the two-stage methodology were discussed and future research directions were suggested according to the limitations of this study.