Transfer, Diffusion and Adoption of Next-Generation Digital Technologies

ChatGPT - it’s the buzzword of the moment in Higher Education with both academics and students discussing this Generative AI tool. The reality, however, is that many do not truly understand it or, at this point, have not been equipped with the skills to use it as an effective tool in the workplace. HE is having to grapple with the widespread use and probable misuse of ChatGPT by students and re-think their business model moving forward to protect the quality and standards of university degrees. Exploration of the challenges and opportunities faced by GenAI within Academia is thus critical at this current point in time. This research paper aims to address this by capturing insight from HE stakeholders (academics, students and educational managers). Challenges as well as opportunities are explored whilst considering the implications this GenAI has on policy and assessment with future direction of potential research considered, in this rapidly evolving arena.

Unplanned pregnancies are a serious public health concern in both developed and developing countries. In many developing countries, such as Arab countries, discussing family planning is surrounded by different barriers such as social and cultural stereotypes, and limited access to information. Chatbots provide women with the opportunity to learn about family planning while overcoming issues such as privacy and stereotypes. Although Chatbots have many benefits for Arab women in the family planning domain little research is conducted to study factors affecting women’s adoption of chatbots to learn about their family planning options or factors affecting them resisting adoption of this technology. Hence, this conceptual paper comes to focus on factors that might lead women in Arab communities to resist adopting chatbots to learn about family planning options regardless of their benefits. The proposed propositions in this paper are based on the Innovation resistance theory (IRT).

Drawing on a case study method, 12 key inhibitors of artificial intelligence (AI) initiatives in the automotive research and development (R&D) unit are identified. The key inhibitors to AI adoption and success are analyzed with industry experts in a semi-structured interview format, helping practitioners formulate suitable strategies. The inter-relationship between each pair of inhibitors is obtained using a survey instrument, and the ISM and MICMAC analyses explored the interconnectedness and categorization. A fuzzy pair-wise matrix of key inhibitors is obtained via focus group discussions (FGD) and prioritized with the fuzzy AHP technique, providing action-oriented insights for the firm. The findings reveal that ‘lack of data acumen’, ‘data-related challenges’, and ‘ambiguity in vendor services’ have higher driving power, and ‘lack of employee commitment and ownership’ and ‘managerial skepticism’ have higher dependence. The study finds that ‘lack of sustained commitment from the leadership team’, and ‘insufficient collaboration and coordination within and across the business functions’ are the most prominent inhibitors, followed by ‘limited experimentation scope’, and ‘lack of data acumen’. These inhibitors essentially indicate necessary cultural changes towards trust, commitment and ownership, complexity and failure tolerance, AI fitment awareness, and alignments.

In-game advertising technology is shifting from two-dimensional to virtual reality graphics. This study aims at examining how a digital game that is either played in a two-dimensional (2D) or head-mounted-display (HMD) virtual reality (VR) graphics is perceived and experienced by the players. More specifically the study illustrates the effect of 2D technology vs. HMD VR technology on players’ feeling of presence and brand recall. Results indicate that subjects who played HMD VR game reported higher level of presence than 2D game players. However, HMD VR players showed lower rates of brand recall than 2D players. This study provides insights for academicians as well as marketing practitioners from the perspectives of attention and elaboration considering 2D and HMD VR technology as important elements for designing effective games in the context of in-game advertising.

Artificial intelligence (AI) technology is being adopted across industries. Adoption is a three-phase process- pre-adoption, adoption, and post-adoption. In this study, a systematic literature review is conducted to extract factors that influence the pre-adoption phase or readiness of the organization for adopting AI. These factors are narrowed down to 20 based on the discussion with the domain experts. These factors are mapped to Technology-Organization-Environment-Individual (T-O-E-I) framework that is derived from the technology-organization-environment (T-O-E) and human-organization-technology fit (H-O-T fit) frameworks. The experts ranked these factors independently. These rankings are used to calculate the global ranking of the factors using the Rough Stepwise Weight Assessment Ratio Analysis Method (R-SWARA), a multi-criteria decision-making (MCDM) method. The top seven factors are the following - perceived benefits of AI, AI system capabilities, data ecosystem in the organization, perceived compatibility of AI systems, perceived ease-of-use of the AI systems, IT infrastructure of the firm, and support from the top management. Sensitivity analysis shows that the ranks are robust.

Generative Artificial Intelligence (GAI) can impact wealth management services; financial institutions actively integrate technology into their operations to acquire a competitive edge and foster innovation. This paper examines the information system success factors that influence the adoption of GAI in wealth management services via the lens of information system success theory. Participants responded to the online structured questionnaire, and structural equation modelling was used for data analysis. Findings indicate that system and service quality significantly influenced generative Net Benefit, whereas Information Quality did not affect generative artificial intelligence adoption. Further moderation of perceived risk on the relation between GAI and net benefit was found to be significant. Also, generative artificial intelligence adoption significantly influenced the net benefits of the information system.The findings indicate that the suggested model can help decision-makers and consumers evaluate the adoption of GAI, improving wealth management efficiency. Wealth management demands sophisticated tools to optimise financial plans and entails complex decision-making. GAI” has demonstrated promise in creating models, data, and strategies.

The surge in popularity of social virtual reality (VR) social platforms, exemplified by platforms like VR Chat, underscores a growing trend of individuals seeking social interactions through immersive digital environments. These platforms offer participants a unique avenue for connection, often attributed to the anonymity of avatars. However, empirical exploration of the depth of personal and emotional experience sharing within such platforms remains limited. This article addresses this gap by investigating the emotional content and nuanced experience shared by US veterans on a prominent social VR platform. Drawing from a dataset of seven YouTube videos from the channel “@syrmor,” capturing conversations on VR Chat, this study employs thematic segmentation to identify recurring topics of discussion. Noteworthy themes include drug abuse, mental health, pre-military circumstances, PTSD, and physical injuries. Furthermore, emotional analysis, utilizing the BERT (Bidirectional Encoder Representations from Transformers) language model, unveils prevalent emotions within these interactions such as fear, sadness, and anger, with significant contrasts to emotions like love and joy. Radar charts visualize these emotions across segments, painting a comprehensive picture of the emotional dynamics within each conversation. Overall, this study sheds light on the significance of VR social platforms as digital spaces for personal narratives and emotional sharing.

This research delves into the adoption of Augmented Reality (AR) technology within the domain of cultural heritage tourism, examining the influence of key determinants on users’ behavioral intentions. Expanding the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model to include personal innovativeness and perceived risk, this study delves deep into the fabric of factors that exert an influence on users’ intentions to embrace AR. Analysing data obtained from 435 participants through a snowball sampling technique, the results emphasize the significant impact of performance expectancy, facilitating conditions, hedonic motivation, and personal innovativeness on behavioral intention, while revealing the negative effect of perceived risk. These findings significantly illuminate the canvas of technology adoption in cultural heritage tourism, providing invaluable insights into how the fusion of these influential factors can effectively foster the integration of AR technology.

The frequency of the occurrence of disasters, and the severity of their effects have both been significantly rising over the past few decades across the world. Recognizing the potential of Artificial Intelligence (AI), particularly its subset, Machine Learning (ML), this study delves into its application in disaster management. More specifically, this study adopted the bibliometric analysis methodology to examine the most active authors, countries, and institutions in research related to the use of ML in disaster management and to investigate the trending themes associated with ML use in disaster management. Based on the results, it can be concluded that the citation networks demonstrate the close collaboration between the USA, India, China, and Australia. India had the most articles cited with 1672 citations, despite China having the largest production of research related to the use of ML in disaster management. Furthermore, besides “disaster management” and “machine learning” which were expected to be part of the key drivers in this research area, “remote sensing” also emerged as a trending topic. Based on the thematic analysis of the various articles retrieved in this study, future research must include fourth industrial revolution (4IR) technologies, as they are crucial to disaster management.

Emerging Next Generation technology such as metaverse, AI systems and Conversational AI are largely open and fluid requiring the enactment of a similarly fluid and agile adoption approach across the enterprise. However, organisations continue to face cultural challenges in their attempts to enact agility despite their formalistic endorsement of it. This study adopts an engaged scholarship approach based on design science research to theoretically and practically develop a tool that supports organisations in their effort to enact and embed agility across the enterprise. The developed tool has been evaluated through a series of interviews, workshops and observations. The finding shows that it acts as a boundary object amongst members of an agile team that helps create a common understanding of the desired agile values and reduces the discrepancy between the endorsement of the values and their enactment through everyday practices. This study contributes to theory and practice by supporting the enactment of the necessary fluidity required for the successful adoption of emerging technology.

The rise of online social media has fostered increasing instances of deviant online behaviour. One of the most lethal is collective bullying i.e., trolling, which has severe impacts including suicides of victims. Yet, it remains a mystery what kind of factors lead social media users to engage in trolling. To explain social media trolling, we contextualized concepts from deindividuation theory. Using fuzzy-set qualitative comparative analysis technique to analyse survey data from 337 Facebook users, three configurations explaining social media trolling have been developed. The results suggest that social media affordances and deindividuation states together give rise to trolling. Our results offer theoretical and practical implications.

Highly evolving economic environment requires from logistics companies fast response and agile solutions. Recently development of digital technologies gives significant advantages to logistics business. Hence many optimized processes belong to operational management level. At the same time the importance of digital technologies adoption to strategic management level should not be underestimated, as it allows gaining competitive advantages alongside the supply chain. In our research we develop a conceptual framework for matching operational and strategic management decisions in order to achieve the stated strategy. The choice of the appropriate strategy is conducted with artificial intelligence tools, machine learning in particular. The present study demonstrates high efficiency of applying artificial intelligence tools both in operational management and strategic decision-making. The research is focused on transportation and inventory management as the most resource consuming and challengeable logistics operations. At the same time these processes makes the most considerable influence on the configuration of supply chains. So the article proposes a multi-level conceptual approach that includes several steps aimed on identifying key metrics for different market strategies in logistics and introduction of artificial intelligence tools to different management levels in order to contribute to decision-making promptly. On the first step we suggest the model targeting to optimization of transportation costs via reduction of logistics cycle duration, and estimation of logistics-related assets. On the second step it is suggested to define the most appropriate market strategy by defining a set of metrics relevant for each strategy. So the proposed approach allows obtaining the most suitable market strategy for logistics companies with artificial intelligence tools. The optimization solutions suggested by the authors are tending to be practically applied and claims their high relevance in terms of digital transformation and adoption in strategic logistics management.

The relationship between human and artificial intelligence has attracted debates and polarized views. A key area of this debate that received research attention is human and AI capability to augment each other to achieve better outcomes. While there is a growing research interest in the topic, research is currently dispersed and spread across the management disciplines making it hard for researchers to benefit from an accumulated knowledge in this domain. This study synthesises the literature and describes the current research findings in order to provide foundation for future research in this area. Based on a systematic review, we identify and discuss three emerging themes in the literature and highlight different possible challenges related to integrating AI in organisations. A future research agenda is also presented.

Air pollution is currently the world’s most dangerous threat to the environment and public health and has a detrimental effect on the ecosystem, climate and human health. Several factors contribute to air pollution, including industrial production of harmful gases, automobile emissions, a rise in the number of hazardous chemicals and particulate matter. Quick decision-making calls for the analysis of information from real-time air quality monitoring are required. This work presents a cost-effective, scalable and flexible Internet of Things (IoT)-based air quality monitoring system developed using a novel approach. The proposed system employs IoT technology to track various vital parameters, such as particulate matter, carbon monoxide, ozone, temperature and humidity, in real-time. Data are sent to the ThingSpeak cloud platform, thereby enabling users to access and analyse the information remotely. The values of particulate matter in the clean surrounding and that in polluted surrounding were two times that of national ambient air quality standards. However, the values of carbon monoxide and ozone were within the permissible limits. The cost effectiveness and user-friendly nature of this system makes it well suited for use in diverse geographic locations, thus enabling prompt decision making in addressing environmental issues.

As the technologically advanced unified payment interface (UPI), enabling cross-bank mobile payment transactions, was launched in India, mobile payment’s popularity in the country grew multi-fold. However, contrary to what mobile payment usage literature suggests about technology features driving usage, we posit that common users often lack the understanding of the detailed technical features and are predominantly driven by what everyone else does – i.e., the herding behavior. Motivated by this, we examine the types of herding – rational and irrational. We develop a research model comprising multi-dimensional scales to capture herding behaviors that impact mobile payment continuance usage. We validated the herding-focused research model using the survey responses from 507 users. The study contributes to the field significantly by adding elements from herding behavior theory to the literature on mobile payment usage, which is of significant value owing to the networked nature of the technology. The results show that there is a balancing influence of rational and irrational herding on continuance usage, which has implications for practice for controlling for certain herding factors to promote technology’s popularity.

Wave of innovative technology is fast replacing the traditional financial services by new innovative financial services provided by Neo Banks. Despite that, Indian consumers are not embracing Neo Banks at the expected rate. Using Neo Bank, people can transact easily, make fund transfer, opening of new bank account etc. 24/7 spread over the year etc. However, its effective adoption is a bottleneck because of inherent and unknown barriers. However, some attempts on the identification of these barriers have been carried out; the literature lacks a thorough investigation into the relationship and interdependencies among them. We have identified ten possible barriers via literate review and in consultation with the experts. These barriers have been analyzed using Fuzzy DEMATEL method based on the expert’s responses. Moreover, the analysis demonstrates Digital Inclusion barrier is the prominent one out of total barriers. Since it will affect the other barriers of Neo Banks adoption but it would not be affected by any other barriers it has a lesser threshold value. Hence, Digital Inclusion barrier is considered as most significant barrier which must be overcome for ensuring the effective adoption of Neo Bank.

Smart home technologies (SHT) are critical for effectively managing homes in a digital society. However, SHTs face challenges related to their limited use in developing country contexts. This study investigates the factors that act as barriers to SHT adoption among individuals in Bengaluru, India. The roles of perceived risk, performance and after-sale service, and demographics in using smart home technologies (SHT). This study used the data from the primary survey of 133 respondents. The collected data were analyzed using regression analysis. The results supported five of the proposed hypotheses, namely, perceived performance risk, perceived financial risk, perceived psychological risk, and technological uncertainty, which influence the Behavioral intention to adopt SHT. However, service intangibility is influenced by performance risk. Income and age influence the psychological risk and adoption of SHT. The study identifies the barriers to SHT adoption. The supportive environment for SHT needs to be strengthened to reduce the associated risks.

Sustainability of agriculture in several countries is frequently associated with inclusion and income of its small-scale producers. Linking farmers directly with consumers is considered as one way to overcome the income problem and in the past decade, India has witnessed a rise of agritech startups, especially those that aim to address this issue. Through a qualitative study in Bangalore Urban district of Karnataka, India and its neighbouring districts, we argue that agritech startup led high-value chains are inclusive for only selected small-scale producers and this is dependent on factors such as the firms’ business model, area of operations, crops cultivated by farmers, agricultural practices employed by them etc. Both agritech startups and farmers pursue their own goals that are often in conflict with each other and it is these goal incongruences that lead to exclusion of large share of small-scale producers from such value chains.

Sustainability is an important issue in the era of extensive development in the manufacturing industry due to increasing the carbon footprint of the activities. Manufacturing activities and logistics practices emit a large number of emissions, which is affecting global sustainability. In this regard, the concept of net zero emissions has become popular, which aims to achieve a net zero emissions agenda by the end of 2070. At the same time, industries are adopting multi-modal transportation, which can reduce their carbon footprint and transportation costs. The other benefits of multi-modal transportation include lower transportation costs, lower emissions, and faster delivery times. Despite these benefits, limited literature is available on net zero emissions in multimodal transportation. Therefore, this paper discusses the research progress in multi-modal transportation and its decarbonization. This study also proposes a net zero emission framework for the industries that will be helpful to achieve net-zero targets.

Purpose: Intermediaries are crucial in determining individuals’ acceptance of cashless transactions (CLT). However, currently, there is a lack of clear evidence regarding the extent to which intermediaries influence individuals’ adoption of CLT. Hence, this study investigates intermediaries’ role of perceived – usefulness, ease of use, trust and attitude towards adopting CLT.Methodology: The survey was carried out by the researchers in Hyderabad, India, among 455 participants from all six zones. Subsequently, the researchers used the PLS-SEM approach to evaluate the relationship between the various factors under investigation, using the data obtained from the participants.Findings: The study’s findings indicate that combining all 13 independent variables can account for 68.8% of the variation in the dependent variable, which is the intention to adopt CLT. Personal innovativeness and self-efficacy indirectly influence users’ behavioural intentions towards adopting CLT through perceived ease of use and usefulness rather than a direct pathway. Furthermore, perceived usefulness, ease of use, and trust are significant mediators for the factors shaping users’ intentions to adopt CLT.Originality/Value: This research formulated eighteen significant mediator hypotheses based on logical reasoning supported by pertinent literature using a comprehensive research framework. Among these eighteen hypotheses, only five have been examined in previous literature, while the remaining thirteen hypotheses are explored, and their results are uncovered for the first time in this study. As a result, this research provides additional insights into individuals’ acceptance of CLT, offering valuable guidance for policymakers and bankers in developing effective strategies to promote the adoption of CLT.

Rapid development in technology and regular identification of new dynamic factors, offer challenges for modeling supply chain. Particularly supply chain of perishable products. These products require a special attention because of quality of product may deteriorate as product moves from one stage to other. India being, a country of large scale production of food, vegetables, meat and milk, hence require most advanced solutions for improving overall supply chain surplus. Presently high amount of wastage, 20–30% which is predominantly related to inefficient supply chain management. This inefficiency can be minimized by suitable modeling. This paper presents modeling of Perishable Product Supply Chain (PPSC) using Petri Net. Modeling of PPSC is based on concept of dynamic and discrete event system. Considering these two concepts, petri net model has been developed. The model provides a theoretical way to discuss the various states and transitions occur in PPSC. Various stages of PPSC starting from farmer, aggregator, processor, distributor, retailer and consumer provides an insight into the system with different drivers such as inventory, logistics, cold chain management, information technology, tracing and tracking of products. Model is developed using colored petri net technique. Purpose of this modeling is to explain a complex system into a simpler and easier way. Graphical notation of the model allows the visualization of complexity of the system and thus provides a mathematical framework for analysis and verification.

In today's smartphone era, digital service applications are essential to most people's daily lives. Even though users get to leverage digital service apps to derive multiple benefits, a big deterrent to continued usage is that users must constantly deal with issues related to invasion of privacy. Borrowing from Technology Threat Avoidance Theory and Multi-motive Information Systems Continuance model, we develop a research model that explains users’ intention to continue usage of digital service apps considering both perceived threat and performance beliefs. We test our hypotheses through an empirical study of 256 data points collected from users of these apps. Our findings reveal that while the perceived threat does not have an effect, performance beliefs strongly influence users’ intention to continue usage. Through this research, we contribute to the broader IT adoption studies specifically focused on user motivations to make decisions on the continuance intention of these apps.

The proliferation of service-based software in the past decade has prompted organizations to reconsider their software development approaches. The scenario is further infused with the complexity with the wave of generative artificial intelligence adoption. This effects on one side how organizations address customer issues, motivate developers to create scalable solutions and on the other side how businesses will evaluate software solutions and their adoption to improve their business processes. This transformation impacts the customer experiences, although harmonizing the experiences of both developers and customers presents a challenge due to existing processes and procedures.To improve both consumer and producer productivity, it is essential to enhance the end-user experiences and usability of all software tools and associated processes within organization’s internal ecosystem. The importance of programs that under-score organizational psychology, mindfulness, design thinking and the nurturing of a growth mindset has been acknowledged by organizations to have influenced digital adoption.Our research tries to understand the paradigm of customer-centric software development, its connection to developer ecosystems and how it influences the adoption of the solution in various industry verticals. It explores the existing and cur-rent state of approaches, improvements, and challenges to come up with a framework to nurture their developer ecosystem by fostering a culture focused on design.

Amid the evolving financial landscape, enhancing customer experience remains paramount for banks. One key area underpinning this experience is Customer Support Services (CSS). While the banking sector has historically employed various technological aids like Interactive Voice Response (IVR) Systems and chatbots, their rule-based nature often renders them less versatile. This research explores the potential of Generative AI in transforming Customer Support Services in banking. Unlike traditional systems, Generative AI’s ability to create unique content offers a more personalized and context-aware interaction. We have compared conventional methods with advanced Generative AI capabilities through a scenario-based approach. The findings provide insights into how Generative AI can revolutionize Customer Support Services across digital platforms, promising an enriched customer experience.

The increasing adoption of online content in education requires adolescents to be frequent internet users. The use of the internet among school-going children has led to problematic internet use (PIU) and internet addiction. Most parents are warned about these adverse effects and are usually advised to monitor the activities of their child. Previous studies have shown the positive effect of parental monitoring in controlling addiction. However, studies show that high levels of parental control cause anxiety among the students. Thus, schools need to be careful when adopting online learning elements. Our exploratory study shows that although monitoring was associated with decreased time spent online, it was also linked to higher internet anxiety. This effect was further found to be dependent on schools. Therefore, parents and schools need to work on promoting healthy internet use among adolescents. These findings emphasize the importance of striking a balance between monitoring and allowing autonomy for adolescents in their internet use.

This paper critiques the tendency of totalising societal problems into the binary of tame or wicked and, in such ways, generalising towards ‘one solution fits all’ since there are no other qualifiers. Taking the premise that every societal problem is unique and highly contextual, we use the lens of the wicked problem concept to explore the case of state-mandated ICTD intervention relating to agriculture market reforms. By operationalising a two-level typology and contingency framework, we place the case in a continuum between the tame and wicked territories. This enables us to typify the issue as wicked and establish the attributes and degree of wickedness in the problem. Our findings show that traits of wickedness in the issue have severely impacted the diffusion and adoption of ICTD itself at the local level and that the state’s push for ICTD-based transformation has led to a multi-dimensional intensification of wickedness traits, including additional complexities, heightened distrust, hardening of divergence and value positions, and increased uncertainty. The outcomes are adverse due to a templated single strategy ICTD intervention, which neither carries a complete understanding of the issues nor is receptive to the local knowledge.

The Fourth and Fifth Generation (4G and 5G) Mobile Communication Technology (MCT) is a crucial development that is expected to bridge digital divides worldwide and has significant economic implications for nations. However, there is a lack of research regarding the interplay of market heterogeneities with the diffusion of such 4G and 5G MCTs, particularly in emerging economies. This study aims to fill this research gap by conducting a quantitative analysis, by taking the case of early diffusion of 4G MCT across the twenty-two administrative regions (aka telecom circles) of India, where a new Mobile Network Operator (MNO) had simultaneously launched the 4G service. Using the Diffusion of Innovations theory and the Heterogenous Markets Hypothesis, the study puts forth several propositions that emphasize the role of market heterogeneity in mobilizing different forces in diffusion, such as imitative influences through word of mouth, and the power of innovation to mobilize Innovators and Early movers.

Understanding the role of e-government in driving governments’ development agenda has received attention from academicians and policymakers. Despite this, there is a dearth of research focusing on the higher-order impact of e-government at a macro level, such as sustainable development goals. This paper explores the relationship between e-government maturity and gender inequality (SDG 5). Drawing on empowerment theory, we examine how e-government maturity may help reduce gender inequality in the country. Moreover, based on institutional theory, we look at the potential role of government effectiveness in influencing the impact of e-government maturity on gender inequality. The study uses publicly available data from well-known sources. We provide empirical results supporting the proposed relationships using two-way fixed effect regression on a balanced panel dataset of 139 countries from 2003 to 2020. Our findings suggest a significant negative relationship exists between e-government maturity and gender inequality. Additionally, government effectiveness provides a considerable moderation effect between e-government maturity and gender inequality. We also validate our results through robustness checks. This study contributes to the literature on e-government impact and the role of institutional factors in realizing the benefits of e-government. Based on the findings, we provide implications for both research and practice.

Healthcare is undeniably a paramount concern for both individuals and nations alike. Its significance extends beyond just individual well-being, as the state of healthcare plays a pivotal role in the overall growth and stability of a nation’s economy. Recognizing this, industry and academics have embarked on various pioneering initiatives aimed at improving the quality of healthcare decisions and services. These efforts have remarkably transitions in healthcare frameworks, progressing from Healthcare 1.0 to Healthcare 5.0. Nowadays, wearable devices make the healthcare analysis of any individual easy. Doctors/hospitals can take the data, analyze it and suggest the diagnosis. But, this is not easy as we think. It is because of security issues where attackers can tamper with the wearable device message before reaching the concerned doctor. The modified message can misguide the doctor with an accurate diagnosis. This leads to a more severe cause for the patent and can endanger the patient’s life. Existing frameworks are mainly focusing on traditional cryptographic and blockchain solutions, which offer some level of security but no anonymity. Motivated by this, we proposed an artificial intelligence (AI), blockchain and onion routing-based secure and anonymous healthcare data management framework for wearable devices in this paper. Onion routing offers high security and anonymity with multi-layer encryption, whereas AI is used to distinguish malicious non-messages from malicious ones. Blockchain is used to authenticate the intermediate nodes of the onion network.

This paper aims to characterize and detect mental health from Twitter users’ posts and how the language, user attributes, and tweet attributes are associated with one of the most prevalent mental illnesses, depression. In this study, the social media analytics CUP framework and the Twitter API are used for the data collection of 27408 unique users. Data analysis uses expert input, natural language processing, and statistical methods. The empirical result from the statistical test confirms that linguistic features can represent the social media user’s mental illness conditions and attributes of social media posts, and users are significantly associated with mental illness and depression. Mental health is a widespread issue worsened by the pandemic. There’s still a stigma attached to it, which discourages open discussions. Digital technology, including social media, can significantly impact our mental health. Hence, the passive mechanism of mental illness characterization and detection from social media can help in timely intervention and provide the necessary support.

Health is the cornerstone for overall well-being, influencing various aspects of life. With the emergence of pandemic-prone diseases, physical and mental health have also been affected. Covid-19, the recent global emergency has devastated the entire world and even today people’s physical and psychological well-being has deteriorated by this pandemic. In these tough times, technology and its innovations have contributed to the management of pandemic-prone diseases. In light of this, the current work concentrates on Internet of Things (IoT) enabled cloud-fuzzy expert systems to assess the severity levels of the infected patients. These severity levels can vary from mild, to moderate, severe, and critical. The vital data is acquired from the patients through wearable healthcare devices every five-minute intervals. If the patient’s health worsens, the doctors and the patient’s family will be notified. This severity level identification system can be a solution to precisely monitor the patient’s health conditions during pandemic-prone diseases like covid-19.

In low-resource settings, effective and impactful decision-making within healthcare systems is essential due to limited resources. Extracting meaningful information from Health Management Systems like the District Health Information System(DHIS2) dashboard has significant implications in improving healthcare planning, and resource allocation. In this study, we investigate values that govern managers’ effective use of DHIS2 Dashboard. We conducted a total of fourteen interviews, which yielded six fundamental objectives (data accuracy, resource optimization, scalability, interactivity, data silos, stakeholder engagement) and thirteen means-ends objectives assessed by managers when operating the dashboard effectively. The developed means-ends objectives network will provide an information springboard for researchers as it uncovers key drivers of effective use of health information systems. Moreover, this research will assist both practitioners and implementers in enhancing features of comparable platforms, to be rolled out in similar contexts.

Healthcare systems, beyond delivering health services, play a pivotal role in shaping national identities. Their resilience is thus crucial. This paper, anchored on the World Health Organization’s resilience framework, investigated the role of AI-powered cloud computing in enhancing the resilience of healthcare systems, specifically focusing on governance within the systems. A qualitative study was conducted using the interpretivism paradigm. Data was collected through interviews with healthcare information technology (IT) experts knowledgeable in healthcare IT. Preliminary findings from the participants reveal that AI-cloud solutions promote participatory leadership by facilitating an integrated system and improved decision-making. Furthermore, these technological solutions foster coordination among healthcare entities, ensuring data security and performance monitoring. AI’s capability in cloud computing helps in top-tier data management and the prediction of potential risks, thereby emphasizing its importance in healthcare. This paper underscores the role of AI-powered cloud computing in reinforcing healthcare governance and overall system resilience.

In recent years, the World Health Organisation (WHO) has noted an increase in the number of young individuals suffering from Non-Communicable Diseases (NCDs). In addition, NCDs are becoming more common among South African young people. The high rate of smartphone usage in South Africa provides an opportunity to develop mobile applications to encourage young people to embrace healthier lives. This study explored the possible use of mobile health applications to encourage youth to engage in healthy physical activity using the Unified Theory of Acceptance and Use of Technology (UTAUT) model as the theoretical lens. A survey questionnaire was used to collect data from 320 participants using convenient sampling. The findings revealed that the factors influencing youth’s adoption of mobile health applications that encourage healthy physical activity are facilitating conditions, social influence (SI), performance expectancy (PE), and effort expectancy (EE). Thus, mobile application-driven interventions that aim to encourage young people to be active should consider the constructs identified in this study that have a higher effect size.

Artificial Intelligence (AI) is transforming various industries, and healthcare is no exception. AI is assisting practitioners in making more accurate diagnoses, improving patient outcomes, and enhancing the overall quality of care. In recent years, scientific publications on healthcare AI have reached unprecedented heights. However, a holistic view of how AI impacts healthcare is scant. Therefore, we rely on a bibliometric approach to explore the interplay between AI and healthcare. This study offers a dynamic, longitudinal analysis of healthcare AI publications, shedding light on the field’s expansion, attributes, and core themes: AI technology, healthcare applications, and human-technology interactions. These insights are of significant value to researchers, policymakers, and healthcare providers, aiding the development and adoption of AI into clinical practice and improving global patient outcomes. Furthermore, the study outlines future research directions that help scholars and practitioners understand and contribute to advancing healthcare.