Abstract: Despite being regarded as the most comprehensive theory in understanding individual technology adoption – UTAUT2 theory with growing number of citations and impetus beyond IS domain face strong criticism on usage of the model in its entirety. This study located UTAUT2 based empirical studies in the Scopus and Web of Science bibliographic database through citied reference search in order to evaluate appropriate usage of UTAUT2 constructs. The meta-analysis results spanning across 60 studies with more than 122,000 cumulative observations found BI➔UB as the strongest path with all significant values. PE➔BI emerged as the most utilized path with most significant values underscoring the emphasis placed by consumers on utilitarian value. Meanwhile, with most non-significant path values the future usage of EE➔BI path is been cautioned and questioned. Finally, trust, personal innovativeness, perceived risk, attitude, and self-efficacy were found as the five topmost UTAUT2 extensions.

Abstract: Mobile payment services have become increasingly important in daily lives in India due to multiple planned and unplanned events. The objective of this study is to identify the determinants of usage satisfaction of mobile payments which could enhance service adoption. The "Digital Service Usage Satisfaction Model" has been proposed and validated by combining technology adoption and service science literature. First the data was extracted from Twitter based on hashtags and keywords. Then using sentiment mining and topic modelling the large volumes of text were analysed. Then network science was also used for identifying clusters among associated topics. Then, using content analysis methodology, a theoretical model was developed based on literature. Finally using multiple regression analysis, we validated the proposed model. The study establishes that cost, usefulness, trust, social influence, credibility, information privacy and responsiveness factors are more important to increase the usage satisfaction of mobile payments services. Also methodologically, this is an endeavour to validate a new approach which uses social media data for developing a inferential theoretical model.

Abstract: Extant literature has increased our understanding of the multifaceted nature of the digital divide, showing that it entails more than access to information and communication resources. Research indicates that digital inequality mirrors to a significant extent offline inequality related to socioeconomic resources. Bridging digital divides is critical for sustainable digitalized societies. Ιn this paper, we present a literature review of Information Systems research on the digital divide within settings with advanced technological infrastructures and economies over the last decade (2010–2020). The review results are organized in a concept matrix mapping contributing factors and measures for crossing the divides. Building on the results, we elaborate a research agenda that proposes [1] extending established models of digital inequalities with new variables and use of theory, [2] critically examining the effects of digital divide interventions, and [3] better linking digital divide research with research on sustainability.

Abstract: Social media plays an important part in the digital transformation of businesses. This research provides a comprehensive analysis of the use of social media by business-to-business (B2B) companies. The current study focuses on the number of aspects of social media such as the effect of social media, social media tools, social media use, adoption of social media use and its barriers, social media strategies, and measuring the effectiveness of use of social media. This research provides a valuable synthesis of the relevant literature on social media in B2B context by analysing, performing weight analysis and discussing the key findings from existing research on social media. The findings of this study can be used as an informative framework on social media for both, academic and practitioners.

Abstract: With the rise in cases of COVID-19, a bizarre situation of pressure was mounted on each country to make arrangements to control the population and utilize the available resources appropriately. The swiftly rising of positive cases globally created panic, anxiety and depression among people. The effect of this deadly disease was found to be directly proportional to the physical and mental health of the population. As of 28 October 2020, more than 40 million people are tested positive and more than 1 million deaths have been recorded. The most dominant tool that disturbed human life during this time is social media. The tweets regarding COVID-19, whether it was a number of positive cases or deaths, induced a wave of fear and anxiety among people living in different parts of the world. Nobody can deny the truth that social media is everywhere and everybody is connected with it directly or indirectly. This offers an opportunity for researchers and data scientists to access the data for academic and research use. The social media data contains many data that relate to real-life events like COVID-19. In this paper, an analysis of Twitter data has been done through the R programming language. We have collected the Twitter data based on hashtag keywords, including COVID-19, coronavirus, deaths, new case, recovered. In this study, we have designed an algorithm called Hybrid Heterogeneous Support Vector Machine (H-SVM) and performed the sentiment classification and classified them positive, negative and neutral sentiment scores. We have also compared the performance of the proposed algorithm on certain parameters like precision, recall, F1 score and accuracy with Recurrent Neural Network (RNN) and Support Vector Machine (SVM).

Abstract: This paper assesses the digital divide between and within the 28 member-states of the European Union. The analysis comprised four socio-demographic contexts: age, education, gender, and income. Because of the digital divide’s complexity, a multivariate approach was applied - factor analysis with oblique rotation, which resulted in two distinct dimensions: e-Services and Social Networks. To test the significant differences of European Union positioning and European Union disparities, Multivariate Analysis of Variance and Squared Rank Test were computed. Findings show that e-Services adoption is influenced primarily by the education level of individuals, while Social Networks adoption is more affected by individuals’ age.

Abstract: Responsible AI is concerned with the design, implementation and use of ethical, transparent, and accountable AI technology in order to reduce biases, promote fairness, equality, and to help facilitate interpretability and explainability of outcomes, which are particularly pertinent in a healthcare context. However, the extant literature on health AI reveals significant issues regarding each of the areas of responsible AI, posing moral and ethical consequences. This is particularly concerning in a health context where lives are at stake and where there are significant sensitivities that are not as pertinent in other domains outside of health. This calls for a comprehensive analysis of health AI using responsible AI concepts as a structural lens. A systematic literature review supported our data collection and sampling procedure, the corresponding analysis, and extraction of research themes helped us provide an evidence-based foundation. We contribute with a systematic description and explanation of the intellectual structure of Responsible AI in digital health and develop an agenda for future research.

Abstract: With the overnight growth in Working from Home (WFH) owing to the pandemic, organisations and their employees have had to adapt work-related processes and practices quickly with a huge reliance upon technology. Everyday activities such as social interactions with colleagues must therefore be reconsidered. Existing literature emphasises that social interactions, typically conducted in the traditional workplace, are a fundamental feature of social life and shape employees' experience of work. This experience is completely removed for many employees due to the pandemic and, presently, there is a lack of knowledge on how individuals maintain social interactions with colleagues via technology when working from home. Given that a lack of social interaction can lead to social isolation and other negative repercussions, this study aims to contribute to the existing body of literature on remote working by highlighting employees' experiences and practices around social interaction with colleagues. This study takes an interpretivist and qualitative approach utilising the diary-keeping technique to collect data from twenty-nine individuals who had started to work from home on a full-time basis as a result of the pandemic. The study explores how participants conduct social interactions using different technology platforms and how such interactions are embedded in their working lives. The findings highlight the difficulty in maintaining social interactions via technology such as the absence of cues and emotional intelligence, as well as highlighting numerous other factors such as job uncertainty, increased workloads and heavy usage of technology that affect their work lives. The study also highlights that despite the negative experiences relating to working from home, some participants are apprehensive about returning to work in the traditional office place where social interactions may actually be perceived as a distraction. The main contribution of our study is to highlight that a variety of perceptions and feelings of how work has changed via an increased use of digital media while working from home exists and that organisations need to be aware of these differences so that they can be managed in a contextualised manner, thus increasing both the efficiency and effectiveness of working from home.

Abstract: Digital technologies are radically changing how established organizations design novel services. Digital transformation (DT) strategies are executed to manage the transition from product-centric to service-centric business models based on digital technologies. However, little is known about what configurations of DT strategies lead to successful digital service innovation (DSI) in established organizations. We employ fuzzy-set Qualitative Comparative Analysis on a set of 17 case studies of DT strategies from established organizations with different industry backgrounds. We identify several distinct configurations of DT strategies that lead to successful and unsuccessful DSI. Based on these configurations, we deduce that the threat of digital disruption negatively impacts an organization’s innovation activities. Furthermore, we find that strategic partnerships can be leveraged by organizations that face an imminent threat of digital disruption while organizations with competitive advantages may rely on “do-it-yourself” approaches. Lastly, we find that the involvement of a C-level executive is a necessary requirement for successful DSI. Our results contribute to theory by integrating research on DSI and DT, providing a perspective on DSI failure, and employing a configurational research approach that allows us to highlight interdependencies between factors as well as insights into the individual factors. Furthermore, we provide actionable recommendations for executives.

Abstract: The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

Abstract: To date, online review usefulness studies have explored the independent influence of central and peripheral cues on online review usefulness Employing the Elaboration Likelihood Model (ELM), however, we argue that central and peripheral cues are jointly, not independently, processed by online users For this exploration, we develop and measure “review consistency” variable (ie, level of consistency between a review text and its attendant review rating), and rating inconsistency (ie, level of inconsistency between a review rating and the average rating) We find a positive effect of review consistency on the review usefulness Contrary to our hypothesis, however, we find a positive effect of rating inconsistency on the review usefulness Our results also indicate that the contingency effect of rating inconsistency on the relationship between review consistency and review usefulness Particularly, we find that rating inconsistency negatively moderates the effect of review consistency on the review usefulness The theoretical and practical implications of the findings are discussed

Abstract: Online impulsive buying has become increasingly prevalent in e-commerce and social commerce research, yet there is a paucity of systematically examining this particular phenomenon in the paradigm of information systems. To advance this line of research, this study aims to gain insight into online impulsive buying through a meta-analysis of relevant research. Derived from 54 articles, this meta-analysis categorized the critical factors that influence online impulsive buying into the website, marketing, and affective stimuli. This study further explores the moderating effect of economic development level. The empirical results reveal that the chosen 13 main factors are significantly and positively related to online impulsive buying except for website security, price, novelty, and negative emotion. Moreover, economic development level moderates the relationship between several factors (i.e., website visual appeal, ease of use, price, promotion, pleasure, and positive emotion) and online impulsive buying. This study contributes to both theory and practice. It not only extends the impulsive buying literature to the online context by emphasizing the IT-supported website stimuli, but also provides implications for future research on online impulsive buying behavior across different economic development levels. Moreover, it provides guidelines for practitioners on how to leverage information technology to induce online impulsive buying.

Abstract: This study investigates the factors that build resistance and attitude towards AI voice assistants (AIVA). A theoretical model is proposed using the dual-factor framework by integrating status quo bias factors (sunk cost, regret avoidance, inertia, perceived value, switching costs, and perceived threat) and Technology Acceptance Model (TAM; perceived ease of use and perceived usefulness) variables. The study model investigates the relationship between the status quo factors and resistance towards adoption of AIVA, and the relationship between TAM factors and attitudes towards AIVA. A sample of four hundred and twenty was analysed using structural equation modeling to investigate the proposed hypotheses. The results indicate an insignificant relationship between inertia and resistance to AIVA. Perceived value was found to have a negative but significant relationship with resistance to AIVA. Further, the study also found that inertia significantly differs across gender (male/female) and age groupings. The study's framework and results are posited as adding value to the extant literature and practice, directly related to status quo bias theory, dual-factor model and TAM.

Abstract: Social media has played a pivotal role in polarising views on politics, climate change, and more recently, the Covid-19 pandemic. Social media induced polarisation (SMIP) poses serious challenges to society as it could enable ‘digital wildfires’ that can wreak havoc worldwide. While the effects of SMIP have been extensively studied, there is limited understanding of the interplay between two key components of this phenomenon: confirmation bias (reinforcing one’s attitudes and beliefs) and echo chambers (i.e., hear their own voice). This paper addresses this knowledge deficit by exploring how manifestations of confirmation bias contributed to the development of ‘echo chambers’ at the height of the Covid-19 pandemic. Thematic analysis of data collected from 35 participants involved in supply chain information processing forms the basis of a conceptual model of SMIP and four key cross-cutting propositions emerging from the data that have implications for research and practice.

Abstract: The Healthcare sector has been at the forefront of the adoption of artificial intelligence (AI) technologies. Owing to the nature of the services and the vulnerability of a large section of end-users, the topic of responsible AI has become the subject of widespread study and discussion. We conduct a mixed-method study to identify the constituents of responsible AI in the healthcare sector and investigate its role in value formation and market performance. The study context is India, where AI technologies are in the developing phase. The results from 12 in-depth interviews enrich the more nuanced understanding of how different facets of responsible AI guide healthcare firms in evidence-based medicine and improved patient centered care. PLS-SEM analysis of 290 survey responses validates the theoretical framework and establishes responsible AI as a third-order factor. The 174 dyadic data findings also confirm the mediation mechanism of the patient’s cognitive engagement with responsible AI-solutions and perceived value, which leads to market performance.

Abstract: The popularity of mobile payment services lies in the convenient transactions they offer to users. In the age of growing cybercrime, however, mobile payment transactions carry risks of financial and data losses. It thus becomes critical to understand how risk and convenience have contrasting impacts on users’ intention to use mobile payments. To investigate this, we consider various dimensions of perceived risk and perceived convenience to understand the net effect of their negative and positive influences on the intention to use. We also examine the actual use behavior predicted by intention along with the influence of habit. The research model, tested using survey responses from a sample of 215 users along with the descriptive answers from the survey respondents, helps us draw crucial insights. The study contributes to the field in a significant manner by providing insights into the balancing effect of risk and convenience on mobile payment service usage, as well as the development of the multi-dimensional scales for the key variables of risk and convenience.

Abstract: Data-driven predictions have become an inseparable part of business decisions. Artificial Intelligence (AI) has started helping the product and support teams perform more accurate experiments in various business settings. This study proposes a framework for businesses based on inductive learnings related to success and barriers shared on social media platforms. Our goal is to analyse the signals emerging from these conversational opinions from the early adoption of AI, with a focus towards facilitators and barriers faced by teams. Factors like efficiency, innovation, business research, product novelty, manual intervention, adaptability, emotion, support, personal growth, experiential learning, fear of failure and fear of upgradation have been identified based on an exploratory study and then a confirmatory study. We present the learnings through a roadmap for practitioners. This study contributes to the IS literature by delineating AI as a determinant of success and introduces a lot of organizational factors into the model.

Abstract: With the proliferation of the use of chatbots across industries, business-to-business (B2B) businesses have started using cognitive chatbots for improved customer service which signifies our research. By extending the Technology Acceptance Model and Information Systems Success Model, this study examines personalised contextual customer service using cognitive chatbot. A quantitative research method is applied to the primary data collected from 300 respondents of B2B businesses. The study contributes to the limited research on chatbots and suggests improvement in customer service. The findings provide evidence of high value by customers, particularly while checking for real-time information on reliability and accessibility of products/services. The automated answers to repetitive questions on the recurrent issues create a seamless experience for the customers. This research makes significant theoretical contributions by integrating two models into a simplified model in chatbot literature and manifest that trust affects the willingness to use the cognitive chatbot which drives automation.

Abstract: This paper tests the meta-analysis based unified theory of acceptance and use of technology (meta-UTAUT) model to predict the behavioral intentions of organizational users and their use behavior to artificial intelligence (AI) integrated customer relationship management (CRM) systems. Data was collected from 315 organizational users in India. The hypotheses draw on the theoretical underpinnings which have been statistically validated. Results show that CRM quality and satisfaction significantly influences an organization’s employees attitudes and intentions to use AI integrated CRM systems. The compatibility of CRM systems has, however, a limited impact on employees attitudes. The findings, which are aligned with the extended UTAUT model, provide useful insights into organizations and decision-makers for designing AI integrated CRM systems.

Abstract: In recent years, the circulation of online misinformation and fake news has drawn our attention, given it has ideologically polarized society and has led to social instability, compromised democracies, and other consequences. Efforts on technical or behavioral dimensions on their identification are not uncommon, but these efforts inadequately addressed their roots, and thus may not stop them from creation or spreading. Using the theoretical lens of computer-mediated communications, we analyzed the ideological polarization phenomenon in Hong Kong, which has been worsening since the Umbrella Revolution in 2014. We proposed a three-stage model to illustrate the mechanism of how online misinformation and fake news leads to ideological polarization. The catalysts and barriers in each stage were also highlighted. Our analyses generate a better understanding of the mechanisms that help hinder the circulation of online misinformation and fake news, and thus, reduces the damages it caused.

Abstract: The use of artificial intelligence (AI) in the healthcare field is gaining popularity. However, it also raises some concerns related to privacy and ethical aspects that require the development of a responsible AI framework. The principle of responsible AI states that artificial intelligence-based systems should be considered a part of composite societal and technological systems. This study attempts to establish whether AI risks in digital healthcare are positively associated with responsible AI. The moderating effect of perceived trust and perceived privacy risks is also examined. The theoretical model was based on perceived risk theory. Perceived risk theory is important in the context of this study, as risks related to uneasiness and uncertainty can be expected in the development of responsible AI due to the volatile nature of intelligent applications. Our research provides some interesting findings which are presented in the discussion section.

Abstract: This study aims to build on the understanding of social commerce in the emerging markets and how it influences online community engagement. The conceptual model was proposed using theories including the social support theory, the trust theory, the social presence theory, the flow theory and the service-dominant logic theory. Using Facebook online community, the data were collected from 400 respondents from Jordan and analysed using AMOS based structural equation modelling. Results revealed that social commerce constructs positively influence social support, community members’ trust and social presence. Furthermore, it was found that social support and social presence positively affect community members’ trust. We also found that community members’ trust positively influence flow whereas both community members’ trust and flow positively influence community engagement.

Abstract: E-learning systems are receiving ever increasing attention in academia, business and public administration. Major crises, like the pandemic, highlight the tremendous importance of the appropriate development of e-learning systems and its adoption and processes in organizations. Managers and employees who need efficient forms of training and learning flow within organizations do not have to gather in one place at the same time or to travel far away to attend courses. Contemporary affordances of e-learning systems allow users to perform different jobs or tasks for training courses according to their own scheduling, as well as to collaborate and share knowledge and experiences that result in rich learning flows within organizations. The purpose of this article is to provide a systematic review of empirical studies at the intersection of e-learning and organizational learning in order to summarize the current findings and guide future research. Forty-seven peer-reviewed articles were collected from a systematic literature search and analyzed based on a categorization of their main elements. This survey identifies five major directions of the research on the confluence of e-learning and organizational learning during the last decade. Future research should leverage big data produced from the platforms and investigate how the incorporation of advanced learning technologies (e.g., learning analytics, personalized learning) can help increase organizational value.

Abstract: Triggered by the development of information and communications technologies, Industry 4.0 opens up a new era for the manufacturing industry.Currently, Industry 4.0 has attracted much attention from industry and academia. Research on Industry 4.0 is still evolving towards the development of frameworks linking Industry 4.0’s enabling technologies to specific goals and to their impact on the manufacturers’ businesses.Accordingly, this study presents a systematic review of the scope of Industry 4.0, its goals and implementations, as well as the barriers to the implementation of Industry 4.0. Solutions for overcoming the barriers and challenges are discussed.

Abstract: Since the inception of Andoroid OS, smartphones sales have been growing exponentially, and today it enjoys the monopoly in the smartphone marketplace. The widespread adoption of Android smartphones has drawn the attention of malware designers, which threatens the Android ecosystem. The current state-of-the-art Android malware detection systems are based on machine learning and deep learning models. Despite having superior performance, these models are susceptible to adversarial attack. Therefore in this paper, we developed eight Android malware detection models based on machine learning and deep neural network and investigated their robustness against the adversarial attacks. For the purpose, we created new variants of malware using Reinforcement Learning, which will be misclassified as benign by the existing Android malware detection models. We propose two novel attack strategies, namely single policy attack and multiple policy attack using reinforcement learning for white-box and grey-box scenario respectively. Putting ourselves in adversary’ shoes, we designed adversarial attacks on the detection models with the goal of maximising fooling rate, while making minimum modifications to the Android application and ensuring that the app’s functionality and behaviour does not change. We achieved an average fooling rate of 44.21% and 53.20% across all the eight detection models with maximum five modifications using a single policy attack and multiple policy attack, respectively. The highest fooling rate of 86.09% with five changes was attained against the decision tree based model using the multiple policy approach. Finally, we propose an adversarial defence strategy which reduces the average fooling rate by threefold to 15.22% against a single policy attack, thereby increasing the robustness of the detection models i.e. the proposed model can effectively detect variants (metamorphic) of malware. The experimental analysis shows that our proposed Android malware detection system using reinforcement learning is more robust against adversarial attacks.

Abstract: Artificial Intelligence (AI) technology is transforming the healthcare sector. However, despite this, the associated ethical implications remain open to debate. This research investigates how signals of AI responsibility impact healthcare practitioners’ attitudes toward AI, satisfaction with AI, AI usage intentions, including the underlying mechanisms. Our research outlines autonomy, beneficence, explainability, justice, and non-maleficence as the five key signals of AI responsibility for healthcare practitioners. The findings reveal that these five signals significantly increase healthcare practitioners’ engagement, which subsequently leads to more favourable attitudes, greater satisfaction, and higher usage intentions with AI technology. Moreover, ‘techno-overload’ as a primary ‘techno-stressor’ moderates the mediating effect of engagement on the relationship between AI justice and behavioural and attitudinal outcomes. When healthcare practitioners perceive AI technology as adding extra workload, such techno-overload will undermine the importance of the justice signal and subsequently affect their attitudes, satisfaction, and usage intentions with AI technology.

Abstract: Co-production and active participation of the consumers are considered to have enhanced the value co-creation activities that would ensure business benefits of a firm. The marketing literature available does not explicitly explain the philosophy that would motivate the consumers to help to increase values for co-creation activities. In this context, attempts have been made to identify the factors that would impact on co-production and consumers’ participation to co-create values. By studying literature and theories such as theory of co-creation, theory of value creation, information processing theory, marketing theory and expectancy value theory, a conceptual model called F-P-C-B (Future Participation (F) - Co-production (P) - Co-creation (C) - Business Benefit (B)) has been developed along with nine hypotheses. The data was from 362 respondents in India and the model was tested using PLS based analysis. The study shows that it is important for the firms to shift from product-oriented activities to customer-related strategies. It is also found that for obtaining more profitability and better business results, customers should be involved in business activities by way of involving in co-design, idea generation, and other relevant activities of the firms. Moreover, the study highlights that knowledge sharing between the customers and the firm authorities ensures better business values.

Abstract: With the unparallel advance of leading-edge technologies like artificial intelligence (AI), the healthcare systems are transforming and shifting for more digital health. In recent years, scientific productions have reached unprecedented levels. However, a holistic view of how AI is being used for digital health remains scarce. Besides, there is a considerable lack of studies on responsible AI and ethical issues that identify and suggest practitioners' essential insights towards the digital health domain. Therefore, we aim to rely on a bibliometric approach to explore the dynamics of the interplay between AI and digital health approaches, considering the responsible AI and ethical aspects of scientific production over the years. We found four distinct periods in the publication dynamics and the most popular approaches of AI in the healthcare field. Also, we highlighted the main trends and insightful directions for scholars and practitioners. In terms of contributions, this work provides a framework integrating AI technologies approaches and applications while discussing several barriers and benefits of AI-based health. In addition, five insightful propositions emerged as a result of the main findings. Thus, this study's originality is regarding the new framework and the propositions considering responsible AI and ethical issues on digital health.

Abstract: The success factor of sentimental analysis lies in identifying the most occurring and relevant opinions among users relating to the particular topic. In this paper, we develop a framework to analyze users’ sentiments on Twitter on natural disasters using the data pre-processing techniques and a hybrid of machine learning, statistical modeling, and lexicon-based approach. We choose TF-IDF and K-means for sentiment classification among affinitive and hierarchical clustering. Latent Dirichlet Allocation, a pipeline of Doc2Vec and K-means used to capture themes, then perform multi-level polarity indices classification and its time series analysis. In our study, we draw insights from 243,746 tweets for Kerala’s 2018 natural disasters in India. The key findings of the study are the classification of sentiments based on similarity and polarity indices and identifying themes among the topics discussed on Twitter. We observe different sets of emotions and influencers, among others. Through this case example of Kerala floods, it shows how the government and other organizations could track the positive/negative sentiments concerning time and location; gain a better understanding of the topic of discussion trending among the public, and collaborate with crucial Twitter users/influencers to spread and figure out the gaps in the implementation of schemes in terms of design and execution. This research’s uniqueness is the streamlined and efficient combination of algorithms and techniques embedded in the framework used in achieving the above output, which can be integrated into a platform with GUI for further automation.