Intention to Adopt E-Commerce: A Comparative Review Across Developed and Developing Economies

The purpose of this study is to conduct a comprehensive literature review of online purchase intention and present a comparative case between developed and developing economies over a 20-year period to reveal insightful implications for academia and industry. Online purchase intention refers to the intended behavior of an individual to buy a product or service from an online store. Prior research has failed to present a review that compares an individual’s online purchase intention across developed and developing economies in order to disclose the largest and smallest influencing factors, theories, and models in e-commerce. Our sample consists of 97 relevant articles focusing on online purchase intention retrieved from various quality databases, specifically from 53 peer-reviewed and validated journals. This research, in brief, will show different phases of analysis to better understand the current landscape of e-commerce behavioral intention and provide useful insights to researchers and professionals

When IS Success Model Meets UTAUT in a Mobile Banking Context: A Study of Subjective and Objective System Usage

The objective usage of mobile banking (MB) reflects more validated measure when compared to subjective usage. Although objective system usage has been seldom studied, it has been never investigated in a MB context to the best of our knowledge. This research in progress develops an integrative conceptual framework that incorporates relevant-context factors into well-established models of IS success and UTAUT to examine their direct and indirect effects on MB usage. This examination can expand our knowledge of system usage in the context of mobile banking. Contribution and implications are discussed

The Role of IT on Entrepreneurial Intention: The Effect of General Computer Self-Efficacy and Computer Anxiety

Entrepreneurs represent valuable assets to any society. They contribute to the economy of nations by creating new ventures and job opportunities. The question of what drives individuals to become entrepreneurs has received much attention by entrepreneurship scholars. However, the entrepreneurship literature is lacking with regard to IT cognitive and emotional factors that can significantly influence individuals to become entrepreneurs. In this study, we propose a theoretical model that extends theory of planned behavior by incorporating the technological role into established entrepreneurial models. In particular, the proposed model explains how general computer self-efficacy and computer anxiety determine entrepreneurial intention. We plan to replicate established hypotheses and test novel ones using a unique design that has a potential methodological contribution

Predictive Analytical Model for Early Detection of Sepsis

This paper proposes the development of a machine-learning model for the early detection of sepsis. This model uses clinical parameters such as vital signs, laboratory values, demographics, and additional information to predict sepsis in patients. The model was trained and tested on data from a U.S. hospital and was found to be effective in detecting sepsis onset and mortality. The study findings suggest to use a random forest algorithm due to its superior performance in predicting sepsis; this can help healthcare workers quickly recognize high-risk patients and provide timely treatment, potentially improving patient outcomes

Understanding Mobile Banking Success Through User Segmentation

Mobile banking (MB) which involves the use of mobile devices to access bank accounts for conducting financial transactions has grown rapidly but unevenly with users. Banks realizes the strategic role of user’s satisfaction and the importance of MB systems in their business models. Yet, the diversity of users and disparity of system usage behaviors make difficult to measure MB success. This study segments the MB users on system use behavior of 4,478 users with objective measures by analyzing the MB system log files on various system usage metrics. Then, a subjective measures study surveys the same users on the system success factors of the information systems (IS) success model by using 445 responses. Results indicate that the influence of success factors significantly varies among user segments for intention to use, which makes an important contribution to enhance interpretation of the IS success model

Moving to Digital-Healthy Society: Empathy, Sympathy, and Wellbeing in Social Media

Background: This research aims to explore the impact of individuals’ demographics and their social media use on empathy, sympathy, and wellbeing in Saudi Arabia. This paper can fill an untapped gap in a developing country (i.e., the Arab context) by shedding light on sympathetic and empathetic behavior and its effect on wellbeing in social media. Method: We manage to obtain a sample of 431 responses across all Saudi regions. Data were analyzed to evaluate reliability and validity of the study’s constructs while the hypotheses were tested using a structural equation modeling (SEM) technique. Results: SEM regression results suggest that there is a significant relationship between both age and income and social media use. In addition, social media use has an indirect relationship to individuals’ wellbeing. This indirect relationship is better manifested through sympathy rather than empathy. Conclusion: Theoretically, this study furthers our understanding of the role of empathy and sympathy on wellbeing in social media among Saudis, whereas practically provides insights to industry experts about what matters to social media users to increase their wellbeing

The Impact of Subjective and Objective Experience on Mobile Banking Usage: An Analytical Approach

This paper aims to investigate mobile banking (MB) usage through the theoretical lens of UTAUT model with its four pillars. The research model will be tested via a hybrid neural networks-based structural equation modeling (SEM-NN) to reveal significant factors. Universal structural modeling (USM) will be then utilized to find the hidden paths and nonlinearity in our research model. To the best of our knowledge, this is the first study to examine the role of subjective and objective experience on MB usage using a multi-analytical approach. Neural network (NN) and USM can identify the most significant determinants and hidden interaction effects, respectively. Thus, both techniques would help to complement SEM and increase our understanding of the influential factors on MB usage. Preliminary results are presented and discussed. Potential contribution and conclusion are communicated to both academia and industry

Antecedent and Consequences of Blockchain-as-a-Service for E-Voting: The Mediating Role of Perceived Trust

Electronic voting refers to internet voting (e-voting), a system for electronically casting and counting votes. The current voting service provided by the government includes balloting paper and e-voting. However, these services cannot be relied on due to several issues such as electoral fraud (e.g., counting, rigging, and election manipulation), circuitry failure (e.g., tampering with the motherboard), and, more importantly, such services do not provide the facility to track back the casted vote. Considering the problems in earlier voting services, the blockchain-as-a-service for e-voting has been introduced to make the voting process more secure, immutable, transparent, and reliable. Within the blockchain-as-a-service for e-voting, we have reviewed the available literature and witnessed that the majority of the studies have given much emphasis on the technical side but lack its focus on the adoption behavior of blockchain-as-a-service for evoting during the election period. Therefore, the foci of this study to explore the antecedent (i.e., digital literacy) and consequences (i.e., consumer wellbeing, users’ referral) of users’ adoption behavior of blockchain-as-a-service for e-voting under the mediating mechanism of users’ perceived trust between digital literacy and adoption behavior. This study collected data from a 315 US sample using the Mturk. Partial least squares – structural equation modeling (PLS-SEM) analyses were used to analyze the study data. The PLS-SEM analysis revealed that the measurement model of the study, including digital literacy as a higher-order reflective-formative construct and other reflective models (e.g., adoption behavior, consumer wellbeing, etc.), have adequate reliability and validity. Upon estimating the study’s structural model, we found that digital literacy of blockchain e-voting positively impacts on perceived trust and adoption behavior of blockchain e-voting technology. Perceived trust in blockchain e-voting also revealed to have a positive impact on users’ adoption behavior of blockchain-as-a-service for evoting. Furthermore, the results of the study indicated that blockchain adoption behavior is a significant predictor of consumer well-being and citizen referral behavior. We also tested the mediating effect of perceived trust between digital literacy and adoption behavior of blockchain-as-a-service for e-voting and found that digital literacy successfully predicts the adoption behavior of blockchain e-voting through perceived trust, signifying the pivotal role of trust. This study theoretically extends the domain of blockchain-as-a-service for evoting via investigating its potential antecedent (i.e., digital literacy) and consequences (i.e., citizen referral behavior and consumer well-being) of users’ adoption behavior of blockchain-as-a-service for evoting. Besides, we also expands the literature of perceived trust via studying it as a mediating mechanism between digital literacy and users’ adoption behavior of blockchain-as-a-service for evoting. It also helps design, prepare, and implement new technologies while considering consumers\u27 digital literacy and trust. Government officials and regulators should promote ways to improve the level of digital literacy to implement the blockchain e-voting service fully. Policymakers should collaborate with industry practitioners to create a well-thought-out plan that targets and improves public digital literacy while also increasing trust in blockchain e-voting to increase people\u27s adoption and usage of this technology

“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts

“So what if ChatGPT wrote it?”:Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts