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The purpose of this paper is to examine the effects of inhibiting, motivating, and technological factors on users’ intention to participate in the sharing economy.

A self-reported online survey was conducted among Uber users in Hong Kong. A total of 295 valid responses were collected. The research model was empirically tested using the structural equation modeling technique.

The results suggested that perceived risks, perceived benefits, trust in the platform, and perceived platform qualities were significant predictors of users’ intention to participate in Uber.

This study bridged the research gaps in the sharing economy literature by examining the effects of perceived risks, perceived benefits, and trust in the platform on users’ intention to participate in the sharing economy. Moreover, this study enriched the extended valence framework by incorporating perceived platform qualities into the research model, responding to the calls for the inclusion of technological variables in information systems research.

The findings provided practitioners with insights into enhancing users’ intention to participate in the sharing economy.

This study presented one of the first attempts to systematically examine the effects of inhibiting, motivating, and technological factors on users’ intention to participate in the sharing economy.

The global market for cloud gaming is growing rapidly. How gamers evaluate the service quality of this emerging form of cloud service has become a critical issue for both researchers and practitioners. Building on the literature on service quality and software as a service, this study develops and validates a gamer-centric measurement instrument for cloud gaming service quality.

A three-step measurement instrument development process, including item generation, scale development and instrument testing, was adopted to conceptualize and operationalize cloud gaming service quality.

Cloud gaming service quality consists of two second-order constructs of support service quality and technical service quality with seven first-order dimensions, namely rapport, responsiveness, reliability, compatibility, ubiquity, smoothness and comprehensiveness. The instrument exhibits desirable psychometric properties.

Practitioners can use this new measurement instrument to evaluate gamers' perceptions toward their service and to identify areas for improvement.

This study contributes to the service quality literature by utilizing qualitative and quantitative approaches to develop and validate a new measurement instrument of service quality in the context of cloud gaming and by identifying new dimensions (compatibility, ubiquity, smoothness and comprehensiveness) specific to it.

With growing interest in the uses of hedonic technologies and gamification in system design, the concept of cognitive absorption (CA) has become increasingly salient in the information systems literature. However, little effort has been made to evaluate the research status and consolidate the current literature findings. To fill these research gaps, the authors conducted a literature review on CA. The authors then proposed an integrative framework that summarises the key elements of and variables related to CA and their relationships. The major findings of the study are discussed, and an agenda for future research is proposed.

The proliferation of social networking sites (SNSs) has drawn attention to different parties in realising their goals. Advertisers utilise SNSs to promote new products and services; politics optimise SNSs to gather support from the public, while ordinary users use SNSs as a unique platform to practice self-disclosure, develop networks, and sustain relationships. This study explores how social anxiety affects self-disclosure on SNSs and well-being. It also examines the moderating effects of two contextual factors, namely, online disinhibition and psychological stress. Two hundred and thirty-four valid responses were collected via an online survey. A positive relationship between social anxiety and self-disclosure, and self-disclosure and well-being was found. Furthermore, a positive moderation effect among social anxiety, online disinhibition, and self-disclosure was revealed. This research contributes to the development of social networking literature. It also enhances the understanding of disclosure patterns on SNSs among socially anxious individuals, thereby providing important insights for practitioners, educators, and clinicians.

This research aims to explore the impact of augmented reality (AR), the digital technology that superimposes virtual elements in a real environment, on consumers in the context of experiential marketing. Specifically, this study proposes a research model based on the stimulus-organism-response model, which considers AR media characteristics as external stimuli, consumers’ value perceptions as the organisms, and purchase intentions as the responses. The research model was tested with 248 consumers using structural equation modelling. The results show that informativeness, ease of use, and telepresence have positive effects on consumers’ utilitarian value perception and that telepresence and interactivity have positive effects on hedonic value perception. Overall, this study contributes to the growing body of knowledge on AR and provides actionable insights for managers implementing digital transformation strategies and AR applications in marketing practices.

Since the emergence of the Internet in the twentieth century and the rapid growth of different types of information technologies (IT), our lives, either personal or professional, have become digitised. Adoption and diffusion of IT enhance individuals and organisational performance, yet scholars discovered a dual nature of IT in which IT usage may have negative aspects too. First, the inability to cope with IT in a healthy manner creates stress in users, termed technostress. Second, digitisation and adoption of new technologies (e.g. IoT and multi-cloud environments) have increased vulnerabilities to information security (InfoSec) threats. Although organisations utilise counteraction strategies (e.g., security systems, security policies), end-users remain the top source of security incidents. Existing behavioural research has approached technostress and InfoSec independently. However, it is not clear how technology-stressors influence employees’ security-related behaviours. This chapter reviews the interaction effect of these concepts in detail by proposing a conceptual model that explains that technostress is the main reason for employees’ non-compliance with security policies in which users with high-level perceptions of technostress are more likely to violate InfoSec policies. Counteraction strategies to mitigate technostress and security threats are also discussed.

Significant advances in digital technologies impact both organisations and knowledge workers alike. Organisations are now able to effectively analyse significant amounts of data, while accomplishing actionable insight and data-driven decision-making through knowledge workers that understand and manage greater complexity. For decision-makers to be in a position where sufficient information and data-driven insights enable them to make informed decisions, they need to better understand fundamental constructs that lead to the understanding of deep knowledge and wisdom. In an attempt to guide organisations in such a process of understanding, this research study focuses on the design of an organisational transformation framework for data-driven decision-making (OTxDD) based on the collaboration of human and machine for knowledge work. The OTxDD framework was designed through a design science research approach and consists of 4 major enablers (data analytics, data management, data platform, data-driven organisation ethos) and 12 sub-enablers. The OTxDD framework was evaluated in a real-world scenario, where after, based on the evaluation feedback, the OTxDD framework was improved and an organisational measurement tool developed. By considering such an OTxDD framework and measurement tool, organisations will be able to create a clear transformation path to data-driven decision-making, while applying the insight from both knowledge workers and intelligent machines.

The nature of technologies that are recognised as Artificial Intelligence (AI) has continually changed over time to be something more advanced than other technologies. Despite the fluidity of understanding of AI, the most common theme that has stuck with AI is ‘human-like decision making’. Advancements in processing power, coupled with big data technologies, gave rise to highly accurate prediction algorithms. Analytical techniques which use multi-layered neural networks such as machine learning and deep learning have emerged as the drivers of these AI-based applications. Due to easy access and growing information workforce, these algorithms are extensively used in a plethora of industries ranging from healthcare, transportation, finance, legal systems, to even military. AI-tools have the potential to transform industries and societies through automation. Conversely, the undesirable or negative consequences of AI-tools have harmed their respective organisations in social, financial and legal spheres. As the use of these algorithms propagates in the industry, the AI-based decisions have the potential to affect large portions of the population, sometimes involving vulnerable groups in society. This chapter presents an overview of AI’s use in organisations by discussing the following: first, it discusses the core components of AI. Second, the chapter discusses common goals organisations can achieve with AI. Third, it examines different types of AI. Fourth, it discusses unintended consequences that may take place in organisations due to the use of AI. Fifth, it discusses vulnerabilities that may arise from AI systems. Lastly, this chapter offers some recommendations for industries to consider regarding the development and implementation of AI systems.