Siew Imm Ng

Mobile live-streaming commerce has quickly changed digital shopping. It lets people interact in real time, see products in different ways, and connect with others through mobile apps. Even though many use these platforms, keeping users engaged remains a major challenge for designers and retailers. This study uses the elaboration likelihood model (ELM) and the stimulus–organism–response (SOR) framework to examine how interactive mobile interface cues affect customer engagement and purchase behaviour in mobile live-commerce settings. This study examined six interface stimuli: product information quality, streamer interaction quality, streamer credibility, bullet-screen consistency, monetary incentives, and popularity cues. Susceptibility to informational influence (SII) was included as a moderating factor. Data came from 500 Chinese Millennial mobile live-streaming shoppers and were analysed using partial least squares structural equation modelling (PLS-SEM). The findings show that all six interface cues help increase customer engagement. Streamer interaction quality and product information quality have the biggest impact. Customer engagement strongly predicts purchase intention and completely mediates the link between each interface cue and purchase intention. Furthermore, SII significantly amplifies the effects of streamer credibility, bullet-screen consistency, and popularity cues on user engagement but does not moderate the effects of information quality, interaction quality, or monetary incentives. These findings advance mobile human-computer interaction (HCI) research by elucidating how interactive interface features influence user engagement and by offering practical guidance for designing more engaging and personalised mobile live-commerce applications.

This study investigates the moderating role of blockchain traceability adoption in enhancing consumer engagement and purchase intention within live-streaming agricultural e-commerce platforms in China. Drawing upon the Stimulus-Organism-Response (S-O-R) framework operationalized at the aggregate market level and information asymmetry theory, this research employs longitudinal market-level time-series data spanning 2019 to 2024, utilizing hierarchical regression analysis with Hayes's conditional process framework to examine main effects, mediation mechanisms, and moderation relationships. The empirical findings reveal that platform development and information transparency exert significant positive effects on market purchase behavior, with consumer engagement serving as a partial mediating mechanism transmitting these effects. The moderation analysis demonstrates that blockchain traceability adoption significantly strengthens the relationships between platform stimuli and consumer engagement, with the information transparency pathway exhibiting substantially stronger moderation effects than the platform development pathway, demonstrating that blockchain technology functions as a selective trust-enhancing mechanism that validates quality signals rather than operating as a general platform enhancer—a distinction representing the central empirical contribution of this study. These findings extend the traditional S-O-R framework by incorporating technological infrastructure as a boundary condition shaping stimulus effectiveness at the market level, while providing practical guidance for platform operators and policymakers to prioritize blockchain traceability infrastructure investment in conjunction with transparency enhancement initiatives for promoting high-quality development of agricultural live streaming e-commerce.

Background/Objectives: Smart home technologies can mitigate the safety risks faced by older adults living alone, yet adoption remains constrained by privacy concerns. Existing research has established that privacy apprehensions deter adoption, but the conditions under which this deterrent effect strengthens or weakens are poorly understood. Drawing on privacy calculus theory, this study examines how health status and children’s support moderate the negative relationship between privacy concerns and smart home adoption intention among solo-dwelling older adults. Methods: A cross-sectional survey of 326 older adults living alone in three Chinese cities was analyzed using partial least squares structural equation modeling. Results: Privacy concerns suppressed adoption intention (β = −0.281, p < 0.001) while perceived safety benefits promoted it (β = 0.358, p < 0.001). Both moderation hypotheses were supported: the deterrent effect of privacy concerns was weaker among participants in poorer health (interaction β = −0.091, p = 0.039) and among those receiving stronger children’s support (interaction β = 0.137, p = 0.001), with the latter reducing the negative effect by approximately two-thirds. Conclusions: These findings demonstrate that the privacy–safety trade-off is contingent rather than fixed and inform health-differentiated privacy design and family co-management strategies for more inclusive smart home adoption.

The authors would like to make the following corrections to the published paper

Background: Air traffic control is a stressful job and vital to aviation safety. Although technological developments have been introduced to enhance and facilitate the tasks of air traffic control officers (ATCOs), ATCOs still experience high levels of job stress. This study explores the influence of mindfulness and social work support (SWS) on the job performance and job stress of ATCOs in Saudi Arabia. Methods: Grounded in Job Demands–Resources (JDR) theory, this study used a cross-sectional design to survey 324 ATCOs, with a 72% response rate. Mindfulness and SWS were treated as individual and situation-specific resources that influence stress and performance outcomes. Results: The results indicated that mindfulness could reduce workplace stress and improve performance. Moreover, SWS was also critical in reducing the adverse impacts of stress on job performance, reflecting the buffering effect posited by JDR theory. Conclusions: This study demonstrates that JDR theory is applicable to the context of ATC since it validates the importance of mindfulness and SWS as critical resources in minimizing stress levels and improving performance. The findings have implications for the viability of mindfulness-based training interventions and peer-support programs in supporting the health of ATCOs and their ability to deal with highly stressful situations.

Air traffic controllers (ATCOs) make a significant contribution to ensuring flight safety, making this profession a highly stressful job globally. Job demands–resources (JDR) theory proposes a health impairment process stemming from job demand (complexity) to mental workload, which causes job stress, resulting in compromised flight safety. This vicious cycle is evident among ATCOs and is recognized as an unsustainable management practice. To curb this process, we propose intra-functional flexibility as a conditional factor. Intra-functional flexibility refers to the flexibility in the reallocation and coordination of resources among team members to help in urgent times. This is a relatively new concept and is yet to be empirically tested in the ATCO context. ATCOs work in a dynamic environment filled with sudden surges of urgent jobs to be handled within short time limits. Intra-functional flexibility allows standby crews to be called to ease these tensions when needed. To ascertain the role of intra-functional flexibility in mitigating health impairment among ATCOs, a questionnaire was administered to 324 ATCOs distributed across Saudi Arabia. Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis exhibited two critical findings: First, the study revealed the prevalence of a vicious cycle of health impairment among Saudi ATCOs, whereby job complexity leads to increased mental workload, resulting in elevated levels of job stress. Secondly, the presence of intra-functional flexibility weakened this vicious cycle by mitigating the influence exerted by mental workload on job stress. That is, the mediation-moderated model proposed in this study provides empirical evidence supporting the applicability of intra-functional flexibility in mitigating the dire suffering of ATCOs. This study discusses limitations and future research directions in the end.

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

The rapid rise of chat generative pre-trained transformer (Chat GPT) has brought huge opportunities for e-commerce platforms to use it for consumer communication and service. This paper proposes a research framework to expand the value-attitude-behavior model by integrating the theory of consumption values. The purpose is to explore key consumption values that affect consumers' attitudes and their continuance intention to adopt ChatGPTdriven chatbots (i.e., LazzieChat) in the e-commerce services context while examining the moderating effect of online shopping self-efficacy in this process. A questionnaire survey was conducted in Singapore, involving 305 participants who had experienced LazzieChat in Lazada. Use the structural equation modelling of Smarts 4 to analyze the data. The results show that consumer 'attitudes towards LazzieChat have significantly affected their continuance intention to adopt LazzieChat, and online shopping self-efficacy has moderated this relationship. In addition, the research confirms that emotional value and epistemic value are the main driving factors of consumer 'attitudes, followed by functional value, social value and epistemic value. Consumers' attitudes towards LazzieChat mediate the relationship between functional value, social value, epistemic value, and continuance intention to adopt LazzieChat. By developing a research framework that follows the logic of value-attitude-behavior model, this study provides theoretical and practical insights based on the theory of consumer values, which could guide e-commerce platforms to more effectively cater to their consumers' continuance intention of adopting ChatGPT-driven ecommerce AI chatbot service.