Exploring user behaviours on mobile technologies combined with payment functions during the COVID-19 pandemic

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information TechnologiesWith the extensive spread of smart mobile devices, mobile technologies and services have revolutionised and pervaded significantly in most aspects of human life, such as social communication, commerce, entertainment, etc. Various industries have integrated services and products with mobile financial transaction technologies, facilitating the payment services combined with various mobile applications. The wide adoption of mobile transactions has increased the efficiency of transaction processes, met the expectations of customers and the requirements of enterprises, and supported the social-economic development in different scenarios, especially under the pandemic situation. Understanding mobile device users’ perceptions and behaviours on mobile technologies combining payment functions under the COVID-19 pandemic situation has reinforced the need to embark on a deeper investigation of customer behaviours during the pandemic. For these reasons, this study contributes to the advancement of knowledge and implementation methods for a better understanding of the determinants of customers’ behavioural intentions of using mobile technologies combined with payment functions in a total of seven separate studies. The investigation begins with a systematic literature review on mobile payment studies presented in chapter two. This research is augmented by investigating users’ continuance usage intention of mobile payments under the COVID-19 pandemic in chapter three. The fourth chapter analyses the determinants of continuance usage intention of food delivery apps during the pandemic. Chapters five and six present two theoretical development studies about the Unified Theory of Acceptance and Use of Technology (UTAUT) and UTAUT2, respectively. The seventh chapter investigates customers’ psychological shopping processes via live-streaming shopping apps during the pandemic lockdown period. In epistemological terms, this study involved conjoint positivist and interpretivist research in behavioural information systems research. A qualitative research method was applied in chapters two, five and six, and a quantitative research method was implemented in the third, fourth and seventh chapters. The main theoretical foundations applied and validated in three empirical studies were UTAUT and UTAUT2. Specifically, chapter three integrates UTAUT with Mental Accounting Theory, the fourth chapter combines UTAUT with the Expectancy Confirmation Model, and chapter seven integrates UTAUT2 with the Stimulus-Organism-Response framework and Flow theory. This study found that performance expectancy, social influence, and trust significantly affect users’ behavioural intentions in all three empirical studies. Customers’ mental cognitions, such as perceived benefits, satisfaction, flow and perceived value, positively formulate users’ behavioural intention in the three studies, respectively. Hedonic motivation and flow significantly influence users' behavioural intention when mobile technologies contain payment and entertainment features. Moreover, this study contributes several theoretical and practical implications. This study facilitates the advancement of knowledge of mobile technologies adoption through three verified theoretical frameworks and two proposed developed theoretical models and appropriate measurement methods. Meanwhile, this study supports relevant stakeholders in mobile technologies, enterprises, policymakers, service providers, and marketing departments with valuable findings and discussions for comprehensively understanding the determinants of customers’ behaviours on mobile technologies combined payment function

How does gender moderate customer intention of shopping via live-streaming apps during the COVID-19 pandemic lockdown period?

Zhao, Y., & Bacao, F. (2021). How does gender moderate customer intention of shopping via live-streaming apps during the COVID-19 pandemic lockdown period? International Journal of Environmental Research and Public Health, 18(24), 1-24. [13004]. https://doi.org/10.3390/ijerph182413004Shopping through Live-Streaming Shopping Apps (LSSAs) as an emerging consumption phenomenon has increased dramatically in recent years, especially during the COVID-19 lockdown period. However, insufficient studies have focused on the psychological processes undergone in different customer demographics while shopping via LSSAs under pandemic conditions. This study integrated the Unified Theory of Acceptance and Use of Technology 2 with Flow Theory into a Stimulus-Organism-Response framework to investigate the psychological processes of different customer demographics during the COVID-19 lockdown period. A total of 374 validated data were analyzed by covariance-based structural equation modelling. The statistical results demonstrated by the proposed model showed a significant discrepancy between different gender groups, in which Flow, as a mediator, representing users’ engagement and immersion in shopping via LSSAs, was significantly moderated by gender where connection between stimulus components, hedonic moti-vation, trust and social influence and response component perceived value are concerned. This study contributed a theoretical development and a practical framework to the explanation of the mental processes of different customer demographics when using an innovative e-commerce tech-nology. Furthermore, the results can support the relevant stakeholders in e-commerce in their com-prehensive understanding of customers’ behavior, allowing better strategical and managerial de-velopment.publishersversionpublishe

An investigation on users’ perspective under the COVID-19 pandemic

Zhao, Y., & Bacao, F. (2021). How does the pandemic facilitate mobile payment? : An investigation on users’ perspective under the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 18(3), 1-22. [1016]. https://doi.org/10.3390/ijerph18031016Owing to the convenience, reliability and contact-free feature of Mobile payment (M-payment), it has been diffusely adopted in China during the COVID-19 pandemic to reduce the direct and indirect contacts in transactions, allowing social distancing to be maintained and facilitating stabilization of the social economy. This paper aims to comprehensively investigate the technological and mental factors affecting users’ adoption intentions of M-payment under the COVID-19 pandemic, to expand the domain of technology adoption under the emergency situation. This study integrated Unified Theory of Acceptance and Use of Technology (UTAUT) with perceived benefits from Mental Accounting Theory (MAT), and two additional variables (perceived security and trust) to investigate 739 smartphone users’ adoption intentions of M-payment during the COVID-19 pandemic in China. The empirical results showed that users’ technological and mental perceptions conjointly influence their adoption intentions of M-payment during the COVID-19 pandemic, wherein perceived benefits are significantly determined by social influence and trust, corresponding with the situation of pandemic. This study initially integrated UTAUT with MAT to develop the theoretical framework for investigating users’ adoption intentions. Meanwhile, this study originally investigated the antecedents of M-payment adoption under the pandemic situation and indicated that users’ perceptions will be positively influenced when technology’s specific characteristics can benefit a particular situation.publishersversionpublishe

Extending the Flow Theory with Variables from the UTAUT2 Model

Zhao, Y., & Bacao, F. (2020). Theoretical Development: Extending the Flow Theory with Variables from the UTAUT2 Model. In 2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020 (pp. 2427-2431). [9345049] (2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCC51575.2020.9345049According to the dramatic development of innovative information technology in worldwide ranges, business climate has changed from traditional commerce to virtual commerce in recent two decades. It is important to synthetically understand customers' adoption intention of new technology for better business management and strategy involved with information technology. Thus, this study extends the Flow theory by integrating variables from the revised Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model and satisfaction to propose a theoretical development for investigating the factors determining customers' behavioral intention on adopting new information technology. In addition, the proposed theoretical development contributes the relevant researches on systematical understanding customers' adoption intention determined from technological perceptions to mental cognition. Moreover, the proposed framework and measurement method can be applied as reference for relevant researchers and stakeholders to investigate customers' behaviors for further research and future business management and strategy.authorsversionpublishe

Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds

Semantic segmentation in 3D indoor scenes has achieved remarkable performance under the supervision of large-scale annotated data. However, previous works rely on the assumption that the training and testing data are of the same distribution, which may suffer from performance degradation when evaluated on the out-of-distribution scenes. To alleviate the annotation cost and the performance degradation, this paper introduces the synthetic-to-real domain generalization setting to this task. Specifically, the domain gap between synthetic and real-world point cloud data mainly lies in the different layouts and point patterns. To address these problems, we first propose a clustering instance mix (CINMix) augmentation technique to diversify the layouts of the source data. In addition, we augment the point patterns of the source data and introduce non-parametric multi-prototypes to ameliorate the intra-class variance enlarged by the augmented point patterns. The multi-prototypes can model the intra-class variance and rectify the global classifier in both training and inference stages. Experiments on the synthetic-to-real benchmark demonstrate that both CINMix and multi-prototypes can narrow the distribution gap and thus improve the generalization ability on real-world datasets

A Cost-effective Shuffling Method against DDoS Attacks using Moving Target Defense

Moving Target Defense (MTD) has emerged as a newcomer into the asymmetric field of attack and defense, and shuffling-based MTD has been regarded as one of the most effective ways to mitigate DDoS attacks. However, previous work does not acknowledge that frequent shuffles would significantly intensify the overhead. MTD requires a quantitative measure to compare the cost and effectiveness of available adaptations and explore the best trade-off between them. In this paper, therefore, we propose a new cost-effective shuffling method against DDoS attacks using MTD. By exploiting Multi-Objective Markov Decision Processes to model the interaction between the attacker and the defender, and designing a cost-effective shuffling algorithm, we study the best trade-off between the effectiveness and cost of shuffling in a given shuffling scenario. Finally, simulation and experimentation on an experimental software defined network (SDN) indicate that our approach imposes an acceptable shuffling overload and is effective in mitigating DDoS attacks

Study on Optimized Elman Neural Network Classification Algorithm Based on PLS and CA

High-dimensional large sample data sets, between feature variables and between samples, may cause some correlative or repetitive factors, occupy lots of storage space, and consume much computing time. Using the Elman neural network to deal with them, too many inputs will influence the operating efficiency and recognition accuracy; too many simultaneous training samples, as well as being not able to get precise neural network model, also restrict the recognition accuracy. Aiming at these series of problems, we introduce the partial least squares (PLS) and cluster analysis (CA) into Elman neural network algorithm, by the PLS for dimension reduction which can eliminate the correlative and repetitive factors of the features. Using CA eliminates the correlative and repetitive factors of the sample. If some subclass becomes small sample, with high-dimensional feature and fewer numbers, PLS shows a unique advantage. Each subclass is regarded as one training sample to train the different precise neural network models. Then simulation samples are discriminated and classified into different subclasses, using the corresponding neural network to recognize it. An optimized Elman neural network classification algorithm based on PLS and CA (PLS-CA-Elman algorithm) is established. The new algorithm aims at improving the operating efficiency and recognition accuracy. By the case analysis, the new algorithm has unique superiority, worthy of further promotion