Dr. Jun Wei

Production-level Autonomous Driving Systems (ADSs), such as Google Waymo and Baidu Apollo, typically rely on the multi-sensor fusion (MSF) strategy to perceive their surroundings. This strategy increases the perception robustness by combining the respective strengths of the cameras and LiDAR, directly affecting the safety-critical driving decisions of autonomous vehicles (AVs). However, in real-world autonomous driving scenarios, both cameras and LiDAR are prone to various faults that can significantly impact the decision-making and subsequent behaviors of ADSs. It is important to thoroughly test the robustness of MSF during development. Existing testing methods only focus on the identification of corner cases that MSF fails to detect. However, there is still a lack of investigation on how sensor faults affect the system-level behaviors of ADSs. To address this gap, we present FADE, the first testing methodology to comprehensively assess the fault tolerance of MSF perception-based ADSs. We systematically build fault models for both cameras and LiDAR in AVs and inject these faults into MSF-based ADSs to test their behaviors in various testing scenarios. To effectively and efficiently explore the parameter spaces of sensor fault models, we design a feedback-guided differential fuzzer to uncover safety violations of ADSs caused by the injected faults. We evaluate FADE on Baidu Apollo, a representative and practical industrial ADS. The evaluation results demonstrate the practical values of FADE, and disclose some useful findings. We further conduct physical experiments using a Baidu Apollo 6.0 EDU AV to validate these findings in real-world settings.

Purpose - The metaverse enables users to create their own avatars in a shared virtual space, giving rise to a new avatar personality that differs from their real-self personality. The aim of this research is to explore how users' real-self and avatar personalities may affect their behavioral engagement and satisfaction in the metaverse context. Design/methodology/approach - This research applies self-discrepancy theory to investigate how the big five traits of both real-self and avatar personalities influence users' engagement and satisfaction in the metaverse. The present research employed a mixed-methods approach, beginning with a qualitative study to identify prevalent personality cues among users on metaverse social media platforms. Subsequently, a quantitative study was conducted to further validate the findings of the qualitative study. Findings - The results indicated that avatar personality scored higher than the real-self personality in the dimensions of openness, conscientiousness and extraversion, while scored lower in the dimensions of agreeableness and neuroticism. Both real-self and avatar personality traits positively influenced metaverse satisfaction via behavioral engagement in the metaverse. Notably, avatar personality traits had a stronger impact on behavioral engagement compared to real-self personality traits, which further influence metaverse satisfaction. Practical implications - The present study offers practical insights for metaverse developers and managers to enhance user satisfaction by focusing on users' big five traits of both real-self and avatar personality. It suggests implementing personalized tools, organizing personality-based social activities and other initiatives to encourage user's behavioral engagement and ultimately enhance metaverse satisfaction. Originality/value - Unlike existing research that concentrates on a single facet of personality traits, this research employs a mixed-methods approach to conceptualize users' real-self personality and avatar personality, further exploring their impacts on metaverse satisfaction.

Considering the technological and institutional nature, along with the inherent complexity, of the rural digital governance context, a data-driven institutional pressure transmission (D-IPT) mechanism is developed in this study. This mechanism aims to facilitate effective regulation of villagers' behaviours, thereby improving rural digital governance performance. Firstly, we proposed a conceptual model based on complex adaptive systems theory and institutional theory to demonstrate how this mechanism can ensure an improvement of the emergent rural digital governance performance. Subsequently, we translated the conceptual model into a computational representation, thereby constructing a theory-informed simulation model. A behavioural dataset consists of 1,255,206 rural households from 119 villages over a span of 18 months was used to instantiate and validate the simulation model based on a specific scenario. Then, we designed five simulation experiments to investigate interventions aimed at harnessing the D-IPT mechanism more effectively to improve the rural digital governance performance. The results show that interventions targeting interaction efficiency, interaction frequency, and institutional environment configuration significantly affect the role of the D-IPT mechanism in enhancing rural governance performance. Finally, we discussed the theoretical and practical implications of the D-IPT mechanism.
•A D-IPT mechanism is developed to improve rural digital governance performance through effective behavioural regulation.•Based on CAS theory and institutional theory, a conceptual model is built to demonstrate the role of the D-IPT mechanism.•The transmission of coercive, normative and mimetic institutional pressures among rural agents is quantified by ABM method.•Interventions targeting interactions among agents and the environment, are explored to better utilize the D-IPT mechanism.

Despite a large number of retailers have embraced the voice shopping channels, the factors that determine consumers' voice shopping value co-creation intentions are still unclear. This study explores the impacts of two social cognition dimensions (competence and warmth) on voice shopping value co-creation intention from the perspectives of confidence in consumers themselves and in AI. Based on the social exchange theory and stimulus-organism-response, a research model was developed and empirically examined by data collected from 363 students at a university in China. The results of structural equation modeling (SEM) analysis indicate that perceived competence and perceived warmth positively affect confidence in consumers themselves and in AI and consequently determine voice shopping value co-creation intention. In addition, the impacts of confidence in consumers themselves and in AI on voice shopping value co-creation intention varies. The results of the fuzzy set qualitative comparative analysis (fsQCA) method further validate these findings.

A green manufacturing supply chain can improve ecological environments and resource utilization in enterprises. The objective of this paper is to assess green supply chain (GSC) risks in manufacturing to help enterprises increase value. Specifically, based on theoretical analysis and literature review, the risk assessment model of green SC in manufacturing is put forward through questionnaire survey data and Principal Component Analysis. Furthermore, to improve the effectiveness of the risk assessment model obtained from Principal Component Analysis, principal component analysis-based Fuzzy Mature-Element (FME) method is used to form the risk assessment model for green SC in manufacturing. Finally, the FME model on manufacturing green SC risk assessment is tested through the simulation of BP neural network. Some findings present that the proposed risk evaluation system of manufacturing green SC is appropriate and the FME based comprehensive evaluation method is reasonable.

Purpose: This research aims to investigate the impact of the collaborative governance mechanism on the implementation of rural governance information systems in developing countries. By integrating institutional logic theory, affordance theory and social identity theory, the authors propose modeling grassroots officials' affordance perception process and exploring the importance of multi-identities' information technology (IT) goals in affordance perception.
Design/methodology/approach: Through an exploratory case study, the authors identified three affordances of rural governance information systems and investigated the mechanisms influencing the perception of these affordances among grassroots officials. Next, the authors established a research model and collected 490 valid questionnaires from grassroots officials in China and analyzed the data using structural equation modeling (SEM).
Findings: The authors' study challenges previous assumptions by integrating institutional logic, affordance and social identity theories. The authors establish a perceived affordance path for rural governance information systems, recognize substitute effects among technological affordances and extend the theory to explain social factors influencing IT perception. The authors' findings suggest providing technical training for grassroots officials to enhance IT capabilities, and governments should prioritize essential functionalities in rural governance information systems to optimize resources. Training on collaborative governance mechanisms can improve IT affordance perception, enhancing digital platform utilization in governance processes.
Research limitations/implications: The study was conducted mainly in China, and therefore, the findings may not be universal to other developing countries. Researchers are therefore encouraged to test the proposal in locations with different rural cultures.
Practical implications: The paper includes implications for the development of IT perception in rural governance, the development of affordance perception theory and studying the relationship between IT goals and affordance perception.
Originality/value: Overall, this paper addresses the need to understand how grassroots officials perceive IT affordances in rural governance and study the relationship between multi-identities' goals and affordance perception.

The purpose of the study is to examine the impact factors of 11 high impact practices (HIPs) of the Association of American Colleges and Universities to identify their influences on the learning experience of college students. A research model is developed based on the theory of planned behaviour to link beliefs of high impacts to the practice of learning behaviours. Structure equation modelling analysis was conducted on the performance metrics of 11 American universities from 2014-2021. Findings indicate that undergraduate research can predict the success of other high-impact practices in Florida. The results will guide administrators in higher education to make financial decisions with a reference to factors of high impact practices. Theoretical and practical implications are also presented.

This paper aims at developing a user-friendly mobile framework to guide mobile shopping via the guided position system (GPS) technology. Specifically, it first studied usability features that are crucial to mobile shopping via GPS by developing a framework with integrated user-friendly features. Second, 23 data flows are mapped and identified based on the framework to illustrate how to integrate these usability features into development. Third, the system analysis is performed based on both dynamic modelling (using data flow diagramming) and static modelling (using entity-relation diagramming). Fourth, the system design including database design and user interface design are performed. Finally, a prototype is developed with usability testing. This study concludes that the mobile shopping guided by GPS with usability emphasis is crucial to develop the efficient mobile shopping systems.

This paper proposed an indexed matrix to assess information security risks' impacts on mobile social media to help organisations address security risks effectively and efficiently. It developed and integrated a static social media information security risk model and a dynamic social media information flow model in the mobile environment to investigate the potential security risks in social networks. The pattern of security risks with three levels was identified using cluster analysis. An indexed matrix is also proposed to assess the information security impacts on social media via the relative-weigh quantitative method. The results can help to develop an effective and safe way to transmit information through social media. This research is the first attempt to integrate static information security threats into the dynamic social media information flow processes to assess the information security risk levels by developing a new indexed method. The theoretical and practical implications are presented.

Purpose – This paper aims to develop a set of tactical electronic business solutions for the electronic credit card issuing industry.

Design/methodology/approach – Specifically, a strategic credit card issuing (SCCI) model is developed to analyze e-business in the credit card issuing industry. Second, a set of tactical solutions is derived on the basis of the SCCI model. Third, pattern analysis is conducted on the basis of data collected from dominant credit card issuing companies to further investigate the implementation status on these electronic business solutions in the credit card issuing industry.

Findings – The findings show that three categories of electronic business solution items can be classified. The average variability of electronic business implementation patterns for business-to-business, business-to-customer and business-to-internal in each company shows a variety of electronic business strategies implemented by these dominant companies.

Originality/value – The results will help managers and executives when they make strategic and tactical decisions on electronic business in the credit card issuing industry.