Shugang Li’s research while affiliated with Shanghai University and other places

Exploring the influence mechanism of user creativity in online learning community is beneficial for improving learning efficiency and increasing stickiness and loyalty of users to online learning community. But the current research on collaborative creation mainly focuses on the effectiveness and innovation of online learning, and lacks the research focusing on the impact of environmental factors like learning group members on online users’ creativity enthusiasm and even creativity. This paper addresses this research gap by exploring the influence of learning team factors like social presence and observational learning on creativity by using self-efficacy and intrinsic motivation as mediators. The theoretical model was validated with data collected from 242 online learning community users. This paper finds that social presence and observational learning have a positive impact on motivational factors, such as self-efficacy and intrinsic motivation, which, in turn, enhance user creativity. Their influences are moderated by challenging research discipline, level of team members and incentive.

For enterprises, exploring the influence mechanism of consumer satisfaction evaluation behavior (CSEB) holds significant research value for the advancement and further development of social e-commerce platforms. The existing literature primarily focuses on quantitative methods in investigating the influence mechanism of CSEB within social e-commerce platforms. This study endeavors to expand the theoretical boundaries of CSEB through qualitative research. This study adopts a mixed-methods approach, combining primary data collected through in-depth interviews with 32 participants and secondary data gathered from 1000 users via web crawlers. Utilizing grounded theory as an analytical framework, the study meticulously summarizes, concludes, and refines the influencing factors of CSEB. Based on these findings, a robust CSEB model is constructed to provide a deeper understanding of the phenomenon. The study reveals that in the decision-making process of consumer evaluation, behavior is primarily driven by evaluation motivations. These motivations are intricately intertwined with product perception, social influence, and perceived behavior control. The interplay among these factors significantly shapes the manner in which consumers engage in satisfaction evaluation on social e-commerce platforms. This study complements existing quantitative research by providing nuanced insights into the complex interplay of factors, which drive consumer evaluation behavior. Furthermore, the study proposes actionable countermeasures and suggestions for businesses and platform managers to effectively promote and enhance consumer satisfaction evaluation activities, thereby contributing to the sustained growth and development of social e-commerce platforms.

The integration of metaverse technology with digital collections of cultural heritage (DCCH) presents a novel paradigm for museum economics and consumer engagement. Despite the burgeoning interest in DCCH, a gap exists in understanding the mechanisms that drive consumers' sticky purchase intention (SPI) in this space. This study addresses this void by examining the role of self-concept triggering (SCT) and perceived scarcity within the metaverse, moderated by platform identity (PI). Utilizing a moderated mediation model with a sample of 763 respondents, we reveal that SCT significantly enhances perceived scarcity and SPI, processes that are positively moderated by PI. This research contributes to the literature by linking individual and group identities to consumer behavior in the metaverse. Our findings highlight the threshold effect of PI, offering museums and metaverse platforms a group-oriented approach to amplify individuals' SPI. This strategy leads to more effective utilization of DCCH, yielding both economic and engagement benefits.

In the digital age, online learning platforms utilizing advanced technologies such as artificial intelligence can provide more diverse and abundant educational resources compared to offline learning environments. However, online learners still face emotional expression and inertia trap problems. Knowledge co-creation virtual teachers (KCVTs) can solve the aforementioned difficulties, because they can increase the emotional interaction between learners and teachers and encourage learners to reduce inertia. But whether the above characteristics of KCVTs can affect learner creativity remains to be studied. Therefore, this study innovatively analyzes the impact of the technical characteristics of KCVTs, including the expertise of resource recommendation (ERR), familiarity with learners’ learning processes (FLLP) and heuristic interaction (HI), on learner creativity from a social facilitation perspective. Meanwhile, the mediating role of internal drive, namely perceived personalization (PP) and intrinsic motivation (IM), as well as the moderating effects of emotional support (ES) and incentive (IN), are considered. Ultimately, the proposed model is empirically tested using a sample of 296 users from existing learning communities. We find: (1) As the Social Facilitation theory suggests, the group factors of KCVTs influence creativity through different mechanisms: the ERR and HI of KCVTs stimulate learners’ creativity (R² = 0.46) through their PP (R² = 0.59) and IM (R² = 0.43), while the FLLP provided by KCVTs fosters learner creativity (R² = 0.46) through their PP (R² = 0.59) in online learning communities. (2) The interactive atmosphere and learning evaluation methods in online communities’ impact creativity. The ES (R² =0.333) provided by KCVTs and the IN (R² =0.348) from online learning communities enhance learners’ spontaneity in generating intrinsic motivation to overcome barriers, thereby fostering creativity. This study extends the social facilitation theory to the context of online learning, enriches the theoretical framework of individual creativity influence mechanisms and offers practical implications for the design and optimization of KCVTs in online learning platforms.

Peer-to-peer (P2P) online experience offers low-cost immersive cultural experiences, but enterprises fear that it will negatively affect local tourism marketing. This study proposes a sequential mediation model to explore how P2P online experience stimulates travel intentions and attraction recommendations. We find that P2P online experience increases cultural presence, which in turn leads to higher situational empathy. This increased empathy then contributes to predicted tourism experience, which ultimately shapes behavioral intentions toward travel and attraction recommendations. Furthermore, the study finds that the impact of P2P online experience on behavioral intentions is stronger for consumers with lower cultural cognitive levels of tourist attractions. Our findings demonstrate the importance of P2P online experience on cultural presence and situational empathy in shaping travel behavior.

Brand equity co-creation can drive up platform value. In real world, celebrities play a crucial role in influencing brand equity co-creation. However, in metaverse celebrity-infused entertainment activities, there is a research gap in the impact of celebrities on brand equity co-create intention, which is crucial for marketing. Through a moderated mediation model, we examine the impact of celebrity virtual image and celebrity real-life influence on experience co-creation (parasocial relationship, presence) and further analyze the impact of experience co-creation on brand equity co-create intention. Additionally, we explore the moderating effect of celebrity real-life influence. Our study provides theoretical contributions by linking celebrity features to brand equity co-create intention from a virtual experiential-dominant perspective. We also find that celebrity real-life influence has a virtual-reality continuum and the characteristic of implicit priming moderating effect. We suggest that new media marketing practitioners recognize that metaverse celebrity virtual image branding and endorsement can be cost-effective while ensuring effectiveness.

In today’s increasingly competitive globalization, innovation is crucial to technological development, and original innovations have become the high horse in the fight for market dominance by enterprises and governments. However, extracting original innovative technologies from patent data faces challenges such as anomalous data and lengthy analysis cycles, making it difficult for traditional models to achieve high-precision identification. Therefore, we propose a Multi-Dimensional Robust Stacking (MDRS) model to deeply analyze patent data, extract leading indicators, and accurately identify cutting-edge technologies. The MDRS model is divided into four stages: single indicator construction, robust indicator mining, hyper-robust indicator construction, and the pioneering technology analysis phase. Based on this model, we construct a technological development matrix to analyze core 3D-printing technologies across the industry chain. The results show that the MDRS model significantly enhances the accuracy and robustness of technology forecasting, elucidates the mechanisms of technological leadership across different stages and application scenarios, and provides new methods for quantitative analysis of technological trends. This enhances the accuracy and robustness of traditional patent data analysis, aiding governments and enterprises in optimizing resource allocation and improving market competitiveness.

This paper addresses the inverse optimal security control problems for a class of stochastic non-strict feedback nonlinear cyber-physical systems under sensor and actuator attacks. The concerned system model includes both stochastic disturbances and more general nonlinearity. First, to make the control design feasible, a linear state transformation is applied to the attacked system. Furthermore, in the process of backstepping design, based on the Nussbaum gain function formula, fuzzy logic system approximation method, and inverse optimal control theory, combing the available output signal, an output feedback inverse optimal controller is proposed. Specifically, the designed controller not only ensures that the system is secure under network attacks but also optimal in terms of the cost function. Finally, two physical examples are given to verify the effectiveness of the proposed control scheme in various network attacks.