The product quality issues of online shopping have not been effectively solved with the rapid development of electronic commerce in China. Faced with the quality issues of online shopping, the main entities, such as online platforms and online sellers, should cooperate to control and assure compliant quality. The adverse selection of buyers caused by information asymmetry and the moral hazard of sellers aggravated by bilateral markets make the quality management of online shopping have a high degree of uncertainty and complexity. This study constructs an evolutionary game model to discuss the dynamic process of the quality behaviours of members and analyse the key triggers of the evolutionary directions in online shopping. By using numerical simulation analysis, the influences of government supervision intensities on the evolution trend of members’ quality behaviours are analysed with various risk attitude combinations in various cases. The current research finds that (1) the decrease of the quality control cost and the increase of the government’s benchmark fine are conducive to increasing the platform’s controlling probability; (2) the decrease of the proportion of rent and the increase of sellers’ fraud cost and the government’s benchmark fines are conducive to increasing the seller’s compliance probability; (3) when both the platform and seller are unwilling to make efforts for quality assurance, risk‐seeking members make the seller gradually choose to provide compliant products; and (4) when the members are all risk‐seeking, strengthening government supervision will help promote the transition of members’ behavioural choices in a benign direction. This paper enriches the theoretical research studies on both online product quality control and government‐led social governance system of quality. Suggestions for government supervision and e‐commerce entities are also provided for quality assurance and improvement.

Industrialized processing has increased the complexity of the food supply chain. Concerns about food-related risks have increased consumer interest in food traceability. Traceability systems are regarded as effective tools for mitigating information asymmetry and enhancing food quality and safety. However, the design of traditional food traceability systems overlooks the risk of information overload. Based on information overload theory, this study designs an artificial intelligence (AI) traceability assistant as an innovative tool to optimize traditional food traceability systems and examines its positive effects. This study focuses on prepared foods as the research objects, selecting three types of prepared foods (Kung Pao chicken, fish-flavored shredded pork, and pickled fish) and three food traceability tasks (preservatives, sweeteners, and drug residues) as experimental stimuli. Through three online scenario experiments, 747 valid responses were collected. This study explores the impact of AI traceability assistant design on positive consumer engagement behaviors and its underlying mechanism. The results reveal that the AI traceability assistant significantly promotes positive consumer engagement behaviors. This positive effect is mediated by perceived system ease of use. Furthermore, perceived product risk positively moderates the impact of the AI traceability assistant on perceived system ease of use. Perceived product risk strengthens the mediating effect of perceived system ease of use. This study contributes a novel theoretical perspective for research on food traceability systems and reveals the underlying mechanism through which the AI traceability assistant exerts its positive effect. In practice, it provides actionable guidance for food producers implementing digital traceability solutions.

Native advertising, a form of online advertising designed to blend seamlessly with the form and function of platform content, is increasingly favored by vendors and publishers. We analyzed how a vendor can use native ads on a platform to stimulate consumer search and purchases. Our model focuses on the vendor's key strategic levers—ad entertainment and targeting—and on how the platform's regulation of ad ambiguity affects these decisions. The study yielded three main findings. First, ambiguity affects the vendor's entertainment and targeting decisions in a region-dependent manner. In particular, although the effect of ambiguity on entertainment depends on the regulatory region, higher ambiguity may expand the vendor's targeted consumer base through the associated pricing decision: some consumers are drawn into search by ambiguity rather than entertainment, while pricing converts a broader set of searchers into buyers. Second, a platform that maximizes economic profit never regulates ambiguity; however, this result no longer holds when the platform considers reputational harm. When consumers have low aversion to advertising, deception costs dominate and the platform adopts strict regulation; when aversion is high, hassle harm dominates and the platform opts for lenient regulation. We further showed that standard contracts, such as profit-sharing and cost-per-click contracts, are generally ineffective in achieving the platform's goals, which explains why direct regulation is essential. Nevertheless, a cost-per-click (CPC) contract may coincide with the government's welfare objective in parameter regions where the CPC-induced ambiguity choice matches the welfare-maximizing boundary. Third, when native-ad content is restricted, stricter regulation can improve social welfare if the quality gap is sufficiently large.