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.
Citation: Yafei Yu, Decheng Wen, Xiao Chen. Ambiguity or transparency? A model for regulating native advertising, entertainment, and targeting decisions[J]. Journal of Industrial and Management Optimization, 2026, 22(8): 3897-3925. doi: 10.3934/jimo.2026139
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.
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Yafei Yu, Decheng Wen, Xiao Chen. Ambiguity or transparency? A model for regulating native advertising, entertainment, and targeting decisions[J]. Journal of Industrial and Management Optimization, 2026, 22(8): 3897-3925. doi: 10.3934/jimo.2026139
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