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Artificial intelligence (AI) is advancing rapidly. The achievements of Chinese tech firms have impressed overseas investors and prompted the United States to step up containment efforts aimed at slowing China’s AI development. Prof. Jack Jiang, the Padma and Hari Harilela Professor in Strategic Information Management at HKU Business School, said China’s AI large models are already world-leading and the gap with the United States is narrowing.

Data donations, where individuals are encouraged to donate their personal information, have the potential to advance medical research and help limit the spread of pandemics, among other benefits. The decision to donate data is fundamentally a privacy decision. In this research, we build on the privacy calculus, a model describing privacy risks and benefits, and examine the impact of privacy concerns on data donation decisions, highlighting the role of societal benefits in privacy decisions. Based on two randomized experiments using the general context of data donation for medical research (experiment 1) and the specific context of data donation for COVID-19 research (experiment 2), we find that individuals who are highly concerned about privacy tend to donate less data (experiments 1 and 2). This effect holds under a variety of conditions and is consistent with prevailing research. However, this effect is contingent on the privacy calculus. When implicit or explicit societal benefits are perceived, particularly in the absence of privacy controls, the association between privacy concerns and data donation decisions is less salient, highlighting the significant role that societal benefits have in privacy decisions. We discuss the theoretical, practical, social, and ethical implications of these findings.

AI image generation is rapidly evolving, driving innovation in marketing, advertising design, and art creation. HKU Business School’s Professor of Innovation and Information Management, Prof. Zhenhui Jack Jiang, along with his research team, recently assessed 22 AI models. They reviewed the performance and potential risks of AI models in image generation.

Online video platforms face the challenge of balancing the needs of their users with those of their advertisers. Although users typically prefer to have less intrusive ads, advertisers aim to effectively catch user attention. This paper investigates how the provision of ad choice affects the effectiveness of video advertising. We argue that allowing users to choose an ad to view may trigger a “conjecture-formation-and-confirmation” process that motivates users to pay more attention to the selected ad. Two online experiments and four laboratory experiments are conducted to test the theorized underlying mechanism of the ad choice effect. Study 1 finds when users are unfamiliar (versus familiar) with the content of ad options (i.e., they need to make conjectures about ad content), ad choice is more likely to increase user attention to the chosen ad. Study 2 and Study 3 show that the impact of ad choice on user attention is more likely to be positive when users are enabled to make conjectures about ad content, such as when choice options provide more relevant information about ad content. Study 4a and Study 4b provide more direct support for the underlying mechanism by showing that the ad choice effect is attenuated when users cannot form conjectures about ad content at the choice stage. Study 5 further demonstrates that the positive effect of ad choice is robust across different ad settings. Taken together, these studies show ad choice is more likely to boost the effectiveness of video advertising when the “conjecture-formation-and-confirmation” process is triggered.

Since ChatGPT was introduced, large language models (LLMs) have quickly become a focus in the global tech competition. LLMs are being applied in various fields, presenting ample opportunities for AI development. The U.S. currently leads in technology development and innovation, with its models excelling at the technological forefront. In contrast, Chinese models focus on optimizing for local languages and practical applications. Striking a balance of competition and cooperation between the two countries will be crucial in the coming years.

Many organizations have adopted internet monitoring to regulate employees’ cyberloafing behavior. Although one might intuitively assume that internet monitoring can be effective in reducing cyberloafing, there is a lack of research examining why the effect can occur and whether it can be sustained. Furthermore, little research has investigated whether internet monitoring can concurrently induce any side effects in employee behavior. In this paper, we conducted a longitudinal field quasi-experiment to examine the impacts of internet monitoring on employees’ cyberloafing and organizational citizenship behavior (OCB). Our results show that internet monitoring did reduce employees’ cyberloafing by augmenting employees’ perceived sanction concerns and information privacy concerns related to cyberloafing. The results also show that internet monitoring could produce the side effect of reducing employees’ OCB. Interestingly, when examining the longitudinal effects of internet monitoring four months after its implementation, we found that the effect of internet monitoring on cyberloafing was not sustained, but the effect on OCB toward organizations still persisted. Our study advances the literature on deterrence theory by empirically investigating both the intended and side effects of deterrence and how the effects change over time. It also has important broader implications for practitioners who design and implement information systems to regulate employee noncompliance behavior.

截至7月底，国内共推出超300个大模型。经过大模型数量之争的上半场，下半场中国大模型该如何押宝？“持续稳定的政策支持、庞大的算力规模和广阔的应用场景是中国独特的竞争优势和巨大的发展潜力。”蒋镇辉教授认为，大模型将从数量到更重视应用端发展。

生成式人工智能（AI）工具發展日新月異，香港大學經濟及工商管理學院今日（12日）發表一項人工智能大語言模型評測綜合排行榜，通過港大深圳研究院建立評分系統，比較十多款大模型表現，顯示由中國科企百度開發的「文心一言」，在中文語境下綜合得分最高，但在「通用語言能力」卻跑輸「ChatGPT4-Turbo」，而大部分國產模型在英文語境下表現均處於「稍微劣勢」。

Many mobile applications use push notifications and reminders to explicitly educate, remind, and motivate users to perform healthy behaviors. However, users do not always act according to these explicit digital interventions. Our study investigates whether users’ self-regulation can be implicitly facilitated with a proper mobile interaction design. Specifically, we investigate the impacts of two touch modes that are supported by force-based interaction technology, that is, pressing and tapping. Drawing on the theory of embodied cognition, which suggests that people automatically infer meanings from their bodily actions, we conjecture that pressing, compared with tapping, enhances self-regulation because the action of pressing on the touchscreen embodies resolute approach motivation toward goals. We test our hypotheses in three experiments. The first experiment investigates beverage choices on a mobile app; the second experiment examines goal setting on a fitness app; and the third experiment focuses on personal hygiene learning on a mobile education app. The results from the three experiments show that pressing actions can improve users’ self-regulation in selecting a healthier but less tasty beverage (Study 1), setting higher exercise goals and performing more physical exercise (Study 2), and reducing lapses in maintaining personal hygiene (Study 3). In addition, such effects were more salient among users with a higher level of health knowledge and a promotion-focused health orientation. This study contributes to healthcare IT research by showing that mobile interaction can be leveraged to nudge users toward enhanced self-regulation.