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Recently, Interactive Brokers announced that users can now connect large language models like ChatGPT, Claude, and Grok directly to their investment accounts for market research, portfolio analysis, and even trade generation. Large language models are evolving from conversation tools that simply answer questions and generate content into intelligent agents capable of calling external tools, connecting to real-world systems, and participating in complex decision-making.

Anthropic’s move to restrict access to its cutting-edge model, Claude Fable 5, is expected to create new hurdles for Chinese artificial intelligence labs, experts say, even as the US firm walks back part of its enforcement plan following a backlash from the global AI research community.

Large language models are increasingly permeating core domains such as knowledge production, business analysis, legal consulting, and medical decision-making. A problem that was once often regarded as a mere technical flaw is rapidly becoming a new focal point in the global technology race: the “hallucination” of large language models—namely, the tendency of a model, when lacking any factual basis, to still produce highly “confident,” fluent, yet false answers.

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.