Featured faculty: Wu Fan
Written by Joanne Madrid

Retail investors traditionally rely on financial news, analyst reports, and online forums to interpret the market. With generative artificial intelligence (GenAI) rewriting the industry playbook, investors are rapidly deploying it into their decision-making. Deloitte expects the share of individuals using GenAI for investment advice to reach 78 per cent by 2028.

“GenAI significantly lowers retail investors’ mental workload by integrating complex data and offering personalised information aggregation much faster than traditional methods,” says Wu Fan, Assistant Professor of Accounting at the Chinese University of Hong Kong (CUHK) Business School. “Yet little is known about the dynamics of retail investors’ interactions with GenAI.”

Having partnered with one of China’s largest GenAI platforms to analyse more than 1.7 million stock-related queries, Professor Wu finds that users typically start with simple questions, such as asking for stock recommendations or market conditions.

When GenAI answers convey a positive signal, and users perceive it as useful, the queried stock tends to yield higher returns on the same day. This effect is stronger when users react to those answers, such as giving a thumbs-up or sharing them with their networks.

However, Professor Wu warns, this doesn’t mean GenAI accurately predicts or even causes market movement. “GenAI aggregates and echoes existing market sentiment rather than exerting an independent influence on trading behaviour. When users trust GenAI’s answers, they might also act on them, and this collective interest could push the stock price up.”

Highly active stock-related queries often signal a few things: a large number of shares are being traded, some informed traders have better information about certain stocks than others, and the gap between the highest stock price a buyer is willing to pay and the lowest price a seller is willing to accept is widening.

Most common queries from retail investors

In a paper titled, How stock market participants use generative artificial intelligence: Evidence from user-platform interaction data, Professor Wu, Frank Ecker of the Frankfurt School of Finance and Management, Li Xitong of HEC Paris, and Li Yilan of ESSEC Business School investigate how Chinese retail investors begin their GenAI journeys. The data shows 40 per cent of users ask just one stock-related question, but only 8.5 per cent follow up with more than 20 queries.

For retail investors who ask more questions, their requests gradually move from general to more specific queries, such as financial statement analysis, assessing the impact of news events, and comparing competitors. This pattern is the most common among financially knowledgeable users, who tend to use GenAI for deeper analysis.

“Retail investors’ journey evolves from passive information consumption to a more targeted information extraction,” Professor Wu adds. “Investors initially use AI to provide clear basic information and then transition to deep-dive analytical needs.”

GenAI delivers the strongest results when asked to compare, organise, and interpret specific financial information. “Tasks involving structured reasoning and synthesis, such as financial statement analysis, seem to yield the most informational benefits,” he adds.

“GenAI aggregates and echoes existing market sentiment rather than exerting an independent influence on trading behaviour.”

Professor Wu Fan

Do retail investors ask the right questions?

The study also spots a missed opportunity. Retail investors rarely use GenAI to summarise company filings and disclosures, one task where AI excels and could add value.

“Retail investors may not fully understand what GenAI can do, or they simply experiment with the technology out of curiosity without deeply exploring its capabilities, so they still struggle to formulate effective prompts to get the summary they want,” Professor Wu says. “Some may also worry about the accuracy of GenAI summaries and prefer to rely on traditional sources.”

Retail investors often prefer information that is already summarised by intermediaries rather than having GenAI create it from scratch. As more sophisticated investors tend to focus on analytical queries, summarisation may also fall into basic tasks they quickly move past, or not be seen as the most efficient way to obtain the insights they seek.

What triggers GenAI queries

Retail investors typically don’t go straight to GenAI for analysis, but often get their first inspiration from reading or watching the news. User queries rise around major corporate events, such as earnings announcements and performance forecasts, and particularly surge only after such events make headlines.

“GenAI does not completely replace the information funnel,” Professor Wu says. “Platform query volumes still closely track contemporaneous media coverage, suggesting that users often still rely on traditional channels to initiate their research.”

GenAI queries peak during trading hours, as users are likely looking for quick checks of time-sensitive information. After hours, a significant number of queries are still submitted, but they are more likely to be for in-depth research.

User queries also tend to decrease when companies publish reports covering longer or broader topics. More detailed disclosures and performance forecasts are associated with fewer GenAI queries, suggesting that when companies provide investors with enough context upfront, there is less need to seek it elsewhere.

Users will also stay engaged when earlier queries about market signals align with actual stock performance, indicating that perceived accuracy builds trust and repeat use. Surprisingly, users react negatively or show lower interaction to long or complex answers, but respond positively to concise, opinion-driven, and direct responses.

“If answers from GenAI simply mimic the density of traditional analyst reports, retail investors may disengage,” Professor Wu adds.

Preparing for AI-assisted investing

As GenAI adoption grows, Professor Wu suggests that companies rethink how they communicate with investors and make disclosures easier to process by both investors and AI tools.

“Firms must comply with regulatory requirements to provide sufficient, relevant and timely information to the market, but they can also consider structuring disclosures in more machine-readable formats to better facilitate AI-assisted processing,” he says.

Given that users want quick answers and GenAI platforms are good at extracting summaries, corporate disclosures should be clear, concise, and use a consistent style to make it easier for AI tools to identify and extract relevant information. The report should also highlight key takeaways and, instead of just presenting numbers, include an explanation of what they mean and why they changed, so GenAI can analyse the context to provide more helpful explanations.

In terms of content, the report should directly address common user questions about the business outlook, financial performance, and operations, to help GenAI provide better information and potentially reduce the need for users to ask follow-up questions.

“Keep in mind that, when using GenAI, users must remain cautious regarding hallucinations or factual errors in AI-generated responses.” Ultimately, GenAI can be a powerful research tool, but it works best when paired with critical thinking, not as a replacement for it.

The post How retail investors use GenAI to navigate stock markets first appeared on China Business Knowledge.

Featured faculty: Li Hongfei
Written by Pan Jingyi

Childhood friends Queenie Fan and Day Lau started their handbag brand, Cafuné, a decade ago in Hong Kong. The label has grown its retail footprint across Asia, yet success did not shield Fan from the kind of scepticism many women founders know well.

“When you start your own business at a young age, people are not always willing to take you seriously, especially in the leather industry, which is typically more traditional and male-driven,” Fan said in an interview.

Her story is far from unique. Katherina‑Olivia Lacey, a co-founder of a Singapore‑based tech startup Quincus, had investors question her role during a seed funding round. A 2025 report by the Organisation for Economic Co-operation and Development also found women are 25 per cent less likely than men to receive bank loans to fund their businesses.

“Different ways of asking questions change the way AI defines successful entrepreneurs.”

Professor Li Hongfei

The myth of the hard-charging leaders who win through aggression and swagger, typical masculine traits, is still alive and prevalent in modern culture. An artificial intelligence (AI) model trained on a myriad of stories and human writing to converse and answer questions, called a large language model, may learn and accept that skewed worldview.

However, Li Hongfei, Assistant Professor in the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong (CUHK) Business School, discovers that AI can be wiser than humans.

“Our study found that large language models generally exhibited a balanced gender perception of entrepreneurship, with a slight preference for feminine traits. However, algorithms may show a notable bias towards masculinity in specific scenarios, or when you use a different approach to ask the questions.”

AI advocates gender equity

In a study titled Detecting gender stereotype biases against women entrepreneurs in large language models, Professor Li, along with Cao Xian of Illinois State University, Xu Qingyu of the City University of Hong Kong, and Zhu Ruoqing of the University of Illinois Urbana-Champaign, asked ChatGPT, a major language learning model, to describe characters of successful entrepreneurs from selected words: dominance, forcefulness, aggressiveness, assertiveness, warmth, sensitiveness, emotiveness, and expressiveness.

The results are encouraging. Rather than choosing purely masculine traits, such as dominance and aggressiveness, the model consistently selects three words for both successful men and women entrepreneurs, namely assertive, expressive, and warm. While assertiveness is traditionally seen as masculine, the other two are closer to feminine traits.

“This contrasts with historical and popular literature that overwhelmingly portrays successful entrepreneurs with stereotypically masculine traits,” Professor Li says. “This pattern might reflect programmed efforts by ChatGPT to minimise unethical gender bias in descriptions of successful entrepreneurs.”

AI companies have used several methods to prevent their systems from repeating sexist ideas, such as filtering out biased content and adding more examples of women in leadership roles. OpenAI, for instance, runs fairness tests to see how ChatGPT responds to a question when the user’s gender or name changes, ensuring its neutrality.

Women entrepreneurs win sometimes

In another experiment, Professor Li and the team fed ChatGPT with texts highlighting masculine traits, feminine traits, a mix of both, and neutral traits to subtly put the model in different mindsets. They then asked it to evaluate a business proposal from a gender-neutral founder to see whether exposure to certain traits would change how AI judges the proposal.

As in earlier results, AI shows little evidence of gender stereotyping. More specifically, ChatGPT does not undervalue feminine traits in general business evaluations and explicitly points out that the entrepreneur’s gender doesn’t affect the outcomes.

To make the test more realistic, the team asked AI to evaluate actual entrepreneurial ideas from Reddit, and it still doesn’t exhibit gender biases. In fact, the algorithms even give higher marks to ideas written in a warmer, more collaborative and people‑focused style, as in a more feminine tone.

When bias finally appears

The picture shifts when ChatGPT was asked to behave less like a conversation partner and more like a venture capitalist analysing investment opportunities.

In the last experiments, ChatGPT was assigned to weigh different personality traits and detailed business ideas, then determine an investment outcome. This test is closer to how AI is used in real life for screening applications, scoring founders, and ranking business opportunities using financial calculations.

Here, the result changes. ChatGPT favours more assertive, risk-embracing, and affirmative pitches that reflect masculine character. In other words, once the AI is pushed to think more technically, masculine traits carry more weight.

“Different ways of asking questions change the way AI defines successful entrepreneurs,” Professor Li says. “When being asked to behave like a data analyst or investor to calculate an investment score using a formula rather than simply ranking an idea, AI doesn’t ‘realise’ it is dealing with a gender question and treats the task as a mathematical exercise.”

Human involvement is crucial

As AI becomes a bigger part of entrepreneurial life — helping with everything from business plans to funding decisions — understanding its strengths and limitations is key. Large language models take the women’s side when describing success or rating ideas in a straightforward way, but when asked to behave like an investor analysing with formulas, AI quietly tilts towards a stereotypical pattern.

“We still need more research, using different methods, to really understand when large language models copy unfair biases and when they help challenge them,” says Professor Li.

For women founders like Fan, Lacey, and others building their ventures, AI can be a useful sounding board that does not automatically downgrade them for being women or for using a more collaborative and emotional tone. However, the study also reminds us that AI outputs should not be treated as completely impartial.

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“When using AI to screen applications or analyse pitches, adding human oversight is still important to avoid unintentional prejudice,” Professor Li adds. “When you ask AI to help you make a decision, you need to understand what the rationale behind its decision is.”

Ultimately, given that gender bias is historically prejudiced human-generated data on which AI is trained, the discerning judgment of humans remains paramount. This vital insight reinforces the indispensable role of humans in ensuring that technology elevates fairness rather than perpetuates inequities.

The post Does AI perpetuate the boy’s club in startups? first appeared on China Business Knowledge.

Featured faculty: Prasanna Karhade
Written by Putro Harnowo

Earlier this year, an X post from a Meta engineer went viral after her artificial intelligence (AI) bot ran amok, deleted emails, and wouldn’t stop, even after being ordered to do so. The engineer was testing the agentic AI that can execute tasks on its own. This AI agent promises autonomous decision-making to solve complex problems without human supervision, but the accident may prove it’s not entirely safe.

While businesses ponder the pros and cons of agentic AI, a safer choice called intelligent process automation (IPA) has been around for years. IPA bots combine AI technologies with robotic process automation to create AI bots. Their strength lies in low-code/no-code (LCNC) toolkits that enable users without coding skills to automate specific tasks, making them more secure and reliable in corporate environments.

Microsoft Power Automate is an example. It can perform repetitive tasks like sending emails or generating reports seamlessly within the Microsoft ecosystem. Software maker SAP also offers Build Process Automation to automate tasks integrated with other SAP products, and IBM’s Cloud Pak for Business Automation caters to larger enterprises seeking cloud-based automation. Many other tech firms provide independent IPA bots for specific needs.

However, despite hefty investments in AI solutions, many companies still struggle to deploy IPA bots widely across their workplaces. Prasanna Karhade, Associate Professor in the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong (CUHK) Business School, argues that companies are still in the dark about how to implement AI solutions effectively.

“AI technologies are rapidly growing, but companies are sometimes stuck with the traditional ways of governing technology solely in the hands of the IT team,” he says. “Employees without coding skills can now leverage IPA bots to create and refine their own automated workflows, bolstering the democratisation of technology development. However, this innovation must also align with company IT policies.”

Professor Karhade notes that this new dynamic has forced businesses to revisit whether centralised technology management remains relevant and, if not, what the best strategies are to roll out AI tools that can be widely accepted across the organisation.

When AI tools are failing, and why

In a study titled, AI governance and the decentralisation of technology production: An investigation of AI-based IPA bots, Professor Karhade and his co-authors examine 176 IPA projects at a Fortune 200 US multinational IT services firm, particularly in the banking, financial services, and insurance domains, to identify the critical factors in AI tool adoption.

“Companies naturally want all employees to use the AI tools, so they demand high utilisation. Apart from that, the AI solutions must be repeatable to ensure broader application within the company beyond the initial use,” says Professor Karhade. “Therefore, utilisation and repeatability are the two key factors in AI governance.”

Having investigated 24 highly underutilised and 54 highly unrepeatable IPA projects, Professor Karhade and the team find that more than 83 per cent of these failed projects are imposed by top management without employee input. Conversely, among the 46 and 35 IPA projects classified as highly utilised and repeatable, respectively, employees are responsible for over 91 per cent of them. These successful projects have low coding intensity, meaning employees apply their own knowledge to deploy AI bots using the LCNC toolkits.

A bottom-up approach turns out to significantly improve the utilisation and repeatability of IPA projects, while a top-down approach results in the opposite. This finding provides a robust starting point for computational experiments to further identify other influential factors in determining the success of AI solutions.

“AI users will be more empowered as they can do many things on their own, but a centralised IT team still plays a crucial role in enabling and overseeing AI infrastructure.”

Professor Prasanna Karhade

The elements of success

The key to the success of an AI solution lies in how it starts, or what the researchers call the “genesis”. IPA projects initiated by employees yield successful adoption, but further analyses find that how the project is deployed, the amount of coding skills required, and the complexity of the tools also contribute significantly.

IPA bots are highly used when deployed by users using the LCNC toolkits, so they require very little coding. Process intricacy, or how many steps an IPA bot has to do to complete a task, is also crucial. Intricate processes are harder to automate, but when users leverage their knowledge to create bots with the LCNC toolkits, the bots are highly likely to be used.

Deployment, or how AI tools work and interact with users, can be divided into three types: attended, where users trigger the process; unattended or fully automated; and hybrid, where AI works alone but sometimes needs human help. Users are still likely to use attended bots in a top-down manner, but they’re not widely accepted.

AI bots may solve the problem at hand, but they are not necessarily repurposed widely. This is where repeatability matters. To create highly accepted bots for a wider user base, unattended AI bots are particularly preferable as they can handle processes autonomously.

Although an AI solution is all about democratising technology, when users implement an AI bot primarily for their own specific needs, the bot becomes too specialised to be reused by others. This contradicts the idea of decentralised technology, or, as Professor Karhade calls it, the limits of democratisation. When this happens, IT support is needed to ensure that the AI tools are reliable and flexible enough to be used across the company.

The evolving roles of the IT team

AI may have democratised technology adoption, but Professor Karhade underlines that the IT team is irreplaceable. “AI users will be more empowered as they can do many things on their own, but a centralised IT team still plays a crucial role in enabling and overseeing AI infrastructure.”

For companies, management should remain receptive to user-initiated AI projects and provide guardrails to enable seamless collaboration between employees and IT teams. This balanced approach will enable businesses to harness the benefits of decentralised technology while safeguarding operational integrity. Otherwise, an accident similar to what happened with Meta’s engineer could happen.

Professor Karhade believes the findings apply to broader contexts and industries dealing with large volumes of documents, such as retail, logistics, healthcare, and human resources. The core ideas revolve around how companies should rethink their technology management in the AI era and build a sustainable ecosystem that enables diverse employees to contribute to AI solutions.

The post Should you let employees build their own AI bot? first appeared on China Business Knowledge.

Featured faculty: Choi Sungwoo
Written by Pan Jingyi

Derek Mobley had applied to more than 100 jobs over several years and had admittedly received rejection notices within minutes or hours. Confused and furious, the US citizen sued Workday, a human resources software firm that handled most of his applications, alleging that its artificial intelligence (AI) screened out his applications based on his age, race, and disabilities.

While Workday has argued that it’s not liable for hiring decisions, a court conditionally certified the age discrimination claims last year. This highly anticipated lawsuit will set a new precedent for AI-driven hiring practices and serves as a reminder that handing over employment decisions to algorithms can lead to backlash.

AI has inevitably reshaped human resource management, as also seen across industries today. A 2025 survey from Resume Builder shows that a majority of US managers have relied on AI for high-stakes decisions, such as promotions, rises and even layoffs. Akin to a dystopian Black Mirror episode, bosses are turning to machines to decide who’s in and who’s out.

Although more and more companies embed automation into their human resource operations, little is known about the impact on employee morale when their career is in the hands of a non-human actor. This is the puzzle that Choi Sungwoo, Assistant Professor of the School of Hotel and Tourism Management at the Chinese University of Hong Kong (CUHK) Business School, seeks to answer.

Professor Choi uncovers a latent repercussion of advanced technology in human resource management: organisational dehumanisation. “Organisational dehumanisation is the feeling of being reduced to a mere functional component of an organisation, much like a single bolt in a large machine, where your unique qualities, emotions and individuality are largely disregarded.”

Why AI can feel dehumanising

In a study titled AI in human resource management: A driver of organisational dehumanisation and negative employee reactions, Professor Choi works with Shin Hyejo of the Hong Kong Polytechnic University and Kim Hyunsu of the University of Macau on three scenario-based online experiments. They recruited nearly 700 participants through Prolific, an online platform widely used in academic research.

“When AI performs human resources operations, employee characteristics are seen as numbers. Therefore, employees would feel like they are not treated as humans.”

Professor Choi Sungwoo

Across all three experiments, participants were asked to imagine that decisions about their promotion or performance review were being made either by an AI system or by a human manager. The results were consistent: people reacted more negatively when the decision-maker was AI.

Those reactions were not trivial. Participants reported lower commitment, stronger turnover intentions, and even greater retaliatory feelings when seeing AI deciding their livelihood. In practice, they are more likely to search for another job and warn others to avoid working for the company.

A couple of factors drive such dehumanising feelings, Professor Choi notes. AI lacks the ability to understand social norms, personal issues and ethical concerns as a human manager can. AI also works in incomprehensible ways to laypeople, and employees may fail to understand how AI reaches its conclusions. As a result, they feel powerless and excluded from the decision-making process.

“Putting it all together, loss of empathy, transparency, and control can leave people feeling objectified,” Professor Choi says. “When AI performs human resources operations, employee characteristics are seen as numbers. Therefore, employees would feel like they are not treated as humans.”

Can human resources automation thrive?

Different companies have different sentiments towards AI. Professor Choi and his collaborators identify what they describe as a cultural paradox: companies with more collaborative and family-like cultures may experience greater resistance to AI in human resources management.

In these collaborative environments, employees believe that the management values cooperation, support and interpersonal relationships. If AI is then used to make major decisions, the technology can clash with such a principle.

“When a collegial workplace adopts AI for major human resource decisions, it can feel like a betrayal,” Professor Choi explains. “Employees experience a dissonance between the human-centred values the culture espouses and the perceived quantification of their worth that AI involvement implies.”

By contrast, the negative effects of AI appear to be less intense in companies with a more outcome-oriented system, where performance and results are already prioritised over interpersonal connections. But that does not mean AI is entirely harmless in such settings.

Even in outcome-focused organisations, AI-driven decisions on the workforce can still backfire and brew dehumanisation. Companies that deploy an AI system to select the most suitable candidates for promotion still see employees feel their aspirations or contributions are overlooked.

In short, whether focusing on outcomes or collaborations, the company needs to take the human aspect into account when adopting AI in human resources operations.

Keep “human” in human resources

Reputation is a valuable asset for a company. When job marketplaces like Glassdoor, Indeed, Seek, and even Google nowadays provide user-generated company reviews, the efficiency gains from AI might be quickly diminished by a wave of criticism from current and former employees.

For companies and business leaders, the message is not to abandon AI, but to use it wisely. One priority, Professor Choi says, is transparent communication. “Companies should clearly explain why AI adoption in human resources is necessary and reassure employees that it does not compromise the organisation’s core commitment to supportive and human-oriented growth.”

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Rather than allowing AI to make decisions on its own, Professor Choi suggests designing systems that enable humans and technology to coexist. “This hybrid approach helps preserve the sense that consequential decisions about people are ultimately made by people. If AI serves only in an assistive capacity with limited input into the final decision, the dehumanisation effect should be substantially mitigated.”

“Human resource practices are highly sensitive, and AI could be most valuable in managing less critical yet voluminous tasks, such as initial application screening, freeing human managers to focus on more complex and high-stakes decisions,” Professor Choi adds. “Despite that, companies should always be mindful of the risks of dehumanising feelings and act accordingly.”

The post Would you trust AI to decide your pay rise? first appeared on China Business Knowledge.

Featured faculty: Cao Xinyu
Written by Sally Ho

Across industries, companies are integrating artificial intelligence (AI) tools to support decision-making and day-to-day operations. It wouldn’t be wrong to assume that once employees understand the benefits, they will automatically embrace AI, but this is often not the case.

Even though AI technology delivers positive results, firms are still struggling to persuade employees to embrace it. A 2026 global survey from the digital adoption platform WalkMe finds that more than half of white‑collar employees abandon their AI tools and revert to manual work.

According to Cao Xinyu, Vice-Chancellor Associate Professor of Marketing at the Chinese University of Hong Kong (CUHK) Business School, algorithm aversion, or a human tendency to distrust computer algorithms, even when they perform better, has been well documented.

Sceptics view machines as inferior to humans, leading them to use AI only for mundane and laborious tasks. Employees may also worry about accountability for algorithmic errors or feel their professional abilities are undervalued.

Such concerns are reasonable, but complete resistance can prevent employees from realising the true benefits of the technology. It can also undermine AI investments, especially when the management has spent a fortune on tools that are used intermittently.

Companies often rely on training, which sometimes includes workshops on the practical application of AI, and encourage employees to use AI tools to improve productivity, but such methods aren’t sufficient. At this point, you may joke that forcing employees to use AI could work, but Professor Cao accidentally confirms it.

“We did not anticipate that temporary mandatory use would lead to sustained AI adoption. Our initial goal was simply to obtain a fair performance evaluation of the AI tool, but we then observed an interesting behavioural change among our research participants, so we explored its underlying mechanism.”

How temporary AI use leads to lasting adoption

In a study titled How forced intervention facilitates AI adoption, Professor Cao and her co-authors, Hu Chenshan of the University of Colorado Boulder, Sun Jiankun of Imperial College London, and Dennis Zhang of Washington University in St. Louis, collaborate with a large online education company in China.

The education platform introduced an AI tool to help sales staff suggest trial class teachers for prospective students. Despite its simplicity and convenience, this tool was underutilised. On average, sales staff use the AI tool only for 20 per cent of the prospective students. Furthermore, they tend to use the AI tool for low-quality leads who are less likely to convert.

“By transparently showing improvements, firms can help correct workers’ biased beliefs about AI and reduce resistance to adoption.”

Professor Cao Xinyu

To investigate the root cause, Professor Cao and the team evaluated the AI tool’s performance by dividing 171 sales employees into three groups. For three weeks, one group was asked to use the tool, another was prevented from using it, and the third group was free to use AI or not.

The AI tool is found to perform comparably to humans in terms of conversion rates, while significantly reducing manual effort and increasing efficiency. Those subject to a short-term mandate are more likely to continue using the AI tool, even after the experiment ends. Some may argue that repeated use makes employees become familiar and eventually form a new habit, but the data suggest more than that.

“If it’s a habit, everyone who was forced to use the AI tool for a while would keep using it in a similar way afterwards, but that’s not what we found. Employees who experienced a larger increase in conversion rate during the mandate-AI-use period tend to use AI more and use AI more on high-quality leads after the experiment,” Professor Cao adds. “Habit alone wouldn’t explain such varied behaviour linked to individual results.”

First-hand experience changes perception

Direct experience allows employees to reassess and change their biased beliefs about AI accordingly. People gain knowledge and skills through real-world practice, which can be more influential than traditional methods such as reading or listening.

The study also offers a clear lesson for company managers. Sometimes, obstacles may not stem from AI tools’ capabilities, but rather from how employees perceive the new tools. When scepticism is high, voluntary uptake alone may not be enough. A fleeting period of structured use allows users to evaluate the system on their own terms.

The caveat, Professor Cao warns, is that mandatory use must be implemented carefully. If employees feel pressured without a clear context, resistance may increase instead of decline. The main goal is not compliance but enabling an informed experience. She also suggests that firms should complement exposure with tangible results. “By transparently showing improvements, firms can help correct workers’ biased beliefs about AI and reduce resistance to adoption.”

Future challenges in AI adoption

As AI becomes increasingly embedded across industries, Professor Cao believes the findings are relevant to other sectors. “Biased beliefs about AI are quite common across industries, especially during the initial deployment in organisations. Employees who hold biased beliefs towards AI tend to underutilise it, and revising such biases plays an important role in addressing algorithm aversion.”

Beyond AI adoption, businesses anticipate the next phase of industrialisation, Industry 5.0, in which technology shifts from digital automation to a human-centric model. Companies will continue to face questions about how employees interact with algorithms, and for Professor Cao, this means more questions to answer.

“For example, how to best allocate tasks between human workforce and AI, how to design the incentives for employees in using AI, how AI transparency influences humans’ trust, learning, and long-term adoption,” she says. “These directions can help deepen our understanding of both the behavioural and market-level implications of AI adoption.”

For now, the study suggests that organisations may need to focus not only on technical deployment, but also on how employees learn to work with technology. In some cases, a brief period of direct experience may be enough to change how workers think about a technology and whether they choose to use it for good.

The post Can force adoption solve AI resistance? first appeared on China Business Knowledge.

Featured faculty: Wu Jing and Michael Zhang
Written by Putro Harnowo

Natural disasters have become more frequent and severe each year. In 2025 alone, devastating wildfires struck California, powerful hurricanes ravaged the Atlantic, and floods and cyclones over South and Southeast Asia inflicted profound economic losses. Across the US, the record-breaking natural disasters last year caused US$115 billion in total damage.

Businesses now operate in an increasingly unpredictable environment. Artificial intelligence (AI) is poised to help navigate turbulence, but its value is more evident in optimising business under stable circumstances. This is not surprising since corporate AI investment, such as from big pharma to tech giants, has historically prioritised building competitive advantage over resilience.

“AI undoubtedly helps productivity in normal times. However, given the current high-velocity environment characterised by disruptive upheavals, a better understanding of how to deal with such unrest becomes more urgent,” says Wu Jing, Professor in the Department of Decisions, Operations, and Technology at the Chinese University of Hong Kong (CUHK) Business School.

Disruptions brought by natural disasters impair business operations and erode investor confidence, leading to lower stock prices for affected companies. Professor Wu’s study reveals that companies that hire more AI talent see a smaller drop in stock value and recover more quickly after disasters as opposed to companies with less AI investment.

The interesting part is that companies with limited budgets are found to benefit more from AI investment during crises. However, their productivity gains still couldn’t match their wealthier peers in normal times due to a lack of organisational systems and infrastructure to fully reap AI potentials.

“Resilience becomes more and more important in today’s ever-changing environment. If crisis is the new norm, infusing AI into firm productions is no longer a luxury,” he adds.

How much AI investment is enough?

Along with Michael Zhang, the Wei Lun Professor of Business AI at the same department, as well as Han Miaozhe at the Hong Kong University of Science and Technology and Shen Hongchuan at the University of Macau, Professor Wu’s latest study, Artificial intelligence and firm resilience: Empirical evidence from natural disaster shocks, assess AI’s impact on firm resilience during challenging periods.

The study focuses on 3,137 firms in the US across agriculture, mining, utilities, construction, manufacturing, trade, transportation, and warehousing sectors. To measure AI investments, the researchers identify AI-related job postings and find that the number of job advertisements seeking AI-related skills was low relative to the overall job postings, but has increased from 8.12 per cent in 2010 to 16.41 per cent in 2019.

“Resilience becomes more and more important in today’s ever-changing environment. If crisis is the new norm, infusing AI into firm productions is no longer a luxury.”

Professor Wu Jing

The team also looks into the International Disaster Database and spots 141 disasters that directly affected sample companies, mainly storms and floods. The researchers then merge the dataset with the stock prices data from the Centre for Research in Security Prices and S&P Compustat.

Stock returns represent adjustments in the general expectation of a firm’s performance, reflecting the firm’s ability to mitigate the damage amid catastrophes. Firms that hire more AI-related positions see moderate losses and higher stock returns during and after the disaster. They can fully recover quickly if at least 2.4 per cent of their job postings require AI-related skills, such as deep learning, image processing, AI tool operation, and the like.

This positive impact is greatest at the peak of the disaster, with the most effective AI-empowered roles focused on cognitive tasks, decision-making, and supply chain coordination. “AI generates significant resilience for firms facing natural disaster shocks primarily by optimising supply chains and production inputs,” says Professor Wu.

Natural disasters create tangible challenges that AI can get around, such as rerouting shipments or optimising output from remaining machinery. In the stock market, this benefit relies on shareholders believing the firm can continue operations amid natural disasters.

However, Professor Wu notes that such resilience may not persist in the face of human-induced shocks, such as cyberattacks, labour strikes, or industrial accidents. The damage in human-caused disasters is often reputational or contractual, and AI-driven operations cannot fully offset it. In this case, AI can only serve as a risk detector, providing data to support human operators rather than mitigating the damage.

“AI serves as complementary support for tangible operations and works more efficiently if it targets physical assets, such as factories,” he adds. “When AI is used on financial assets, intellectual property, or market access, its effects are limited.”

How to invest in AI wisely

Many may conflate AI with information technology (IT), as both often go hand in hand, so the researchers seek to examine them more deeply. IT is meant to improve efficiency by coordinating, communicating, and monitoring a wide range of activities, whereas AI focuses on applications where data and algorithms generate predictions to assist decision-making.

The team then measures investments in non-AI technologies by weighing job postings with general IT skills, such as robotics, data analytics, or cloud-related skills. The analysis finds that while IT is great for enhancing day-to-day operations and cutting costs, AI plays a distinct role in helping firms to be more resilient during crises.

Professor Wu suggests that AI investment should not be spread evenly across all technical functions. Instead, focus on encouraging managers who can maximise output with AI when resources are scarce. By concentrating AI capabilities in high-level cognitive and operational roles, firms can improve their resilience.

“Therefore, roles like supply chain coordinators should be prioritised to be empowered with AI skills for better predicting materials arrivals and planning alternative routes to address the disruptions directly,” says Professor Wu. “Strategic decision-makers and production operations managers should also be equipped with AI tools to make quicker, better decisions in resource allocation.”

For financially constrained firms, rather than investing solely in AI tools, the most critical takeaway is to prioritise organisational design, such as training manpower and setting up procedures to ensure AI can make a real difference. “These firms should view AI investment as an insurance premium for resilience instead of an immediate profit engine, ensuring they have the IT backbone to support it,” he adds.

“They should also focus on deploying AI in areas that immediately reduce operational costs or mitigate certain risks, such as vendor monitoring or financial planning. This will allow them to generate the savings needed to fund further resilience tools while avoiding the trap of investing in technology they cannot utilise efficiently.”

The post Can AI help businesses weather any storm? first appeared on China Business Knowledge.

Featured faculty: Lisa Wan
Written by Pan Jingyi

It was supposed to be a fun summer trip to Puerto Rico last year, as a Spanish couple had done everything ChatGPT planned, until they were refused to board the plane for not obtaining proper paperwork. In another case, two tourists were lost in a rural Peruvian town trying to find an imaginary destination suggested by AI.

AI has been hailed as the new technological evolution, but these stories remind us not to take technology at face value. On the other hand, these cases also highlight how trip planning has moved from a search bar of internet browsers to ChatGPT, DeepSeek, Grok, and the like. Scrolling through a sea of blue links is gradually replaced with a single prompt.

“Opting for an unfamiliar and novel search method like GenAI can be seen as a risky choice for making concrete plans.”

Professor Lisa Wan

“When ChatGPT was first introduced, we immediately sensed its strong potential for tourism information search, which largely depends on context and user preferences,” says Lisa Wan, Associate Professor of the School of Hotel and Tourism Management and the Department of Marketing at the Chinese University of Hong Kong (CUHK) Business School.

“Unlike traditional search engines that primarily provide fragmented information through hyperlinks, generative AI, or GenAI, can synthesise information, generate narratives, and adapt responses to users’ preferences.”

With much positive and negative news surrounding GenAI, Professor Wan seeks to understand what travellers actually perceive of the new technology. Working with Li Yuan of Zhejiang University, along with Luo Xiaoyan and Ding Xu of Sun Yat-Sen University, she conducted the research Advancing information search through GenAI: the roles of search type, travel motive and GenAI customisation level.

Across a series of studies involving more than 800 participants from different countries, the team examined when people lean towards GenAI or retreat to traditional search engines. They find that travellers’ willingness to use GenAI depends on their search purpose, travel motives, and whether the AI agent is tailored for trip planning.

When GenAI is less trustworthy

Trip planning often starts from curiosity and then turns into concrete actions. Individuals who come across a destination on social media or over casual conversation may want to find more about the must-sees, the overall vibe, and, as their interest deepens, may seek further information on specific prices and booking options.

Based on the above process, the researchers grouped these behaviours into two search types: non-decision-based, where individuals browse for general information about a destination, and decision-based, when more detailed information is sought for final decision-making.

“These differences can influence which search tools people choose,” Professor Wan says. “In the decision-based search, a small bad decision based on inaccurate information can turn into bigger problems, causing consumers to be more cautious.”

When participants are in decision-making mode, they prefer to gather information using traditional search engines. “Opting for an unfamiliar and novel search method like GenAI can be seen as a risky choice for making concrete plans,” she adds.

Professor Wan notes that new technologies often face a natural trust gap, especially when mistakes have significant consequences. Moreover, scepticism towards GenAI also reflects a rational assessment of its limitations in providing real-time and verified data, as reported in recent news.

In non-decision-based situations, however, the pattern shifts. When people are causally exploring or thinking about a destination, GenAI’s conversational style and ability to synthesise broad information become more appealing.

Traveller’s mindset and customisation make a difference

Looking further into what factors might encourage people to use AI in decision-making scenarios, Professor Wan and her collaborators found that the travel motive is the crucial piece. Specifically, participants motivated by a utilitarian goal that focuses on efficiency and convenience reported a higher preference for GenAI, whereas those with a hedonic motive of prioritising fun and pleasure are more likely to stick with traditional search engines like Google.

For utilitarian travellers, GenAI is preferred for its ability to filter information and organise search results, reducing the effort to compare options manually. Meanwhile, hedonic travellers enjoy the traditional browsing experience, mostly because search engines feature a richer mix of photos, videos, maps, reviews, and unexpected discoveries.

“Those prioritising fun and pleasure may find the variety and richness of multimedia content more appealing, providing a more immersive and enjoyable searching experience compared to the textual responses generated by GenAI,” says Professor Wan.

Customisation levels also affect user preference for AI. As booking platforms increasingly embed AI plugins for specific tasks, such as suggesting available hotels based on user preferences and providing customer service via AI chatbots, the study finds that such customisations can boost trust in GenAI.

How the tourism industry should adopt and develop GenAI

Given that GenAI is often more preferred in the non-decision stage, Professor Wan suggests platforms make an AI assistant visible in the main search bar to inform travellers general information about destinations, such as major attractions and cultural highlights. Another application is to display GenAI responses alongside traditional search results, allowing travellers to cross-check information easily.

While AI transformation continues to gain momentum, Professor Wan observes that fundamental challenges remain. “Many firms invest heavily in AI solutions but see limited results in daily operations. Two common obstacles are the lack of in-house talent to integrate AI into workflows and the tendency to adopt generic tools that don’t match real user demand.”

Furthermore, she observes that rapid AI advancements and shifting customer demand require firms to continually adapt this technology. “Rather than treating GenAI adoption as a one-off technological upgrade, firms need to view it as an organisation transformation process that involves gradual development, cross-functional collaboration and iterative experimentation.”

The future of travel planning

Professor Wan believes that AI will not completely replace search engines just yet, at least in the near future. Instead, travel information would be more distributed, with different tools serving different purposes. “GenAI is more likely to complement travel planning rather than substitute the traditional way,” she adds.

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Interestingly, she suggests that social media will be the close contender for search engines. In Chinese Mainland, for example, RedNote has already become a starting point for many travellers for its first-hand reviews. “The real shift is towards interactive, experience-rich and peer-validated information, something that social media and GenAI offer in different ways.”

Another takeaway is a concern about how GenAI can subtly change how travellers engage with places and experiences. Therefore, she encourages travellers to keep interacting with locals and communities. “The goal is not to reject intelligent tools, but to remain attentive to how they reshape human capabilities and experience.”

The post Can AI beat search engines for trip planning? first appeared on China Business Knowledge.

Featured faculty: Cao Xinyu and Wang Jingbo
Written by Putro Harnowo

Many may call it cliché or cheesy when watching a short movie about a bullied employee who turns out to be the CEO’s son or a poor husband revealing himself as a billionaire, but believe it or not, these microdramas have stolen the show. Their plot twists and cliff hangers have helped them find a way to Hollywood, as Fox Entertainment and Disney recently announced investments in producing bite-sized movies.

Microdramas have less than two-minute duration and a vertical format because they are meant to be watched on a smartphone. Their popularity can be traced back to 2018, when Chinese short-video platforms began featuring them. Fast forward to 2024, the country’s microdrama industry surpassed its box-office revenue with US$6.9 billion, according to the China Netcasting Services Association.

Nowadays, China’s microdrama industry has become highly competitive, powered by more than 100,000 enterprises producing around 3,000 series monthly. Video platforms recognise these miniseries as profitable content and not only host but also actively incentivise them through financial rewards, algorithm optimisation, and the like, to encourage more original and high-quality content.

However, the ever-increasing number of creators and limited financial resources have made selecting creators for such incentives more challenging. After analysing data from a major video platform, Wang Jingbo, Assistant Professor in the Department of Marketing, and Cao Xinyu, Vice-Chancellor Associate Professor of Marketing at the Chinese University of Hong Kong (CUHK) Business School, find that creators with a large number of followers tend to be selected for financial support.

This method may be counterproductive since the supports might not provide a significant boost to their already high performance. “The platform may simply select creators who already have the highest content quantity and quality, rather than those who were likely to experience the greatest improvement due to the incentives,” Professor Cao says.

Therefore, in a study titled A Deep-DiD method to estimate heterogeneous treatment effects: Application to content creator selection, Professors Cao and Wang, as well as Cheng Yan of Shanghai University of Finance and Economics, Shen Zuo-Jun (Max) of the University of Hong Kong, and independent researcher Zhang Yuhui, propose a new method to optimise a digital platform’s selection process.

Practical experiment and application in the digital world

The short video platform launched a signing programme in three countries in 2022. Selected creators were asked to sign a contract and receive monthly payments based on their performance.

Professor Cao and her team then try to measure how effective the signing programme was, using three key indicators: the number of video uploads per day, the time users spent on each video, and user engagement from likes, comments, shares, and follows. To create a fair comparison, they match 2,343 creators who signed the programme with the same number of other creators who did not sign up based on their performance trajectories.

Through a statistical method called difference-in-differences (DiD), which has been widely used in economics and social sciences to estimate the effect of specific interventions, the researchers find that the signed group shows a significant boost in all key indicators. After signing up, their average number of videos per day temporarily increases, while the positive impacts on user time and engagement last longer.

However, the researchers also find that the effects vary widely across different creators. “The DiD method calculates the overall average impact by blending all the individual effects,” says Professor Cao. “If an intervention affects different individuals in several ways, or if the impact changes over time, the analyses could lead to an inaccurate picture of the actual impact.”

“The platform may simply select creators who already have the highest content quantity and quality, rather than those who were likely to experience the greatest improvement due to the incentives.”

Professor Cao Xinyu

Therefore, Professor Cao and the team further examine the specific impact on each creator with an advanced method by leveraging a computer programme called a deep neural network. Dubbed as the Deep-DiD method, it possesses extra layers to process information and find hidden patterns that traditional techniques overlook.

How does Deep DiD work, and how good is it?

First, the researchers develop a Deep DiD model by integrating deep neural networks into a difference-in-differences framework to flexibly estimate individual-level heterogeneous treatment effects as nonparametric functions of high-dimensional pre-treatment features.

By inspecting rich data from the platform, the model can predict which creators would have improved the most if they joined the programme. “With this advanced predictive analysis, we can examine how a specific programme helped some individuals more than others,” Professor Cao adds.

Creators selected by the Deep DiD model as favourable candidates show 57 to 123 per cent higher performance than those selected by the platform. Overall, creators chosen by the model consistently show a 72 to 114 per cent higher performance.

In out-of-sample evaluations, creators selected by the Deep-DiD model exhibit substantially larger performance gains than those selected by the platform. Among signed creators, those also identified by the model experience 70 to 80 per cent higher realised performance jumps relative to the average signed creator, across both user time contributed and user engagement.

When comparing selection rules directly, creators ranked highest by the model have 57 to 123 per cent higher estimated treatment effects than platform-selected creators, indicating significant scope for improvement in targeting. Notably, nearly half of the creators identified by the model were not signed by the platform, reflecting systematic differences in selection criteria.

What makes the Deep DiD method remarkable is its flexibility. While the three key indicators above reflect outcomes that platforms are likely to consider, platforms can tailor any other metrics depending on their goals. This way, they can maximise the impact of their programmes by investing only in those who will gain the most.

“If a platform’s revenue depends on users’ watching time, then this metric can be prioritised. The goal can also be defined as a weighted combination of metrics or other customised outcomes,” Professor Cao says. “The process can be repeated multiple times to reduce randomness and improve the stability of the results. Whenever the platform plans to implement a new intervention, new rounds of estimation should be conducted using the corresponding data and inputs.”

Beyond the digital platform, the Deep DiD method may also be applied in other settings. For instance, a retail shop launching a rewards programme must choose its target wisely to ensure only the right customers contribute to revenue, a company deploying bonus systems needs to determine which employees would achieve the largest productivity boost, and a government introducing a subsidy scheme should predict which individuals would benefit most.

In the real world, figuring out how much an intervention truly impacts a beneficiary is never easy since many variables interact in complex and hidden ways. These intricate and unknown linkages are nearly impossible to figure out using traditional mathematical methods, but the deep neural network acts much more like a sophisticated brain and is exceptionally good at discovering complex and subtle patterns.

The post How should video platforms spot their next big stars first appeared on China Business Knowledge.

Featured faculty: Wu Jing
Written by Putro Harnowo

The meeting between Chinese President Xi Jinping and his US counterpart Donald Trump in late October brought a cooling breeze over the simmering trade tensions between the global superpowers. After months of tariff war, which reached 145 per cent at some point, both countries finally struck a deal to de-escalate with a year-long trade truce.

The summit signals a new hope that competition between the world’s two largest economies can be managed without sliding into open conflict. It was a positive development for both after years of deteriorating trade relations, while also giving a well-deserved time for other countries to revisit their supply chain resilience.

“Since 2018, or the onset of the US-China trade war, we have witnessed the global supply chain enter an era of high volatility, although it may have started as early as 2015,” says Wu Jing, Professor in the Department of Decisions, Operations, and Technology at the Chinese University of Hong Kong (CUHK) Business School.

Speaking during the 2025 Global Supply Chains and Logistics Summit organised by the Asian Institute of Supply Chains and Logistics, Professor Wu, who is also an associate director at the institute, delves into strategies for businesses in navigating uncertainty and fragile supply chains.

He explains that before the frictions, the world had seen globalisation peak. Cost optimisation drove companies headquartered in developed economies to move manufacturing activities to emerging markets. This offshoring was particularly popular in the wake of the 2008 financial crisis.

However, global trade started to slow down in 2015, and companies began to relocate back. “In China, the appreciation of the Chinese yuan against the US dollar and increasing labour costs have made the manufacturing cost more expensive,” Professor Wu adds.

The US-China trade war further fuelled the drift as the increasing tariffs and the decoupling risk forced significant manufacturing orders to move outside of China. In 2022, the Russia-Ukraine war and the pandemic brought the trends of nearshoring and friendshoring, where companies relocate operations to nearby or politically aligned jurisdictions to reduce geopolitical risks.

New regulations have also changed the landscape of the traditional supply chain. For instance, the EU Carbon Border Adjustment Mechanism imposes tariffs on imported carbon-intensive products, which, although it sounds good for the planet, has raised concerns among trading partners like the US, China, India, and Brazil.

“The traditional global trade systems are becoming more challenging to solve the current problems,” says Professor Wu. “We probably need to say goodbye to a certain level of trade globalisation and hello to economic regionalisation.”

“The traditional global trade systems are becoming more challenging to solve the current problems. We probably need to say goodbye to a certain level of trade globalisation and hello to economic regionalisation.”

Professor Wu Jing

Restructuring global trade with regionalisation and technology

In a recent interview with Reuters, the director of the World Trade Organisation admitted that only 72 per cent of global trade is now happening under the organisation’s rules, the largest disruption to global trade rules in the past 80 years, and the number could fall further. This means the rest of the cross-border trade currently happens through special agreements between countries.

“We’re seeing a growing number of regional trade agreements, such as the Regional Comprehensive Economic Partnership and the US-Mexico-Canada Agreement, among roughly 400 regional deals covering goods, investment, labour, and technology,” says Professor Wu. “This isn’t an adjustment, but a restructuring.”

In this new trend, regional integration will be crucial in allowing countries to facilitate the flow of trade, capital, energy, people, and ideas. However, global opinions seem to be at a crossroads. The July report from the Pew Research Centre shows that among 28,000 respondents in 24 countries, opinions of the US had worsened, while China is increasingly seen in a more favourable light.

Most of the countries still prioritise the US when it comes to economic ties, but the views among high-income countries have moved in the direction of China. “This opinion storm could shift alliances, trade flows, and investments going forward,” he adds.

As the world shifts from global to regional networks and from cost-cutting to flexibility-first strategies, Professor Wu highlights that supply chain resilience is born from granular data and forged in lightning-fast decisions. Therefore, building supply chains using AI-driven tools and data-powered insights would be a significant advantage, and China may hold a relative advantage in this area.

The UN 2024 Patent Landscape Report shows the country dominates in generative AI patents by filing more than 38,000 patents between 2014 and 2023, surpassing the US as the closest contender with 6,276 patents. Overall, China has been leading in global patent applications by filing 1.64 million applications in 2023, leaving the US as runner-up with 518,364 filings, according to the World Intellectual Property Organisation’s report.

“By transitioning technological capabilities into an AI-driven model and strategically utilising data assets, firms would be able to position themselves to navigate the restructuring of the supply chain,” Professor Wu adds. “The integration of big data and AI will facilitate enhanced adaptability and enable informed, real-time decision-making across logistics operations.”

The post Can AI and regionalisation restructure global trade? first appeared on China Business Knowledge.

Featured faculty: Philip Zhang Renyu
Written by Putro Harnowo

Social media has become more than just a means to connect, but also a way to build communities and markets beyond borders. When TikTok was on the verge of a US ban due to national security concerns earlier this year, a mass exodus of American users to another Chinese social media platform, RedNote, ensued as a form of protest.

A few months after the ban was postponed, ByteDance, the company behind TikTok, aims for 20 per cent annual revenue growth to US$186 billion, just a smidge below Meta’s projection of US$187 billion, this year. Platforms are not the only ones living the good life. Social media users can earn money from sponsors as content creators, and if they have amassed a large following, they can earn celebrity-level income as influencers.

The secret to keeping users engaged is matching content with viewers accurately through organic recommendations, where algorithms scan a vast array of video titles to suggest content relevant to users based on their viewing history and preferences. Users can also find content through a search query, but this accounts for only a fraction of total viewership.

“That’s the reason why content creators are encouraged to add titles with hashtags or descriptions, which are the only metadata used by the recommender system to improve content relevance,” says Philip Zhang Renyu, Associate Professor of the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong (CUHK) Business School. “This metadata provides structured and concise details about the video, enabling the system to better understand the content crucial for the platform’s recommender system.”

Unfortunately, most user-generated content platforms suffer from metadata sparsity, where much of the content lacks descriptive titles or hashtags. Many of them then leverage generative AI to create and fill in missing metadata, but the outcome leaves much to be desired, as Professor Zhang and his co-authors found in their latest work, The value of AI-generated metadata for UGC platforms: Evidence from a large-scale field experiment.

“Although human-generated titles tend to outperform AI-generated ones, AI-generated titles remain valuable as they reduce the effort of title creation and at least provide a usable starting point for humans,” he says. “Even if AI performs worse overall, its output can still offer guidance, particularly for low-skilled producers who may struggle to craft effective titles on their own.”

When AI becomes a game changer

In the study, Professor Zhang, along with Goh Khim Yong of the National University of Singapore and his PhD student Zhang Xinyi, as well as researcher Sun Chenshuo, partnered with a leading short-video platform in Asia to conduct a series of experiments. While the platform boasts more than 300 million daily active users, only 60.7 per cent of the videos have titles, similar to other user-generated content platforms.

The team analysed more than two million users who produced more than 10 million videos. The videos were categorised into utilitarian videos, whose primary purpose is practical, informational, and educational, such as news, reviews and tutorials; and hedonic videos like entertainment or lifestyle content, such as vlogs, comedy sketches and travel videos. Before the experiment, 61.3 per cent of utilitarian and 57.3 per cent of hedonic videos had titles.

“AI-generated metadata can enrich the input to recommender systems, thereby improving the accuracy of user-content matching and enhancing overall personalisation within the system.”

Professor Philip Zhang Renyu

The platform introduced generative AI tools in July 2023 to create metadata by capturing multiple frames from the video, then extracting visual elements and text to write metadata that reflects the content. The researchers assigned creators randomly to either the treatment group, where they were given access to AI-generated titles, or the control group, which could only make titles by themselves.

The AI tool was found to increase the likelihood of videos having titles by 41.4 per cent and hashtags by 72.4 per cent. Overall, videos with AI-generated titles had an average 1.6 per cent increase in viewership and a 0.9 per cent increase in watch duration.

For low-skilled creators or those with a total number of followers and videos below the median among all creators, AI-generated titles helped increase viewership by 1.6 per cent and watch duration by 1.3 per cent. Looking beyond viewership, a deeper analysis of an additional dataset of almost 94 million videos found that the number of likes, shares, and follows was significantly higher among the group with AI tools.

“AI-generated metadata can enrich the input to recommender systems, thereby improving the accuracy of user-content matching and enhancing overall personalisation within the system,” Professor Zhang says. “Videos with AI-generated titles are associated with higher viewership diversity, suggesting that improved metadata enables the system to surface content to a broader and more varied audience.”

Human brain vs. machine intelligence

While the results seem positive, AI may not be suitable for everyone. Utilitarian content saw a 3.1 per cent decrease in viewership and a 3.0 per cent decrease in watch duration with AI-generated titles. For content that already had titles written by humans, changing them with AI decreased viewership by 37.9 per cent and 32.6 per cent in watch duration.

However, when creators edited the AI-generated titles, each 10 per cent change would bring 9.8 per cent more views and 8.2 per cent more watch duration. When the similarity between the AI-generated and the revised titles drops to 20 per cent, the videos perform better than those without AI-generated titles. This suggests that content creators should significantly edit AI-generated titles to maximise their performance.

Further analyses revealed that AI produces a basic thematic summary but often misses context-specific descriptions helpful for clarity and engagement. For instance, AI would create a title that says, “Enjoy the beauty of nature #ScenicNature,” and the content creator could enhance it to “Lush mountains and flowing streams: embrace nature’s serenity”. More detailed descriptors improve lexical richness and enable the recommender system to match videos with users more accurately.

Collaborative forces across platforms

Generative AI has been praised for its ability to create content, but it also raises concerns about originality and copyright issues. The study offers another perspective on how AI can enhance metadata to boost recommendation systems and content discovery.

Given that AI-generated metadata is beneficial for low-skilled and hedonic content creators, platforms should consider focusing on these segments when deciding to scale up AI tools. Prioritising new content creators to access AI would have the most immediate and noticeable impacts. Additionally, rather than automatically integrating AI titles, platforms should consider providing an option to edit to enhance the generated metadata. This strategy is not only beneficial for user-generated content platforms but also for other platforms where recommendations drive content consumption, including e-commerce.

“On e-commerce platforms, products often require metadata such as material, colour, and size, which can be time-consuming for sellers to input due to the large number of items. AI-generated metadata can help streamline this process and is likely to produce similar improvements in recommendation accuracy and efficiency,” says Professor Zhang.

With advances in large language models and multimodal AI that can process and integrate information from multiple data types, Professor Zhang believes that AI-generated metadata is likely to evolve far beyond simple titles to include richer and more structured descriptions. Future AI would automatically generate summaries, hashtags, emotional tones, scene-level annotations, and even inferred narratives that integrate visual, audio, and textual cues.

“Such metadata would offer deeper semantic insights into content, enabling recommender systems to make more nuanced and context-aware matching decisions, especially valuable for addressing cold-start problems and enhancing personalisation across diverse content types.”

The post While AI whispers, content creators make magic first appeared on China Business Knowledge.