Featured faculty: Ying Chao
Written by Sally Ho

Capital markets have been on a tear this year, driven by a massive bet on AI investments. For instance, Chinese chipmaker CXMT’s recent debut raised US$8.6 billion to deliver China’s largest listing since 2010, while a month earlier, SpaceX made history with a record-breaking US$75 billion IPO.

On top of that, two rival AI pioneers, OpenAI and Anthropic, have announced their plans to go public in the near future, each aiming to become a trillion-dollar company. Older giants like Alphabet, Amazon, and Microsoft have also seen their stocks soar following their AI ventures.

Ironically, the labour market has taken the opposite path, marked by layoffs and hiring freezes across big tech. While overall market adjustments later often bring the job market back on track, a clear pattern emerges: waves of innovation bring short-term crises for workers despite market optimism.

“There is a glaring disconnect between financial markets and the real economy. For example, stock prices sometimes surge when hiring is weak, and stock returns often move in the opposite direction of physical investment,” says Ying Chao, Assistant Professor of the Department of Finance at the Chinese University of Hong Kong (CUHK) Business School.

“When a technological breakthrough happens, markets immediately price in the expectation of future productivity, but the real-world gears, like hiring, production, and physical investment, take much longer to catch up,” he adds.

Professor Ying’s new paper, Innovation‑driven contractions: A missing link for asset pricing puzzles, co-authored with Li Zhonghao of Nanjing University and Gill Segal of the University of North Carolina at Chapel Hill, examines why technology investment is often accompanied by short‑run contractions in the real economy.

Good news for tech, bad news for workers?

Having examined financial data on US companies in Compustat from 1953 to 2019 and measured their patent values, Professor Ying and the team find that, despite boosting productivity and efficiency, technological advances shrink the workforce with a negligible impact on capital expenditure on physical assets.

This is not simply creative destruction or technological unemployment, as it doesn’t fit the typical notion that when superior businesses overtake traditional ones, both jobs and physical investments dwindle simultaneously. “Nowadays, new technology initially reduces labour input while moving the needle only slightly on capital growth,” he says.

Professor Ying and the team then grouped companies by earnings, asset values, and investment levels. After observing how often companies adjust their product prices and tracking stock returns across the entire economy, they find that when companies rarely change their product prices, higher hiring activity and capital expenditures are associated with lower stock returns.

Companies often keep prices steady for extended periods for many reasons, such as staying competitive, retaining loyal customers, and so on, even as economic conditions shift. The more rigid or “sticky” product prices are, the more likely the stock market is to react in the opposite direction of real economic activity.

This price stickiness acts as a hidden force that explains why financial markets disconnect from the real economy and why it sometimes intensifies.

“When a technological breakthrough happens, markets immediately price in the expectation of future productivity, but the real-world gears, like hiring, production, and physical investment, take much longer to catch up.”

Professor Ying Chao

“The negative contemporaneous relationship between stock returns and investment growth or labour-market surprises becomes stronger when prices are stickier,” says Professor Ying. “Stock prices are forward-looking, reflecting expected future profits from innovation. Real economic activity, like capital expenditure or labour usage, can temporarily contract or lag behind.”

“Therefore, when a company’s valuation skyrockets before a corresponding surge in real economic activity, it doesn’t necessarily indicate an irrational market bubble. Instead, it often reflects the market rationally pricing the long-term benefits of innovation, while the real economy is still undergoing short-term adjustments.”

How sticky prices drive higher stock prices

To better understand the dynamics of technological breakthroughs affecting the real economy, the team built an economic model comprising two sectors: one producing consumption goods, and the other producing machinery and equipment for other firms. Firms in both sectors have some control over their product prices, but these prices are rigid even when costs fluctuate.

After a technology shock, firms can produce more with the same level of labour and materials. But since their prices are sticky, firms charge the same price even as production costs fall, allowing them to make larger profits on each sale. “Because product prices adjust more slowly than marginal costs after a positive technology shock, markups temporarily rise. These higher markups increase valuations while restraining short-run hiring,” says Professor Ying.

A technological breakthrough also tends to lower the price of new machinery with more efficiency. Although companies want to buy more of these advanced tools and hire more workers, it takes time for product prices and wages to catch up. This stickiness across the broader economy temporarily reduces demand in real economy activities.

Put together, workers might feel the pinch of fewer jobs in the short term, while investors see the potential for higher profits and stock returns. If product prices could change easily, the stock markets and hires would move in the same direction.

“Keep in mind that whether a specific company can sustain high stock prices depends on longer-term fundamentals, including market demand, how fast they expand capacity, and how well they fend off competitors,” Professor Ying adds.

A technology-based signal for future returns

For shareholders, the disconnect between the stock market and the real economy can make it harder to predict how a stock will perform in the short term. Therefore, based on the above model, the team develops a mathematical formula called the investment-based dividend yield to track how technological innovations, rather than stock market data, influence economic dynamics.

This investment-based dividend yield is a broad economic indicator derived from the above model, rather than a measure of the cash-flow potential of an individual company’s investments. “This tool is a macro indicator that helps predict the overall stock market’s future risk, not a tool for short-term trading on individual stocks. This indicator strongly and negatively predicts future aggregate stock returns,” says Professor Ying.

Looking ahead, Professor Ying hopes to determine whether this tool can be applied with more specific information, such as data from individual industries, and with more precise ways to track innovation, especially those related to AI. “With AI specifically, the biggest challenge now is untangling genuine productivity shocks from other noise, like government subsidies, hype-driven demand, financing conditions, or geopolitical shifts,” he adds.

“Going forward, we also need to look more closely at cross‑sectional differences: how these tech shocks impact different types of workers, the role of intangible capital, and how the ultimate gains and losses of technological change are distributed across the broader economy.”

The post Why the AI boom spells short-term labour gloom 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: 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: Li Jingyu
Written by Joanne Madrid

The pandemic was a poignant reminder of the importance of information technology, or IT. Almost overnight, businesses scrambled to go digital, with video calls replacing boardroom meetings and cloud platforms keeping teams connected.

The rush to adopt technology has not slowed since. According to McKinsey & Company’s 2025 report, 88 per cent of firms across 105 countries are now using artificial intelligence in at least one area of their business, a spike from 20 per cent in 2017.

For China, technology is the core of its economic powerhouse. Private companies contribute 70 per cent of the country’s technological innovation, and Chinese tech firms, whether in semiconductors, artificial intelligence, or robotics, have been rushing to go global.

Companies now have a wealth of options to choose tech tools, but does piling on more technology automatically make a company better prepared for the next crisis? Not necessarily, according to a study co-written by Li Jingyu, an Assistant Professor of the Department of Management at the Chinese University of Hong Kong (CUHK) Business School.

“Having too much variety of IT tools can actually make it more costly and difficult for companies to come up with new plans, decide on the best ones, and then put them into action,” says Professor Li. “This problem gets much, much worse during unexpected shocks, where companies have to make decisions and act quickly.”

The paper, titled Navigating the storm: Towards a theory of IT portfolio diversity, leadership power, and organisational resilience to major shocks, found that companies with a moderate range of digital tools were generally more resilient during crises than those with either very few or many digital tools.

The safe zone of digital tools

Along with Li Mengxiang of Hong Kong Baptist University, Hsieh Po-An at Georgia State University, Wang Xincheng at Tongji University, and Gu Bin at Boston University, Professor Li examined 2,926 listed Chinese firms, tracking their revenues before, during and after the pandemic. Firms that are better at absorbing a shock would see smaller drops in income, or even manage to grow.

At the heart of the study is the concept of IT portfolio diversity, which refers to the variety of digital tools a company can deploy. This includes everything from cloud computing and video-conferencing systems to mobile applications and enterprise management software.

The analyses find that companies with a moderate level of IT portfolio diversity have genuine advantages. They have sufficient technologies to allow them to shift operations online, coordinate staff remotely, and keep information flowing when normal routines are upended.

“Having too much variety of IT tools can actually make it more costly and difficult for companies to come up with new plans, decide on the best ones, and then put them into action.”

Professor Li Jingyu

Yet the benefits did not keep rising in a straight line. Firms that had accumulated a very wide array of technologies often struggled during the crisis. A sprawling and fragmented technology setup can be expensive to maintain and difficult to coordinate.

These problems become acute when speed and clarity are most needed, such as during a crisis. In normal times, firms felt less pressure to reorganise their systems. After the crisis, many had already adapted to new ways of working. “When IT portfolio diversity is too low or too high, it can lead to inferior performance related to organisational resilience,” Professor Li adds.

Think of it as a chef’s kitchen. A good range of knives, pans and appliances makes cooking faster and more versatile. But cram every kitchenware imaginable onto the worktop and the cook room becomes cluttered and confusing, especially when hundreds of unexpected guests arrive.

Giving technology leaders a seat at the table

Having the right tools is only half the equation, because the man behind the gun pulls the trigger. Who is in charge of those tools and how much authority they hold matters significantly.

Companies where senior technology executives sat on the board or held top-level positions were far better at turning a diverse set of digital tools into a real edge. These leaders could coordinate resources across departments, resolve conflicts between teams, and ensure that technology decisions supported the company’s broader strategy under intense pressure.

“Firms that intend to build digitally enabled organisational resilience to withstand unforeseeable shocks should pay special attention to enhancing the power of their IT leaders,” Professor Li says.

Without a high level of authority, even well-stocked technology tools can go to waste. If the head of technology lacks the standing to redirect resources or overrule competing departmental priorities during a crisis, the firm’s digital tools may sit underused or, worse, pull in different directions.

It is worth mentioning that what constitutes too much varies from company to company. Companies with very strong IT leaders might be able to manage a much wider array of digital tools effectively before getting overwhelmed.

Industry context changes the calculus

Not all industries experienced the crises in the same way. Transport and hospitality were devastated during the pandemic, but pharmaceuticals and express delivery boomed. These contrasting fortunes shaped how much the technology-leadership combination mattered.

In hard-hit sectors, the payoff from owning diverse digital tools with strong tech leaders was amplified. Affected firms benefit more from well-coordinated digital responses, but if their digital systems are disjoint, more severe consequences ensue. In this case, technology becomes a high-stakes game.

For businesses that thrive during crises, their digital portfolios and powerful tech leaders aren’t critical to survival since they already meet what the market needs.

In short, digital expansion alone does not make a company crisis-proof. Organisations need to think carefully about how many different technologies they can realistically manage, and ensure that the people responsible for those technologies have decision-making authority.

“Our findings underscore the need for firms to deliberately cultivate organisational resilience, enabling them to strategically align IT portfolio diversity and the authority of tech leaders in responding to different environments,” says Professor Li.

While the study covers only Chinese listed companies, she believes the core insight is likely to resonate well beyond borders. In a crisis, the winners are not necessarily the companies with the most technology, but those with the right amount and the leadership to make it count.

The post Why too much tech hurts resilience 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: Cen Ling and Wu Jing
Written by Pan Jingyi

In recent years, artificial intelligence (AI) has become a key tool for companies, helping with everything from demand forecasting and procurement to streamlining and optimising processes. For global supply chains that have been under pressure lately due to geopolitical uncertainties, trade conflicts, sanctions, and environmental concerns, AI could be a game changer.

According to a recent EY report, around 40 per cent of supply chain organisations are investing in generative AI for managing knowledge and information. Technologies tend to spread along economic networks, either horizontally among competitors or vertically along the supply chain. However, the diffusion of emerging technologies such as AI remains underexplored.

“We uncover a clear pattern of AI diffusion along supply chains, where AI adoption among downstream sectors leads to subsequent adoption among their upstream suppliers,” says Cen Ling, Associate Professor at the Department of Finance of the Chinese University of Hong Kong (CUHK) Business School.

Downstream industries are those closer to the final stage of production and delivery to end customers, including service and manufacturing sectors, while upstream industries refer to sectors providing raw materials or basic inputs, like mining and agriculture, for downstream industries.

Professor Cen highlights Foxconn, a key supplier for Apple and Nvidia, as a notable example. The rapid AI applications of its major customers have turned Foxconn from a labour-intensive company to one that produces AI-driven electric vehicles and AI servers to house Nvidia chips.

Along with Wu Jing, Associate Professor at the Department of Decisions, Operations and Technology of the School, as well as Han Yanru of Stevens Institute of Technology (A Graduated PhD student of CUHK Business School) and Qiu Jiaping of Shanghai University of Finance and Economics, Professor Cen conducted a study titled Artificial intelligence along the supply chain to delve deeper into critical questions like whether supply chain can serve as a diffusion channel for AI technologies and what economic mechanisms propel this diffusion.

Lead-lag patterns

To measure how American public companies are adopting AI, the team tracked the hiring of AI-skilled employees using data from Revelio Labs, an analytics firm that gathers workforce information from employment platforms like LinkedIn and Indeed, from 2009 to 2019.

The results show a significant increase in AI employees over the past decade, with AI use growing in almost all industries. However, considerable variation exists among various sectors, with downstream industries adopting AI technologies much faster than upstream industries. This is because, Professor Cen explains, downstream companies in the supply-chain data are typically large and reputable industry leaders with direct access to big data of customer profiles.

“We uncover a clear pattern of AI diffusion along supply chains, where AI adoption among downstream sectors leads to subsequent adoption among their upstream suppliers.”

Professor Cen Ling

Further analysis unveils that the increase in AI adoption by main customers precedes and potentially causes an increase in AI adoption among their suppliers in the following year. The researchers call this a “lead-lag” within firm-pair relationships and found that this pattern is not driven by market-wide or industry-specific trends.

In a controlled experiment where the team replaced the suppliers with those who have no prior connections to customers, the results showed that the AI adoption at customer firms does not impact that of suppliers. This suggests the diffusion is indeed driven by firm-to-firm interactions.

Learning and catering mechanism

Suppliers may enhance their AI adoption in response to their primary customers through two key channels: learning and catering. Under the learning scenario, primary customers, often large firms or industry leaders, typically adopt AI technologies before their suppliers, which then absorb and apply these technologies in their operations through routine supply chain interactions.

“Under this mechanism, close strategic relationships with customers equipped with AI technologies reduce suppliers’ costs of learning and adopting AI, which may improve suppliers’ own operational activities and performance,” Professor Cen says.

In the catering scenario, suppliers respond to the needs of major customers who have embraced AI technologies to sustain crucial partnerships. “The effectiveness of the catering channel depends on the relative bargaining power of customers against their suppliers,” he adds. “The learning channel is influenced by the relative size of supply-chain partners, which affects the applicability of the knowledge transferred.”

Based on the collected data, Professor Cen and his collaborators found that the learning mechanism is the primary driver. Suppliers mostly adopt AI to enhance their own capabilities rather than just to cater to customers’ demands.

What makes AI spread faster?

To examine the factors that can affect the suppliers’ learning mechanism, the team examined the employee mobility and geographic distance between customers and suppliers and found that when suppliers hire managers who previously worked for their main customers, the AI adoption is notably higher. Managers normally have a broader view of their company compared to rank-and-file employees, who may lack knowledge of AI advancements unless they work in AI-related roles.

“Our results validate that labour mobility from customers to suppliers, particularly employees with AI-related visions or skills, promotes the AI learning along the supply chain,” Professor Cen adds.

The diffusion of AI technology is also stronger when the geographical distances are shorter. Moreover, when customers relocate farther from suppliers, the suppliers’ AI adoption becomes less influenced by their customers.

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“A shorter distance between supplier and customer facilitates more frequent interactions and leads to faster transfer of knowledge and information,” Professor Cen says, highlighting the crucial role of geographic proximity in stimulating learning.

Positive economic outcomes

Finally, the team examined whether AI diffusion from customers to suppliers actually improves business outcomes. The result confirms that suppliers that have learned AI from their customers are more likely to enhance product quality and manage costs more effectively. More specifically, every standard increase in AI hiring is linked to a 0.74 per cent higher chance of boosting product quality.

As AI continues to spread across industries and borders, understanding how it diffuses along supply chains offers valuable lessons for both business leaders and policymakers.

Professor Cen suggests corporate managers can mitigate risks and maintain competitiveness by tapping into AI knowledge within their supply chain partners. For instance, they can identify the optimal point to acquire such knowledge from trade partners or hire AI experts from these partners.

Compared to traditional technologies, AI requires a significant initial setup cost. However, once established, the ongoing operational costs of using it are comparatively low. Professor Cen argues that government subsidies for downstream customer firms could help kickstart the adoption, “then the positive externalities will diffuse along economic networks to achieve a socially optimal level of AI adoption.”

The post Beyond cargo, supply chain also transfers AI first appeared on China Business Knowledge.

By Pan Jingyi, Principal Writer, China Business Knowledge @ CUHK

Following the launch of ChatGPT in late 2022, artificial intelligence (AI) has supercharged the possibility to revolutionise the modern industry, rushing Microsoft to invest US$10 billion in OpenAI. This step signalled a race among big tech to incorporate AI into their products. A few months later, Meta introduced its own AI called LLaMa, while Apple recently announced its “Apple Intelligence” system by integrating AI into its gadgets.

The US is not the only one that is trying to get its hands on AI. When OpenAI launched its video-generation model Sora in February this year, many were amazed by its ability to create videos just by typing a cue on a keyboard. Fast forward in June, China’s short-video platform Kuaishou unveiled a text-to-video model named Kling, making it a worthy opponent for Sora. The UK, France, Germany, Israel, India, Japan, and Singapore have also entered the ring in the AI competition.

“AI will not replace humans, but those who don’t make good use of AI [will be replaced].”

Professor Dominic Chan

According to a 2024 AI index report by Stanford University, generative AI funding surged to US$25.2 billion in 2023, nearly nine times higher than the previous year and 30 times the amount recorded in 2019, with generative AI made up more than a quarter of all AI-related private investment. The report also indicates that AI outperforms humans in certain benchmarks like image classification and English understanding, but it still falls short in complex cognitive tasks.

But not everyone is amused. A Global views on AI 2023 survey by market research firm Ipsos found widespread concern about the negative impacts of this advanced technology on employment. Approximately, 57 per cent of 14,782 working adults across 31 countries anticipated that AI would change the way they do their current jobs, while 36 per cent were worried about AI taking their jobs.

How is AI going to impact our society and transform industries? What can people do to get themselves ready for this new era? Dominic Chan, Associate Professor of Practice in Entrepreneurship of the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong (CUHK) Business School, shared his thoughts on these questions in a masterclass for the school’s EMBA programme titled Embracing technological disruption: Thriving as leaders in the age of AI in April.

Assisted intelligence

In a nutshell, AI refers to the technology that enables computers and machines to emulate human intelligence and tackle problem-solving tasks. The words people hear a lot such as machine learning, deep learning, natural language processing and generative AI, are all interconnected fields within the broader domain of AI. These advanced technologies have been applied to various sectors and industries to streamline processes and improve efficiency. In the realm of business, AI encompasses a wide range of applications, including chatbot assistants, fraud detection, and task automation, among others.

“AI can help you to organise your information or help you automate the process, which makes a big difference in productivity and quality of the work,” Professor Chan says, adding that AI can amplify human abilities.

Professor Chan also highlights that people should view AI as “assisted intelligence”. This perspective underscores the notion that AI serves as a tool to assist people rather than replace them. He notes that AI lacks the ability to think independently and operates based on the training it receives from human input. “AI makes decisions based on mathematics not ethics,” he adds.

Will I lose my job to AI?

Along with the emergence of new technologies throughout history, the topic of job security has once again become a prominent concern, with people expressing apprehensions about being replaced by AI.

To illustrate which types of jobs are most threatened by AI and which types of jobs are likely to be safer, Professor Chan refers to a model proposed by a renowned businessman and computer scientist, Lee Kai-Fu, in his book titled AI superpowers: China, Silicon Valley, and the New World Order. According to this model, jobs that primarily require optimisation rather than compassion to be most likely to be replaced, such as truck drivers. Conversely, jobs involving compassion, creativity, or strategic thinking, such as social workers, are less likely to be replaced.

Furthermore, from the analysis of Age Earning Profile, Professor Chan observed very clear shifts in income trends across generations as people age, indicating that the income curve peaks at an earlier age with each subsequent generation. Separately, Professor Chan observed those born between 1986 and 1995 earning less than the previous generations. This phenomenon is partly attributed to the rapid development of technology. Knowledge and skills accumulation held significant value before the 1990s because computers, the internet and AI had not yet become widely available. Now, with the advancement of technology, human knowledge and skills depreciate rapidly.

“The way for most people to create value today is the ability to use technology,” says Professor Chan.

AI poses challenges not only for rank-and-file employees but also for the management. A 2023 report titled Leadership in the age of AI by IBM indicates that 82 per cent of more than 1,600 senior leaders surveyed have deployed or planned to implement generative AI in 2024. Alongside opportunities, the report highlights that business leaders across all sectors grapple with challenges related to skills, ethics, privacy and data security.

“In the past, managers primarily managed people, but today they have to manage the machines as well,” says Professor Chan. “Managers also need to oversee and facilitate the technological interactions between their own organisations and other firms.”

Make good use of technology

While AI has undoubtedly reduced tedious work and enhanced productivity, it is not without flaws. For instance, if you ask AI to generate a picture on the subject of a secluded temple on a mountain, it can present you with glorious images of temples and mountains. However, a human tasked with the same assignment may have different outcomes, which would feature symbols or metaphors that go beyond the literal interpretation of the request. Professor Chan notes that AI doesn’t think by itself but relies on what humans have already done.

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Whether their current job falls into a safe or dangerous zone, everyone needs to be well-prepared for the future coexisting with AI. Professor Chan suggests that individuals who possess complex problem-solving skills, critical thinking, creativity, communication, and compassion (or he calls them 5C abilities) will be better equipped to deal with the challenges ahead.

Finally, Professor Chan encourages people to embrace AI technology and enjoy the ride, as it will make individuals more productive. “AI will not replace humans, but those who don’t make good use of AI [will be replaced].”

The post Why you should and shouldn’t be afraid of AI first appeared on China Business Knowledge.

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