Biz@HKUST, Spring 2022 (Issue 66)

Abstract

1SPRING 2022.01 www.BM.UST.HI< @HKUST II Contents // Biz THE VALUE OF DATA @Hl<UST Insights and News from the HKUST Business School 34 38 41 44 4 Victor LAM, Government Chief Information Officer, HKSAR Government IN MAsTER PLAN FORA SMART CilY 8 Ada CHUNG, Privacy Commissioner, Office of the Privacy Commissioner for Personal Data IN PERSONAL DATA PRIVACY MATTERS 12 Michael YUE, General Manager of Sales and Operations, Google Hong Kong IN MEETING THE NEED 16 Jelly ZHOU, CEO (HK) of HKTVmall IN SHOPPING MADE EAsY 20 S0ren J0RGENSEN, Director of the Responsible Digital Leadership Project, Stanford University IN MAKING IT PERSONAL 24 Tech lawyer Frankie TAM and HKUST students contribute to data ethics IN KEEPING AI HONEST By Professor HUI Kai-Lung, Professor Allen HUANG, and Postdoctoral Fellow Dr LIM Hyungsoo of the Center for Business and Social Analytics FROM DATA TO ACTIONABLE INSIGHTS: HARNESSING THE POWER OF BUSINESS AND SOCIAL ANALYTICS By Professor Lancelot F. JAMES Department of Information Systems, Business Statistics & Operation Management, HKUST Business School BIG BUSINESS By Professor YOU Haifeng Department of Accounting, HKUST Business School A MAN WITH A MACHINE By Professor LIU Jia Department of Marketing, HKUST Business School How DATA ANALYTICS ARE USED IN MARKETING By Professor HUANG Yangguang, Department of Economics, HKUST Business School DATAAND REPUTATION BUILDING ON ONLINE PLATFORMS :.:.:.:,: 48 HONOR RECOGNIZES TEACHING EXCELLENCE % Faculty receive local and global recognition in their chosen f ields C ~ ::::: 50 POINTING THE WAY Influential business leaders offer advice for education of tomorrow 51 IN RECOGNITION OF TALENT Support to foster and retain the very best minds 52 POSITIVE OUTCOMES Achievements attest to leading positions in the region 54 MAKING A STRONG CASE Asia-related business cases hit an all-time high in global reach 56 THE FIRST BSC IN SUSTAINABLE & GREEN FINANCE IN HONG KONG New program bridges the talent gap in the emerging field 59 COLLABORATING FOR THE NEXT-GENERATION FINANCE SERVICES Partnerships with respected organizations in promoting green finance and fintech 60 DEVELOPING Al AND BIG DATA TALENT Recommendations to meet the surging demand for Al and big data talent ~HKUST llll llJ11 1 BUS INESS SCHOOL \~ if ~ ~ * iffj ~ 11% WORLD CLASS IN ASIA ''''''''' Biz H (UC,T THE VALU E OF Ht..lJST tj ~,,}~,!., The magazine is available for download or can be delivered via email on request. Published by External and Alumni Relations HKUST Business School The Hong Kong University of Science and Technology Address/ Clear Water Bay, Kowloon, Hong Kong Tel/ (852) 2358 8934 Fax/ (852) 2719 8231 Email/ bmpr@ust.hk ~-?&~:m~~ Im ,,.~ --~ I!] ' - Stay Connected l!]~-f,:l!]a ? . . YouTube In all absolute terms, data is powerful. Its uses are limitless, and it can be repurposed to suit any need. Along with providing incredible insights, the collection of facts can tell a story about the past and the present, and even predict the future. For this reason, the value of data is said to be greater than oil and gold. II From the Dean II In this issue, we explore what data means to businesses today. By making a deep dive into the world of data, we try to understand its massive potential, and learn how it can contribute to the development of applications and open up new business models. With this in mind, we spoke to leaders and experts who discussed their experiences and the challenges they face as they navigate their way through an age of dat a-led world. To introduce the issue, we interviewed the Office of Government Chief Information Office (OGCIO) and the Office of the Privacy Commissioner for Personal Data (PCPD), some of Hong Kong's foremost innovators and gatekeepers. Victor LAM, the Government Chief Information Officer, talks about the benefits of a Smart City and how Hong Kong is embracing technology and data to succeed as 'Asia's World City', while the Privacy Commissioner for Personal Data Ada CHUNG explains how the public and businesses should handle personal data. Turning to the private sector, we hear from our Kellogg-HKUST EMBA and MBA alumni who now work for the highly successful global and local companies Google and HKTVmall. Michael YUE, General Manager of Sales and Operations of Google HK, talks about the incredible potential of data, and Jelly ZHOU, CEO (HK) of HKTVmall, shares her experience of growing a business using the power of information and data analytics. With great power comes great responsibility, and big data presents a host of ethical concerns. We went overseas to get some views from S0ren J0RGENSEN, Director of the Responsible Digital Leadership Project at Stanford University, who has been working on a project that helps industries foster an ethical at t itude. Frankie TAM, a tech lawyer in Hong Kong who has also involved in the project along with our students, talks about identifying ethical dilemmas that arise from technology. On the educat ional front, our School is proud to play a central role in nurturing professionals for a digital era. Our dedicated faculty and research centers have been working in different but related fields. In this issue, they share their insights via a series of art icles looking at how data fits into the bigger picture of society and business. From the Center for Business and Social Analytics, Professor HUI Kai-Lung, Professor Allen HUANG and Dr LIM Hyungsoo co-authored an article about how big data can help solve social problems and aid businesses. Professor Lancelot JAMES, our Director of MSc in Business Analytics, presents his views from the perspective of an educator. Account ing Professor YOU Haifeng explains w hy computers are not ready to replace human intelligence, while Market ing Professor LIU Jia discusses the importance of data analytics in her f ield. We also hear from our Economics faculty Professor HUANG Yangguang, and he demonstrates how on line reviews can improve market efficiency and consumer welfare. With such a wide range of views and perspectives, we feel this issue can help to better prepare businesses and society as they navigate the world of big data. Professor TAM Kar Yan Dean Blz@HKUST Traversing Big When it comes to big data, the future is truly limitless. In this interview series, we hear from a number of leaders and industry experts who talk about what it means to harness big data to capitalize on opportunities, and to grow businesses and further improve society. However, they also note that access to data is a privilege, and it should be used with care and caution. Hailing from a range of backgrounds, they provide readers with first-hand accounts of the power of data. It is their firm belief that data and technology will be the key drivers to success in the future. 4 Biz@HKUST Biz@HKUST 5 // Cover // Thought Leader Master Plan for a Smart City Victor LAM, the Government Chief Information Officer, explains the benefits of a smart city and how Hong Kong embraces technology and data to succeed as ?Asia?s World City?. Hong Kong was accelerating plans to roll out the Government?s Smart City Blueprint long before the COVID-19 pandemic. First introduced in 2017, the blueprint aims to improve all aspects of Hong Kong citizens? daily lives by using technology and data. The blueprint sets out 76 initiatives in six smart areas to develop the city?s digital infrastructure. These include ?Smart Mobility?, ?Smart Living?, ?Smart Environment?, ?Smart People?, ?Smart Government? and ?Smart Economy?. The city has started to experience some of the major benefits stemming from these developments during the last three years, including the introduction of free Wi-Fi hotspots, the Faster Payment System (FPS), and the ?iAM Smart? platform. Due to the COVID-19 pandemic, the city rapidly learnt how technology and data could help to control the spread of a virus, most notably through the ?StayHomeSafe? system that supports home quarantine, and the ?LeaveHomeSafe? app. Catalyst for Change All these initiatives are underpinned by technology and data, and they show how the power of data can affect people?s daily lives, Lam says. He also emphasizes that in Hong Kong, ?It is just the beginning, and there?s still so much more to do.? Since the start of the pandemic, the Government has been updating and accelerating many of its SMART city plans under an updated version called Blueprint 2.0. This says that, ?Our work in combating the epidemic in 2020 has given us some insights on promoting I&T development. In particular, the adoption of an innovative mindset to transform established service modes and the wider adoption of technology to combat the ?new normal?.? New plans also include initiatives such as the SMART village pilot, which intends to improve the livability for people in rural areas. Building a SMART city is no small feat for a city of more than 7.5 million people, but Hong Kong is doing an excellent job. The city ranked second in the world in the IMD World Digital Competitiveness Ranking for 2021, which measures the capacity and readiness of economies to explore and adopt digital technologies for economic and social transformation. Furthermore, the massive uptake and download rate of the city?s apps and data is a testament to the popularity of the digital realm. ?We have released the HK eMobility app, and it has proved really popular,? Lam says. ?We recorded 2.6 million downloads, and had a daily hit rate of 80,000. Last year, we had 10 billion downloads of open data, and this year we have already exceeded 11 billion because many citizens require real time data of facilities, such as buses and minibuses, to facilitate their daily journeys.? This trend is in line with findings from the Organization for Economic Co-operation and Development (OECD) that showed the proliferation of data from devices is growing at an unprecedented rate, thanks to the use of devices, cloud computing and AI. That?s why data is the core component of the SMART city plans, Lam says. Aside from innovation and technology, economic and urban development requires that the public sector joins hands with the private sector to utilize data to improve the city?s operations. ?Through open data collaboration, we can even use social media analytics to gauge a citizen?s feeling about public policies. We can make use of big data analytics in the management of public toilets, air mail and postal services, and analyze traffic data,? Lam says. Big data is also a powerful way to make predictions. For instance, it can combine weather data and transport data to predict the arrival time of transportation on a rainy day. Such innovation can only be made possible by the combined strengths of technology and data. Greater Responsibility Lam recognizes that with great power also comes great responsibility. He says that the HKSAR Government takes data privacy seriously. ?Safeguarding personal data is a high priority. Open data is open, but there are big concerns about personal data because all these factors affect public trust,? Lam says. The Victor Lam Government Chief Information Officer HKSAR Government 6 Biz@HKUST Biz@HKUST 7 // Cover // Thought Leader Government ensures that multiple safeguards are in place to protect personal data, and privacy impact assessments are carried out before new projects are started, he says. To carry out its digital plan effectively, the Government needs to have the right talent in place. Developing a pipeline of graduates with the right interdisciplinary skills is top of the agenda when it comes to making Hong Kong a premier information and technology hub. The Government has been actively investing into IT programs at the primary and secondary levels, emphasizing IT education as a core part of today?s curriculum and promoting extra-curricular IT activities in schools. Furthermore, it has rolled out many exciting programs and award recognition to encourage students to consider future jobs in innovation and technology. Lam points out that the HKSAR Government also recognizes the important role that universities play in nurturing talent, and has consequently been injecting more resources into research and interdisciplinary research to identify new areas of innovations and programs. To develop Hong Kong into a worldclass I&T hub, collaborative efforts need to take place between the Government, academia, and the private sector, as well as leverage on the opportunities that have been brought about by the Greater Bay Area Region SMART Mobility SMART CITY Blueprint 2.0 Over 95% of Hong Kong citizens use the Octopus card for travel and spending Smart check-in kiosks, indoor wayfinding, and self-bag drop offs at the airport Over 41,800 free Wi-Fi hotspots in Hong Kong Mobile subscriber penetration rate is 283% A reduction of carbon intensity by 35% in 2019 compared to 2005 Government buildings reduced electricity consumption by 5% 60% of senior secondary students studied one or more STEM-related elective subjects in 2019/2020 R&D funding in 2019 reached HK$26,333m, an increase of 8% compared with 2018 Public sector information portal has over 4,690 unique datasets and 1,800 application programming interfaces GovHK makes information and services easier for the public and now includes around 850 e-services Internet Banking saw 15.8 million accounts with over HK$11.9 trillion monthly transactions in 2019 SMART Living SMART Environment SMART People SMART Government SMART Economy Victor Lam Government Chief Information Officer HKSAR Government M O R E But at the end of the day, Lam says, in order to develop Hong Kong into a world-class I&T hub, collaborative efforts need to take place between the Government, academia, and the private sector, as well as leverage on the opportunities that have been brought about by the Greater Bay Area Region. ?The National 14th Five-Year Plan explicitly mentions the objective to develop Hong Kong into an international technology hub, so I?m confident that innovation and technology will prosper in the years to come,? Lam says. ?Universities will have a strong role to play. They will help not only the development of Hong Kong, but also turn Greater Bay Area into a global I&T hub.? 8 Biz@HKUST Biz@HKUST 9 // Cover // Thought Leader Personal Data Privacy Matters The Office of the Privacy Commissioner for Personal Data aims to inspire citizens to be more careful about how they use their personal data online. But this involves challenges, says the Privacy Commissioner Ada CHUNG. When it comes to developing a culture of privacy, protection and respect for personal data, Ada Chung, the Privacy Commissioner for Personal Data (Privacy Commissioner) in Hong Kong, has a massive job on her hands. As an independent watchdog, the Office of the Privacy Commissioner for Personal Data (PCPD) is designed to monitor, supervise, promote and enforce compliance in relation to the Personal Data (Privacy) Ordinance (PDPO), which came into effect in 1996. Aside from supervising and enforcing protection of personal data privacy, the PCPD provides guidance, public education and best practice notes on the lawful and responsible use of personal data. In today?s digital world, this is no easy task. The rise of technology and the growing use of social media and apps are driving a proliferation of data, and online activities have increased dramatically, especially during the pandemic. Consequently, the PCPD has its hands full putting out fires while trying its best to educate the public and businesses about respecting and safeguarding personal data. According to the results of a survey released by the PCPD in January 2021, over 85 per cent of Hong Kong citizens are active users of social media, and share personal information online, Chung says. This information often includes their date of birth, residential address, and health information. Ominously, most online users are unaware that they are sharing these details. ?This is dangerous,? Chung says. ?If personal data is leaked, it can be misused, and that can lead to the perpetration of crimes or fraud. In Hong Kong, in the past two years, doxxing has become rampant, which has caused serious and long-lasting effects on its victims.? Doxxing is the act of disclosing the personal data of a data subject without the relevant consent of the data subject, and the discloser is being reckless or has an intent to cause harm to the data subject or any family member of the data subject. There has been a notable rise in doxxing in Hong Kong in recent years. The privacy watchdog noted that over 5,800 doxxing cases were handled between June 2019 and June 2021. Data breaches are also becoming more frequent worldwide. The number of high-profile data breaches which have affected a huge number of individuals has been increasing. One recent example occurred in April 2021, when networking data associated with 500 million LinkedIn users was posted on a forum on the Dark Web. Hong Kong citizens were certainly among those affected. ?In the past few years, the scale of this has been unprecedented,? Chung says, noting that things may get worse. Fundamental Human Rights For this reason, the PCPD plays an increasingly important role when it comes to protection of personal data privacy. Hong Kong recently made amendments to its privacy laws which gave significant powers to the Privacy Commissioner to remove doxxing messages. This legislation also carries extra-territorial powers, so the Privacy Commissioner can serve cessation notice to internet service providers having a place of business in Hong Kong, or operators of overseas social media platforms which are outside of Hong Kong, to take down any information that is deemed to be doxxing, within a designated timeframe. ?Privacy is a fundamental human right. Protection of personal data is indispensable in protecting individuals? privacy,? Chung says. ?Protection of personal data is particularly important in a digital era where anyone?s personal data can be widely shared in a split second.? Given the boundless nature of the internet, and the global increase in digitalization, the PCPD will not be able to achieve its mission alone. Chung says that everyone must play a part to protect personal data privacy. This is the key message that the PCPD has been promoting to the public. Chung emphasizes that the development of mobile applications and data-driven technology mean that businesses and individuals who are data users have an equal responsibility to meet the legal requirements that are set out in the PDPO. They are required to comply with the six Data Protection Principles when collecting, holding, processing and using personal data, Chung says. Part of the PCPD?s job is to ensure that everyone is familiar with the PDPO and knows how to apply them. That includes Ada Chung Privacy Commissioner for Personal Data 10 Biz@HKUST Biz@HKUST 11 // Cover // Thought Leader M O R E Privacy is a fundamental human right. Protection of personal data is particularly important in a digital era Accountability Organisations should be responsible for what they do, and provide justification for their actions. The PCPD proposes seven ethical principles for the ethical development and use of AI Organisations should disclose how they use AI, and the relevant policies, to stakeholders while improving the interpretability of automated AI decisions. The level of human involvement should be proportionate to the risks and impact of using AI. The use of AI should provide benefits and minimise harm to stakeholders. Effective data governance should be put in place to protect an individual?s personal data privacy during both the development and use of AI. Bias and discrimination should be avoided. AI systems should operate reliably, be free of errors, and be protected against attacks. Transparency & Interpretability Human Oversight Fairness Reliability, Robustness & Security Beneficial AI Data Privacy Ada Chung Privacy Commissioner for Personal Data both organizations and individuals. Promotion and publicity work have to take place in order to raise the awareness of how to protect personal data, especially for the more vulnerable segments of society such as the youth and the elderly. ?A lot of people are simply not aware of these issues,? says Chung in an urgent tone. ?They need to be aware that their personal data is a valuable asset and that they shouldn?t be giving it away too easily, and in arbitrary manner,? she says. The watchdog has also been visiting schools to educate students about personal data protection, and to increase the general awareness of scams and fraud. Chung also wants to teach students to be wary of online communications from unknown sources. Regulations Set to Increase As technology such as artificial intelligence (AI) becomes more commonplace, the PCPD will continue to have a greater role to play in safeguarding personal data. So looking to the future, Chung says that regulations will likely become more common, and there will be a greater focus on automated technology like AI. The PCPD recognizes that AI is developing rapidly, and is aware of the many privacy concerns and risks to fundamental human values that could arise. That?s why the PCPD has been working hard to create ethical frameworks. In August this year, the PCPD issued the ?Guidance on the Ethical Development and Use of Artificial Intelligence? to help organisations understand and comply with the relevant requirements of the PDPO when they develop or use AI. ?The next ten years will be the era of AI, and it will bring fundamental changes to the way people behave, and to our daily lives,? Chung says. ?The effect of AI on human beings will be tremendous.? It is for these reasons that Chung thinks frameworks and guidelines must be put in place to regulate how data is being used. ?I believe that in the years to come, there will be more emphasis on accountability and what businesses should do to comply with the law,? Chung says. Such developments will include the development and implementation of Personal Data Privacy Management Programme (PMP) by organizations, to gain trust from customers and other stakeholders, as well as the appointment of Data Protection Officers to oversee organisations? compliance with the PDPO. Cybersecurity will also become a hot issue in the boardroom in the coming years. The Ethics of AI 12 Biz@HKUST Biz@HKUST 13 // Cover // Thought Leader Meeting the Need Digital technology is not a ?nice-to-have? thing today and data is not something that sits in a silo, says Michael YUE, General Manager of Sales and Operations at Google Hong Kong. When it comes to the future of data and technology, it is really up to businesses and individuals to decide how they want to apply data to solve their problems or create new user experiences, says Michael Yue, Google?s General Manager of Sales and Operations in Hong Kong. The COVID-19 pandemic has forced organizations to accelerate their strategies and adoption of digital technology, Yue says. Digital technology used to be regarded as something that was nice to have, but now it is a must-have. For this reason, organizations must realize that data can no longer operate in silos, and as at Google, companies must look at how they can break down the data silos and use them to their advantage. ?When we look at collaboration across Google, it?s how we bring all data together, how we break down the silos of data that will hopefully improve strategy, and bring better opportunities for businesses and employees to connect,? Yue says. Data is not something that sits in a silo, it is a critical element of all aspects of a business, Yue notes. For small and medium enterprises (SMEs), Yue refers to Google Hong Kong?s Smarter Digital City initiative. This initiative which includes research took place between 2017 and 2020 and explored how Hong Kong could become a smarter digital city and accelerate its digital transformation. The research highlights that the ability to demonstrate impact and Return on Investment (ROI) early on with insights from data can be critical towards the success of a business. Digital Transformation Journey Yue says that data plays a critical role when it comes to driving businesses and creating value. Citing some case studies, businesses could use analytics software to drive more efficient marketing, or look at the global supply chain to see what the constraints are, he says. They could use data to predict what inventory to stock in the future, for example. ?Organizations that piece it all together can add really great value to their businesses,? he says. Yue says that companies must think about the business challenges they are facing and see how data can become insights and inform companies about the best possible solutions. ?For data to bring value to businesses, they need to be very clear about what business challenges they want to solve with the data. They need to apply that knowledge to drive business value,? he says. Digital transformation is a journey, and Yue says that all companies need to think about the innovation that data can provide, along with using it to solve existing problems. ?Businesses can take innovative approaches to the ways they use their data,? he says. Google is at the forefront of innovation, and Yue firmly believes that technology can help improve the lives of citizens. Data to him is about insights, and insights can help a business to grow. ?I believe organizations can start by connecting the dots. I think that one of the great things that Google does is connect the dots internally within our organization,? he says. Yue says that a bright future awaits those who want to pursue a career in data analytics, especially for those who can connect the dots and understand how to draw insight from data to learn new things. ?That?s a powerful combination,? Yue says. Yue cautions that companies need to take due care when it comes to issues of privacy and security. ?When people use our products, they are trusting us to protect their privacy and security,? he says. ?I encourage all businesses to do the same. You must create a good experience for the user and build trust. That way, I believe you will bring long term sustained value.? Michael Yue General Manager of Sales and Operations, Google Hong Kong (Kellogg-HKUST EMBA Graduate) Digitize Hong Kong for Good 14 Biz@HKUST Biz@HKUST 15 // Cover // Thought Leader Talent is a Challenge Looking into the future, Yue says that the outlook for data use is bright, and digital transformation can be whatever companies want to make of it. But for now, the best approach for companies is to put the right talent in place if they plan to harness the power of data. According to the Smart City Digital research findings, talent continues to be one of the challenges that businesses will face when they drive digital transformations and acceleration. Talent supply should come from both reskilling and upskilling in-house, as well as from education. ?The role of the educator is to continue to inspire curiosity,? says Yue. Both the private and public sector should join forces to create learning environments that foster agility. Students can build new digital skills through practical applications, and complement those with great experiences, he notes. Yue says that digital transformation must stem from positive role modelling. He extols the virtues of reverse mentoring ? where executives can learn from a junior ? calling it a powerful tool that can create a positive learning culture. You must create a good experience for the user and build trust The pandemic has triggered an immense increase in digital solutions and game-changing moments for both private and public sectors to rethink and reset the journey ahead. Overall, both residents and businesses recognize the value of technology and products powered by artificial intelligence (AI), according to Google Hong Kong?s Smarter Digital City research findings. The latest economic impact report by AlphaBeta, commissioned by Google, shows that if leveraged fully, digital technologies could create an annual economic value of HK$387 billion (US$50 billion) by 2030. To put this in perspective, this is equivalent to twothirds of the annual economic value-add of the city?s financial services industry today. Equally important, both private and public sector shall join hands to promote trust in technology for good, embrace new security models and modernize their approach to protect user privacy, as well as help people understand how their data is being used and how to manage their digital footprint. If the pace of its digital skilling efforts is accelerated over the next decade, the smarter workforce with digital skills can contribute up to a fifth of the city?s GDP in 2030. Building data capabilities across the business helps make smarter strategic decisions, while bridging the knowledge and talent gaps bring longterm success. Michael Yue General Manager of Sales and Operations, Google Hong Kong (Kellogg-HKUST EMBA Graduate) M O R E 16 Biz@HKUST Biz@HKUST 17 // Cover // Thought Leader Shopping Made Easy HKTVmall is a popular online platform for shoppers. Even though people in Hong Kong can mostly find a shop just a stone?s throw away, CEO (HK) of HKTVmall Jelly ZHOU explains the reasons for its success. When it comes to using big data and technology to improve everyday shopping experiences, HKTVmall delivers. Established in 2015, HKTVmall is the eCommerce business unit under Hong Kong Technology Venture Company Limited, a listed company in Hong Kong. HKTVmall covers the Group?s operations in Hong Kong to run an online shopping mall and provide one-stop shop services including online shopping, marketing and digital advertising, big data analysis, smart logistics and fulfilment as well as physical O2O stores. Since the onset of the pandemic, the company?s online retail sales in Hong Kong have risen to take a 7 to 8 per cent share of the market, and this is expected to increase. This is a stellar figure considering that the technology company is operating in a city where most citizens can access a shopping mall within 15 minutes. According to Jelly Zhou, CEO (HK) of HKTVmall, the migration to online shopping was a natural progression that was accelerated by the pandemic lockdowns. ?It?s really all about convenience for the shopper,? she says. ?When we think back seven years ago to when we started HKTVmall, you could find a shopping mall quite quickly in any direction, so why would you shop online?? The answer, Zhou and her team realized, is convenience. Online shoppers can shop at any time of the day, even after midnight, and can get any product that they want with the click of a button. ?Hong Kong consumers are the same as those in other countries. They are looking for convenience, so there is a demand for e-commerce in Hong Kong. That?s how we grew our online shopping businesses,? she says. Data and Insight With a view to transforming all its operations, trading, retail, finance and daily life onto a single digital online platform and establish a unique digital ecosystem, HKTVmall offers more than just convenience in the e-commerce space. Zhou defines the over-the-top shopping services as an entertainment platform, presenting it as a first-class ?shoppertainment? platform. Then HKTVmall harnesses the power of data and insights to deliver an online sales channel that could cater to specific needs. ?We didn?t want our clients to just purchase on our platform, we wanted them to live with our apps. So we have more features like a live shopping channel, an EcoMart, and a simplified version app for the elderly,? she says. ?We want them to do everything they need to do in their daily life in the HKTVmall app.? Zhou also credits the company?s success to the fact that before demand grew, the company had the foresight to build a warehouse, and to install an automation and software system. ?When the demand came, we were able to meet it,? she says. The company?s success means that it has been able to capture an incredible amount of data across numerous categories. Along with commercializing the data, they hope to use it for good. This year, the company launched one of its biggest projects, the Open Databank. The company firmly believes that data and insight is the key to success for companies, and for society. But Zhou agrees that there is still much that is unknown about the way it is used. ?Everyone knows big data is important, but how do you actually use it? All this requires more research,? she says. The company has plenty of first-hand experience when it comes using data, and Zhou says that there are many applications for it. As an example, she notes that data has been a powerful tool for inventory forecasting, especially during promotional periods where the sales increment could be well over 100 per cent. The pain point comes when retailers don?t have enough stock. But, by having a historical record and data, companies can use algorithms to better forecast and calculate how much inventory is needed for the next wave of promotions. This in turn optimizes their warehouse and inventory control. Data is also useful for the company?s Customer Relationship Management (CRM) program. Because HKTVmall has so much information based on a customer?s browsing history and purchase behaviors, companies are able to launch new products and easily target promotions at prospective buyers. This leads to higher conversion rates. Jelly Zhou CEO (HK) of HKTVmall (HKUST MBA Graduate) 18 Biz@HKUST Biz@HKUST 19 // Cover // Thought Leader Data and insight is the key to success for companies, and for society A data analysis team in-house to retrieve and perform analysis Trial-and-error modelling to enhance the commercial rate Jelly Zhou CEO (HK) of HKTVmall (HKUST MBA Graduate) Privacy Concerns HKTVmall?s product recommendation feature for addon offering is a good example. After a customer makes a purchase, they are prompted to buy further products that might interest them. Through this feature, the company has managed to increase conversion rates by up to 15 per cent, or HKD100 per basket. ?That?s one way we can use big data,? she says. ?Big data is a must for today. Without big data, it?s like you?re riding on a bicycle while everyone else is driving a sport car on a highway.? Zhou recognizes that privacy concerns are an issue, and she advises other companies to take this seriously. She also thinks that data proves a headache for some. ?Because the volume of the data is huge, you need servers and infrastructure to run the algorithms. You need to set up infrastructure to do this, or find a solution provider,? she says. So what is the secret of HKTVmall?s success? Zhou says that it lies in the company?s culture of not being afraid to manage risks, a willingness to make changes, a winning attitude, and a desire to apply technology for the benefit of the public. A Winning Formula Big data has its rightful place within business structures, but without the manpower to do data analytics, it is just a bunch of numbers. For this reason, investment in manpower is equally important for any company that wants to venture into the big data space. M O R E Training staff who have shown a passion for an area and designed a localized system Rotating staff through various departments to ensure that they have the exposure and understanding of how other departments operate 20 Biz@HKUST Biz@HKUST 21 // Cover // Thought Leader Making It Personal The Stanford Responsible Digital Leadership Project helps industry foster an ethical attitude to data privacy and artificial intelligence. S�ren J�RGENSEN, the Project Director, explains how to find a balance between privacy and data use. Technology and data have transformed the lives of many, but their misuse can be detrimental to personal privacy and safety. For this reason, S�ren J�rgensen, a research fellow at Stanford University in the US, joined with Dr Elise St John of the Digital Transformation Hub at California Polytechnic State University and Radhika Shah, an angel and impact investor from Silicon Valley and also a fellow at Stanford University, to launch the Responsible Digital Leadership project at the end of 2019. The aim of the project is to provide guidelines for the responsible use of new technologies such as Artificial Intelligence (AI) and data, and to provide a learning platform for global businesses to exchange ideas and establish better practices. Working with a group of banks and insurance companies, the project hopes to find ways for companies to implement ethical principles and guidelines when using data. The project also considers data ethics in the context of responsible digital leadership, especially its impact on society, human rights, and it assesses how the use of data complies with the United Nations Sustainable Development Goals (SDGs). ?Globally, it is a critical time, as we are starting to realize the challenges that the use of technology brings,? says J�rgensen. ?We realize that responsible digital leadership is about culture and learning, and about promoting a mindset of responsible behavior. We must develop the ability to face these challenges when they come up. We need guidelines, but guidelines alone won?t do that, it?s a result of how we behave.? In what could be classified as the largest global project of its kind, J�rgensen says that the Responsible Digital Leadership Project is the culmination of the ideas of more than 70 PhD, MBA and master?s level students from some of the world?s top universities. HKUST is included in that list. This global effort ?will define the risks of technology use, with a mission to future-proof the financial sector against getting the ethics of data and AI wrong.? J�rgensen says that the project has so far uncovered around 60 concrete dilemmas and challenges when it comes to the ethical use of data and technology. Such challenges include looking at how companies can use technology and data to build trust, while also protecting the interests, privacy and the rights of the individual. Companies must strive to be flexible enough to comply with local regulations, laws and cultural expectations. They must also drive innovation while benefitting society as well as themselves. Tough Challenges J�rgensen says these are difficult challenges, and there are no straightforward solutions. But the project is certainly taking a step in the right direction by creating useful frameworks. First and foremost, the project has put privacy on the top of the agenda, and made it the central pillar of its activities. The question is, J�rgensen says, how can we enable those in the financial sector to benefit from the power of data without risking the privacy and fundamental rights of customers? ?At the core, it?s about how we relate to data and manage it. That concerns privacy, and how the data is being used, and manipulated. That is an important issue,? he says. Another issue is bias and discrimination in the way the data is used. A central concern is the contradiction between an individual?s feelings about privacy issues and the way they take care of their personal data. For example, J�rgensen notes that people are hesitant to leave social media platforms such as Facebook, even though they aware of the problems such platforms may cause them due to the access the companies have to their data. ?People have just accepted the trade-off, as it?s convenient,? J�rgensen says. We all look for convenient platforms and solutions, and the most convenient solutions are those where your data is being collected, even though giving access to that data may be a risk, J�rgensen notes. S�ren J�rgensen Director of the Responsible Digital Leadership Project Stanford University 22 Biz@HKUST Biz@HKUST 23 // Cover // Thought Leader Awareness is Growing The good news is that there is now a growing awareness of security and privacy concerns. J�rgensen notes that people are becoming more mindful and quicker to react to instances of abuse. A recent Pew Research Center survey conducted in April 2021 shows that 56 per cent of Americans think that major technology companies should be regulated more than they are now, and 68 per cent believe that some of the tech firms have too much power. Humankind has taken an enormous digital leap during the COVID-19 pandemic. The world got to see the enormous advantages and possibilities in using digital platforms, but the pandemic also highlighted tech?s limitations and shortcomings, such as a lack of human connection. As the world journeys further into the age of digital transformation, education must play a key role in meeting the demand for new skills and competences, and providing relevant research, J�rgensen says. He thinks that education can help companies to set the right boundaries, and allow them to find a balance between privacy and data use. Responsible digital leadership is about culture and learning Will there be a decrease in central bank digital currency because people are afraid to use it? Will we see a more or less equitable world with the increased adoption of crypto currencies? Since the ?right to privacy? and the ?right to be forgotten? does not apply to the traceable crumbs left behind by digital payment, what resources do customers have to protect themselves from the state and disproportionate power of corporations? Is the future of transparency about transforming all information into ?open data? so that there isn?t an imbalance of information? S�ren J�rgensen Director of the Responsible Digital Leadership Project Stanford University Dilemmas & Challenges Identified Post-Graduate Students Worldwide Involved 60 70 Blockchain and Cryptocurrency - Food for Thought The Responsible Digital Leadership project hosted an inspiration session in mid of 2021, with a focus on blockchain and cryptocurrency regulation. The project?s researchers presented a number of ethical dilemmas that were imposed by the adoption of the Central Bank Digital Currencies (CBDG). Questions and ethical challenges that were raised included: M O R E M O R E 24 Biz@HKUST Biz@HKUST 25 // Cover // Thought Leader An Ethical and Responsible Culture ?HKUST postgraduate students have attended workshops virtually with global leaders to develop data governance guidelines,? Tam says. ?These efforts are further supplemented by an increased focus on developing an ethical and responsible culture of technology use, and a focus on training for industry professionals, teams and larger organizations.? Tam recounts that when she opened discussions with the team at Stanford University at the beginning of the project, they were keen to have Hong Kong students participate in the joint research. Since then, students from HKUST have been part of an international group working with international banks to identify and discuss possible ethical dilemmas that could arise from the use of technology in the financial sector and other industries. Taking part in this project is important for students as technology is now an integral part of people?s lives. Those in the workplace must have the skills to balance monetizing data to achieve more value-added services with a duty to protect customers? data. ?How can banks ethically monetize customers? data to provide value-added services while upholding customers? best interests? That is a dilemma,? she says. The Risks of AI Tam explains in some parts of the world, such as Europe, there are already laws to regulate how banks can share customers? data while seeking to innovate. ?Banks and insurance companies in Hong Kong can develop their strategy freely to cope with this dilemma,? she says. ?So far, most Hong Kong regulations are ?technology-neutral?, which means that the current laws have not addressed risks which are specific to the use of AI.? There may be risks arising from the use of AI. For instance, the use of some algorithms may result in unfair or biased decisions. If AI is used to screen out candidates in an unfair manner, it may be difficult for a person to be held accountable for the discriminatory decision. ?Should the programmer, the HR department, or the AI be accountable for that decision?? Tam says. Keeping AI Honest Frankie TAM is an international technology lawyer and is on the advisory board of Stanford University?s Responsible Digital Leadership Project. This project aims to identify global ethical dilemmas related to the use of technology. She serves as a bridge between Stanford and HKUST - the only Hong Kong university that is taking part in the Project - in contributing to this meaningful international study of data ethics. Frankie Tam, an international technology lawyer at Eversheds Sutherland who also teaches regtech and fintech programs for postgraduate degrees, is confident that data is the ?new oil?. Data is a valuable asset for any organization, she says, adding that it is important to put best practices in place to guide responsible innovation. Tam has been a staunch advocate for HKUST?s acceptance of an advisory role on the board of the Responsible Digital Leadership Project ? a global project launched by Stanford University - that helps to identify ethical dilemmas related to the use of technology and the processing of data in financial organizations. ?I am delighted to serve as a bridge between the two universities in organizing the meaningful international study of data ethics in our increasingly digital world,? she says. ?I?ve had the opportunity to share my views from a practitioner?s perspective with leading scholars and outstanding students.? The project aims to build a ?library? of ethical dilemmas which relate to the use of technology in the financial sector. It is a joint effort conducted by around 70 postgraduate students from more than 26 top universities around the world. HKUST is the only Hong Kong university that is taking part in the project. Student Voices HKUST students share their experiences of participating in the Responsible Digital Leadership Project. CHEN Xin MSc in Business Analytics As soon as I understood what the organizer was trying to achieve through ?collecting ideas from people from all fields globally?, I felt really connected. This multidisciplinary project was a huge magnet for me when I saw it for the first time. This project stirred my interest in areas such as education, Chinese classics, and even philosophy. It encouraged me to think about why there are issues and dilemmas between financial technology and climate change. I firmly believe that this is now a future development plan. The contradictory positions between eco and econ is another important matter. Now I have a stronger responsibility towards something bigger. I feel that while technological development is crucial and fundamental, if it is guided by good will and fortitude, this seemingly ?heartless?, data-driven process can lead to harmony and world peace. This experience has helped me understand that my routine daily work can be a building block that leads to world peace in the future. Ezekiel CHIM MBA I believe that we are now at one of the most critical times in our history where we are striving for a better society with technological advancements. Since the latest emerging technologies are so powerful and revolutionary, we must maintain a proper balance between ethics and fairness and the benefits of technology, and make sure we are not leaving any minorities behind. As an industry practitioner, I can?t wait to be part of this vital stage, and to define the framework and approach to identifying and proposing solutions to the ethical dilemmas that arise when using these emerging technologies. One of the biggest challenges that we have observed is the difficulty in justifying why something could be correct. For example, in one of our studies, we saw that geographic data was used to calculate lending risks, but this could be unfair and biased against those who have negative geographic data but good credit history. If historical data shows that people who live in ?City A? have a higher chance of defaulting, and banks therefore reduce the lending or impose a higher interest rate, is this an ethical way of classification? What if AI is doing the classification instead of humans? By listening to the views of consumers, industry professionals, financial institutions, regulators, and researchers, we are learning how to define these dilemmas and make proposals. 26 Biz@HKUST // Cover // Thought Leader Valeria STADALNINKAS MBA Today, we live in the context of an AI-enabled economy, and data is evolving into an essential and strategic resource. This research project cannot be more relevant and meaningful in the era of digital transformation and prevalence of emerging technologies. I am appreciative and grateful to be given this chance to be part of a truly global initiative aimed to make a digital space, particularly in the financial sector, a responsible, transparent and safe ecosystem for all the parties involved. The challenge and utter beauty of this project was the diversity of our group. It really fascinated me that we could bring completely different perspectives to the same issue which consequently encouraged us to think out of the box. Just the ability to listen to each other, make efforts to understand the rationale behind the ideas and suggestions, respect each other?s opinions, and perhaps the most crucial aspect, the common goal, is to make our journey productive and fun! WANG Can MSc in International Management I majored in finance in college and learned about many aspects of the industry, but not from an ethical point of view. As new technologies are changing the industry and people?s lives, it?s essential to discuss ethical challenges facing the industry today. Many countries have ?regulation policies? on AI and blockchain technologies, but all have different attitudes and ethical principles. For example, the regulations in Singapore are based on the principles that AI should be explainable, transparent and fair. The most unforgettable experience we had on the project was co-presenting our project during the Copenhagen FinTech Week in September 2021. It was a great opportunity to receive feedback from real industry insiders. I would highly recommend this valuable experience because of the chance to get to work in a large global team and meet professionals from different backgrounds. We met experts and industry insiders through a series of workshop sessions. It is great to have the chance to work on such a meaningful project, and it also helps us achieve our personal goals. HKUST students participate in a research symposium, exploring solutions that make the world a better place with fintech. There are many reasons why Hong Kong must take the lead in hashing out these specific dilemmas, Tam says. Firstly, Hong Kong is one of the first places in the region to have data privacy regulations, so the city must set an example and act as a helpful reference for other Asian countries. Secondly, the Hong Kong Monetary Authority?s Fintech Talent 2025 strategy means that attention is focused on the fintech sector. How can Hong Kong continuously facilitate the breeding of what Tam calls a versatile ?T-shaped? talent ? someone who has deep expertise in a core field while being able to collaborate horizontally? ?I think the current ?Responsible Digital Leadership Project? which HKUST is taking part in is a good start in this regard,? she says. Looking to the future, if Hong Kong wants to develop as a fintech hub, it needs a large talent pool which has an open mind towards new, creative technology ideas. The talent must also be aware of the current issues and dilemmas facing the world of fintech. ?In the future, we hope to have more general cybersecurity regulations which cover more than just personal data. This is expected to better protect interests in the digital space,? Tam says. Depth and Breadth of Big The HKUST Business School is committed to the advancement of big data and conducting research into the field. Making use of a multi-disciplinary approach, our esteemed researchers have taken this opportunity to share their in-depth knowledge and insights into data and technology. In the following articles, they touch on a number of topics, including how to solve social problems using both data and artificial intelligence. This is a unique opportunity to hear from a whole range of experts whose subject areas span information systems, accounting, marketing and economics. Their research demonstrates our commitment to enabling an informed discussion about the transformations brought by a new digital age. 30 Biz@HKUST Biz@HKUST 31 // Cover // Insight From Data to Actionable Insights: Harnessing the Power of Business and Social Analytics HKUST?s Center for Business and Social Analytics (CBSA) generates quality insights from massive amounts of social data. Chair Professor HUI Kai-Lung CBSA Director Associate Professor Allen HUANG CBSA Associate Director Dr LIM Hyungsoo Postdoctoral Fellow at CBSA British mathematician Clive Humby famously coined the phrase ?Data is the new oil? in 2006. Since then, many organizations have scrambled to embrace this maxim, ensuring they have a robust data strategy to inform decision-making for every function from finance to marketing and logistics to production. Today, data is being generated, stored, and analyzed at staggering rates worldwide. According to a Forbes study in 2018, over 2.5 quintillion bytes of data are produced in the digital universe every single day. Along with this rapid growth of data, the rise of social media has encouraged researchers and data users to tap unconventional data sources and methods to explain and predict economic or behavioral outcomes in various domains such as finance (e.g., stock price), marketing (e.g., brand reputation, consumer purchasing behavior), and politics (e.g., voting behavior). Unconventional data, more commonly referred to as big data, is unstructured and undefined. Such data is characterized as high-volume, high-velocity, high-variety, and/ or highly semantic, and advanced techniques and applications, as well as high-performance computing, are often required to collect and process it. Big data analytics, including data mining methods and machine learning algorithms, allow users to gain a deep understanding of behavior. The Internet has become a part of modern life, and it is almost second nature for people to express their views and preferences on social media. The emergence of platforms such as Facebook, Uber, Taobao, and peer-to-peer applications, and new computing and communications technologies such as Internet of Things (IoT) and 5G cellular networks, has generated tremendous amounts of new data that opens up the possibility to study a wide range of human behaviors. Against this backdrop, the Center for Business and Social Analytics (CBSA) of the HKUST Business School leverages massive amounts of social big data to generate quality insights that aid business and social decision and policy making. To date, a majority of academic and industry research is based on conventional data from opinion polls, surveys, and lab or field experiments, which suffer from sample selection bias or lack generalizability. The Center takes advantage of social big data to tackle complex business and social problems by listening to what the crowd actually says. The following are sample projects undertaken by the Center. Hong Kong Tourism Index The tourism industry is one of the four pillar industries in Hong Kong. Mainland China is the major tourism market of Hong Kong, accounting for nearly 80% of visitor arrivals in 2019. In view of the tendency of Chinese tourists to share their travel experiences and sentiments on social networks, the Center has collaborated with Wisers, the world's leading expert in big data and AI analytics, to construct a series of predictive tourism indexes for government monitoring and other business interests. We collect massive volumes of data from digital platforms frequently used by Chinese travelers, including Sina Microblog, Baidu Tieba, Douban, Mafengwo, Xiaohongshu, and Douyin. The total amount of data extracted from these platforms exceeds 10 million observations per day. To account for the seasonality in tourism demand and key sociopolitical events in Hong Kong, we focus on the period of January 2018 to December 2020. It is imperative to gain a systematic understanding of the various factors impacting Hong Kong's tourism industry. We draw on the latest natural language processing technologies and advanced statistical models to process and quantify the data, and establish a multivariate time series forecasting model and a series of predictive indexes that are updated in real time. With the error rate as low as 4% (in terms of Symmetric Mean Absolute Percentage Error, sMAPE), these indexes predict the occupancy rates of Hong Kong hotels, the number of visitors to Hong Kong, and the average daily rates of Hong Kong hotels. We anticipate that our work will enable the Hong Kong Government and the tourism industry to obtain accurate forecasts, trends, and insights in the future concerning the local tourism industry, and formulate more sustainable and data-driven tourism policies and strategies. 32 Biz@HKUST Biz@HKUST 33 // Cover // Insight Sentiment of next content Sentiment of current content Negative Neutral Positive Negative 42.39% 40.52% 17.09% Neutral 32.09% 47.77% 20.14% Positive 29.30% 43.11% 27.60% Variable Model Sentiment Index 0.0481*** (0.0036) User fixed effects Yes Team x day fixed effects Yes Controls Yes Observations 3,608,654 R-squared 0.111 Adjusted R-Squared 0.095 FinSent ? An Interactive Sentiment Analysis Dashboard Based on FinBERT The Center has been developing a first-of-its-kind sentiment analysis dashboard based on FinBERT, a BERT model that outperforms other language models in the sentiment classification of financial texts. Aptly named FinSent, the fully automated dashboard visualizes sentiments of financial texts using cuttingedge natural language processing technologies, enabling users to gain insights into the financial sentiments of publicly traded companies through charts, graphs, and colorcoded sentiment scores. FinSent comprises an automated module that collects and processes corporate 10-K and 10-Q filings and earnings call transcripts of public companies on a daily basis. The sentiments towards the companies are presented via an interactive dashboard that allows users to freely customize the content. The following diagram shows an example of the FinSent interface: Spillover of Social Media Sentiments In recent decades, the rise of social media has aided the free flow of public information. It has also become a reliable platform for people around the world to interact and share their opinions in an affordable, anonymous, and uncensored manner. While countless benefits have been accrued from the use of social media, some complicated and worrying challenges have surfaced in recent years, catching the attention of researchers, policymakers, and members of the public. It is necessary to understand the difference between real-life and online communications, and how this affects our behavior and mental health. To address this issue, we study the spillover of social media sentiments in the English Premier League (EPL) soccer community using data from Reddit. We apply sentiment analysis with a machine learning model to all the contents in our dataset to capture the sentiment of the contents. The model outputs the likelihood of those contents being positive, neutral, or negative. We trace the contents created by each author and provide evidence of how their next sentiments will change from the current one with a transition matrix. The transition matrix shows that sentiments of the next content differ depending on their current sentiments. Users will more likely produce negative sentiment contents when they generate negative contents (42.39%). On the other hand, sentiments of the next contents are more likely positive when they produce positive sentiment contents (27.60%) rather than negative ones (17.09%). Thus, we consider the sentiment of the current content as one of the controls when we estimate the model. With 3.6 million pieces of content from 20 EPL subreddits in the 2017/2018 season, we run a regression model to check whether the sentiments of what users see would affect the sentiment of their next piece of content. The dependent variable of the model is the difference between the likelihood for the content to be positive and negative. A positive index means the content is more likely to be positive. Similarly, a negative index means the content is more likely to be negative. The main independent variable is a sentiment index, which measures the sentiments of contents that users see between their posts. Furthermore, we control various confounding factors, including the sentiment of their previous posts. We find strong evidence of spillover effects from the sentiments. The estimate of the sentiment index (0.0481) is significant and positive. It means that emotions on social media are contagious and can easily intensify. Social media users should recognize this spillover to their emotions and avoid being manipulated by other social media users. Learning how to deal with an increasingly polarized online environment and offensive conversations is important if we are to be responsible netizens. Notes. Robust standard errors clustered by user are in parentheses. * p < 0.1; ** p < 0.05; *** p < 0.01. The Center tackles complex business and social problems by listening to what the crowd actually says BUSINESS ANALYTICS Master of Science in We are now accepting applications for the 2022/23 Intake, so grasp the chance and be ready to keep leading in the future! Please feel free to contact us to learn more: Website ________ https://msba.hkust.edu.hk/ Email ________ msba@ust.hk Learn how to apply AI, machine learning, and statistics to core business functions ranging from finance to human resources. Business analytics is among the hottest fields for the following reasons: Progressive industry growth rate Opportunity to progress quickly and make an impact HKUST?s Master of Science in Business Analytics prepares you for a career in the fields of Consulting, Data Analytics, Finance, Information Technology, Product Management, etc. MSBA Program Features: ? One-year full time / two-year part time master?s degree ? Equip students with data analytic skills to optimize business performance and apply to real business problems ? Cutting edge curriculum taught by world class faculty to meet industry needs ? Supportive alumni and industry network ? Career enhancement in the digital era C M Y CM MY CY CMY K 34 Biz@HKUST // Cover // Insight The National Security Law (NSL) and Social Media Sentiments One of the Center's research projects investigates how the actions initiated by the different authorities?such as the Chinese government, HKSAR, and international governments?and the implementation of the NSL on 1 July 2020 influenced the sentiments in various media channels in Hong Kong. We construct our sentiment model through manual labeling and then train it by a state-of-the-art machine learning model called MacBERT. Based on the fine-tuned model, we predict not only the sentiments of conventional media (e.g., newspapers and magazines), but also social media (e.g., Facebook, LIHKG) users? sentiments towards the China-Hong Kong relationship. As a piece of model-free evidence, we depict the different trends of the daily proportion of sentiments (positive, neutral, and negative) across various media types. We observe that the total proportion of positive sentiment contents had increased during our empirical period (May 2020 to July 2020). This pattern is clearer for BBS sentiments. On the other hand, we see that the pattern is different for sentiments on newspapers. In sum, we find strong evidence of different impacts on the sentiments across various types of media and significant impacts of different authorities on the sentiments. Our sample comprises around 0.4 million posts and comments. About the Center for Business and Social Analytics The Center is committed to studying timely business and social problems by utilizing the big data available on the Internet. The data used in our research has a wide span, including social media posts, publicity, user-generated content, user likes and follows, and other social, business, and interpersonal exchanges. We draw on the strengths of faculty and researchers from diverse disciplines, such as business accounting and economics, information systems, decision analytics, finance, computer science, and humanities and social sciences, to produce evidence-based research with the aim of informing business practices and public policies. The Center actively collaborates with other academic, public, and private organizations who share the same vision of cultivating and analyzing social big data to uncover novel insights and knowledge. We welcome academic scholars and industry practitioners to work with us on addressing emerging business and social problems for the long-term betterment of Hong Kong and beyond. 36 Biz@HKUST Biz@HKUST 37 // Cover // Insight Big Business Big data is important for business, and the HKUST Business School is taking note of developments in business analytics and reacting accordingly. Chair Professor Lancelot F. JAMES Department of ISOM In this article I present my views on some challenges faced by the HKUST Business School in this era of big data, how it has been addressed so far, and some further points to consider. These are based on my experience as a statistician in the Department of Information Systems, Business Statistics & Operation Management (ISOM) at the HKUST Business School for 20 years, and as the new Director of our Master?s of Science in Business Analytics. Statistics and Data Science Data science is the term often ascribed to the emerging field concerned with the analysis and management of (big) data. While naturally arising in many fields, the technical aspects generally involve statistics, computer science (machine learning/artificial intelligence) and a considerable amount of data engineering with a view towards automation. However, to some who are unfamiliar with the various rumblings over domain and terminology, the admittedly simplified description sounds quite a bit like statistics. The key word of separation in the description is not ?big? but rather management that leads to the conclusion that, while statisticians may be concerned with all aspects of data science, they (like all experimental scientists) generally want access to data, and are not directly involved with the engineering or computer science associated with database management. While it is pertinent to mention, this article is not meant to be a prolonged discussion about differences between statistics and data science. For that I would recommend an article1 by the eminent Stanford statistician David DONOHO, who, among many other accolades, is the 2013 recipient of the Shaw Prize in the Mathematical Sciences. It is also noteworthy that departments at Yale and UPenn Wharton Statistics, among other places, have changed their names from Statistics to Statistics and Data Science. This article is also not about the role of Statistics in a business school, as that thankfully has been addressed by Professor Gareth JAMES2, of the USC Marshall Business School. Nonetheless, I will add that my statistics colleagues in ISOM have published in the top A-listed finance, economics, information systems and operations management journals. Big data occurs in many scientific investigations which may generally be outside the scope of interest of business schools. One can also conjure up myriads of examples of pre-information age big data, for instance data points corresponding to all the atoms on the earth. Below I present an anecdotal view of one of the primary, and now widely recognized, sources of big data that is of interest to corporate entities. Within the subtext, are my musings about how new tech quickly becomes commonplace or outdated technology. Intro to WWW, Internet, Big Data It was 1994, while in my friend?s, Patrice BERTAIL, office as a visiting scholar at the INRA3 near Paris, that I wandered over to his desktop computer and first saw the World Wide Web. I recall exclaiming, ?What is that?? I did not think much of it at the time. I was a regular user of emails by then, and welcomed the changes of size of floppy disks, for data storage, from 8 inches, to 5.25 inches, to finally 3.5 inches. DVDs had not quite arrived. MTV was still a primary source for new music. Apple was a moribund company, and needless to say, there were no smartphones, WhatsApp/WeChat, Netflix, Amazon/Alibaba, Facebook, Instagram. Twitter, LinkedIn, Google, Tencent, etc. I still used Normal Distribution tables, and regularly checked out stacks of books from the library. Despite the remarkable advances of that time, emails were not quite the same as phone calls, and social media, as we know it, was yet to emerge. We are well aware in 2021 that Sir Tim BERNERS-LEE?s invention of the web enabled billions of people to interactively connect with each other, academic institutions, libraries, scientific resources, social media and various corporate entities. Data generated from activity related to the web and smartphones is a large contributor to what we now refer 1 David Donoho (2017) 50 Years of Data Science, Journal of Computational and Graphical Statistics, 26:4, 745-766 2 James G.M. (2018). Statistics within business in the era of big data. Statistics and Probability Letters (2018), Elsevier, vol. 136(C), pages 155-159. 3 Institut National de la Recherche Agronomique in Ivry Sur Seine, France. to as ?big data?. However, the web does not reflect the entirety of data available on the internet, indeed the web is not the internet itself, and there are also less accessible massive data stores housed by banks, governments and many other entities. Besides the companies mentioned above, and certainly other digital marketing or e-commerce enterprises, a growing number of other types of companies have recognized that there is potential value in data, and are eager to invest resources to capitalize on this. While a company such as Google or Alibaba is perhaps much more mature and prepared in this sense, they are all seeking talented individuals and the utilization of automated processes to help them achieve added value. Such talents are often referred to generically as data scientists, and more specifically as data analysts or data engineers. Business School Research The commercialization of big data, how it arises, and how it is used in various enterprises, issues of ethics, data privacy and compliance, employee training and the future of employment, fraud detection and risk management, and indeed adding to the discipline of Data/Business Analytics, are matters addressed by business schools worldwide. Within HKUST, despite big data creating new sources for commerce, it is still business, and many of the issues mentioned, such as data privacy, are readily tackled by our more seasoned forward-thinking faculty, based on applications of robust fundamentals. Nonetheless, given the scope of the impact of big data, the HKUST Business School has aggressively pursued new faculty hires from the best schools with appropriate new data analytic and machine learning skillsets. This also reflects an ongoing trend in institutions worldwide where Corporate partnership and their sharing offer a holistic view for business analytics students 38 Biz@HKUST Biz@HKUST 39 // Cover // Insight machine learning talents are hired in various academic departments. It may not be surprising to know that many are housed in our multi-disciplined department of ISOM, and obviously Marketing. One can find such faculty in all departments including Accounting and Management. Furthermore, the utility of big data within fintech is showcased in the Business School?s RGC-funded theme-based research project, ?Contributing to the Development of Hong Kong into a Global Fintech Hub?, under the leadership of Dean TAM Kar Yan. As is the general case when new subfields that employ new methodologies and viewpoints emerge, there are challenges, in terms of competition for more talents, retention and promotion. MSBA Business School Education In reaction to the commercialization of big data, the Business School, like many academic institutions, has introduced various courses and professional programs. Most of our masters of science programs have significant elements of analytics within their curriculum. Here I will focus on our Master?s of Science in Business Analytics (MSBA), which was, until recently, under the directorship of Professor Mike K.P. SO. The MSBA is a one-year technically intensive interdisciplinary program which is housed in our department of ISOM. ISOM faculty members deliver the core curriculum, and the majority of electives. There are also electives offered by other departments. The program is designed so that graduates leave with much more than the typical technical skills to execute data analytics, but also with developed data-analytic thinking, business acumen and communication skills to play front office roles in the process of leveraging data for adding value to their firm or clients. A director?s task, especially in a dynamic field such as business analytics, is to always look for ways to add value for students and prospective employers. Furthermore, it is important to understand the issues that these parties may encounter. One of my sources for a general view of the field has been the internet. After spending time on LinkedIn, blogs and in other formats, one sees for example, problems with ambiguous job titles. More alarming is the frequent post lamenting mismatches between expected deliverables and employee capabilities. I am also quite conscious of the growing role, and inevitability, of automation and AI within analytics. A Holistic View of Business Analytics at HKUST I have taken a holistic view of the Business School departments as all having potential relevance to the added value of students in Business Analytics without changing the usual core competencies and valued electives. Naturally this cannot be done without the cooperation of departments, individual professors, and the MSc office. I will discuss some details and acknowledge some individuals who have shown enthusiastic support for the program in various ways. This year, responding to the need in the industry, new electives will be introduced. Professor Chris DORAN and Professor Joon Nak CHOI from Management, will teach management consulting courses with some tailor-made adjustments for the MSBA, and Professor Song LIN and Professor Coral PUIGGARRIGO, of Marketing, will teach Marketing Analytics related courses for the program. Industry-based projects will be directed by some of these faculty as well as Professor Kris Baoqian PAN, the Associate Director of MSBA. In general, there is a welcome sharing of electives across the various MSc programs, now headed by Salad KWOK. Professor Veronique LAFON-VINAIS, of Finance, and Executive Director, Career Development & Corporate Outreach SBM, has helped me with public relations, and my search for Business Analytics-specific corporate partners. As an important start, with the help of the Kellogg- HKUST EMBA Office (KH-EMBA), the program has been fortunate to establish a formal collaborative agreement with Sprint Milestone, which is a data management advisory firm specializing in data warehouse, automation, analytics and AI solutions for companies. Its founders, Akihito KATAYAMA and Khai San BAN, are alumni of the KH-EMBA, with extensive analytics experience in the banking sector prior to starting their company. Their willingness to graciously, and frequently, share their expertise is invaluable for understanding how to prepare students for the future of business analytics. ?Exactly what I was looking for!? While there are many developments envisioned specifically for the MSBA program, including a professional business analytics advisory board, I believe that the business school itself can treat Business Analytics in a more holistic fashion. There is currently the recently established Center for Business and Social Analytics (CBSA), under the directorship of Professor HUI Kai-Lung. However, as a vehicle to expand the MSBA and other MSc program capabilities, there is room for a complementary entity for the taught professional postgraduate programs, which can serve as a nexus for fostering Business Analyticsspecific corporate partnerships to help provide a practical training ground for MSc students, and perhaps expanded to include MPhil/ PhD students who may wish to pursue a career in industry. The Business School?s positioning in Hong Kong and its strengths, for instance in Logistic and Supply Chain Analytics, as in our MSc program in Global Operations, suggest that training could also target individuals in Belt and Road nations4 and likewise train local students for a similar purpose. These considerations will serve to make the university an enduring goto place for students, and potential employers, for an elite Business Analytics education which develops value driven data-analytic thinking for a cybernetic5 future. 4 One may find the 2019 report by the British Chamber of Commerce in China on ?Education on the Belt and Road? to be of interest. BritCham Report - Education on the Belt and Road - British Chamber of Commerce in China | Beijing 5 Thanks to Prof. Joon Nak Choi (JC) for the terminology within this context and discussions related to this. A director?s task, in a dynamic field such as business analytics, is to always look for ways to add value for students and prospective employers 40 Biz@HKUST Biz@HKUST 41 // Cover // Insight A Man with a Machine The investment industry is increasingly using AI technology to make investment decisions, but computers are not ready to replace human intelligence just yet. Professor YOU Haifeng Department of Accounting, HKUST Business School Artificial intelligence (AI), which is powered by machine learning technology, has affected many aspects of our lives. For example, Apple?s Siri uses natural language processing to understand the commands of users and then frame the appropriate responses. Medical image analysis, which uses deep neural networks, is also widely used in the healthcare industry to provide more accurate medical diagnoses. The adoption of machine learning technology in the investment industry has also picked up steam in recent years. Hedge fund managers and other institutional investors are increasingly turning to machine learning for opportunities to generate superior returns. Many have attempted to identify outperforming stocks and estimate the likelihood of bond default, using mathematical algorithms to facilitate their investment decisions. This accelerated adoption is partially driven by an explosive growth in alternative data. Such data is compiled from sources such as financial transactions, sensors, mobile devices, satellites, and the internet, and cannot be handled by traditional software such as Microsoft Excel. Machine learning technology makes it substantially easier to extract information from this hard-toprocess data, and this has led to an ?arms race? in alternative data among institutional investors. While alternative data has certainly taken the spotlight in recent years, traditional data, particularly data relating to financial statements, also merits a fresh look through the lens of AI. Financial statements, together with other information contained in corporate reports, have long been one of the most important sources of information for investors. The tradition of analyzing financial statements for investment decision making can be traced back at least as far as Graham and Dodd (1934). In their canonical book of value investing, Graham and Dodd devote several hundred pages to analyzing financial statements to arrive at the intrinsic value of a company. Financial Statement Data This ?fundamental analysis? has been a dominant approach to investing for almost a century. Traditional fundamental analysis primarily relies on ?human intelligence? which requires investors to painstakingly decipher financial statements, together with other information in corporate reports. For example, it is well known that Warren Buffett likes to read corporate filings. He famously got his start as an investor by reading Moody?s manual of publicly listed companies from cover to cover. However, corporate financial reports have become increasingly complex and lengthy. A study finds that the median text length of corporate annual reports in the US doubled from 23,000 words in 1996 to nearly 50,000 words in 2013.1 Firms also report an intimidating amount of financial statement items. The most commonly used machine-readable financial statement database, COMPUSTAT, currently reports nearly 1,000 items of data for 840,469 firm-year observations of 41,159 unique firms (as of Oct 10, 2021). Complex financial reports impose considerable challenges to investors when processing the information for a large number of firms, and this prevents them from fully appreciating the information content of the financial statements. Indeed, researchers have demonstrated that complex annual reports lead to a significant market underreaction to the information contained in these reports.2 Thus, it is conceivable that significant information remains hidden in financial statements and is not yet fully understood by investors. Can machine learning technology come to the rescue? The answer is likely to be yes. Machine learning has been developed to efficiently handle high-dimensional data, and is capable of accommodating more complex relationships. As discussed above, financial statements, together with the footnotes, include nearly 1,000 data items. It is a formidable task for a human to process such a large volume of data for thousands of publicly listed companies, but it could be a breeze for a powerful computer armed with advanced AI algorithms. Furthermore, financial statement data is the accumulated result of numerous transactions and it involves a complicated generation process. Rich information is consequently 1 Travis Dyer, Mark Lang, Lorien Stice-Lawrence, 2017, The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation, Journal of Accounting and Economics, 64(2?3): 221-245. 2 Haifeng You, Xiao-Jun Zhang, 2009, Financial reporting complexity and investor underreaction to 10-K information, Review of Accounting Studies 14: 559-586 3 Kai Cao, Haifeng You, 2021, Fundamental analysis via machine learning: https://papers.ssrn.com/sol3/papers. cfm?abstract_id=3706532 4 Steven J. Monahan, 2018, Financial statement analysis and earnings forecasting. Foundations and Trends� in Accounting 12 (2), 105-215. hidden in the relationships among the line items of different financial statements. These relationships can be very subtle and non-linear in nature, and therefore cannot be easily understood by investors. In contrast, machine learning has the capability to deal with complicated nonlinear relationships, making it a promising technology to navigate through these subtle relationships and extract useful information for investors. Forecasting Corporate Earnings A recent study adopted machine learning technology to perform one of the most important tasks of fundamental analysis, that is, forecasting corporate earnings. This provides clear evidence about the usefulness of the technology in equity investment using financial statement data.3 Corporate earnings are one of the most important drivers of equity valuation and stock returns. Both academics and industry practitioners have devoted tremendous effort to forecasting earnings accurately. For example, as suggested by Thomson Reuters, over 30,000 analysts from 3,000+ contributing brokers have contributed their earnings forecasts to its IBES database. The revision in these forecasts often triggers dramatic stock price changes. Academics have also developed a battery of statistical models to produce earnings forecasts. However, researchers have challenged the performance of these model-based forecasts, and concluded that they are not much more accurate than the simple guess that future earnings will remain the same as the prior year.4 The simple approach of combining machine learning and analysts? forecasts outperforms both individually 42 Biz@HKUST Biz@HKUST 43 // Cover // Insight The predictive power of the extant models is limited because most of them focus only on highly aggregated measures (e.g. net income) and fail to account for the differential effects of other detailed financial statement line items. Furthermore, these models are largely designed to take a linear form, and cannot capture the subtle nonlinear relationships that could be very important in driving future earnings. The research shows that machine learning forecasts outperform the extant models substantially in terms of forecast accuracy. Moreover, machine learning forecasts almost completely subsume the information content in the extant models regarding future earnings changes. Finally, the new information extracted by machine learning models predicts analyst forecast errors and future stock returns, suggesting that both financial analysts and the stock market fail to make full use of the information in financial statements. Machine Learning Limitations While machine learning shows great promise in fundamental analysis and investment decision making, it is not without limitations. Compared to other applications such as image recognition and voice recognition, the application of machine learning to investment faces several distinct challenges. First, there is much more noise in stock prices than in images or voices, and this lead to a low information-to-noise ratio for many of the prediction problems on the financial markets. The low information-to-noise ratio exacerbates the risk of overfitting, and therefore may render machine learning models useless for out-ofsample predictions. Meanwhile, non-stationarity is another problem when we use machine learning models to make investment decisions. Stock markets are highly dynamic, and the innate relationships may experience significant changes due to reasons such as changing market conditions and investor preferences, and arbitrage activities by investors. In 2020, the market witnessed a significant drop in the investment return of Renaissance Technologies, a legendary hedge fund known for adopting machine learning in its investment process.5 In a letter to its clients, Renaissance admitted that their models are trained on historical data, so the results were not surprising considering that 2020 was unusual year. Adam Taback, chief investment officer of Wells Fargo Private Wealth Management, also notes that quantitative models might have difficulty capturing useful information when markets are volatile. These examples highlight the importance of human judgment and experience in overcoming the limitations of machine learning in the investment process. Human knowledge can be useful to determine whether patterns detected by machines reflect overfitting due to noise or reflect a sensible and sustainable relationship. Humans may also make better forward-looking logical judgments or predictions with a small amount of historical data. By better isolating information from noise and taking a forward-looking approach, human intelligence is still of paramount importance in making sound investment decisions. Thus, at least in the foreseeable future, humans are unlikely to be completely replaced by machines in the investment industries. Both humans and machines have their own parts to play in the game. The best approach would be man+machine. As Paul Tudor Jones, a renowned billionaire hedge fund manager said, ?No man is better than a machine, and no machine is better than a man with a machine.?6 The aforementioned study on earnings forecasts demonstrates the promise of this approach. The authors find that machine learning forecasts compete well against, and most of the time outperform, the collective wisdom of financial analysts from thousands of contributing brokerage firms. More importantly, the simple approach of combining machine learning and analysts? forecasts outperforms both individually. The combined forecasts are more accurate than analysts? consensus forecasts in 29, 31, and 32 years out of the total 33 years testing period for one, two and three years ahead forecasts respectively, indicating that human analysts can substantially improve their performance by incorporating the insights from machine learning models. 5 https://www.institutionalinvestor.com/article/ b1q3fndg77d0tg/Renaissance-s-Medallion-Fund-Surged- 76-in-2020-But-Funds-Open-to-Outsiders-Tanked 6 https://dealbreaker.com/2017/05/paul-tudor-jonesthrowing- money-letters-ai How Data Analytics Are Used in Marketing A closer look into three main kinds of data-driven marketing analytics ? descriptive, predictive and prescriptive. Assistant Professor LIU Jia Department of Marketing, HKUST Business School Data-driven marketing simply put means using quantitative methods to derive meaning from data to make informed marketing decisions. Thanks to the availability of a massive amount of data, data analytics in marketing has become more important than ever. According to a BMO Capital Markets report, marketers spend US$50 billion per year on big data and advanced analytics to improve marketing?s impact on business. Research by McKinsey1 shows companies that invest in big data and analytics yield a five to six percent average increase in profits, which jumps to nine percent for investments spanning five years. In many cases, companies have focused on more open-ended efforts to gain novel insights from big data. These efforts were fuelled by analytics vendors and data scientists who were eager to take data and run all types of analyses in the hope of finding diamonds. Depending on the stage of the workflow and the requirement of data analysis, there are three main kinds of data driven marketing analytics ? descriptive, predictive and prescriptive. Their underlying techniques include, but are not limited to, statistical modelling, data mining, machine learning, and AI. In this article, I will present some applications of each of the three analytics using recent research in marketing, most of which reflect my own (working) papers on marketing analytics. Descriptive Analytics Descriptive analytics refers to the interpretation of historical data to identify trends and patterns, answering the question ?What has happened??. This means descriptive analytics can help identify potential problems or future opportunities for business. In my collaboration with the Microsoft Bing search engine2, we have developed an interpretable machine learning model that can track, quantify, and interpret users? topical preferences underlying each search query and across search contexts (e.g., time, location, and demographics). The proposed model leverages data on user queries, subsequent click-through on search results, and all textual information 44 Biz@HKUST Biz@HKUST 45 // Cover // Insight encountered on the search engine. The outputs of our proposed model can help search engines (advertisers) improve their search results (keyword and ad copy strategies). A group of researchers at Humboldt University Berlin3 proposed a methodology to analyze the cooccurrences of products in consumer shopping baskets, by leveraging the recent advances in natural language processing and machine learning. Their proposed method is well-suited to retailers because it relies on data that is readily available from their checkout systems and facilitates their analyses of cross-category product complementarity, in addition to within-category substitution. The proposed approach also has high usability because it is automated and scalable. In a similar spirit, my working paper on the vending market4 aims to help retailers improve their product assortment strategies across different vending locations and within each vending location simultaneously. We propose an automatic machine learning model using consumer transaction data across different vending locations. Our proposed model can (i) profile different consumer segments driven by location preferences, (ii) quantify the differences in product preferences across these consumer segments, and (iii) understand the relationships (e.g., substitution and References 1 Gordon, Jonathan, Jesko Perrey, and Dennis Spillecke. 'Big data, analytics and the future of marketing and sales.' McKinsey: Digital Advantage (2013). 2 Liu, Jia, Olivier Toubia, and Shawndra Hill (2020), ?Content-based Model of Web Search Behavior: An Application to TV Show Search.? forthcoming at Management Science. 3 Gabel, Sebastian, Daniel Guhl, and Daniel Klapper. 'P2V-MAP: Mapping market structures for large retail assortments.' Journal of Marketing Research 56.4 (2019): 557-580. 4 Liu, Jia, and Kohei Kawaguchi, ?Location-Based Market Structure: A Dynamic Analysis of Product Assortment and Consumer Purchases in Panel Data.? Working paper. 5 Gharibshah, Zhabiz, and Xingquan Zhu. 'User Response Prediction in Online Advertising.' ACM Computing Surveys (CSUR) 54.3 (2021): 1-43. 6 Ali, Muhammad, et al. 'Discrimination through optimization: How Facebook's Ad delivery can lead to biased outcomes.' Proceedings of the ACM on Human- Computer Interaction 3.CSCW (2019): 1-30. 7 Liu, Jia, and Shawndra Hill (2021), ?Moment Marketing: Measuring Dynamics in Cross-channel Ad Effectiveness.? Marketing Science, 40 (1), 13-22. 8 Liu, Jia, Shawndra Hill, and David Rothschild (2021), ?The Impact of Temporally Turning off TV Ad on Search: A Generalized Synthetic Control Estimator under Interference.? Working paper. 9 Cong, Ziwei, Jia Liu, and Puneet Manchanda. 'The Role of 'Live' in Livestreaming Markets: Evidence Using Orthogonal Random Forest.' Working paper. complementarity) across products and vending locations. Predictive Analytics Predictive analytics refers to the process of using current and/ or historical data, combined with statistical techniques, to assess the likelihood of a certain event happening in the future by answering the questions: ?What will happen?? or ?When will it happen??. Predictive analytics have been used for a variety of prediction problems in marketing, such as credit scoring, new customer acquisitions, customer churn, customer lifetime value, clickthrough on ads, purchase incidence, and product recommendations. Therefore, predictive analytics can help improve many areas of a business, including efficiency, customer service, and risk reduction. For example, the reason that our proposed method in [2] can be useful to search engines and search advertisers is that it can lead to improved predictions of user clickthrough rates on search results. [5] provides an excellent review on the existing methodologies for user response prediction in online advertising. These methods can also be applied for prediction problems in other contexts. In particular, Deep Learning (DL) has received significant attention for prediction tasks in marketing over the past few years. DL is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks, in which multiple layers of processing are used to extract progressively higher level features from data. In addition to superior prediction accuracy, DL can process much more information from many different sources. Not only is it able to do this on an unparalleled scale, but it is also capable of bringing together disparate types of information ? images, audio, app data, clickstream data, location data, social network data ? in a way that other systems cannot handle. Nevertheless, one of the common complaints about the existing predictive analytics is that the tools are too much like black boxes, with no insight into what goes on during the process after input. Hence, there may be potential negative consequences when blindly relying on algorithms to optimize predictions. For example, a recent study6 shows that algorithms underpinning Facebook?s advertising platform started to discriminate by showing different people different types of housing or employment opportunities. That ultimately resulted in the algorithm showing more ads for secretarial jobs to women and more ads for jobs in the lumber industry to men, as one example. Therefore, it is important for companies to decide how to trade-off between accuracy and Diaphragm 1: The Relationship between Predictive and Prescriptive Analytics interpretability/transparency for a given problem on hand. Prescriptive Analytics Prescriptive analytics refer to the application of testing and other techniques to determine which outcome will yield the best result in a given scenario by answering the questions: ?why it will happen?? or ?What should I do??. Prescriptive Analytics often extends beyond predictive analytics by specifying both the actions necessary to achieve predicted outcomes and the interrelated effects of each decision. Please see Diaphragm 1 for an illustration of the relationship between predictive and prescriptive analytics. Randomized experiments, such as A/B testing, are commonly used by companies to determine the best ad copy, pricing point, targeting strategy, etc. The basic idea is to randomly allocate the experimental units across different treatment conditions, and then identify the treatment condition that yields the best outcome of interests. The biggest advantage of randomization is to reduce bias by equalising other factors that have not been explicitly accounted for the in study design. However, randomized experiments are either too costly or infeasible in most business problems. Therefore, a major class of prescriptive analytics generate proactive decisions on the basis of the predictive analytics outcomes using quasi-experimental or observational data. In my recent publication7, we aim to help practitioners understand the value of investigating in ?moment marketing? which entails the ability to synchronize online advertising (e.g., sponsored search) in real time with relevant offline events such as TV ads. We conduct causal estimation by leveraging large variations in TV advertising expenditure over a long period for a major brand in the U.S. fast food industry. Based on statistical analysis, we show that TVmoment- based search advertising could be effective for optimizing sponsored search advertising for both TV-advertised brands and their competitors. We also document the mechanisms driving such cross-channel advertising effects. Specifically, TV advertising can change the quality of online search traffic (e.g., who searches, where they search, and how they search) in the moments following a TV ad, so that an average searcher responds differently to subsequent search results. In a similar project8, we measure the effects of major TV advertisers? temporary discontinuation of TV advertising on consumer keyword search behavior. We leverage a field experiment in the US wireless industry in which the focal brand stopped its TV advertising for one randomly chosen week. We develop a statistical method to simulate the search volume for different topical search keywords under different TV advertising expenditure at one point in time. These simulations can provide insights for advertisers to improve their cross-channel advertising strategies. In my working paper on livestreaming markets9, we estimate how consumer price elasticity (or willingness-topay) for knowledge goods varies over the entire product life-cycle. We achieve this by leveraging the most recent advances in machine learning and statistics. Our proposed model is trained using large-scale historical transaction records, along with high-dimensional information about products, sellers, consumers, and the platform. Our proposed methodology can not only provide descriptive insights on consumer purchase patterns, but also derive optimal pricing point for a give product over each time period of the product life-cycle. Looking Forward Businesses are increasingly utilising data to discover insights that can aid them in creating business strategy, making decisions, and delivering better products, services and personalised online experiences. The three distinct marketing analytics are complementary and valuable in this process. While models and algorithms often outperform humans, domain knowledge, reasoning, and, ultimately, decision making rest in the hands of an end user. Since many of the algorithms underlying the analytics take a black-box approach, it often leaves little room for injecting domain expertise, and can result in frustration from analysts when results seem spurious or confusing. Therefore, it is very important for business leaders to create interdisciplinary teams to continuously monitor and evaluate data for bias. Last but not least, with the growing concern about privacy, new regulations have driven companies to take steps and examine how they can make their data more transparent to customers, as well as more reliable and relevant. As a result, this calls for efforts and techniques that can deliver a solution that doesn?t need a wealth of data to function but is also able to process, whenever possible, large volumes of data. 46 Biz@HKUST Biz@HKUST 47 // Cover // Insight Data and Reputation Building on Online Platforms Studies show online reviews can improve market efficiency and consumer welfare. Assistant Professor HUANG Yangguang Department of Economics, HKUST Business School Reputation systems operated by online platforms play an important role in mitigating the problems caused by asymmetric information in internet marketplaces. Successful reputation systems can crowdsource information from consumers and provide a large volume of reviews about product quality, attributes, seller credibility, and so on. Such review data may take the form of ratings, stars, textual comments, badges, or recommended lists. One important reason for the great success of leading e-commerce platforms, such as Amazon, eBay, and Taobao, is their ability to alleviate asymmetric information by their reputation systems. Reviews recorded by online platforms are valuable public goods that greatly improve market efficiency and consumer welfare. For example, scholars have found that online review platforms improve the welfare of restaurant consumers by US$2.50 per person per meal.1 The Effectiveness of Reputation Systems The effectiveness of reputation systems depends on two key conditions. First, on the consumer side, reviews are mostly usergenerated content and likely to be underprovided due to the nature of public goods. Reviews may suffer from various credibility problems such as loopholes, fraud, manipulation, and bias. Even if reviews are truthful, the information in them usually includes a lot of noise with regards to assessments of quality. For some markets, reviews written by consumers are based on subjective experiences and may not be applicable to consumers with heterogeneous tastes. For example, many Chinese people do not trust the ratings of Chinese restaurants on Yelp because most reviews are provided by non-Chinese people. In some markets, the nature of credence goods can hinder consumers from leaving informative reviews and learning from others' experiences. Hence, the design of the reputation system and quality transparency greatly affect how much consumers can learn from reviews. Second, on the firm side, a successful reputation system must provide firms with a sufficiently strong dynamic incentive to build a reputation by accumulating reviews. Firms must be eager to improve their quality and maintain good services because they expect to gain benefits from obtaining good reviews. For example, eBay sellers have a strong incentive to compete for the ?eBay Top Rated Seller? badge because consumers are willing to pay an average of 8.1% more to these reputable sellers.2 However, intense competition can slow down review accumulation and reduce the reputation premium, which weakens the firm?s dynamic incentive of building a reputation. Sketch of the Theory Consider that a market consists of a mixture of high-quality and lowquality firms. Later-stage consumers learn about quality from reviews provided by earlier-stage consumers through a quality inference function that maps reviews into beliefs about quality. A high-quality firm will adopt an increasing price path because the low initial price leads to more favorable information being released through earlier-stage sales. As a result, the firm will have a higher perceived quality and can increase its price and enjoy the reputation premium in later stages. In contrast, a low-quality firm will employ a declining price path because setting a higher price results in less negative reviews. The quality inference process of consumers depends on the volume and informativeness of review data. In a market with higher quality opaqueness, consumers update their beliefs by a smaller margin given the same number of reviews. Similarly, intense competition makes setting a low introductory price less effective in promoting sales and generating positive reviews. These two factors reduce high-quality firms' dynamic incentives to build their reputation. Therefore, quality opaqueness and intense competition slow the process of quality inference, and high-quality firms need a longer duration to build a good reputation. Specifically, in a more competitive market, or a market with a poor reputation system, a high-quality firm needs to maintain a low price for a longer duration before it can convince the market of its high quality. Empirical Evidence We tested the theory using data from Zaihang (www.zaih.com), a Figure 1: Competitive Intensity and Market Outcome Figure 2: Quality Opaqueness and Market Outcome leading online consulting platform in China. Zaihang is a platform in which individuals can register as ?experts? in certain areas and provide consulting services (e.g., career development, psychology, education ...) to clients. Consumers search for experts by their service categories and information on webpages of experts, ratings, and reviews left by previous clients. If a consumer wants to purchase the consulting service, he or she makes an appointment through Zaihang. After receiving the service, the client can leave a rating and review the consulting experience. Zaihang focuses on offline services, so the market is segregated into many small markets with different geographic locations and service categories. The intensity of competition and quality opaqueness vary across these markets, which enable us to study their influence on the effectiveness of the reputation system. We measured the competitiveness of a market by the number of experts providing service products. In Figure 1, groups 1, 2, 3, and 4 represent the first, second, third, and fourth quartiles, respectively, of the number of experts among the 553 markets. There is a clear pattern that, for both high-quality and low-quality experts, the sales and duration of the first product are higher for more competitive markets. Hence, in more competitive markets, experts wait longer to accumulate sales and reviews before they adjust prices. We used the average length of textual reviews per sale to measure quality opaqueness. In Figure 1, groups 1, 2, 3, and 4 correspond to the fourth, third, second, and first quartile of market opaqueness. Markets with higher quality opaqueness tend to have longer first-product duration, more first-product sales, and a smaller price difference between new and old products. If consumers in some markets have a low tendency to leave reviews, or only write short and uninformative reviews, after purchasing a product, then experts in these markets will not have a strong incentive to accumulate reviews to build their reputation. Hence, we observe the pattern that the process of reputation building is slower and less transparent. 1 Fang, Limin. ?The effects of online review platforms on restaurant revenue, survival rate, consumer learning and welfare.? Management Science (2021). 2 Resnick, Paul, et al. ?The value of reputation on eBay: A controlled experiment.? Experimental Economics (2006). 48 Biz@HKUST Biz@HKUST 49 // Cover // Insight I decided to join the KH program simply because it is the best. ? ? Michael Yue (Class of 2018) General Manager, Sales & Operation Google Hong Kong The Kellogg-HKUST EMBA Program is an exempted course under the Non-local Higher and Professional Education (Regulation) Ordinance Cap 493 (the Ordinance) of Hong Kong. It is a matter of discretion for individual employers to recognize any qualification to which this course may lead. Managerial and Policy Implications Our findings offer several managerial and policy implications. The fact that intense competition jeopardizes the effectiveness of a reputation system suggests the possibility that platforms can mitigate asymmetric information by entry restriction. By maintaining an appropriate level of competition, high-quality firms will have a strong incentive to participate in the market, offer a low introductory price, and build a reputation. This process, in turn, can increase the overall attractiveness of the platform. If the entry barrier is too low, excessive competition may cause each firm to receive few reviews and lose the incentive to provide a high-quality product. As a result, the platform may be filled with low-quality products and becomes less attractive due to adverse selection. For example, Taobao is often blamed for inferior and counterfeit products.3 In contrast, Tmall, another e-commerce platform by Alibaba, sets up higher entry barriers and has grown much faster than Taobao in recent years.4 Like online platforms, regulators and professional associations face a trade-off between promoting competition and the effectiveness of the reputation system. Inadequate competition results in high prices and reduces the variety of products, but excessive competition may worsen the asymmetric information problem. From a social welfare perspective, the desirable level of competition should strike a balance. Moreover, this optimal level of competition varies with the level of quality opaqueness. For goods with transparent quality, consumers can easily discover highquality firms and transmit quality information to other consumers. In this scenario, intense competition is desirable from a welfare perspective. However, certain entry restrictions may be desirable for goods with strong quality opaqueness, especially for credence goods. For example, the American Medical Association restricts the number of physicians by means of licensing exams and certificates to maintain a high quality of medical practice. Therefore, platforms and regulators should take heed of the quality opaqueness of different product categories and set entry restrictions accordingly. Moreover, new sellers have a strong incentive to accumulate reviews than established sellers. On Taobao, many sellers often provide rebates to attract more consumers to experience the product and write reviews.5 The platform can promote new sellers and curb the competition faced by them by granting special advantages in the recommendation system. High-quality new sellers are less likely to be buried if they become more visible with the help of the recommendation algorithm. Platforms have the natural incentive to develop better reputation systems. In the presence of competition between online platforms, the platform with a higher expected reputation is more attractive to both consumers and high-quality firms. Platforms should understand that quality opaqueness and competition are likely to interact with other factors during the process of quality inference and reputation building. Hence, for large e-commerce platforms carrying goods from vastly different categories, adopting a uniform rating and reputation system may not be desirable. For example, in markets with intense competition and opaque quality, the platform can take actions to reduce opaqueness by granting platform certification and posting experts' comments. The optimal design of the rating system and rating aggregation algorithm should also vary for different categories of products. References Fang, Limin. ?The effects of online review platforms on restaurant revenue, survival rate, consumer learning and welfare.? Management Science (2021). Huang, Yangguang, Chenyang Li, and Si Zuo. ?Building reputation on online platforms.? Working Paper (2021). Li, Lingfang, Steven Tadelis, and Xiaolan Zhou. ?Buying reputation as a signal of quality: Evidence from an online marketplace.? RAND Journal of Economics (2020). Resnick, Paul, et al. ?The value of reputation on eBay: A controlled experiment.? Experimental Economics (2006). 3 Fortune.com, Why Alibaba Can?t Complain About Its Return to the ?Notorious? Counterfeit Market List, Dec 22, 2016 4 Fortune.com, Alibaba Shows Resilience And Posts Blowout Revenue Growth Of 61% YoY, Aug 23, 2018 5 Li, Lingfang, Steven Tadelis, and Xiaolan Zhou. ?Buying reputation as a signal of quality: Evidence from an online marketplace.? RAND Journal of Economics (2020). Pointing the Way Consisting of 27 influential business leaders from a wide spectrum of industries, the HKUST Business School Advisory Council (SAC) Annual Meeting is one of the most important occasions where the School?s management team receives strategic advice and guidance from the SAC to improve teaching and research endeavors. Presided by Council Chairman Dr Hans Michael JEBSEN, this important meeting was held on 28 October 2021 in a hybrid form, comprising both virtual and physical formats. The meeting began with a welcome remark by HKUST President Professor SHYY Wei as he shared the latest developments of the University. Notable highlights include the yearround celebration program for the University?s 30th anniversary, the opening of the Shaw Auditorium, and the new HKUST(Guangzhou) campus. Following a presentation made by the School?s Dean Professor TAM Kar Yan, members offered meaningful insights and solid recommendations on the School?s strategic focus areas. 50 Biz@HKUST Biz@HKUST 51 // Cover // Feature In Recognition of Talent Thanks to the generosity of our SAC Members, their continued and new support provide the much-needed recognition and additional resource for the School to foster and retain the very best minds. Fung Term Professorships for Accounting and Management Our Donor Dr William FUNG Kwok Lun Group Deputy Chairman Fung Group Chair Professor GONG Yaping Fung Term Professor of Management Associate Professor Alminas ZALDOKAS Adjunct Associate Professor Winnie PENG Associate Professor Martha JEONG Associate Professor LIN Song Associate Professor LEE Dongwon Associate Professor AU Pak Hung Chair Professor HUNG Mingyi Fung Term Professor of Accounting Our Donor Mr Maximilian Y K MA Chairman Lee Heng Diamond Group Lee Heng Fellowship for Young Scholars 52 Biz@HKUST Biz@HKUST 53 // Feature Senior Research Fellow by RGC Professor Songnian CHEN, Chair Professor of the Department of Economics, has been named a Senior Research Fellow by the Research Grants Council. This prestigious scheme selects ten awardees every year and provides HK$7.8 million in funding to support a five-year extensive research period for each awardee. The scheme also facilitates the professors? dedication to research and development and helping universities attract and retain research talent. Professor CHEN?s project examines the ?Quantile Regression Subject to Sample Selection with Continuous and Binary Outcomes?. Honor Recognizes Teaching Excellence Flying High The Michael G Gale Medal for Distinguished Teaching is the highest accolade in teaching at HKUST that presents to only one faculty member each year. Associate Professor of Business Education in the Department of Information Systems, Business Statistics and Operations Management (ISOM), Jeevan JAISINGH, becomes the award recipient of 2021 and the honor exemplifies the uncompromising commitment of our faculty in pursuit of excellence. In addition to accolades presented by HKUST, our faculty have received respect and recognition locally and globally in their chosen fields. Clarity, Adaptability, Responsiveness and Empathy (CARE) teaching philosophy has guided Professor Jaisingh, to empathize students? needs, design engaging courses and teaching materials that combine theory and practice. In addition to the Michael G Gale Medal, he has been the winners of multiple teaching awards, including the Business School?s Franklin Prize for Teaching Excellence for twice, and the Common Core Excellence Course Award. ?My guiding light has always been the belief that I can make a difference, every single day, for students in the classroom and beyond,? says Professor Jaisingh. He adds that teaching can have its highs and lows. Teachers can cherish the highs, while use the lows as a stepping stone to be a better professor of tomorrow. ?Winning the award provides immense satisfaction that my contributions in shaping student development are being recognized by the University,? he says. Professor Jaisingh joined HKUST in 2003. A member of the Steering Committee on Review of the Common Core and the Working Group on Review of the Common Core, Professor Jaisingh has been instrumental in revamping the Common Core program to which ?Critical Thinking and Data Literacy? was introduced as a new Foundation course. He also served various teaching and learning committees in the Business School. Professor Jaisingh is the sixth faculty from the Business School to receive the Michael G. Gale Medal. He is particularly indebted to all ISOM department heads for supporting his teaching journey. The Michael G. Gale Medal for Distinguished Teaching was established in 1994 to commemorate the late Founding Council Member, Mr Michael G. Gale, for his distinguished service and outstanding contributions to the development of HKUST. Chief Economist of the Asian Development Bank Chair Professor Albert PARK, Head of Economics and Director of the Center for Economic Policy at HKUST, has been appointed by the Asian Development Bank (ADB) as its new Chief Economist. Professor PARK will lead ADB?s work on policy research and regional cooperation and integration. Professor Park is a development and labor economist who is an expert on China?s economic development. Launch of New Book on RMB Professor Edwin LAI, Department of Economics and Director of Center for Economic Development has launched a new book entitled ?One Currency, Two Markets: China's Attempt to Internationalize the Renminbi?. His book is written for anyone ?interested in the economic analysis of the future of the international monetary system and the USD, and the rising importance of the RMB?. By applying economic theories, Professor LAI examines in detail the real reasons for China to internationalize its currency and how financial development and openness are crucial for the internationalization of RMB to succeed. Best Online Courses of All Time Delivered by Professor Garvin Percy DIAS, Associate Professor of Business Education of the Department of Information Systems, Business Statistics and Operations Management, the MOOC Class ?Information Systems Auditing, Controls and Assurance? offered via Coursera has been awarded Class Central's Best Online Courses of All Time (2021 Edition). The award was granted based on 170,000+ reviews written by the learners. 54 Biz@HKUST Biz@HKUST 55 // Feature Positive Outcomes 2021 has concluded with more encouraging results achieved by the HKUST Business School and its academic programs. These achievements attest to the leading position of our performances in the Asia Pacific Region and beyond. Striving for Social Impact and Sustainability The School is the only business school in Hong Kong recognized for its social impact and sustainability achievements in the Positive Impact Rating (PIR), an initiative developed by business school experts in collaboration with global NGOs WWF, Oxfam, and UN Global Compact. The 2021 edition of PIR was launched at the UN PRME Global Forum, where our School was featured as one of the few business schools that advanced to the Level 4 (Transforming Schools) in the five-level rating. Master of Science in Finance: No.1 in Asia for 5 Consecutive Years Recognized by QS as the No.1 Master of Finance Program in Asia, the Program was developed by integrating the MSc in Investment Management and MSc in Financial Analysis programs. The Program has been widely recognized by financial professionals as a premier academic program, including the Chartered Financial Analyst (CFA) Institute, the Chartered Alternative Investment Analyst (CAIA) Association, and the Global Association of Risk Professionals (GARP). Among the Top Business Schools in Far East Asia The HKUST Business School has been ranked the BEST business school in Hong Kong and TOP 3 in Far East Asia, according to the 2021 Eduniversal Business Schools Ranking. The Eduniversal system rates business schools according to their international reputation and historical importance. Every year, it announces the rankings of the best 1,000 business schools in 154 countries in nine geographical zones. The results are based on academic peers who have participated in its annual Deans? Vote Survey. Master of Science in Global Operations: No.1 in the Asia Pacific Region The Program is acknowledged as No.1 in the Asia Pacific Region in the QS Business Masters Rankings: Supply Chain Management 2022. Offering the M2M Double Degree Master?s option with Yale University and FGV EAESP (in Sao Paulo Brazil), the Program allows students to study at two prominent business schools in two countries and graduate with two master?s degrees. Since the 2020-21 intake, the Program has launched an optional concentration in Operations Analytics. Kellogg-HKUST EMBA Program: World?s Leading Position In the Financial Times Global EMBA Ranking 2021, the Kellogg-HKUST Executive MBA Program continues to maintain its global leading position at No.2 overall. To be consistently ranked on top is no easy feat. While being rated No.2 this year, the Program has set an unrivaled record of being No. 1 in the world ten times since 2005. Master of Science in International Management: Top Five in the Asia Pacific Region The Program has been recognized as one of the top five master's programs in the region in the QS Business Masters Rankings 2022: Management. Offered in collaboration with CEMS (a global alliance of leading business schools, multinational companies and NGOs), the Program provides students with a dual qualification and an extensive global alumni and career development network. Concurrently, the M2M Double Degree Master?s option is available with Yale University, HEC Paris, and FGV EAESP. 56 Biz@HKUST Biz@HKUST 57 // Feature Making a Strong Case HKUST?s extensive collection of Asia-related business case studies has not only led to a distribution partnership with Harvard Business Publishing, but also hit an alltime high in its global reach. When it comes to a business education, nothing beats learning from real-life situations. Case studies are where theory intersects with real life and that people?s authentic experiences can provide valuable lessons. For this reason, case studies are held in high esteem and are valued by business educators worldwide. Since 2018, HKUST?s Thompson Center for Business Case Studies has partnered with Harvard Business Publishing (HBP) to distribute the University?s business cases online via HBP?s case catalog. Its global distribution has now hit a record high, signaling a strong world-wide demand for HKUST case studies. Cases into the Classroom ?Local case studies are really important for us, because we are able to bring the protagonist of the cases into the classroom. That way the students can hear some of the analysis, and also get some feedback directly from the protagonist,? says Professor Roger KING, Senior Advisor and Founding Director of the Thompson Center for Business Case Studies. Professor King, who is also Founding Director of the HKUST?s Tanoto Center for Asian Family Business and Entrepreneurship Studies, says that these local cases including those which feature Asian family business and entrepreneurship studies add a new dimension to teaching. Asian cases are bicultural and that?s another important and unique aspect of HKUST?s case studies, he says. ?There is another reason for its importance,? says Professor Winnie Qian PENG, who is the Director of both the Thompson Center for Business Case Studies and the Tanoto Center for Asian Family Business and Entrepreneurship Studies. ?Quality Asia-specific case studies are in demand.? Professor Peng says that HKUST case studies hold incredible values, because most of them are written from the perspective of the key decision-maker and reflect real-life issues in Asia, and the region had the highest economic growth in the past decade and still has a high growth potential for the future. ?More and more people in and out of Asia are interested in understanding Asian businesses and doing businesses in Asia,? Professor Peng added. ?You?re dealing with a real-life situation, with management decisions that have real consequences. So do you go left, right or straight? Through case study methods, the students get to put themselves in the shoes of the protagonist,? says Professor Michael CHEN Shih-Ta, Advisor for the Center whose previous experiences included leadership positions with the Harvard Business School Asia Pacific Research Center and the Asian Development Bank. Hitting a New Record The volume of cases that are being accessed from around the world demonstrates the popularity of the case studies. Over 8,000 copies of HKUST case studies were distributed to international business schools during the second quarter of 2021. This is compared to 5,000 copies in the previous quarter, making it a record high for the Center since it began the partnership with HBP. Global institutional users of the case studies include Harvard University, HEC Paris, INSEAD, the University of British Columbia, the University of California, and more. Within just three years? partnership with HBP, nearly 100 HKUST case studies have been listed by HBP, nearly 40,000 copies of the case studies are being used around the world, and over 40 HKUST faculty members participated in case writing so far. ?The faculty has realized that students want to study these cases because of the pedagogy,? says Professor Chen. Professor Peng adds that in recent years, she has noticed that it?s not just EMBA students who are interested in the case studies. Undergraduates and MSc students are interested too, and they?re looking to find insights by reading them. Case studies aren?t always a part of undergraduate classes. The students are very excited especially when they get to study the companies that they have heard of or have been a customer before. HKUST?s case studies are Asian focused, and that can fit the growing demand. ?Asian companies are a very important element of Asian business school education,? says Professor Peng. This is one of the key reasons why the Thompson Center for Business Case Studies started the partnership. Both Professor King and Professor Peng say they want to facilitate a better understanding of the unique cultures, management styles, ethics and business environments in Asia through Asia?s own cases. The cases cover a variety of disciplines and fields, and are compiled by expert HKUST faculty members. ?We?re a premier business school with lots of diverse, worldclass faculty, and we have a great network. It gives us a great advantage,? adds Professor Chen. HKUST?S outstanding reputation and high-quality case studies brought about the partnership with Harvard. ?This is a really good opportunity to let more people around the globe learn what?s really happening in Asia. Our case center is one of the most active in existence,? says Professor Peng. Asian cases are bicultural and that?s another important and unique aspect of HKUST?s case studies >120 Cases >40 HKUST Faculty Nearly 100 Case Studies Nearly 40,000 Copies developed by the Center up to date participated in case writing listed by HBP within 3 years listed by HBP within 3 years 58 Biz@HKUST Biz@HKUST 59 // Feature The First BSc in Sustainable & Green Finance in Hong Kong This new programme bridges the talent gap in this emerging field and is perfectly positioned to bring exceptional talent for a sustainable future. As climate change continues to take center stage, profit alone can no longer underpin business activities. In fact, there is already an expectation that businesses will be geared towards sustainability and will join the fight against climate change. With this in mind, sustainable and green finance is now a fast-emerging field, and there is a corresponding and strong demand for professionals who have an understanding of green development. HKUST has launched the first Bachelor of Science in Sustainable and Green Finance (SGFN) Program in Hong Kong. The aim of the Program is to foster talent to support green and sustainable development, and to support the Hong Kong Government?s objectives to turn the city into a leading green and sustainable finance center. Green finance plays a huge role in protecting the environment, and the transition from brown-to-green requires the mobilization of large amounts of funding from the public and private sectors. ?We can see that many countries have started to undertake climate-resilient development pathways and aim to achieve carbon neutrality, something which requires a considerable investment,? explains Professor Jimmy FUNG Chi-hung, SGFN Program Co-Director, and an accomplished scholar in meteorology and air pollution. ?Green finance activities will definitely help to mobilize resources across different sectors and support environmental protection.? Investing to Meet Asia?s SDGs Professor Veronique J. A. LAFON-VINAIS, SGFN Program Co-Director, and a seasoned financial market professional, says that Asia is significantly exposed to the impact of climate change. According to the Asian Development Bank, mitigation and adaptation efforts will require at least (From left) Professor TAM Kar Yan, Dean of HKUST Business School, SGFN Program Co-Directors Professor Veronique J A LAFON-VINAIS and Professor Jimmy FUNG, and Professor Alexis LAU, Head of the Division of Environment and Sustainability US$26 trillion dollars of investment. The United Nations estimated that Asia may have to invest US$3 to 5 trillion annually to meet its sustainable development goals. ?This is a massive financing requirement which can only come from a combination of public and private investments,? she says. The silver lining is that governments are already doing a lot through regulation. Professor Lafon-Vinais says that China is leading the way in Asia, and the country?s financial regulators are spearheading the development of green finance and working with their European Union counterparts to adopt common standards. As sustainable and green finance continues to emerge, and development priorities are set at national and global levels, the need for people with the relevant knowledge, skill sets, and experience will increase. This is where the SGFN program will fill the gap. Effective Leadership Skills ?The Program will teach students how to be effective leaders and managers in Environmental, Social and Governance (ESG), impact investment, the financing of sustainable and green projects, the development of sustainable and green instruments and products, and other related fields,? Professor Fung says. Working in sustainability and green finance will require crossover skills, and that is why the program is organized by both the Business School and the Division of Environment and Sustainability. Its interdisciplinary nature will provide students with a broad-based learning experience that includes environment, science and technology. This will enable students to develop a global outlook which will enhance the development of sustainable and green finance. After graduating, SGFN students will be equipped to tackle issues that extend far beyond a single discipline. ?It is more than a finance degree, and it is more than an environmental management degree,? says Professor Lafon- Vinais. ?The new Program gathers an interdisciplinary team of academic specialists in the area of finance and investment, environmental science and technology, to equip students with professional knowledge and a global outlook.? The Program is taught by expert faculty members, and maintains strong connections with industry, as well as HKUST alumni. Such connections ensure there are plenty of practical and enriching co-curricular activities. An Interdisciplinary Approach The first intake for the inaugural program will be during the Fall of 2022, when 30 students will be admitted to the Program. Eighteen admissions will be program-based admissions, and 12 will be school-based. A program-based admission means that students will be directly admitted from secondary schools, if they fulfil the minimum requirements of admission by the Business School. Schoolbased admissions will be for students already enrolled in the Business School, who can then apply for a BSc in SGFN as a major. 60 Biz@HKUST Biz@HKUST 61 // Feature SGFN Program Highlights Business & Finance | Managing CSR | Derivative Securities | | ESG Investing | Green Finance Case Analyses | | Social Sustainability Risks and Challenges | | Circular Economy and Life Cycle Assessment | | Introduction to Sustainability | | Green Business Strategy | Environment & Sustainability More New Funding for Green Finance Research In addition to nurturing green finance talent, HKUST has taken active steps in expanding its understanding of green finance. In our ongoing effort to support this realm, we are pleased to receive major funding from the Theme-based Research Scheme of the Research Grants Council for our research project entitled ?Developing Hong Kong as a Global Green Finance Centre?. Our funded proposal will gather an interdisciplinary team of academic specialists in the areas of finance, environmental science, public policy, and technology, as well as former policymakers and experienced practitioners, to address As part of the Program, students will study a wide range of topics across disciplines. These include ESG investing, sustainable supply chain management, green business strategy, the use of engineering technology to solve environmental problems, and more. According to Professor Fung, the four-year programme is particularly suited to students who have a strong interest in green development and green projects. They will be able to apply a knowledge of finance to enhance the financial sustainability of projects. ?Sustainability will be at the heart of the economy in the 21st century and beyond. The new program is set to equip students with the essential knowledge and skills to fill the talent gap for green and sustainable finance.? fundamental research questions of green finance through a series of cohesive studies. Professor Chu ZHANG, Head of the Finance Department and Project Coordinator says, ?Our research team represents six main research areas and will contribute towards helping various stakeholders achieve the ?brown to green? transition. Adopting a ?think and do? approach, we will gather academic insights, work with industry partners and government units, and guide them in implementation and execution phases of a broad range of green finance tasks.? Professor TAM Kar Yan, Dean of HKUST Business School, says, ?The cross-disciplinary nature of this project emphasizes the importance of collaboration across various industries and disciplines to ensure the success of building and financing better solutions for our planet.? Themed ?Scaling FinTech Future Together?, the Hong Kong FinTech Week 2021 was held between 1-5 November. Our faculty joined many respected speakers, in several panels, to share their insights into different timely topics. In a panel on ?AI and Big Data Adoption in APAC: A Talent Development Perspective?, Professor TAM Kar Yan, Dean of HKUST Business School, discussed with experts about the adoption status and talent needs of AI and Big Data technologies. Senior Associate Dean and Elman Family Professor of Business, HUI Kai-Lung, talked about fintech talent development in Hong Kong and beyond, while Associate Professor HONG Weiyin explained how to use AI to derive insights from data analytics. Recognizing the growing need for fintech talent, HKUST has signed an MoU with the Hong Kong Monetary Authority (HKMA) to form a new partnership in talent development as well as research and application to the development of fintech. Under the Industry Project Masters Network (IPMN) of the HKMA, a Mentorship Network will be established. Through this network, each fintech or industry project of the IPMN scheme will be supervised by mentors from various fields. HKUST together with three other local universities will collaborate with the HKMA and other stakeholders on the IPMN scheme. Postgraduate students of these universities in the discipline of fintech will be eligible to join IPMN. The School has worked with Hong Kong Exchanges and Clearing Limited (HKEX) to launch an executive education course on Sustainable Finance. This is the first time that HKEX has partnered with a university in Asia to offer this kind of course in this important field for MBA students The elective course ?Sustainable Finance with Business Practices? introduces the framework of sustainable finance and responsible investing and examines various approaches to ESG investing, relevant products and strategies, and the governed practices. With experienced faculty from HKUST and experts from HKEX, MBA students will be able to understand the key challenges and opportunities in different contexts of green and sustainable finance. Collaborating for the Next-Generation Finance Services In Support of InvestHK for HK Fintech Week MoU with HKMA to Open Up Mentorship Opportunities Pioneers with HKEX in Sustainable Finance Education Through advocacy and education, the HKUST Business School has partnered with respected organizations in promoting green finance and fintech. ..!..HKUST I llllJI BUSINESS SCHOOL DBA wfiiff~*illl!l!IIJ'l WORLD CLASS IN ASIA Doctor of Business Administration FROM INSIGHT TO IMPACT Program Commences in September 2022 HKUST Business School Tel: (852) 2358 5015 The Hong Kong University of Science and Technology Email: dba@ust.hk Clear Water Bay, Kowloon, Hong Kong Website: http://www.dba.ust.hk