19 January 2022 10:00 – 12:00 GMT (18:00 – 20:00 Taipei time)

Speakers

• Dr. Abdul Kader Al Jandali (Royal Holloway)
• Dr. Mu-En Wu (Taipei Tech)
• Dr. Wesley Yuen & Prof. Yu-Lun Liu (Royal Holloway & Taipei Tech)
• Dr. Wei-Lun Chang (Taipei Tech)
• Organized by Prof Hari Harindranath (Royal Holloway) & Prof Jung-Fa Tsai (Taipei Tech)

Agenda

Time	Topic	Speaker
09:55 (17:55)	Seminar Opening	Prof Jung-Fa Tsai & Dr Nisreen Ameen (DOS Co-Director)
10:00 (18:00)	FinTech Innovations: A Review of the Recent Developments and Prospects Q & A	Dr. Abdulkader Aljandali
10:30 (18:30)	A Quantitative Model for Option Sell-Side Trading with Stop-Loss Mechanism by Using Random Forest Q & A	Dr. Mu-En Wu
11:00 (19:00)	The taxonomic fit and thematic fit of brand extension and tourist’s inspiration: The moderation effects of brand familiarity and implicit beliefs Q & A	Dr. Wesley Yuen & Dr. Yu-Lun Liu
11:30 (19:30)	Exploring Service Journey by AI Chatbots for Customer Experience Q & A	Dr. Wei-Lun Chang
12:00 (20:00)	Closing Remarks	Nisreen & Jung-Fa

---

Further details

Dr Abdul Kader Al Jandali
Position: Senior Lecturer in Financial Management
Topic: FinTech Innovations: A Review of the Recent Developments and Prospects

Research Areas:

• Fintech and Financial inclusion
• ESG investing
• Sustainable Finance

Abstract

The dramatic surge of interest in FinTech over the past few years has highlighted the need for a better understanding of the value of technological innovations. In line with this reasoning FinTech has emerged as a mechanism that enables banking and nonbanking institutions to cut their costs, to enhance the quality of their services and to create a much more resilient and diverse financial environment. This FinTech development, which is supported by Artificial intelligence (Ai) and automation allows FinTech adopters to enter different segments of the financial services industry. However,despite the broad application of FinTech, little academic research has explored the development of this new wave of technological innovations. Therefore, our study aims to fill this gap in the literature and examines the recent developments in selected Fintech technologies including peer-to-peer financing, digital-only banks, machine learning and Ai, Blockchain, Robo-advice, InsurTech and RegTech. The paper also reviews the FinTech ecosystem including the impact of FinTech business models and their related, regulatory and non-regulatory, challenges.

Bio

Dr Aljandali is a Senior Lecturer in Financial Management and Financial Technologies at Royal Holloway University of London. Authority in Quantitative Research with published output in Financial Modelling, Foreign exchange Forecasting, Economic Modelling and Multivariate Methods. Current research includes studies on the role FinTech as a key driver for financial inclusion with a focus on emerging markets. Data Analytics passionate with knowledge of Python, Tenserflow, GitHub, Colab, Google Hub and AWS. Specialist knowledge and expertise in SPSS, SAS and EViews computer packages.

---

Dr. Mu-En Wu
Position: Associate Professor, Department of Information and Finance Management, National Taipei University of Technology, Taiwan
Topic: A Quantitative Model for Option Sell-Side Trading with Stop-Loss Mechanism by Using Random Forest

Research Areas:

• Money management
• Financial data analysis
• Information theory
• Cryptography

Abstract

Option trading is increasingly being used for hedging and speculation; however, inherent risk, makes money management an important issue in controlling profits and losses. In this study, we sought to quantify the risk associated with options by constructing a sell-side trading strategy with a mechanism aimed at predicting the win-rate via statistical analysis and the random forest algorithm. AUC-ROC curves verified the effectiveness of our random forest predictive model with a greater than 75 percent likelihood of correctly identifying a trade. In experiments, our random forest predictive model outperformed the statistical predictive model, thereby demonstrating the effectiveness of our trading strategy with stop-loss mechanism in controlling risk in options trading.

Bio

Mu-En Wu is an Associate Professor at Department of Information and Finance Management at National Taipei University of Technology, Taiwan. Dr. Wu received his Ph.D. degree with major in computer science from National Tsing Hua University, Taiwan, in 2009. After that, he joined Institute of Information Science, Academia Sinica at Taipei City, Taiwan as a postdoctoral fellow during 2009~2014. During February 2014 to July 2017, he served as an assistant professor of Department of Mathematics at Soochow University. He has a wide variety of research interests covering cryptography, information theory, prediction market, money management, and financial data analysis. He has published more than 100 research papers in referred journals and international conferences.

---

Dr. Wesley Yuen & Dr. Yu-Lun Liu
Position: Wesley Yuen: Lecturer in Marketing, Department of Marketing, Royal Holloway, University of London; Yu-Lun Liu: Assistant Professor of Marketing, Department of Business Management, National Taipei University of Technology
Topic: The taxonomic fit and thematic fit of brand extension and tourist’s inspiration: The moderation effects of brand familiarity and implicit beliefs

Research Areas:

• Brand extension
• Consumer inspiration
• Tourism Marketing

Abstract

The significant growth in the number of overseas travelers has encouraged many multinational brands to enter and seek to dominate the international tourist market. Brand extension (BE) has been used to introduce new product lines to an existing and/or new market. Previous research suggests that the success of BE is determined by the similarity between the parent brand and the expended product line(s) (i.e. fit). Such BE fit can be created taxonomically or thematically. While marketers are engaging in utilizing BE fit strategies to improve the adoption and purchase intentions of tourists/consumers towards BE product(s), there have also been many unsuccessful BE examples. The research finds initial evidence that tourists’ adoption of the BE product is determined by discovering their inspirations about the level of BE fit. That is, the BE fit needs to be able to appropriately trigger the tourists’ interest in the BE product lines in order to encourage their willingness to purchase/adopt the brand-extended product/service. Furthermore, this effect of BE fit on tourist (consumer) inspiration is moderated by an individual’s level of familiarity with the parent brand and their implicit beliefs. The findings suggest marketing literature and practitioners a new phenomenon and antecedent of BE on inspiration.

Bio

Yu-Lun Liu is an Assistant Professor of marketing at National Taipei University of Technology. He holds a PhD in Business and Management, and an MBA in Commerce Automation and Management. Yu-Lun obtained years of work experience in an IT company as a software engineer. He has a particular interest in information systems and technology management. He also has interest in online consumer psychology and consumer behavior.

Wesley Yuen is a lecturer in Marketing at Royal Holloway, University of London. Wesley obtained his fully-funded Economic and Social Research Council (ESRC) PhD in Marketing at the Alliance Manchester Business School. His main research areas are brand management, tourism marketing and consumer psychology (mainly in personality, cognition and emotion). Wesley has published in internationally-recognized journals, such as Journal of Business Research, International Journal of Contemporary Hospitality Management, Resources, Conservation & Recycling and Journal of Marketing Communications.

---

Dr. Wei-Lun Chang
Position: Associate Professor, Department of Business Management, National Taipei University of Technology, Taiwan
Topic: Exploring Service Journey by AI Chatbots for Customer Experience

Research Areas：

• Data/Text Mining
• Artificial Intelligence
• Digital Technology and Application
• Service Science

Abstract

The emergence of digital age brings AI robots (chatbots) for intelligent interaction and conversation. The functions of AI chatbots cover utilitarian to entertainment that may change physical or online shopping behavior. The COVID-19 pandemic fosters the usage of chatbots. However, the impact of AI chatbots as medium on customer experience and journey still lacks and needs further investigation. This research uses design science to create a customer service journey with three stages (recognition, consideration, and decision) to integrate chatbots to evaluate demands and behavior. The designed AI chatbot contains rule-based and case-based systems. The conversations will be analyzed by sentiment analysis to support information and emotional responses. We also investigate how to furnish appropriate service and guide customers from recognition to decision via interactive chatbot. We also aim to provide clues to help companies implement AI chatbots to create best interactive experience. The outcomes showed customer service journey not only enhance current services but explore unfulfilled customer needs. The results also revealed the direction of conversational intelligent chatbots and optimization of customer service journey.

Bio

Wei-Lun Chang, is an Associate Professor of the Department of Business Management in the National Taipei University of Technology. He completed his PhD at the Department of Management Information Systems at National Chengchi University. His research areas include social media, sentiment and technology in education. He has been published in international journals with various topics, such as Communications of the ACM, Journal of Information Science, Information Systems Frontiers, Internet Research, European Journal of Operational Research, Transportation Research Part E, Journal of Organizational and End User Computing, and Electronic Commerce Research and Applications.