【 Webinar Series - Innovation, Productivity, and Challenges in the Digital Era: Asia and Beyond 】
Why are Firms Slow to Adopt Profitable Opportunities?
Date: 5 Oct 2022 (Wed)
Time: 10am – 11:10am (Hong Kong Time, UTC+8)
Abstract: Why are small businesses often slow to adopt new profitable opportunities, even in the absence of informational frictions, fixed costs, or misaligned incentives? The authors explore three potential mechanisms: present bias, memory, and trust in other firms. In partnership with a financial technology (FinTech) company in Mexico, the authors randomly offer businesses that are already users of the payment technology the opportunity to be charged a lower merchant fee for each payment they receive from customers. The median value of the fee reduction is 3% of profits. The authors randomly vary the size of the fee reduction, whether the businesses face a deadline to accept the offer, whether they receive a reminder, and whether the authors tell them in advance that they will receive a reminder. While deadlines do not affect take-up, reminders increase take-up of the lower fee by 18%, and anticipated reminders by an additional 7%. The results point to limited memory in firms, but not present bias. Additional survey data suggests trust as the mechanism behind the significant additional effect of the anticipated reminder. Upon receiving an anticipated reminder from the FinTech company, firms value the offer more and accept it even if they generally distrust advertised offers.
Speaker:
Sean HIGGINS
Assistant Professor of Finance, Kellogg School of Management, Northwestern University
Co-authors:
Paul J. GERTLER, Li Ka Shing Professor of Economics and Professor, School of Public Health, University of California, Berkeley
Ulrike MALMENDIER, Edward J. and Mollie Arnold Professor of Finance, Berkeley Haas and Professor of Economics, University of California, Berkeley
Waldo OJEDA, Assistant Professor, William Newman Department of Real Estate, Baruch College, Zicklin School of Business, City University of New York
Discussant:
Jie BAI
Assistant Professor of Public Policy, Harvard Kennedy School Harvard University
Session Chair:
Pulak GHOSH
IIMB Chair of Excellence and Professor of Decision Sciences, Indian Institute of Management Bangalore (IIMB)
About the Webinar
Artificial Intelligence (AI), Big Data, multilevel neural nets, the Internet of Things (IoT) and other digital technologies are transforming the world. They are strengthening innovation and productivity and innovation by rendering the future more predictable and reshaping individual, business, social, and government behavior. Asia leads the world in some of these endeavors, e.g., digital platforms. The OECD lists 40% of big new digital technologies as Asian. Almost half of global digital platform business-to-consumer revenues are Asian, versus only 22% from the U.S. and 12% from the Eurozone. Profound new policy challenges arising, in consequence, include: shifting skills demanded in labor markets and “digital divide” inequality, (ii) AI expanding financial inclusion or encoding inequality, expanding or obscuring accountability, increasing transparency or obscuring amoral decision-making, and (iii) digital privacy, unsanctionable on-line libel, misinformation, manipulation, and propaganda. The ABFER, therefore, plans a monthly e-seminar series spotlighting important new research, particularly the Asia-pacific related, into these issues and providing “state-of-the-art” overviews by prominent scholars. We hope policy makers and practitioners will find the e-seminars helpful and will alert researchers to issues needing attention.
Collaborating Organizers
ABFER, The Chinese University of Hong Kong-Zhejiang University Joint Research Center for Digital Economy, The Chinese University of Hong Kong (CUHK) Department of Economics and Center for Internet Development and Governance, Fanhai International School of Finance (FISF), Fudan University, National Tsing Hua University College of Technology Management and Tsinghua University School of Economics and Management (Tsinghua SEM)