Human–Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending

References

• Arnold V, Collier P, Leech S, Sutton S (2000) The effect of experience and complexity on order and recency bias in decision making by professional accountants. Accounting Finance 40(2):109–134.Crossref, Google Scholar
• Attenberg J, Ipeirotis P, Provost F (2015) Beat the machine: Challenging humans to find a predictive model’s “unknown unknowns.” J. Data Inform. Quality 6(1):1–17.Crossref, Google Scholar
• Blackwell M, Iacus S, King G, Porro G (2009) CEM: Coarsened exact matching in Stata. Stata J. 9(4):524–546.Crossref, Google Scholar
• Campbell D, Frei F (2010) Cost structure, customer profitability, and retention implications of self-service distribution channels: Evidence from customer behavior in an online banking channel. Management Sci. 56(1):4–24.Link, Google Scholar
• Cushing B, Ahlawat S (1996) Mitigation of recency bias in audit judgment: The effect of documentation. Auditing 15(2):110–122.Google Scholar
• D’Acunto F, Prabhala N, Rossi AG (2019) The promises and pitfalls of robo-advising. Rev. Financial Stud. 32(5):1983–2020.Crossref, Google Scholar
• Davis J, Heller SB (2017) Using causal forests to predict treatment heterogeneity: An application to summer jobs. Amer. Econom. Rev. 107(5):546–550.Crossref, Google Scholar
• Deese J, Kaufman R (1957) Serial effects in recall of unorganized and sequentially organized verbal material. J. Experiment. Psych. 54(3):180–187.Crossref, Google Scholar
• D’Hondt C, De Winne R, Ghysels E, Raymond S (2019) Artificial intelligence alter egos: Who benefits from robo-investing? Preprint, submitted November 6, https://dx.doi.org/10.2139/ssrn.3415981.Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2015) Algorithm aversion: People erroneously avoid algorithms after seeing them err. J. Experiment. Psych. Gen. 144(1):114–126.Crossref, Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2016) Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Sci. 64(3):1155–1170.Link, Google Scholar
• Duarte J, Siegel S, Young L (2012) Trust and credit: The role of appearance in peer-to-peer lending. Rev. Financial Stud. 25(8):2455–2483.Crossref, Google Scholar
• Featherman M, Pavlou P (2003) Predicting e-services adoption: A perceived risk facets perspective. Internat. J. Human Comput. Stud. 59(4):451–474.Crossref, Google Scholar
• Friedberg B (2018) Six of the newest trends in robo advisors. USNews (June 27), https://money.usnews.com/investing/investing-101/articles/2018-06-27/6-of-the-newest-trends-in-robo-advisors.Google Scholar
• Friedberg BA (2019) How do robo-advisors work? Roboadvisorpros (January 27), https://www.roboadvisorpros.com/how-do-robo-advisors-work.Google Scholar
• Fügener A, Grahl J, Gupta A, Ketter W (2019) Collaboration and delegation between humans and AI: An experimental investigation of the future of work. ERIM Report Series Research in Management, Erasmus Research Institute of Management, Erasmus University Rotterdam, Rotterdam, Netherlands.Google Scholar
• Ge R, Feng J, Gu B (2017) Predicting and deterring default with social media information in peer-to-peer lending. J. Management Inform. Systems 34(2):401–424.Crossref, Google Scholar
• Germann M, Merkle C (2019) Algorithm aversion in financial investing. Preprint, submitted November 6, https://dx.doi.org/10.2139/ssrn.3364850.Google Scholar
• Hitt LM, Frei FX (2002) Do better customers utilize electronic distribution channels? The case of PC banking. Management Sci. 48(6):732–748.Link, Google Scholar
• Iacus SM, King G, Porro G (2012) Causal inference without balance checking: Coarsened exact matching. Politcal Anal. 20(1):1–24.Crossref, Google Scholar
• Iyer R, Khwaja I, Luttmer P (2016) Screening peers softly: Inferring the quality of small borrowers. Management Sci. 62(2):1554–1577.Link, Google Scholar
• Jiang Y, Ho Y, Yan X, Tan Y (2020) When online lending meets real estate: An empirical investigation of lender behavior in real-estate crowdfunding. Inform. Res. Systems 31(3):715–730.Link, Google Scholar
• Jung D, Dorner V, Glaser F (2017) Robo-advisory. Bus. Inform. Systems. Engrg. 60(1):81–86.Crossref, Google Scholar
• Jung D, Dorner V, Weinhardt C, Pusmaz H (2018) Designing a robo-advisor for risk-averse, low-budget consumers. Electronic Marketing 28(3):367–380.Crossref, Google Scholar
• KPMG (2016) Robo advising: Catching up and getting ahead. https://home.kpmg/content/dam/kpmg/pdf/2016/07/Robo-Advising-Catching-Up-And-Getting-Ahead.pdf.Google Scholar
• Lin M, Prabhala R, Viswanathan S (2013) Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management Sci. 59(1):17–35.Link, Google Scholar
• Lopez C, Babcis S, De-laOssa A (2015) Advice goes virtual: How new digital investment services are changing the wealth management landscape. J. Financial Perspect. 3(2):156–164.Google Scholar
• Ludwig L (2020) Best robo advisors for 2020. InvestorJunkie (February 1), https://investorjunkie.com/best-robo-advisors/.Google Scholar
• Luo X, Lu X, Li J (2019) When and how to leverage e-commerce cart targeting: The relative and moderated effects of scarcity and price incentives with a two-stage field experiment and causal forest optimization. Inform. Systems Res. 30(4):1203–1227.Link, Google Scholar
• Markowitz H (1952) Portfolio selection. J. Finance 7(1):77–91.Google Scholar
• Murdock B (1962) The serial position effect of free recall. J. Exp. Psychol. 64(5):482–488.Crossref, Google Scholar
• Musto C, Semeraro G, Lops P, De Gemmis M, Lekkas G (2015) Personalized finance advisory through case-based recommender systems and diversification strategies. Decision Support Systems 77(9):100–111.Crossref, Google Scholar
• Paravisini D, Rappoport V, Ravina E (2016) Risk aversion and wealth: Evidence from person-to-person lending portfolios. Management Sci. 63(2):279–297.Link, Google Scholar
• Park J, Ryu J, Shin H (2016) Robo-advisors for portfolio management. Adv. Sci. Tech. Lett. 141(1):104–108.Crossref, Google Scholar
• Pompian M (2011) Behavioral Finance and Wealth Management: How to Build Investment Strategies that Account for Investor Biases (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Schanke S, Burtch G, Ray G (2021) Estimating the impact of ‘humanizing’ customer service chatbots. Inform. Systems Res., ePub ahead of print May 24, https://doi.org/10.1287/isre.2021.1015.Link, Google Scholar
• Schierz PG, Schilke O, Wirtz BW (2010) Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Res. Appl. 9(3):209–216.Crossref, Google Scholar
• Srivastava SC, Chandra S, Theng YL (2010) Evaluating the role of trust in consumer adoption of mobile payment systems: An empirical analysis. Commun. Assoc. Inform. Systems 27:561–588.Google Scholar
• Tversky A, Kahneman D (1974) Judgment under uncertainty: Heuristics and biases. Science 185(4157):1124–1131.Crossref, Google Scholar
• Venkatesh V, Morris M, Davis G, Davis F (2003) User acceptance of information technology: Toward a unified view. MIS Quart. 27(53):425–478.Crossref, Google Scholar
• Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. J. Amer. Statist. Assoc. 113(523):1228–1242.Crossref, Google Scholar
• Wang J, Ipeirotis PG, Provost F (2017) Cost-effective quality assurance in crowd labeling. Inform. Systems Res. 28(1):137–158.Link, Google Scholar
• Xin D, Ma L, Liu J, Macke S, Song S, Parameswaran A (2018) Accelerating human-in-the-loop machine learning: Challenges and opportunities. DEEM’18 Proc. Second Workshop Data Management End-to-End Machine Learn. (Association for Computing Machinery, New York), 1-4.Google Scholar
• Xu J, Chau M (2018) Cheap talk? The impact of lender-borrower communication on peer-to-peer lending outcomes. J. Management Inform. Systems 35(1):53–85.Crossref, Google Scholar
• Yin J, Luo J, Brown SA (2021) Learning from crowdsourced multi-labeling: A variational Bayesian approach. Inform. Systems Res. Forthcoming.Google Scholar
• Zhou T (2013) An empirical examination of continuance intention of mobile payment services. Decision Support Systems 54(2):1085–1091.Crossref, Google Scholar