Prof. Oliver Hinz
Goethe University Frankfurt
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Academic Career Length
21Years (2006-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications13 March 2023 in Business & Information Systems Engineering
Journal Article
TTTimm Teubner
Timm TeubnerCFChristoph M. Flath
Christoph M. FlathCWChristof Weinhardt
Christof Weinhardt
Wil M.P. van der AalstOliver Hinz28 February 2023 in Management Review Quarterly
Journal Article
Digitalization and technologization affect numerous domains, promising advantages but also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance implement these technological advances—especially Artificial Intelligence—regulators prescribe high levels of transparency, assuring the traceability of decisions for third parties. Explainable Artificial Intelligence (XAI) is of tremendous importance in this context. We provide an overview of current research on XAI in Finance with a systematic literature review screening 2,022 articles from leading Finance, Information Systems, and Computer Science outlets. We identify a set of 60 relevant articles, classify them according to the used XAI methods and goals that they aim to achieve, and provide an overview of XAI methods used in different Finance areas. Areas like risk management, portfolio optimization, and applications around the stock market are well-researched, while anti-money laundering is understudied. Researchers implement both transparent models and post-hoc explainability, while they recently favored the latter.
1 December 2023 in Information Systems Research
Journal Article
Although future regulations increasingly advocate that AI applications must be interpretable by users, we know little about how such explainability can affect human information processing. By conducting two experimental studies, we help to fill this gap. We show that explanations pave the way for AI systems to reshape users' understanding of the world around them. Specifically, state-of-the-art explainability methods evoke mental model adjustments that are subject to confirmation bias, allowing misconceptions and mental errors to persist and even accumulate. Moreover, mental model adjustments create spillover effects that alter users' behavior in related but distinct domains where they do not have access to an AI system. These spillover effects of mental model adjustments risk manipulating user behavior, promoting discriminatory biases, and biasing decision making. The reported findings serve as a warning that the indiscriminate use of modern explainability methods as an isolated measure to address AI systems' black-box problems can lead to unintended, unforeseen problems because it creates a new channel through which AI systems can influence human behavior in various domains.
16 May 2019 in Information Systems Journal
Journal Article
1 September 2024 in Information Systems Research
Journal Article
UGUlrich Gnewuch
Ulrich GnewuchSMStefan Morana
Stefan MoranaOliver HinzRKRalf Kellner
Ralf Kellner
AMAlexander Maedche
Alexander MaedcheTo leverage the complementary strengths of humans and artificial intelligence (AI) in online service encounters, firms have begun to use hybrid service agents: combinations of AI agents (e.g., chatbots) and human agents (e.g., service employees) behind a single interface. However, it is unclear whether firms should be transparent about behind-the-scenes employees working in tandem with an AI-based chatbot to serve customers. Against this backdrop, we investigated the impact of human involvement disclosure on customer interactions with hybrid service agents. Our findings suggest that disclosing human involvement before or during an interaction with the hybrid service agent leads customers to adopt a more human-oriented communication style. This effect is driven by impression management concerns that are activated when customers become aware of humans working in tandem with the chatbot. The more human-oriented communication style ultimately increases employee workload because fewer customer requests can be handled automatically by the chatbot and must be delegated to a human. These findings provide novel insights into how and why disclosing human involvement affects customer communication behavior, reveal its negative consequences for employees working in tandem with a chatbot, and highlight the potential costs and benefits of providing transparency in customer–hybrid service agent interactions.
16 January 2023 in Research in International Business and Finance
Journal Article
MCMichele Costola
Michele CostolaOliver HinzMNMichael Nofer
Michael Nofer
LPLoriana Pelizzon
Loriana Pelizzon
The recent COVID-19 pandemic represents an unprecedented worldwide event to study the influence of related news on the financial markets, especially during the early stage of the pandemic when information on the new threat came rapidly and was complex for investors to process. In this paper, we investigate whether the flow of news on COVID-19 had an impact on forming market expectations. We analyze 203,886 online articles dealing with COVID-19 and published on three news platforms (MarketWatch.com, NYTimes.com, and Reuters.com) in the period from January to June 2020. Using machine learning techniques, we extract the news sentiment through a financial market-adapted BERT model that enables recognizing the context of each word in a given item. Our results show that there is a statistically significant and positive relationship between sentiment scores and S&P 500 market. Furthermore, we provide evidence that sentiment components and news categories on NYTimes.com were differently related to market returns.
About Me
Oliver Hinz studied at the TU Darmstadt Business Administration and Information Systems with main focus on Marketing, Software Engineering and Computer Graphics. After receiving his diploma (equiv. master degree) he worked several years for the Dresdner Bank as a consultant for business logic.
Oliver worked as a Research Assistant at the Chair of Electronic Commerce (2004-2007) and received his Ph.D. in October 2007. He has been awarded with the dissertation prize of the Alcatel-Lucent-Stiftung 2008, the Erich-Gutenberg-Prize 2008 and the science prize "Retailing 2009" of the EHI Retail Institute. He is also the winner of the honorable Schmalenbach prize for young researchers in 2008, the ECIS Ciborra Award, the Science Prize 2017 and the Sheth Foundation/Journal of Marketing Award 2018 for his Long-Term Impact on research in this area.
He supported the E-Finance Lab as Assistant Professor for E-Finance & Electronic Markets from 2008 to 2011 and joined the TU Darmstadt in April 2011 and headed the Chair of Information Systems | Electronic Markets. Oliver is Chair of Information Systems and Information Management at Goethe University since September 2017.
His research has been published in journals like Information Systems Research, Management Information Systems Quarterly, Journal of Marketing, Journal of Management Information Systems, Decision Support Systems, Electronic Markets, Business & Information Systems Engineering and others.