New publications at the 26th Pacific Asia Conference on Information Systems (PACIS 2022)

New research papers at PACIS 2022

The following three articles have been accepted at PACIS 2022

Neuss, N. and Zielke, V. 2022. “Nudging the Private Investor - A Systematic Literature Review,“ in: Proceedings of the 26th Pacific Asia Conference on Information Systems, Taipei/Sydney, AISeL.

Abstract: Nudging describes influencing human behavior to improve individuals’ decision-making in various aspects of life such as health, social behavior, consumption, and savings through small interventions in the offline as well as increasingly in the digital world. In the aspect of individual financial decisions such as investments and retirement savings, financial providers use nudges to motivate investors to reach their goals without restricting their freedom of choice. However, a comprehensive overview of scientific literature examining the implementation of nudges on private financial investors is missing. In this study, we conduct a systematic literature review to give an overview of studies examining the implementation and the effects and underline the role of digital nudges. With this review, we contribute to the understanding of Nudge Theory and derive future research directions.

Bankamp, S. and Muntermann, J. 2022. “Understanding the Role of Document Representations in Similarity Measurement in Finance and Accounting,“ in: Proceedings of the 26th Pacific Asia Conference on Information Systems, Taipei/Sydney, AISeL.

Abstract: Document similarity is an important concept for many research questions. It can be applied to trace information exchanged on the capital market. For similarity calculations, the document must be transformed into a vector (document representation). Researchers can choose from a variety of document representations. We review the finance and accounting literature and find many different practices for estimating document similarity but little guidance on how to choose the right approach. To address this gap, we propose a framework of three similarity dimensions (object, author, and time). Based on this framework, we conduct an experiment on a corpus of analyst reports to quantify the accuracy of the estimated similarity. Our results help researchers and practitioners to choose an appropriate document representation for their analysis. Doc2vec achieves the overall highest accuracy, while Latent Dirichlet Allocation performs well on the object dimension. Bag-of-words models achieve surprisingly promising results despite their simplicity.

Torno, A., Bähnsch, S., and Dreyer, M. 2022. “Taming the Next Wolf of Wall Street – Design Principles for Ethical Robo-Advice,“ in: Proceedings of the 26th Pacific Asia Conference on Information Systems, Taipei/Sydney, AISeL.

Abstract: Automated investing in form of Robo-Advice (RA) has promising qualities, e.g., mitigating personal biases through algorithms and enable financial advice for less wealthy clients. However, RA is criticized for its rudimentary personalization ability questioning its fiduciary duties, nontransparent recommendations and violations of data privacy and security. These ethical issues pose significant risks, especially for the less financially educated targeted clients, who could be exploited by RA as illustrated in the movie “Wolf of Wall Street”. Yet, a distinct ethical perspective on RA design is missing in literature. Based on scientific literature on RA and international standards and guidelines of ethical financial advice we derive eight meta-requirements and develop 15 design principles, that can guide more ethical and trustworthy RA design. We further evaluated and enhanced the design artifact through interviews with domain experts from science and practice. With our study we provide design knowledge that enables more ethical RA outcomes.