Jan Muntermann

A Method for Taxonomy Development and its Application in Information Systems

A fundamental problem in many disciplines is the classification of objects in a domain of interest into a taxonomy. Developing a taxonomy, however, is a complex process that has not been adequately addressed in the information systems (IS) literature. The purpose of this paper is to present a method for taxonomy development that can be used in IS. First, this paper demonstrates through a comprehensive literature survey that taxonomy development in IS has largely been ad hoc. Then the paper defines the problem of taxonomy development. Next, the paper presents a method for taxonomy development that is based on taxonomy development literature in other disciplines and shows that the method has certain desirable qualities. Finally, the paper demonstrates the efficacy of the method by developing a taxonomy in a domain in IS.

Towards ubiquitous information supply for individual investors: A decision support system design

This paper introduces an IT artifact called MoFiNDSS that comprises hard- and software components that provide the basis for a prototype of a financial decision support system (DSS) to support individual investors reacting to unforeseen market events. We have derived our motivation for building such a system design from behavioral finance research. Analyses of the behavior of individual investors provide evidence that this segment does react more significantly to any public news published compared to institutional investors. On the other hand, the analyses show that they react significantly slower than their institutional counterparts. Since empirical intraday event study analyses show that capital markets react promptly to new information and that excess returns decrease over a specific period of time, individual investors miss significant trading opportunities due to their current strategies of information research. We address the problem that this market segment is not able to continuously observe diverse information channels and to assess all the new information available. Our prototype decision support system continuously observes company announcements and forecasts their potential impact on the corresponding stock price. After identifying those events for which significant market reactions can be expected, wireless push-based message services provide the technical basis for prompt and location-independent information supply. Based on a novel simulation-based evaluation methodology we have developed, we demonstrate and quantify the advantages that the developed system provides to the individual investors.

Research Note-Heuristic Theorizing: Proactively Generating Design Theories

Design theories provide explicit prescriptions, such as principles of form and function, for constructing an artifact that is designed to meet a set of defined requirements and solve a problem. Design theory generation is increasing in importance because of the increasing number and diversity of problems that require the participation and proactive involvement of academic researchers to build and test artifact-based solutions. However, we have little understanding of how design theories are generated. Drawing on key contributions by Herbert A. Simon, including the ideas of satisfice and bounded rationality and reviewing a large body of information systems and problem-solving literature, we develop a normative framework for proactive design theorizing based on the notion of heuristic theorizing. Heuristics are rules of thumb that provide a plausible aid in structuring the problem at hand or in searching for a satisficing artifact design. An example of a problem-structuring heuristic is problem decomposition and an example of an artifact design heuristic is analogical design. We define heuristic theorizing as the process of proactively generating design theory for prescriptive purposes from problem-solving experiences and prior theory by constantly iterating between the search for a satisficing problem solution, i.e., heuristic search, and the synthesis of new information that is generated during heuristic search, i.e., heuristic synthesis. Heuristic search involves alternating between structuring the problem at hand and generating new artifact design components, whereas heuristic synthesis involves different ways of thinking, including reflection and learning and forms of reasoning, that complement the use of heuristics for theorizing purposes. We illustrate the effectiveness of our heuristic theorizing framework through a detailed example of a multiyear design science research program in which we proactively generated a design theory for solving problems in the area of intelligent information management and so-called big data in the finance domain. We propose that heuristic theorizing is a useful alternative to established theorizing approaches, i.e., reasoning-based approaches. Heuristic theorizing is particularly relevant for proactive design theorizing, which emphasizes problem solving as being highly intertwined with theorizing, involves a greater variety of ways of thinking than other theorizing approaches, and assumes an engaged relationship between academics and practitioners.

The Economic Impact of Privacy Violations and Security Breaches

Privacy and security incidents represent a serious threat for a company’s business success. While previous research in this area mainly investigated second-order effects (e.g., capital market reactions to privacy or security incidents), this study focuses on first-order effects, that is, the direct consumer reaction. In a laboratory experiment, the authors distinguish between the impact of privacy violations and security breaches on the subjects’ trust and behavior. They provide evidence for the so-called “privacy paradox” which describes that people’s intentions, with regard to privacy, differ from their actual behavior. While privacy is of prime importance for building trust, the actual behavior is affected less and customers value security higher when it comes to actual decision making. According to the results, consumers’ privacy related intention-behavior gap persists after the privacy breach occurred.

On the Effectiveness of Privacy Breach Disclosure Legislation in Europe: Empirical Evidence from the US Stock Market

Several U.S. states have enacted laws that require organizations to notify the affected individuals if personal data under their control is believed to have been acquired by an unauthorized person. In the EU, where a similar legislation is still missing, several researchers have recommended the introduction of a security-breach notification law. The intention of these laws is twofold. On one hand, they should enable affected individuals to take appropriate steps to protect themselves against malicious impacts resulting from the breach. On the other hand, it was intended to create incentives for companies to undertake steps to improve their security measures. In this contribution, we explore these incentives and present an event study in order to examine the effects of privacy incident announcements on the stock prices of affected companies. Our results show that there are significant price reactions on the next day following the announcements. By comparing these price reactions with those observed for other event types, we detect that disclosed privacy incidents are perceived as marginal by the market. The results show that existing disclosure regulation provides little to no incentives to invest in security measures to prevent the occurrence of privacy breaches, since they are widely ignored by the capital markets. From the widely discussed incentive perspective, the privacy breach disclosure legislation does not appear to be effectively addressing this goal.

Users’ Willingness to Pay for Web Identity Management Systems

Electronic services such as virtual communities or electronic commerce demand user authentication. Several more or less successful federated identity management systems have emerged to support authentication across diverse service domains in recent years. In this paper, we explore the determinants for success and failure of such systems with a focus on Germany representing one of the largest markets in Europe. To achieve this goal, we analyze the preferences and willingness to pay of prospective users by conducting a choice-based conjoint analysis. Our results indicate that users prefer simple systems where an intermediary takes care of their data. An additional market analyses confirms these findings and contradicts the assumptions of many researchers, especially in the fields of engineering and computer science, supporting systems with higher and higher levels of privacy and security.

Stock analysts vs. the crowd

Exploration of the drivers of crowd wisdom and their impact on prediction quality.Operationalization of measures based on constructs of crowd wisdom theory.Suggestion of future research avenues for crowd wisdom research.Recommendation of aggregation procedures for crowd-based platforms. We examine the drivers of crowd wisdom in the financial domain by relating analyst report and social media sentiment via Granger causality (GC) testing based on the wisdom of crowds (WoC) theory. The significance of a large number of the tested time series indicates that analyst reports and social media content are suitable for mutual prediction. We elaborate on the conditions under which crowd cognitive diversity matters, and we derive related measures. The results suggest that the WoC theory can partially explain the GC between the two media types and that both professional analysts and the crowd can outperform one another under favorable circumstances.

Discovering Intraday Market Risk Exposures in Unstructured Data Sources: The Case of Corporate Disclosures

Capital markets react promptly and significantly to critical events that have not been anticipated by market participants. Prominent examples of such market behaviour which risk management activities turn their attention to became evident during the last few months. Today, classic risk management tools perform complex calculations on the basis of structured data sources such as historical price series. In contrast, unstructured data sources, such as corporate disclosures, are widely disregarded. However, such data that has been released newly could carry information that is not reflected in the structured data available in such a situation. We aim to utilize such unstructured data sources and present a text mining-based approach to detect intraday market risk exposures that result from the event of newly published information. Our results provide evidence that unstructured data contains valuable information to discover intraday risk exposures promptly, i.e. represent valuable data sources in this context.

The Economic Impact of Privacy Violations and Security Breaches - A Laboratory Experiment

Privacy and security incidents represent a serious threat for a company’s business success. While previous research in this area mainly investigated secondorder effects (e.g., capital market reactions to privacy or security incidents), this study focuses on first-order effects, that is, the direct consumer reaction. In a laboratory experiment, the authors distinguish between the impact of privacy violations and security breaches on the subjects’ trust and behavior. They provide evidence for the so-called “privacy paradox” which describes that people’s intentions, with regard to privacy, differ from their actual behavior. While privacy is of prime importance for building trust, the actual behavior is affected less and customers value security higher when it comes to actual decision making. According to the results, consumers’ privacy related intention-behavior gap persists after the privacy breach occurred.

The Role of Misbehavior in Efficient Financial Markets: Implications for Financial Decision Support

The analysis of different data sources to support financial decision making has been a subject of research for several decades. While early approaches mostly focus on structured data, recent studies also take into account unstructured data. In this paper, we build upon these two research streams and explore potential benefits that can be achieved by combining both approaches. Therefore, we present an approach that integrates both data types. From a theoretical perspective, our research angle is based on two fundamental theories in Finance: while the Efficient Market Hypothesis states that capital markets are information efficient, Behavioral Finance theory stresses that market efficiency may be limited, e.g. due to irrational behavior of market participants or market barriers. While the two theories provide arguments for and against the functioning of our approach, we can illustrate its superiority compared to other approaches. The implications are discussed from a methodological and theoretical perspective.