Results

Found 5 articles

Clear filters

Consequences of User Manipulation through Dark Patterns

2023 | International Conference on Information Systems | Citations: 0
Authors: Witte, Janis; Kenning, Peter; Brock, Christian

Abstract: With increasing competition in the online market, companies frequently apply "d ... Expand

Abstract: With increasing competition in the online market, companies frequently apply "dark patterns" to steer user behavior in ways that benefit the company but may harm the user. To date, consequences of dark patterns use are rather unknown. Prior research demonstrated positive effects (e.g., an increase in acceptance rates) and negative effects (e.g., negative emotions) of dark patterns use. To explain these contradictory effects, we draw in information manipulation theory. In a survey experiment we confronted participants with the dark patterns scarcity and sneaking. The results indicate that exposure to dark patterns increases perceived violations of communication maxims, which increase perceived user manipulation. This, in turn, reduces attitude toward the website and website design. Further, the results show that perceived user manipulation is significantly higher for users with low familiarity with online shopping. We plan to validate the findings in a field experiment to be conducted in the near future. Collapse

Semantic filters: information manipulation theory

Topics: website design website electronic commerce user behavior data quality

Methods: ANOVA survey parametric test structural equation modeling experimental group

Theories: information manipulation theory
Who Is the Next “Wolf of Wall Street”? Detection of Financial Intermediary Misconduct
PDF
2020 | Journal of the Association for Information Systems | Citations: 0
Authors: Lausen, Jens; Clapham, Benjamin; Siering, Michael; Gomber, Peter

Abstract: Financial intermediaries are essential for investors’ participation in financial ... Expand

Abstract: Financial intermediaries are essential for investors’ participation in financial markets. Because of their position within the financial system, intermediaries who commit misconduct not only harm investors but also undermine trust in the financial system, which ultimately has a significant negative impact on the economy as a whole. Building upon information manipulation theory and warranting theory and making use of self-disclosed data with different levels of external verification, we propose different classifiers to automatically detect financial intermediary misconduct. In particular, we focus on self-disclosed information by financial intermediaries on the business network LinkedIn. We match user profiles with regulator-disclosed information and use these data for classifier training and evaluation. We find that self-disclosed information provides valuable input for detecting financial intermediary misconduct. In terms of external verification, our classifiers achieve the best predictive performance when also taking regulator-confirmed information into account. These results are supported by an economic evaluation. Our findings are highly relevant for both investors and regulators seeking to identify financial intermediary misconduct and thus contribute to the societal challenge of building and ensuring trust in the financial system. Collapse

Semantic filters: information manipulation theory

Topics: LinkedIn website data breach social network fraud detection

Methods: machine learning support vector machine sensitivity analysis classification method McNemar's test

Theories: information manipulation theory interpersonal deception theory
Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach

2020 | Information & Management | Citations: 2
Authors: Liu, Rong

Abstract: The detrimental effects of insider trading on the financial markets and the econ ... Expand

Abstract: The detrimental effects of insider trading on the financial markets and the economy are well documented. However, resource-constrained regulators face a great challenge in detecting insider trading and enforcing in sider trading laws. We develop a text analytics framework that uses machine learning to predict ex-ante potentially opportunistic insider trading (using actual insider trading allegation by shareholders as the proxy) from corporate textual disclosures. Distinct from typical black-box neural network models, which have difficulty tracing a prediction back to key features, our approach combines the predictive power of deep learning with attention mechanisms to provide interpretability to the model. Further, our model utilizes representations from a business proximity network and incorporates the temporal variations of a firm’s financial disclosures. The empirical results offer new insights into insider trading and provide practical implications. Overall, we contribute to the literature by reconciling performance and interpretability in predictive analytics. Our study also informs the practice by proposing a new method for regulators to examine a large amount of text in order to monitor and predict financial misconduct. Collapse

Semantic filters: information manipulation theory

Topics: database system accounting Python open source application programming interface

Methods: machine learning deep learning experiment artificial neural network word embedding

Theories: information manipulation theory behavioral theory
Information Processing on Online Review Platforms
PDF
2019 | Journal of Management Information Systems | Citations: 9
Authors: Siering, Michael; Janze, Christian

Abstract: Online reviews represent an important decision aid for consumers. Therefore, the ... Expand

Abstract: Online reviews represent an important decision aid for consumers. Therefore, the question of whether online reviews reflect the currently available information is of high importance. Nevertheless, previous research neglects information processing on online review platforms. We address this research gap and analyze whether restaurant health inspection results have an impact on the review generation process of online restaurant reviews. We find that while severe health inspection results lead to changes of online review star ratings, information processing depends on the current environment. We find indications for corrective actions after critical health inspections: on the one hand, the restaurant health score improves, which shows an increase in restaurant quality. On the other hand, we observe indications for an increased amount of fake reviews in case of poorly-graded restaurants. We contribute to theory by providing an understanding of the nature of information processing on online review platforms. For practitioners, our findings allow for an understanding of the dynamics of online review generation. Furthermore, we outline the importance of considering the risk of deceptive behavior. Collapse

Semantic filters: information manipulation theory

Topics: online review user-generated content fake review logistics management mobile health

Methods: event study longitudinal research product review machine learning support vector machine

Theories: information manipulation theory
Deception: Toward an Individualistic View of Group Support Systems

2008 | Journal of the Association for Information Systems | Citations: 6
Authors: George, Joey F.; Marett, Kent; Giordano, Gabriel

Abstract: Many of the ideas we take for granted about group support systems originated fro ... Expand

Abstract: Many of the ideas we take for granted about group support systems originated from work conducted at the University of Minnesota by DeSanctis and colleagues. One of the lesser known concepts is an analysis of the theoretical basis for group support system research. This analysis groups five theories that support GSS research into two camps: individualistic and collective. Individualistic theories are seen as prevalent in GSS research. From the individualistic perspective, technology is an active tool that works to enhance individual power. Our recent work has focused on deception and its detection, including deception stemming from individual agendas among group members in a GSS setting. This work demonstrates how group members can take advantage of the individually-focused emphasis in GSS design to successfully advance their own agendas. The purpose of this paper is to further examine the individualistic theoretical underpinnings of GSS research. To do so, we examine deception and explore its implications for groups and for GSS. Collapse

Semantic filters: information manipulation theory

Topics: group support system group decision support system deception detection task complexity system support

Methods: experiment simulation survey experimental task qualitative interview

Theories: interpersonal deception theory information manipulation theory

Page 1 of 1

AIS11

Journals

Conferences

Semantic Filter

Found 5 articles

Consequences of User Manipulation through Dark Patterns

Who Is the Next “Wolf of Wall Street”? Detection of Financial Intermediary Misconduct

Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach

Information Processing on Online Review Platforms

Deception: Toward an Individualistic View of Group Support Systems