Rating deviation and manipulated reviews on the Internet—A multi-method study

2023 | Information & Management | Citations: 0

Authors: Xu, Yukuan; Xu, Xin

Abstract: Manipulated reviews can mislead consumers to make inappropriate purchase decisio ... Expand

Abstract: Manipulated reviews can mislead consumers to make inappropriate purchase decisions and reduce consumers’ dependency on online reviews, jeopardizing the platforms’ reputation. Existing studies mainly focus on the detection and the impact of manipulated reviews but are limited in examining the determinants of review legitimacy from the perspective of consumer perception. The current research introduces perceived review manipulation, defined as the extent to which an individual perceives a review as non-authentic with the goal of misleading others and influencing product sales. Using psychological reactance theory as a general framework, we investigate the impact of reviews with deviation from the average ratings on perceived review manipulation and review adoption. We future examine two boundary conditions of the above relationship—the moderating effects of review content concreteness and reviewer rating distribution. We adopt a multi-method approach to the empirical test of the research model. First, three online randomized experiments reveal that: (1) reviews with deviant ratings are more likely to be perceived as manipulated; (2) the relationship in (1) is enhanced when review content is abstract rather than concrete and when a reviewer is usually negative/positive (i.e., his/her rating distribution has positive/negative skewness) based on the deviation direction; and (3) perceived review manipulation negatively influences review adoption. Second, a field study was conducted to support the external validity of our research model. Our findings have academic and practical implications. Collapse

Topics: online review online word of mouth user-generated content word of mouth disinformation

Methods: experiment parametric test Student's t-test field study ANOVA

Theories: reactance theory

Privacy Risks in Digital Markets: The Impact of Ambiguity Attitudes on Transparency Choices

2022 | International Conference on Information Systems | Citations: 0

Authors: Sachs, Nikolai; Schnurr, Daniel

Abstract: Transparency is viewed as an essential prerequisite for consumers to make infor ... Expand

Abstract: Transparency is viewed as an essential prerequisite for consumers to make informed privacy decisions in digital markets. However, it remains an open research question whether and when individuals actually prefer transparency about privacy risks when given a chance to avoid it. We investigate this question with a randomized controlled online experiment based on an Ellsberg-type design, where subjects repeatedly choose between risk and ambiguity while facing the threat of an actual disclosure of their personal data. We find empirical support for ambiguity attitudes as a novel behavioral mechanism underlying people's transparency choices in privacy contexts. In particular, we find that most individuals avoid ambiguity and prefer transparency for low likelihood privacy losses. However, this pattern reverses for high likelihood losses and when subjects perceive data disclosure as a gain. Most notably, a significant share of people seek ambiguity and thus prefer to avoid transparency when facing high likelihood privacy risks. Collapse

Topics: privacy video conference decision making mobile system internet of things

Methods: experiment archival research chi squared test exact binomial test experimental design

Theories: behavioral economics transaction cost economics

It’s all about the text: An experimental investigation of inconsistent reviews on restaurant booking platforms

2022 | Electronic Markets | Citations: 0

Authors: Steur, Andreas J.; Fritzsche, Fabian; Seiter, Mischa

Abstract: Consumer-generated reviews play a decisive role in creating trust and facilitati ... Expand

Abstract: Consumer-generated reviews play a decisive role in creating trust and facilitating transactions on digital platforms. However, prior research shows various problems, e.g., only a small number of consumers providing reviews, fake reviews, and inconsistent reviews. We use an experiment in the context of a restaurant booking platform to examine the impact of inconsistent reviews on the duration of consumers’ transaction decisions. In a second experiment, we investigate the relative importance of the review components in the case of inconsistent reviews. Drawing on the dual-process theory and media richness theory, we predict that inconsistent reviews result in a longer time required for consumers’ transaction decisions (H1) and lead to users’ transaction decisions being predominantly based on the qualitative component (H2). Although we do not find general support that inconsistent restaurant reviews negatively determine the duration of transaction decisions, we find evidence that in the case of inconsistent restaurant reviews, the polarity of the qualitative component is crucial for both the duration of the transaction decision and the decision itself. Collapse

Topics: decision making online review media richness decision quality digital platform

Methods: experiment product review sentiment analysis Student's t-test survey

Theories: media richness theory dual-system theory

Model identification for ARMA time series through convolutional neural networks

2021 | Decision Support Systems | Citations: 0

Authors: Tang, Wai Hoh; Röllin, Adrian

Abstract: We use convolutional neural networks for model identification in ARMA time serie ... Expand

Abstract: We use convolutional neural networks for model identification in ARMA time series models, where our networks are trained on synthetic data with known ground truths. Comparing the performance of these networks with traditional likelihood-based methods, in particular the Akaike and Bayesian Information Criteria, we are able to show that when it comes to statistical inference on ARMA orders, neural networks can significantly outperform likelihood-based methods in terms of accuracy and, by orders of magnitude, in terms of speed. We also observe improvements in terms of time series forecasting. Our approach shows the feasibility of using artificial neural networks for statistical inference in situations where classical likelihood-based methods are difficult or costly to implement. Collapse

Methods: time series analysis longitudinal research autoregressive moving average model artificial neural network residual neural network

Theories: mathematical theory

Making Open Data more Personal Through a Social Value Perspective: a Methodological Approach

2020 | Information Systems Frontiers | Citations: 2

Authors: Cabitza, Federico; Locoro, Angela; Batini, Carlo

Abstract: In this paper, we address the problem of assessing the social value of open data ... Expand

Abstract: In this paper, we address the problem of assessing the social value of open data. While the number of open data initiatives increases and many data sets are currently available to lay people, common citizens and end users, a still limited number of studies specifically address how to improve open data understandability, their usability by common users and the measurability of their value in terms of concrete outcomes and benefits for the intended communities and the individual who appropriate those data, to make them more personal and hence more valuable. Our goal is to contribute to the success of open data initiatives by defining a methodology by which to assess their perceived social value. In this paper, we present the conceptual content of the methodology, that is its main concepts and logic structure, and discuss it by means of an empirical user study in which we applied it to real-life open data sets and involving a large sample of prospective consumers of those open data. In particular, we focus on the health care domain in order to improve the welfare of the citizens that need health care services and use the Web to look for relevant information to address those situated needs. Among our main findings, we discovered a clear preference for visual information formats by women with respect to men, and a clear preference for hospitals ranking by disease for senior people with respect to younger. Collapse

Topics: open data usability personalization information graphic decision making

Methods: case study survey exploratory data analysis statistical hypothesis test nonparametric test

Evaluating Explainable Artifical Intelligence – What Users Really Appreciate

2020 | European Conference On Information Systems | Citations: 0

Authors: Förster, Maximilian; Klier, Mathias; Kluge, Kilian; Sigler, Irina

Abstract: Explainable Artificial Intelligence (XAI) is an aspiring research field address ... Expand

Abstract: Explainable Artificial Intelligence (XAI) is an aspiring research field addressing the problem that users of AI do not trust AI systems that act as black boxes. However, XAI research to date is often criticized for not putting the user in the center of attention. We develop a generic and transferable human-based study to evaluate explanations generated by XAI methods from the users' perspective. The design of the study is informed by insights from social sciences into how humans construct explanations. We conduct the study with 164 participants evaluating contrastive explanations generated by representative XAI methods. Our findings reveal characteristics of explanations users appreciate in the context of XAI. We find concreteness, coherence, and relevance to be decisive. These findings provide guidance for the design and development of XAI methods. Collapse

Topics: explainable artificial intelligence artificial intelligence mobile application data quality open source

Methods: experiment computational algorithm descriptive statistic decision tree classification theoretical contribution

Fostering Human Agency: A Process for the Design of User-Centric XAI Systems

2020 | International Conference on Information Systems | Citations: 0

Authors: Förster, Maximilian; Klier, Mathias; Kluge, Kilian; Sigler, Irina

Abstract: The emerging research field of Explainable Artificial Intelligence (XAI) address ... Expand

Abstract: The emerging research field of Explainable Artificial Intelligence (XAI) addresses the problem that users do not trust or blindly follow AI systems that act as black boxes. XAI research to date is often criticized for not putting the user at the center of attention. Against this background, we design a process to systematically guide the instantiation, calibration, and quality control of XAI systems such that they foster human agency and enable appropriate trust in AI systems. The process can be applied independent of the XAI method, application domain, and target user group. It incorporates the principles of usercentric design, insights into explanations from the social sciences, and established XAI evaluation scenarios. Following the Design Science methodology, we demonstrate the practical applicability of our artifact and evaluate its efficacy in a realistic setting. Our work contributes to the design of user-centric XAI systems and the quest for human agency in AI. Collapse

Topics: explainable artificial intelligence artificial intelligence mobile application user requirement smartphone

Methods: usability testing design artifact machine learning experiment design science

Head, Heart, or Hands: How Do Employees Respond to a Radical Global Language Change over Time?

2019 | Organization Science | Citations: 0

Authors: Reiche, B. Sebastian; Neeley, Tsedal B.

Abstract: To understand how recipients respond to radical change over time across cognitiv ... Expand

Abstract: To understand how recipients respond to radical change over time across cognitive, affective, and behavioral dimensions, we conducted a longitudinal study of a mandated language change at a Chilean subsidiary of a large U.S. multinational organization. The engineering-focused subsidiary aiming to facilitate cross-border interactions embedded language-acquisition experts to transition all employees from Spanish to English full time. We gathered survey data and objective fluency scores from the language change recipients at five points over a period of two years. Using variable- and person-centered exploratory analyses, our results suggest that recipients’ negative affective responses to the language change precede their cognitive responses or self-efficacy, predicting their current language learning. Further, we find that recipients’ cognitive and affective responses over time differentially influence two future behavioral outcomes: intention to leave the organization and willingness to adopt the change. Although cognitive rather than affective responses over time drive recipients’ intentions to leave, affective responses influence recipients’ willingness to adopt English. Finally, we show that change recipients followed three trajectories of cognitive responses and two trajectories of affective responses over time. We discuss theoretical and practical implications to the literature on organizational change, emotions, and language in global organizations. Collapse

Topics: self efficacy IT personell turnover organizational change missing data mobile application

Methods: survey longitudinal research chi squared test mediation analysis statistical hypothesis test

Theories: appraisal theory affective events theory core competency theory theory of bounded rationality

AKEGIS: automatic keyword generation for sponsored search advertising in online retailing

2019 | Decision Support Systems | Citations: 4

Authors: Scholz, Michael; Brenner, Christoph; Hinz, Oliver

Abstract: Sponsored search advertisers face several complex decisions when planning and im ... Expand

Abstract: Sponsored search advertisers face several complex decisions when planning and implementing a new sponsored search advertising campaign. These decisions include the selection of keywords, the definition of landing pages, and the formulation of bidding strategies. Relatively low attention has been paid on supporting the selection of keywords in recent research and most studies on sponsored search advertising focus on the formulation of bidding strategies and strategies for budget planning. We present a novel approach for automatically generating sponsored search keywords that relies on the theory of consumer search behavior. Our approach uses an online store's internal search log to extract keywords used by consumers within their search process, because recent research has shown that especially consumers with a high conversion probability that exhibit goal-directed instead of exploratory search patterns use an online store's internal search engine. We empirically test our approach based on a store's internal search engine and identify the effects of this approach by comparing it to a state-of-the-art approach. Our analysis reveals that our approach substantially increased the number of profitable keywords, improved the store's conversion rate by approximately 41%, and decreased the average cost per click by more than 70%. Collapse

Topics: search engine electronic commerce search engine marketing Google+ advertising management

Methods: experiment experimental group laboratory experiment Student's t-test computational algorithm

I3DermoscopyApp: Hacking Melanoma thanks to IoT technologies

2017 | HICSS | Citations: 0

Authors: Di Leo, Giuseppe; Liguori, Consolatina; Paciello, Vincenzo; Pietrosanto, Antonio; Sommella, Paolo

Abstract: The paper introduces I3DermoscopyApp, a new declination of the Internet of Thing ... Expand

Abstract: The paper introduces I3DermoscopyApp, a new declination of the Internet of Things (IoT) paradigm, designed to allow the early detection of melanoma. Even though artificial intelligence programs cannot outperform the diagnostic accuracy of expert dermatologists yet, they reveal to be very useful in providing second opinions to physicians with short clinical experience, thus improving significantly their diagnostic performance. Following this trend, an original integration of mobile app technology and well-known image processing algorithms allows the automatic analysis of pigmented skin lesions to help physicians apply a diagnostic method (Seven Point Check List) based on dermoscopy. The web-based platform makes the physician able to: i) store digital images captured by smartphones featured with a dermatoscope; ii) measure morphological and chromatic parameters of the skin lesion; iii) make a diagnostic decision according to the Seven Point Checklist method. A detailed description of the adopted techniques, together with the first validation results are reported. Collapse

Topics: database system internet of things mobile system smartphone Apple iPhone

Methods: computational algorithm exact binomial test image processing structured literature research literature sample