Decide Now or Later: Making Sense of Incoherence Across Online Reviews

2023 | Information Systems Research | Citations: 0

Authors: Yin, Dezhi; de Vreede, Triparna; Steele, Logan M.; de Vreede, Gert-Jan

Abstract: Mixed or inconsistent opinions are commonplace in online reviews. Prior research ... Expand

Abstract: Mixed or inconsistent opinions are commonplace in online reviews. Prior research shows that review inconsistency has different effects: its product-level manifestation in the form of inconsistent product ratings is associated with poorer sales, but its review-level manifestation in the form of two-sided arguments is associated with greater helpfulness and credibility evaluations of the review. In practice, consumers rarely consult all reviews or just a single review before they make purchase decisions. Instead, they often read a set of featured reviews (i.e., a review set) and some additional reviews if needed. Focusing on inconsistency in a review set, we introduce a new type of inconsistency across reviews that does not exist within a single review or at the level of product ratings: cross-review incoherence, which refers to disagreement among reviewers about specific product features. Based on cognitive dissonance theory, we explore how and when cross-review incoherence influences helpfulness and credibility judgments of the review set, revealing that consumers’ reactions to such incoherence might operate differently beyond an individual review. In addition, we examine how and why consumers’ judgments of a review set influence their purchase deferral—that is, the likelihood of making a buy-or-not-buy decision immediately after consulting the top reviews or deferring it until after obtaining more information. Two laboratory experiments demonstrate that cross-review incoherence increases purchase deferral via more negative evaluations of the review set that, in turn, reduce attitude certainty. In addition, the negative effect of cross-review incoherence on review set evaluations is weaker when the reviewers provide more contextual information behind their opinions. These findings deepen our understanding of inconsistency across multiple pieces of information; reveal the consequences of review evaluations during an understudied stage of consumers’ decision-making process; and provide critical implications for review platforms, companies, and reviewers. History: Paul Pavlou, Senior Editor; David (Jingjun) Xu, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1150. Collapse

Topics: decision making online review user-generated content data relevance enterprise information system

Methods: experiment chi squared test laboratory experiment conceptual modelling theory development

Theories: cognitive dissonance theory

Investigating the Market Success of Software-as-a-Service Providers: the Multivariate Latent Growth Curve Model Approach

2023 | Information Systems Frontiers | Citations: 0

Authors: Lee, Sun-Pyo; Kim, Kyungji; Park, Sungbum

Abstract: Software-as-a-Service (SaaS) Cloud computing as the next stage of internet evolu ... Expand

Abstract: Software-as-a-Service (SaaS) Cloud computing as the next stage of internet evolution provides all the computing resources as services over the Internet. In the SaaS cloud computing research area, there are many studies from the user’s point of view, but there is relatively little research on the supplier’s success strategy. The purpose of this study is to empirically analyze the factors that determine the market competitiveness necessary for SaaS cloud computing providers to survive in the mid to long term. We presented application dimension and technology maturity as SaaS idiosyncratic factors and show how these factors influence the business performance of SaaS providers through a differentiation strategy and low-cost strategy. Using a multivariate latent growth curve model, this study analyzed 199 strategic business units of SaaS cloud computing providers in Korea for three years. Our results find that SaaS cloud computing idiosyncratic factors did not significantly enhance the software providers’ business performance in the early stage. However, they significantly affected the growth rate of their customer base and financial performance as the SaaS technology became mature over time. In addition, this study identifies a set of business, strategic, and technical considerations to guide the practitioners’ decision-making process for selecting an appropriate SaaS cloud computing model. Collapse

Topics: software as a service cloud computing cloud computing provider organizational productivity IS maturity

Methods: longitudinal research chi squared test survey design latent growth curve modeling survey

Undesirable framing effects in information systems projects: Analysis of adjective usage in IS project business cases

2022 | Information & Management | Citations: 0

Authors: Benschop, Nick; Nuijten, Arno L.P.; Hilhorst, Cokky A.R.; Keil, Mark

Abstract: Many information systems (IS) projects encounter significant problems. The liter ... Expand

Abstract: Many information systems (IS) projects encounter significant problems. The literature suggests that decisionmakers can be misled by overoptimistic estimates. We argue that such overoptimism may also be present in the choice of language in business cases. In this study, we analyzed the usage of such framing in 20 business cases for large IS projects of the Dutch government. Our findings show that newly proposed systems are systematically framed using positive adjectives, whereas the existing systems are framed using negative adjectives. This pattern of framing may subconsciously bias the decision-makers toward investing in new IS projects. Collapse

Topics: IT project IT project management project management agile software development

Methods: linguistic analysis chi squared test Z-test qualitative coding nonparametric test

Theories: technological frames of reference

Exploring interdependent privacy – Empirical insights into users’ protection of others’ privacy on online platforms

2022 | Electronic Markets | Citations: 0

Authors: Franz, Anjuli; Benlian, Alexander

Abstract: Recent information privacy research has started to spark a debate about privacy ... Expand

Abstract: Recent information privacy research has started to spark a debate about privacy infringements that happen not on an individual, but on a multi-party level. Here, a person’s own information privacy is affected by the decisions of others – a phenomenon referred to as interdependent privacy. Building on the 3R Interdependent Privacy Protection Framework, we explore the underlying mechanisms of how and why interdependent privacy violations happen and how they can be remedied. Drawing on an online vignette experiment (N = 330), we investigate the efficacy of an interdependent privacy salience nudge and reveal that it can decrease the likelihood that users disclose others’ personal information by 62%. Furthermore, we develop a novel measurement instrument and empirically validate that users’ decision to disclose others’ personal information to an online platform is formed via a serial mediation mechanism through users’ realization of the data transfer, recognition of others’ ownership, and respect for others’ rights. We discuss important implications for both theory and practice. Collapse

Topics: privacy Instagram information privacy concern decision making mobile application

Methods: experiment mediation analysis experimental group survey archival research

When Trading Becomes Social: How Social Trading Platforms Affect the Disposition Effect

2022 | International Conference on Information Systems | Citations: 0

Authors: Jabr, Wael; Rai, Arun

Abstract: Social trading platforms have, over the last decade or so, been gaining a stron ... Expand

Abstract: Social trading platforms have, over the last decade or so, been gaining a strong foothold in individual investment markets. Users on these platforms can observe ("view") traders' detailed transactions over time. They can also ''follow'' anyone of those traders, just like with other social media platforms, investing their money in accordance with the strategies of their trader of choice. We study whether and how the disposition effect bias of individual traders is affected by two social features of the platform, "Views" and "Followers." We find a differentiated impact on this bias from those two social features, which is conditional on the level of market turbulence. We attribute this to how traders assess the signal originating from Views and Followers in relation to how committal it is. Collapse

Topics: social trading platform decision making electronic finance logistics management financial performance

Methods: theory development Z-test survival analysis cluster analysis hazard function

Theories: disposition effect behavioral economics

What translates big data into business value? A meta-analysis of the impacts of business analytics on firm performance

2022 | Information & Management | Citations: 0

Authors: Oesterreich, Thuy Duong; Anton, Eduard; Teuteberg, Frank

Abstract: The main purpose of this study is to examine the factors that are critical to cr ... Expand

Abstract: The main purpose of this study is to examine the factors that are critical to create business value from business analytics (BA). Therefore, we conduct a meta-analysis of 125 firm-level studies spanning ten years of research from across 26 countries. We found evidence that the social factors of BA, such as human resources, management capabilities, and organizational culture show a greater impact on business value, whereas technical aspects play a minor role in enhancing firm performance. Through these findings, we contribute to the ongoing debate concerning BA business value by synthesizing and validating the findings of the body of knowledge. Collapse

Topics: organizational value organizational productivity value creation analytical information system IS value

Methods: meta analysis literature sample literature study synthesis ANOVA

Theories: socio technical theory

White knight in dark days? Supply chain finance firms, blockchain, and the COVID-19 pandemic

2022 | Information & Management | Citations: 0

Authors: Paul, Samit; Adhikari, Arnab; Bose, Indranil

Abstract: We investigate the impact of the announcement of the COVID-19 pandemic on the ma ... Expand

Abstract: We investigate the impact of the announcement of the COVID-19 pandemic on the market value and trading volume of supply chain finance (SCF) firms. Using an event study, we observe a significant valuation loss and higher trading volume of SCF firms. However, blockchain-enabled SCF firms are protected from such valuation loss and volatility in trading. We find that higher research and development (R&D) and capital expenditures by firms prevent the loss. Moreover, the firm value of blockchain-enabled SCF firms is impacted by their mem­ bership in a blockchain consortium and progress in blockchain implementation. Investors’ confidence in blockchain reduces the market uncertainty. Collapse

Topics: blockchain pandemic research and development supply chain management price management

Methods: longitudinal research event study regression analysis method cross sectional research Student's t-test

Theories: lemon market theory

Cross-Review Incoherence and Purchase Deferral

2021 | International Conference on Information Systems | Citations: 0

Authors: Yin, Dezhi; Vreede, Triparna de; Steele, Logan; Vreede, Gert-Jan de

Abstract: This paper focuses on inconsistency among a set of reviews and explores how, wh ... Expand

Abstract: This paper focuses on inconsistency among a set of reviews and explores how, why, and when the presence of cross-review incoherence influences consumers' purchase deferraltheir likelihood of making a "buy/not buy" decision immediately after consulting the reviews or deferring it until after obtaining more information. Based on the cognitive dissonance theory, we hypothesize that (a) the presence of cross-review incoherence or attribute-level contradictions among a set of reviews leads to lower certainty in consumer attitudes and a higher likelihood of purchase deferral, (b) this effect transpires through consumers' helpfulness and credibility evaluations of the review set, and (c) the effect of cross-review incoherence on review set evaluations is weakened when the specificity of the context behind reviewers' opinions is high. We conducted two experiments and found support for these predictions. Collapse

Topics: decision making online review user-generated content user friendliness data relevance

Methods: laboratory experiment experiment product review conceptual modelling digital trace data

Theories: cognitive dissonance theory

Trajectory Outlier Detection: Algorithms, Taxonomies, Evaluation, and Open Challenges

2020 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Belhadi, Asma; Djenouri, Youcef; Lin, Jerry Chun-Wei; Cano, Alberto

Abstract: Detecting abnormal trajectories is an important task in research and industrial ... Expand

Abstract: Detecting abnormal trajectories is an important task in research and industrial applications, which has attracted considerable attention in recent decades. This work studies the existing trajectory outlier detection algorithms in different industrial domains and applications, including maritime, smart urban transportation, video surveillance, and climate change domains. First, we review several algorithms for trajectory outlier detection. Second, different taxonomies are proposed regarding application-, output-, and algorithm-based levels. Third, evaluation of 10 trajectory outlier detection algorithms is performed on small, large, and big trajectory databases. Finally, future challenges and open issues with regard to trajectory outliers are derived and discussed. This survey offers a general overview of existing trajectory outlier detection algorithms in industrial informatics applications. As a result, mature solutions may be further developed by data mining and machine learning communities. Collapse

Topics: database system global navigation satellite system climate change big data mobile phone

Methods: computational algorithm anomaly detection machine learning experiment survey

Theories: Dempster-Shafer theory

Off-the-Shelf Technologies for Sentiment Analysis of Social Media Data: Two Empirical Studies

2020 | Americas Conference on Information Systems | Citations: 0

Authors: Carvalho, Arthur; Harris, Lucas

Abstract: Off-the-shelf technologies provided by major cloud platforms promise to facilit ... Expand

Abstract: Off-the-shelf technologies provided by major cloud platforms promise to facilitate and democratize the use of artificial intelligence techniques. Organizations can now apply highly sophisticated, pre-trained models in a variety of situations, such as when analyzing the sentiment behind social media posts. Among other uses, this enables organizations to better understand their consumers' opinions regarding products and/or services. In this paper, we first review technologies for sentiment analysis provided by major cloud platforms. We then compare the accuracy of these technologies against a technique widely used in managerial and information systems studies, namely the bag-of-words approach. Our two empirical studies use social media data collected from Twitter (short posts) and Facebook (long posts). We find that all the studied off-the-shelf technologies for sentiment analysis are vastly more accurate than the bag-of-words approach. We conclude the paper by discussing our results in light of the recent rise of low/no-code development practices. KeywordsSentiment analysis, artificial intelligence, social media analytics, software as a service. IntroductionRecent years have seen tremendous growth in interest in artificial intelligence (AI), with application domains ranging from playful settings, such as soccer robot (Oliveira et al., 2009;Carvalho and Oliveira, 2011) and artificial poker players (Brown and Sandholm, 2018), to energy trading (Babic et al., 2017) and life-saving scenarios, such as rescue robots (Davids, 2002) and the prediction of heart transplant outcomes (Dag et al., 2016). Although not consensual, AI can be defined as "the study of agents that receive percepts from the environment and perform actions" (Russell and Norvig, 2016). Agents can be from very basic scripts written in any computational language to fully autonomous robots. This agent-centric perspective brings together several research areas under a common framework. For example, machine learning is about how agents can learn in/from an environment, knowledge representation is about how agents represent their knowledge, and natural language processing (NLP) is about how agents can communicate and understand each other and human beings. It is this latter area that we focus on in this paper.One of the primary focuses of NLP is to acquire information from written language (Russell and Norvig, 2016). This is a challenging task given the nature of the underlying data, i.e., textual data might be ambiguous, contain typos, grammar mistakes, jargon, slangs, etc. Using computational, statistical, and linguistic techniques, there are many types of textual analysis one can perform, e.g., text categorization, text clustering, automatic summarization, topic modeling, and sentiment analysis. We concentrate on sentiment analysis, with a particular focus on the analysis of social media data.Sentiment analysis aims at understanding and quantifying the sentiment behind a text (Liu, 2012), e.g., it is used when one wants to determine whether a certain piece of text is positive, negative, or neutral. This, Collapse

Topics: Twitter social media cloud computing artificial intelligence Facebook

Methods: sentiment analysis natural language processing experiment machine learning Z-test