I Like My Anonymity: An Empirical Investigation of the Effect of Multidimensional Review Text and Role Anonymity on Helpfulness of Employer Reviews

2023 | Information Systems Frontiers | Citations: 0

Authors: Parameswaran, Srikanth; Mukherjee, Pubali; Valecha, Rohit

Abstract: Employer review sites have grown popular over the last few years, with 86 percen ... Expand

Abstract: Employer review sites have grown popular over the last few years, with 86 percent of job seekers referring to reviews on these sites before applying to job positions. Though the antecedents of review helpfulness have been studied in various contexts, it has received limited attention in the employee review context. These sites provide review text in multiple dimensions, such as pros and cons. Besides, to solicit unbiased reviews, these sites allow an option of keeping reviewer information anonymous. Rooted in the diagnosticity perspective, we investigate review helpfulness focusing on the role of review text in multiple dimensions and the anonymity of the reviewers. We use a publicly available Glassdoor dataset to model review helpfulness using a Tobit regression. The results show that the review length in multiple dimensions of review text and anonymity positively impact review helpfulness. Moreover, anonymity positively moderates the review length in the cons section. As a post-hoc analysis, we perform topic modeling to gain better insights on the review text in multiple dimensions and anonymity. The post-hoc analyses show that non-anonymous reviewers discuss firm reputation in the pros section, which anonymous reviewers do not. In the cons section, non-anonymous reviewers discuss politics, unfair and unethical treatment, and prospects of the employer, while anonymous reviewers discuss incompetency of the leadership. This research has important practical implications for online review sites’ design and crafting guidelines and policies for employees writing reviews. Collapse

Topics: anonymity online review word of mouth online word of mouth Python

Methods: topic model qualitative content analysis theory development descriptive statistic post-hoc analysis

Online Sex Trafficking Policy Research: A Systematic Review

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Srivastava, Smriti; Dhillon, Gurpreet

Abstract: Online sex trafficking cases have been increasing every day. The field of Infor ... Expand

Abstract: Online sex trafficking cases have been increasing every day. The field of Information Systems (IS) has not significantly explored research on online sex trafficking specifically to evaluate existing literature and provide future research directions. In this paper, we use Latent Semantic Indexing (LSI) to review the literature on online sex trafficking. Our focus is on policies related to online sex trafficking. We then summarize our findings and suggest a future research agenda. Collapse

Topics: database system

Methods: latent semantic indexing literature sample principal component analysis descriptive statistic data transformation

Unifying Algorithmic and Theoretical Perspectives: Emotions in Online Reviews and Sales

2023 | Management Information Systems Quarterly | Citations: 2

Authors: Yu, Yifan; Yang, Yang; Huang, Jinghua; Tan, Yong

Abstract: Emotion artificial intelligence, the algorithm that recognizes and interprets v ... Expand

Abstract: Emotion artificial intelligence, the algorithm that recognizes and interprets various human emotions beyond valence (positive and negative polarity), is still in its infancy but has attracted attention from industry and academia. Based on discrete emotion theory and statistical language modeling, this work proposes an algorithm to enable automatic domain-adaptive emotion lexicon construction and multidimensional emotion detection in texts. Using a large-scale dataset of China's movie market from 2012 to 2018, we constructed and validated a domain-specific emotion lexicon and demonstrated the predictive power of eight discrete emotions (i.e., surprise, joy, anticipation, love, anxiety, sadness, anger, and disgust) in online reviews on box office sales. We found that representing overall emotions through discrete emotions yields higher prediction accuracy than valence or latent emotion variables generated by topic modeling. To understand the source of the predictive power from a theoretical perspective and to test the cross-culture generalizability of our prediction study, we further conducted an experiment in the U.S. movie market based on theories on emotion, judgment, and decision-making. We found that discrete emotions, mediated by perceived processing fluency, significantly affect the perceived review helpfulness, which further influences purchase intention. Our work shows the economic value of emotions in online reviews, generates insight into the mechanism of their effects, and has managerial implications for online review platform design, movie marketing, and cinema operations. Collapse

Topics: online review Python user-generated content microblog marketing management

Methods: computational algorithm word2vec parametric test product review experiment

Theories: psychological theory

Network text analysis: A two-way classification approach

2020 | International Journal of Information Management | Citations: 0

Authors: Celardo, Livia; Everett, Martin G.

Abstract: Text clustering is a well-known method for information retrieval and numerous me ... Expand

Abstract: Text clustering is a well-known method for information retrieval and numerous methods for classifying words, documents or both together have been proposed. Frequently, textual data are encoded using vector models so the corpus is transformed in to a matrix of terms by documents; using this representation text clustering generates groups of similar objects on the basis of the presence/absence of the words in the documents. An alternative way to work on texts is to represent them as a network where nodes are entities connected by the presence and distribution of the words in the documents. In this work, after summarising the state of the art of text clustering we will present a new network approach to textual data. We undertake text co-clustering using methods developed for social network analysis. Several experimental results will be presented to demonstrate the validity of the approach and the advantages of this technique compared to existing methods. Collapse

Topics: Twitter information management information retrieval

Methods: computational algorithm cluster analysis social network analysis network analysis qualitative content analysis

Theories: social network theory

Evolution of information systems research: Insights from topic modeling

2020 | Information & Management | Citations: 21

Authors: Jeyaraj, Anand; Zadeh, Amir Hassan

Abstract: Information systems (IS) research has continued to consistently evolve with the ... Expand

Abstract: Information systems (IS) research has continued to consistently evolve with the transitions in the IS discipline over time. There has been a general interest in the IS discipline over time as researchers have examined various aspects such as the intellectual core, diversity, and impact of IS research. On the basis of 2962 articles published in six leading IS journals between 2003 and 2017, topic modeling using latent semantic indexing was applied to author-supplied keywords. Topics such as IS development, IT adoption, and IS usage had endured over time; topics such as e-commerce and IT outsourcing had gained momentum and then waned over time; and topics such as social media, design science, and online communities have gained momentum in recent times. Results suggest that IS research has evolved considerably over time and generally included new frontiers while serving its core strength and identity. Collapse

Topics: blockchain IT workforce social media systems development mobile system

Methods: literature sample topic model design artifact post-hoc analysis computational algorithm

Theories: technology acceptance model unified theory of acceptance and use of technology

Peer Recognition Increases User Content Generation but Reduces Content Novelty

2019 | International Conference on Information Systems | Citations: 2

Authors: Burtch, Gordon; He, Qinglai; Hong, Yili; Lee, Dokyun

Abstract: Platforms that depend on user-generated content spend a great deal of effort cra ... Expand

Abstract: Platforms that depend on user-generated content spend a great deal of effort crafting policies and mechanisms that can yield a steady stream of useful content. In this work, we consider the effects of awards offered by peers, a feature that platforms often provide to enable users to recognize the quality of peers’ contributions. We set out to conduct a large-scale field experiment on Reddit, one of the biggest social news aggregation and discussion platforms in the world, evaluating the effect of peer recognition on content generation as well as content novelty. Leveraging the plentiful textual content and its innovative peer recognition mechanism, Gold Award, we purchased and then randomly assigned Gold Award to approximately 900 posts on Reddit, anonymously, over the course of two months. Collecting and analyzing users’ behavioral trace data and content postings in the post-treatment period via Reddit’s API, our results suggest that the probability of submission increased by 6.6% among treated users, relative to control users, and posts were 95.3% longer on average. Interestingly, however, the content that users post becomes more similar to their awarded content, and thus declines in its novelty. Based on this result, we conclude that peer recognition is a double-edged sword. On the one hand, peer recognition does indeed foster increased engagement and content production among users, but the additional content that results is less novel than content that arrives in the absence of recognition. Collapse

Topics: Reddit application programming interface productivity website Python

Methods: experiment experimental group term frequency–inverse document frequency doc2vec natural language processing

Focusing the customer through smart services: a literature review

2019 | Electronic Markets | Citations: 20

Authors: Dreyer, Sonja; Olivotti, Daniel; Lebek, Benedikt; Breitner, Michael H.

Abstract: Smart services serve customers and their individual, continuously changing needs ... Expand

Abstract: Smart services serve customers and their individual, continuously changing needs; information and communications technology enables such services. The interactions between customers and service providers form the basis for co-created value. A growing interest in smart services has been reported in the literature in recent years. However, a categorization of the literature and relevant research fields is still missing. This article presents a structured literature search in which 109 relevant publications were identified. The publications are clustered in 13 topics and across five phases of the lifecycle of a smart service. The status quo is analyzed, and a heat map is created that graphically shows the research intensity in various dimensions. The results show that there is diverse knowledge related to the various topics associated with smart services. One finding suggests that economic aspects such as new business models or pricing strategies are rarely considered in the literature. Additionally, the customer plays a minor role in IS publications. Machine learning and knowledge management are identified as promising fields that should be the focus of further research and practical applications. Concrete ideas for future research are presented and discussed and will contribute to academic knowledge. Addressing the identified research gaps can help practitioners successfully provide smart services. Collapse

Topics: database system business-to-consumer business-to-business communication service infrastructure search engine

Methods: literature study literature sample qualitative content analysis latent semantic indexing

Playing to the Crowd? Digital Visibility and the Social Dynamics of Purchase Disclosure

2019 | Management Information Systems Quarterly | Citations: 6

Authors: Rhue, Lauren; Sundararajan, Arun

Abstract: As people share more of their lives on social media, their consumption and purch ... Expand

Abstract: As people share more of their lives on social media, their consumption and purchase behaviors become increasingly visible to their peers, spawning a new form of digitally enabled conspicuous consumption. We examine how the ensuing dynamics of social commerce affect these visibility choices. We use data from a platform dedicated exclusively to facilitating the display of verified online and offline purchases with optional product commentaries. We use fixed effects and matching techniques to control for endogeneity in commentary and consumer disclosure, while embedding a new text-based model of product similarity into our econometric analysis and capturing product complementarity using network-based measures of similarity. Our findings demonstrate that disclosed purchase patterns are shaped by other users, even those with whom the discloser has no previously stated relationship. We find that the source of attention shapes disclosure behavior, and we uncover a relationship between comments and purchase similarity of disclosed purchases, suggesting that consumers play to the crowd. The negative relationship between product reviews and purchase disclosure suggest that consumers may disclose a purchase because of a relationship with that particular item and not a desire to habitually reveal purchases. Our results shed new light on the distinction between digital visibility and social buzz, with managerial implications for retailers and social media platforms that support social consumption. Collapse

Topics: social network social media online word of mouth online review word of mouth

Methods: product review experimental group logistic regression latent semantic indexing econometric modeling

Update Assimilation in App Markets: Is There Such a Thing as Too Many Updates?

2018 | International Conference on Information Systems | Citations: 13

Authors: Saffarizadeh, Kambiz; Jabr, Wael; Keil, Mark

Abstract: Extant literature suggests that faster app evolution (in terms of changes in qua ... Expand

Abstract: Extant literature suggests that faster app evolution (in terms of changes in quality and functionality) leads to increased app success (in terms of survival and demand). This evolution-success relationship, however, does not account for users’ limited capacity to assimilate the changes resulting from a continual stream of app updates. Drawing from extant literature, we theorize that the limited absorptive capacity of users leads to an assimilation gap that results in an inverted curvilinear relationship. Specifically, as app evolution increases app success increases but only to a certain point, after which as app evolution continues to increase, app success begins to decrease. We further argue that users’ readiness for change positively moderates this relationship. Additionally, as the marginal adoption costs of updates outweigh their benefits, app evolution hampers app success. In this paper, we advance our understanding of the effect of app evolution on app success and provide implications for app developers. Collapse

Topics: mobile application absorptive capacity mobile application market innovation management diffusion of innovation

Methods: natural language processing latent semantic indexing theory development econometric modeling

TOPIC MODELLING METHODOLOGY: ITS USE IN INFORMATION SYSTEMS AND OTHER MANAGERIAL DISCIPLINES

2017 | European Conference On Information Systems | Citations: 0

Authors: Eickhoff, Matthias; Neuss, Nicole

Abstract: Over the last decade, quantitative text mining approaches to content analysis ha ... Expand

Abstract: Over the last decade, quantitative text mining approaches to content analysis have gained increasing traction within information systems research, and related fields, such as business administration. Recently, topic models, which are supposed to provide their user with an overview of themes being discussed in documents, have gained popularity. However, while convenient tools for the creation of this model class exist, the evaluation of topic models poses significant challenges to their users. In this research, we investigate how questions of model validity and trustworthiness of presented analyses are addressed across disciplines. We accomplish this by providing a structured review of methodological approaches across the Financial Times 50 journal ranking. We identify 59 methodological research papers, 24 implementations of topic models, as well as 33 research papers using topic models in Information Systems (IS) research, and 29 papers using such models in other managerial disciplines. Results indicate a need for model implementations usable by a wider audience, as well as the need for more implementations of model validation techniques, and the need for a discussion about the theoretical foundations of topic modelling based research. Collapse

Topics: database system

Methods: topic model literature study regression analysis method literature sample structured literature research