How Review Quality and Source Credibility Interacts to Affect Review Usefulness: An Expansion of the Elaboration Likelihood Model

2023 | Information Systems Frontiers | Citations: 0

Authors: Aghakhani, Navid; Oh, Onook; Gregg, Dawn; Jain, Hemant

Abstract: This study extends our understanding of what makes an online review useful by ex ... Expand

Abstract: This study extends our understanding of what makes an online review useful by examining the effects of review quality (i.e., as a composite variable of review comprehensiveness and review topic consistency) on review usefulness, and the moderating effects of source credibility on the relationship between review quality and review usefulness. The Elaboration Likelihood Model, convergence theory, and cueing effect literature are used to define the variables of review comprehensiveness and review topic consistency. Analyses of 27,517 restaurant reviews from Yelp show that review topic consistency has a positive effect on review usefulness, but, contrary to our hypothesis, review comprehensiveness has a negative effect on review usefulness. We also found source credibility positively moderates the effect of review comprehensiveness on review usefulness, but negatively moderates the effect of review topic consistency on review usefulness. Theoretical and practical implications are discussed. Collapse

Topics: online review user-generated content Python website data breach

Methods: topic model term frequency–inverse document frequency NLTK statistical hypothesis test robustness check

Theories: elaboration likelihood model

From Groups to Communities: A Resource Mobilization Theory Perspective on the Emergence of Communities

2023 | Information Systems Frontiers | Citations: 1

Authors: Angelopoulos, Spyros; Canhilal, Kubra S.; Hawkins, Matthew A.

Abstract: Groups and communities have been key topics in the information systems (IS) rese ... Expand

Abstract: Groups and communities have been key topics in the information systems (IS) research agenda. While communities are assumed to emerge at the intersection of overlapping groups and their practices, prior research has mainly focused on their dynamics and evolution. This has resulted to limited empirical support regarding the emergence of communities. We address that lacuna by tracing the emergence of communities through the prism of resource mobilization theory. In doing so, we make use of a unique longitudinal dataset and incorporate Topic Modelling, Bipartite Network Analysis, and Community Detection. We show that new communities are formed at the intersection of overlapping groups and practices. In addition, we contribute to the IS literature by demonstrating that their emergence occurs due to resource mobilization that gives rise to a shared mindset. We also reveal that multiple resources are incorporated into the practices of an emerging community. By combining large datasets and innovative computational approaches, we help IS theory and practice to move away from traditional "what" questions towards the more insightful "how" ones. We discuss the theoretical and practical implications of our work and delineate an agenda for future research on the topic. Collapse

Topics: accounting identity theft database system

Methods: longitudinal research theoretical contribution theory development network analysis topic model

Theories: information systems theory

User Privacy, Surveillance and Public Health during COVID-19 – An Examination of Twitterverse

2023 | Information Systems Frontiers | Citations: 0

Authors: Bhatt, Paras; Vemprala, Naga; Valecha, Rohit; Hariharan, Govind; Rao, H. Raghav

Abstract: Online users frequently rely on social networking platforms to transmit public c ... Expand

Abstract: Online users frequently rely on social networking platforms to transmit public concerns and raise awareness about societal issues. With many government organizations actively employing social media data in recent times, the need for processing public concerns on social media has become a critical topic of interest across academic scholars and practitioners. However, the growing volume of social media data makes it difficult to process all the issues under a single umbrella, causing to overlook the main topic of interest within communication technologies, such as privacy. For example, during the COVID-19 pandemic, arguments on privacy and health issues exploded on Twitter, with several threads centered on contact tracking, health data gathering, and its usage by government agencies. To address the challenges of rising data volumes and to understand the importance of privacy concerns, particularly among users seeking greater privacy protection during this pandemic, we conduct a focused empirical analysis of user tweets about privacy. In this two-part research, our first study reveals three macro privacy issues of discussion distilled from the Twitter corpus, subsequently subdivided into 12 user privacy categories. The second study builds on the findings of the first study, focusing on the primary difficulties highlighted in the macro privacy subjects—contact tracing and digital surveillance. Using a document clustering approach, we present implications for the focal privacy topics that policymakers, agencies, and governments should consider for offering better privacy protections and help the community rebuild. Collapse

Topics: privacy Twitter social media pandemic information privacy concern

Methods: topic model qualitative content analysis cluster analysis hierarchical clustering Gensim

Low-Coders, No-Coders, and Citizen Developers in Demand: Examining Knowledge, Skills, and Abilities Through a Job Market Analysis

2023 | International Conference on Business Informatics | Citations: 0

Authors: Binzer, Björn; Winkler, Till J.

Abstract: The emergence of low-code/no-code (LCNC) platform technologies and the resultin ... Expand

Abstract: The emergence of low-code/no-code (LCNC) platform technologies and the resulting increase in citizen development programs are facilitating the democratization of the design, development, and deployment of digital solutions. Citizen developers, non-technical employees who leverage LCNC platforms, are at the heart of this trend. While many firms perceive LCNC and citizen development as a crucial component of their digital transformation strategy, little is known about the evolving roles in this field or the necessary knowledge, skills, and abilities (KSA). To address this knowledge gap, we processed 113,106 job postings published on Indeed.com. Our topic modeling methodology identified 34 KSA topics and classified them into the three domains platform, business, and technology. We contribute to research by empirically demonstrating which competencies are required to successfully work in the LCNC field. Our findings can guide individual professionals and organizations alike. Collapse

Topics: online labor market Indeed.com low code development platform Python project management

Methods: topic model natural language processing computational algorithm latent dirichlet allocation NLTK

The state of eHealth research across information technologies: A longitudinal analysis using topic modeling to address future scholarship

2023 | HICSS | Citations: 0

Authors: Britt, R.; Chou, S.; Omah, O.; Chakraborty, A.

Abstract: eHealth research has been marked by the last two decades of scholarship spurred ... Expand

Abstract: eHealth research has been marked by the last two decades of scholarship spurred by technological advances and the potential of health promotion and behavior change.The study examined the state of eHealth scholarship across social, behavioral and information technologies through a systematic, machine-based learning approach of the last 19 years across 811 articles. The study analyzes topics that were published using latent Dirichlet allocation of studies from 2002 to 2021; it also raises ethical challenges for researchers related to those in prior health initiatives by the CDC and in current scholarship. Results show the common topics, terms, and linguistic attributes within the state of eHealth scholarship and disparities in other areas based on topics published. Suggestions are offered for interdisciplinary collaboration to facilitate the growth and the optimal, practical use of eHealth and directions for the future. Collapse

Topics: electronic health database system pandemic Python health information system

Methods: topic model literature sample latent dirichlet allocation meta analysis synthesis

Theories: technology acceptance model

Infrastructural and network support in the illness experience: The role of community crowdsourcing in self-care

2023 | HICSS | Citations: 0

Authors: Britt, Rebecca; Doss, Erin Faith; Hayes, Meredith

Abstract: Social sharing within pseudonymous online communities can assist- or hinder- the ... Expand

Abstract: Social sharing within pseudonymous online communities can assist- or hinder- the self-management of health care and emphasize challenges associated with chronic illness. For those who are affected by chronic illness who participate in a public online community, the breadth of topics discussed in a corpus of a pseudonymous group can lend insight into tracking the interaction processes and outcomes. Examining the topics discussed in the endometriosis subreddit (r/endo) informs public health strategies as well as ethical considerations in health care surrounding stigmatized illness in a public community. In the present study, the data corpus of r/endo was analyzed and scraped, employing computational data mining techniques to uncover the discursive practices within the community. The topics of self-management is constructed by diagnosed and undiagnosed patients; endometriosis etiology and understanding symptoms negotiated within the community. In an online community for those who face unique health challenges from endometriosis, we argue that users engage in a form of community crowdsourcing via information exchange and network support, spurred by the platform affordances of Reddit. Collapse

Topics: Python information exchange Reddit online community pseudonymity

Methods: machine learning qualitative content analysis natural language processing latent dirichlet allocation

Theories: theory of affordance

Divergent Innovation: Directing the Wisdom of Crowd to Tackle Societal Challenges

2023 | International Conference on Information Systems | Citations: 0

Authors: Cao, Chuhan; Zhu, Jiantao; Xiong, Bingqing; Lim, Eric; Liu, Hefu; Cai, Zhao; Tan, Chee-Wee

Abstract: Crowdsourcing is acknowledged as a promising avenue for addressing societal cha ... Expand

Abstract: Crowdsourcing is acknowledged as a promising avenue for addressing societal challenges by drawing on the wisdom of the crowd to offer diverse solutions to complex problems. Advancing a new conceptual framework of 'divergent innovation' which delineates between topic and quality divergence as focal metrics of performance when crowdsourcing for solutions to societal challenges, this study investigates the impacts of four ideation stimuli on divergent innovation. These four stimuli include task description concreteness, resource richness, topic entropy, and judging criteria comprehensiveness. Empirical analysis based on data sourced from an online crowd-ideation platform reveals that task description concreteness negatively affects topic divergence but positively influences quality divergence, whereas resource richness positively affects topic divergence but negatively influences quality divergence. Additionally, the relationship between topic entropy and topic divergence is U-shaped, with no significant impact on quality divergence. These findings contribute to extant literature on crowdsourcing and offer invaluable insights for practitioners. Collapse

Topics: innovation management societal challenge crowdsourcing evaluation criteria media richness

Methods: BERT literature study statistical hypothesis test regression analysis method language model

A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction

2023 | Information Systems Research | Citations: 0

Authors: Chen, Gang; Xiao, Shuaiyong; Zhang, Chenghong; Zhao, Huimin

Abstract: As artificial intelligence and digitalization technologies are flourishing real- ... Expand

Abstract: As artificial intelligence and digitalization technologies are flourishing real-time, online interaction–based commercial modes, exploiting customers’ purchase intention implied in online interaction processes may foster huge business opportunities. In this study, we target the task of voice chat–based customer response prediction in an emerging online interaction–based commercial mode, the invite-online-and-experience-in-store mode. Prior research shows that satisfaction, which can be revealed by the discrepancy between prior expectation and actual experience, is a key factor to disentangle customers’ purchase intention, whereas black-box deep learning methods empirically promise us advantageous capabilities in dealing with complex voice data, for example, text and audio information incorporated in voice chat. To this end, we propose a theory-driven deep learning method that enables us to (1) learn customers’ personalized product preferences and dynamic satisfaction in the absence of their profile information, (2) model customers’ actual experiences based on multiview voice chat information in an interlaced way, and (3) enhance the customer response prediction performance of a black-box deep learning model with theory-driven dynamic satisfaction. Empirical evaluation results demonstrate the advantageous prediction performance of our proposed method over state-of-the-art deep learning alternatives. Investigation of cumulative satisfaction reveals the collaborative predictive roles of theory-driven dynamic satisfaction and deep representation features for customer response prediction. Explanatory analysis further renders insights into customers’ personalized preferences and dynamic satisfaction for key product attributes. History: Yong Tan, Senior Editor; Jingjing Zhang, Associate Editor. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971067 and 72271059] and the China Postdoctoral Science Foundation [Grant 2022M722394]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1196. Collapse

Topics: customer satisfaction willingness to pay decision making marketing information system customer service

Methods: deep learning experiment machine learning BERT theory development

Token Incentives in a Volatile Crypto Market: The Effects of Token Price Volatility on User Contribution

2023 | Journal of Management Information Systems | Citations: 0

Authors: Chen, Kun; Fan, Yifan; Liao, Shaoyi Stephen

Abstract: Crypto tokens, issued and managed via smart contracts, function as rewards in bl ... Expand

Abstract: Crypto tokens, issued and managed via smart contracts, function as rewards in blockchain systems to encourage user participation. Distinct from monetary incentives, token incentives are uncertain in reward magnitude due to the large swings in token prices on crypto markets. By focusing on token price volatility, this study investigates how the reward uncertainty affects user contribution in a tokenized digital platform. Our empirical setting is Steemit, a platform where bloggers write posts and share token rewards based on their posts' popularity. We find that while high token price volatility induces a large volume of blog posts, it diminishes post quality. The dichotomous effects are explained via two mechanisms: users' direct reactions to reward uncertainties and their indirect reactions, mediated by altered token preference amid volatility shocks. Deeply exploring this dynamic process, our results reveal that token price volatility facilitates a platform's network short-term effect but impairs long-term user creativity. Our empirical findings thus extend the literature on blockchain economics and cryptocurrencies and have practical implications for the design of incentive mechanisms on tokenized digital platforms. Collapse

Topics: blog blockchain digital platform cryptocurrency user behavior

Methods: longitudinal research instrumental variables estimation data transformation principal component analysis vector autoregression

Theories: motivation crowding theory

Conversation Analytics: Can Machines Read between the Lines in Real-Time Strategic Conversations?

2023 | HICSS | Citations: 0

Authors: Chen, Y.; Rui, H.; Whinston, A.

Abstract: Strategic conversations involve one party with an informational advantage and t ... Expand

Abstract: Strategic conversations involve one party with an informational advantage and the other with an interest in the information. This paper proposes machinelearning based measures to quantify the degrees of evasiveness and incoherence of the informed party during real-time strategic conversations. The specific empirical context is the questions and answers (Q&A) part of earnings conference calls during which managers endure high pressure as they face analysts' scrutinizing questions. Being reluctant to disclose adverse information, managers may resort to evasive answers and sometimes respond less coherently due to increased cognitive load. Using data from the earnings calls of the S&P 500 companies from 2006 to 2018, we show that the proposed measures predict worse nextquarter earnings. Moreover, the stock market perceives incoherence as a negative signal. This paper contributes methodologically by developing two novel machinepowered measures to automatically evaluate behavioral cues during real-time strategic conversations. The proposed analytical tools are particularly beneficial to resource-constrained and informationally disadvantaged parties such as retail investors who may not be able to effectively trade on signals buried deep in unstructured conversational data. Collapse

Topics: database system artificial intelligence electronic finance missing data accounting

Methods: computational algorithm BERT deep learning machine learning next sentence prediction

Theories: theory of economic growth