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

TEA-IS: A hybrid DEA-TOPSIS approach for assessing performance and synergy in Chinese health care

2023 | Decision Support Systems | Citations: 0

Authors: Antunes, Jorge; Hadi-Vencheh, Abdollah; Jamshidi, Ali; Tan, Yong; Wanke, Peter

Abstract: This paper presents an assessment of the Chinese healthcare system in 31 provinc ... Expand

Abstract: This paper presents an assessment of the Chinese healthcare system in 31 provinces for a 10-year period in light of relevant physical and human resource variables. First, a novel TEA-IS (Trigonometric Envelopment Analysis for Ideal Solutions) model is developed to assess healthcare efficiency at the province level. Machine learning methods are also employed to predict high-low performance and the synergistic Chinese healthcare province in terms of contextual variables. The results indicate that synergy has played a pivotal role in the Chinese healthcare systems, not only by triggering higher performance levels due to the progressive adoption of best practices over the course of time, but also by being closely related to different socioeconomic and demographic variables, such as the illiteracy rate. It is possible to claim that healthcare performance has remained stable in China over the past two decades, performance and synergy at the province level are still heterogeneous. Collapse

Topics: health information system IT productivity human resource management service quality productivity

Methods: TOPSIS trade-off analysis method machine learning artificial neural network literature study

Motivating User-Generated Content: The Unintended Consequences of Incentive Thresholds

2023 | Management Information Systems Quarterly | Citations: 0

Authors: He, Liuyi; Luo, Jifeng; Tang, Yisong; Wu, Zhiyan; Zhang, Han

Abstract: While monetary rewards have been widely used by online platforms to motivate us ... Expand

Abstract: While monetary rewards have been widely used by online platforms to motivate user-generated content (UGC) contributions, users may not always demonstrate the expected behaviors. Unintended consequences of reward policies, exemplified by unchanged or reduced UGC contributions, may occur. Through two natural experiments, this study investigates the implications of providing users with an incentive structure that rewards users' continued contribution according to the volume of UGC. Using a unique data set on two completion-contingent incentive programs from a popular online aesthetic medicine platform, we examine user responses to reward thresholds. We found that after users reach a threshold to obtain a monetary reward, they are less likely to continue contributing UGC, suggesting a minimal-effort effect. Our findings indicate that social approval from peers helps to mitigate unintended user responses to monetary reward policies. We also observed that monetary rewards primarily improve the quality and website traffic of low-to moderate-quality contributions. Collapse

Topics: unintended consequence website online community social norm intrinsic motivation

Methods: experimental group natural experiment robustness check computational algorithm simulation

Exploiting Connections among Personality, Job Position, and Work Behavior: Evidence from Joint Bayesian Learning

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Shen, Dazhong; Zhu, Hengshu; Xiao, Keli; Zhang, Xi; Xiong, Hui

Abstract: Personality has been considered as a driving factor for work engagement, which s ... Expand

Abstract: Personality has been considered as a driving factor for work engagement, which significantly affects people’s role performance at work. Although existing research has provided some intuitive understanding of the connection between personality traits and employees’ work behaviors, it still lacks effective quantitative tools for modeling personality traits, job position characteristics, and employee work behaviors simultaneously. To this end, in this article, we introduce a data-driven joint Bayesian learning approach, Joint-PJB, to discover explainable joint patterns from massive personality and job-position-related behavioral data. Specifically, Joint-PJB is designed with the knowledgeable guidance of the four-quadrant behavioral model, namely, DISC (Dominance, Influence, Steadiness, Conscientiousness). Based on the real-world data collected from a high-tech company, Joint-PJB aims to highlight personality-job-behavior joint patterns from personality traits, job responsibilities, and work behaviors. The model can measure the matching degree between employees and their work behaviors given their personality and job position characteristics. We find a significant negative correlation between this matching degree and employee turnover intention. Moreover, we also showcase how the identified patterns can be utilized to support real-world talent management decisions. Both case studies and quantitative experiments verify the effectiveness of Joint-PJB for understanding people’s personality traits in different job contexts and their impact on work behaviors. Collapse

Topics: personality IT personell turnover IT job missing data decision making

Methods: experiment artificial neural network survey topic model case study

DeepPsych: Harnessing Market Psychology with Deep Learning

2023 | International Conference on Information Systems | Citations: 0

Authors: Shen, Jiancheng; Wang, Jia; Zhu, Hongwei; Liu, Benyuan

Abstract: Investor psychology provides an important avenue for modeling non-fundamental b ... Expand

Abstract: Investor psychology provides an important avenue for modeling non-fundamental behaviors in financial analysis. Yet, whether market psychological information has a practical application in predicting asset returns is still under debate. Thus, a burgeoning number of machine learning algorithms have been developed to test the effectiveness of investor psychology in financial predictions. With all the merits of machine learning approach, the drawbacks are prediction biases, data overfitting issues and poor performance. To address these issues, we developed a DeepPsych system to harness the power of high frequency TRMI psychology data for market prediction. In a "hybridization-generalization-dual-channel-fusion" three-stage experiment, we evaluate each proposed module and the entire framework against the state-of-art machine learning benchmarks on investor psychology and trading data of the SPY (SP500 ETF). Results demonstrate that our deep learning framework can automatically identify features that are more effective than fundamental factors and support profitable trading. Collapse

Topics: social media electronic trading big data innovation management

Methods: experiment deep learning machine learning design science convolutional neural network

Theories: transaction cost economics

sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics

2023 | Information Systems Research | Citations: 0

Authors: Yang, Yi; Zhang, Kunpeng; Fan, Yangyang

Abstract: Topic modeling methods such as latent Dirichlet allocation (LDA) are powerful to ... Expand

Abstract: Topic modeling methods such as latent Dirichlet allocation (LDA) are powerful tools for analyzing massive amounts of textual data. They have been used extensively in information systems (IS) and business discipline research to identify latent topics for data exploration and as a feature engineering mechanism to derive new variables for analyses. However, existing topic modeling approaches are mostly unsupervised and only leverage textual data, while ignoring additional useful metadata often associated with text, such as star ratings in customer reviews or categories of posts in online forums. As a result, the identified topics and variables derived based on the learned topic model may not be accurate, which could lead to incorrect estimations that affect subsequent empirical analysis and to inferior performance on predictive tasks. In this study, we propose a novel supervised deep topic modeling approach called sDTM, which combines a neural variational autoencoder model and a recurrent neural network. sDTM leverages the auxiliary data associated with text to enhance the topic modeling capability. We conduct empirical case studies and predictive analytics on an online consumer review data set and an online knowledge community data set. Experimental results show that in comparison with benchmark methods, sDTM can enhance both the empirical estimation and predictive performance. sDTM makes methodological contributions to the IS literature and has direct relevance for research using text analytics.History: Alok Gupta, Senior Editor; Huimin Zhao, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1124. Collapse

Topics: online review open source user-generated content Wikipedia accounting

Methods: topic model deep learning machine learning BERT data transformation

A deep learning approach for detecting fake reviewers: Exploiting reviewing behavior and textual information

2023 | Decision Support Systems | Citations: 0

Authors: Zhang, Dong; Li, Wenwen; Niu, Baozhuang; Wu, Chong

Abstract: Ensuring the credibility of online consumer reviews (OCRs) is a growing societal ... Expand

Abstract: Ensuring the credibility of online consumer reviews (OCRs) is a growing societal concern. However, the problem of fake reviewers on online platforms significantly influences e-commerce authenticity and consumer trust. Existing studies for fake reviewer detection mainly focus on deriving novel behavioral and linguistic features. These features require extensive human labor and expertise, placing a heavy burden on platforms. Therefore, we propose a novel end-to-end framework to detect fake reviewers based on behavior and textual information. It has two key components: (1) a behavior-sensitive feature extractor that learns the underlying patterns of reviewing behavior; (2) a context-aware attention mechanism that extracts valuable features from online reviews. We rigorously evaluate each proposed module and the entire framework against state-of-the-art benchmarks on two real-world datasets from http://Yelp.com. Experimental results demonstrate that our method achieves state-ofthe-art results on fake reviewer detection. Our method can be considered a tentative step toward lowering human labor costs in realizing automated fake reviewer detection on e-commerce platforms. Collapse

Topics: fake review online review electronic commerce deception detection shapley additive explanation

Methods: machine learning digital trace data experiment parametric test deep learning

Does Fake News in Different Languages Tell the Same Story? An Analysis of Multi-level Thematic and Emotional Characteristics of News about COVID-19

2023 | Information Systems Frontiers | Citations: 0

Authors: Zhou, Lina; Tao, Jie; Zhang, Dongsong

Abstract: Fake news is being generated in different languages, yet existing studies are do ... Expand

Abstract: Fake news is being generated in different languages, yet existing studies are dominated by English news. The analysis of fake news content has focused on lexical and stylometric features, giving little attention to semantic features. A few studies involving semantic features have either used them as the inputs to classifiers with no interpretations, or treated them in isolation. This research aims to investigate both thematic and emotional characteristics of fake news at different levels and compare them between different languages for the first time. It extends a state-of-the-art topic modeling technique to extract news topics and introduces a divergence measure to assess the importance of thematic characteristics for identifying fake news. We further examine associations of the thematic and emotional characteristics of fake news. The empirical findings have implications for developing both general and language-specific countermeasures for fake news. Collapse

Topics: fake news pandemic Twitter social media internet technology

Methods: topic model descriptive statistic transformer model Student's t-test literature study

A text-based measure for digital innovation - uncovering digital innovation and its impact on firm performance

2023 | European Conference On Information Systems | Citations: 0

Authors: Zimmermann, Thomas; Kalscheuer, Ulrich; Fischer-Kreer, Denise

Abstract: ... Expand

Abstract: Collapse

Topics: organizational productivity innovation management research and development organizational success accounting

Methods: descriptive statistic latent dirichlet allocation computational algorithm longitudinal research machine learning

A critical assessment of consumer reviews: A hybrid NLP-based methodology

2022 | Decision Support Systems | Citations: 0

Authors: Biswas, Baidyanath; Sengupta, Pooja; Kumar, Ajay; Delen, Dursun; Gupta, Shivam

Abstract: Online reviews are integral to consumer decision-making while purchasing product ... Expand

Abstract: Online reviews are integral to consumer decision-making while purchasing products on an e-commerce platform. Extant literature has conclusively established the effects of various review and reviewer related predictors to­ wards perceived helpfulness. However, background research is limited in addressing the following problem: how can readers interpret the topical summary of many helpful reviews that explain multiple themes and consecutively focus in-depth? To fill this gap, we drew upon Shannon’s Entropy Theory and Dual Process Theory to propose a set of predictors using NLP and text mining to examine helpfulness. We created four predictors - review depth, review divergence, semantic entropy and keyword relevance to build our primary empirical models. We also reported interesting findings from the interaction effects of the reviewer’s credibility, age of review, and review divergence. We also validated the robustness of our results across different product categories and higher thresholds of help­ fulness votes. Our study contributes to the electronic commerce literature with relevant managerial and theo­ retical implications through these findings. Collapse

Topics: online review electronic commerce computer virus protection information retrieval digital media

Methods: natural language processing robustness check term frequency–inverse document frequency topic model moderation analysis

Theories: dual-system theory chaos theory mathematical theory of communication elaboration likelihood model