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

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

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

A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media

2023 | Information Systems Frontiers | Citations: 0

Authors: Chung, Wingyan; Zhang, Yinqiang; Pan, Jia

Abstract: The spreading of disinformation in social media threatens cybersecurity and unde ... Expand

Abstract: The spreading of disinformation in social media threatens cybersecurity and undermines market efficiency. Detecting disinformation is challenging due to large volumes of social media content and a rapidly changing environment. This research developed and validated a theory-based, novel deep-learning approach (called TRNN) to disinformation detection. Grounded in social and psychological theories, TRNN uses deep-learning and data-centric augmentation to enhance disinformation detection in financial social media. Temporal and contextual information is encoded as specific knowledge about human-validated disinformation, which was identified from our unique collection of 745,139 financial social media messages about four U.S. high-tech company stocks and their fine-grained trading data. TRNN uses multiple series of long short-term memory (LSTM) recurrent neurons to learn dynamic and hidden patterns to support disinformation detection. Our experimental findings show that TRNN significantly outperformed widely-used machine learning techniques in terms of precision, recall, F-score and accuracy, achieving consistently better classification performance in disinformation detection. A case study of Apple Inc.’s stock price movement demonstrates the potential usability of TRNN for secure knowledge management. The research contributes to developing novel approach and model, producing new information systems artifacts and dataset, and providing empirical findings of detecting online disinformation. Collapse

Topics: disinformation social media Twitter fake news usability

Methods: machine learning experiment design artifact deep learning recurrent neural network

Theories: psychological theory

Incorporating Multiple Knowledge Sources for Targeted Aspect-based Financial Sentiment Analysis

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

Authors: Du, Kelvin; Xing, Frank; Cambria, Erik

Abstract: Combining symbolic and subsymbolic methods has become a promising strategy as re ... Expand

Abstract: Combining symbolic and subsymbolic methods has become a promising strategy as research tasks in AI grow increasingly complicated and require higher levels of understanding. Targeted Aspect-based Financial Sentiment Analysis (TABFSA) is an example of such complicated tasks, as it involves processes like information extraction, information specification, and domain adaptation. However, little is known about the design principles of such hybrid models leveraging external lexical knowledge. To fill this gap, we define anterior, parallel, and posterior knowledge integration and propose incorporating multiple lexical knowledge sources strategically into the fine-tuning process of pre-trained transformer models for TABFSA. Experiments on the Financial Opinion mining and Question Answering challenge (FiQA) Task 1 and SemEval 2017 Task 5 datasets show that the knowledge-enabled models systematically improve upon their plain deep learning counterparts, and some outperform state-of-the-art results reported in terms of aspect sentiment analysis error. We discover that parallel knowledge integration is the most effective and domain-specific lexical knowledge is more important according to our ablation analysis. Collapse

Topics: microblog domain-specific language Twitter social media knowledge graph

Methods: sentiment analysis RoBERTa transformer model BERT XLNet

Addressing Learners' Heterogeneity in Higher Education: An Explainable AI-based Feedback Artifact for Digital Learning Environments

2023 | International Conference on Business Informatics | Citations: 0

Authors: Haag, Felix; Günther, Sebastian A.; Hopf, Konstantin; Handschuh, Philipp; Klose, Maria; Staake, Thorsten

Abstract: ... Expand

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Topics: explainable artificial intelligence online learning system analytical information system project management IT project management

Methods: design science survey design principle design artifact two sample t-test with equal variance

Why some products compete and others don't: A competitive attribution model from customer perspective

2023 | Decision Support Systems | Citations: 0

Authors: Qian, Yang; Jiang, Yuanchun; Shang, Jennifer; Chai, Yidong; Liu, Yezheng

Abstract: Competitive intelligence uses information collected about competitors to derive ... Expand

Abstract: Competitive intelligence uses information collected about competitors to derive better managerial insights. In this study, we focus on identifying the competitors and detecting the competitive dimensions concurrently. To achieve this goal, we propose an aspect-level competitive attribution model (a variation of the topic model) to leverage consumer-reviewed products and their review texts. To better analyze product relations and the un­ derlying competitive aspects, we consider consumer limited attention when modeling consumers' preferences and introduce a background aspect to filter out the trivial and maintain the valuable competition-related in­ formation in review texts. We validate this approach using a dataset of 785 products reviewed by 15,669 con­ sumers in the auto industry. Based on the empirical experiments, we show that our model can accurately infer high-quality competitive segments and decipher competition-related aspects corresponding to these segments. To highlight differences, we conduct comparisons and find our approach outperforms the benchmark models meaningfully in the literature when predicting consumers' online behaviors. Collapse

Topics: online review competitive intelligence marketing management competitive advantage user-generated content

Methods: topic model experiment Gibbs sampling natural language processing computational algorithm

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

Relationships between Involuntary Visual Cueing and Sensemaking of YouTube Videos

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Shen, Zixing; Tan, Songxin

Abstract: Sensemaking plays a critical role in the everyday use of social media, which ar ... Expand

Abstract: Sensemaking plays a critical role in the everyday use of social media, which are increasingly visual as video, images, and other visual content permeate. Yet, research on the sensemaking of visual information on social media is minimal. This study addresses this research gap by investigating the relationships between involuntary visual cues (color contrast and visual complexity) and making sense of videos on YouTube and Reddit. It finds that color contrast has a significant association with sensemaking on both platforms and that visual complexity is a significant predictor of sensemaking only on Reddit. Our study adds to the research on sensemaking on social media. It expands social media analysis from textual to visual information and contributes to the knowledge of visual sensemaking on social media. It also provides practitioners with guidelines for designing videos that facilitate visual sensemaking. Collapse

Topics: sensemaking YouTube social media Reddit

Methods: survey design multiple linear regression image processing statistical hypothesis test computational algorithm

Theories: information theory

Extracting Actionable Insights from Text Data: A Stable Topic Model Approach

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Yang, Yi; Subramanyam, Ramanath

Abstract: Topic models are becoming a frequently employed tool in the empirical methods r ... Expand

Abstract: Topic models are becoming a frequently employed tool in the empirical methods repertoire of information systems and management scholars. Given textual corpora, such as consumer reviews and online discussion forums, researchers and business practitioners often use topic modeling to either explore data in an unsupervised fashion or generate variables of interest for subsequent econometric analysis. However, one important concern stems from the fact that topic models can be notorious for their instability, i.e., the generated results could be inconsistent and irreproducible at different times, even on the same dataset. Therefore, researchers might arrive at potentially unreliable results regarding the theoretical relationships that they are testing or developing. In this paper, we attempt to highlight this problem and suggest a potential approach to addressing it. First, we empirically define and evaluate the stability problem of topic models using four textual datasets. Next, to alleviate the problem and with the goal of extracting actionable insights from textual data, we propose a new method, Stable LDA, which incorporates topical word clusters into the topic model to steer the model inference toward consistent results. We show that the proposed Stable LDA approach can significantly improve model stability while maintaining or even improving the topic model quality. Further, employing two case studies related to an online knowledge community and online consumer reviews, we demonstrate that the variables generated from Stable LDA can lead to more consistent estimations in econometric analyses. We believe that our work can further enhance management scholars' collective toolkit to analyze ever-growing textual data. Collapse

Topics: online review user-generated content open source usability social media

Methods: topic model experiment word embedding Gibbs sampling computational algorithm