Guanqing Liang, Jingxin Zhao, Helena Yan Ping Lau, C. Leung
The outbreak of COVID-19 has caused huge economic and societal disruptions. To fight against the coronavirus, it is critical for policymakers to take swift and effective actions. In this article, we take Hong Kong as a case study, aiming to leverage social media data to support policymakers’ policy-making activities in different phases. First, in the agenda setting phase, we facilitate policymakers to identify key issues to be addressed during COVID-19. In particular, we design a novel epidemic awareness index to continuously monitor public discussion hotness of COVID-19 based on large-scale data collected from social media platforms. Then we identify the key issues by analyzing the posts and comments of the extensively discussed topics. Second, in the policy evaluation phase, we enable policymakers to conduct real-time evaluation of anti-epidemic policies. Specifically, we develop an accurate Cantonese sentiment classification model to measure the public satisfaction with anti-epidemic policies and propose a keyphrase extraction technique to further extract public opinions. To the best of our knowledge, this is the first work which conducts a large-scale social media analysis of COVID-19 in Hong Kong. The analytical results reveal some interesting findings: (1) there is a very low correlation between the number of confirmed cases and the public discussion hotness of COVID-19. The major public concern in the early stage is the shortage of anti-epidemic items. (2) The top-3 anti-epidemic measures with the greatest public satisfaction are daily press conference on COVID-19 updates, border closure, and social distancing rules.
{"title":"Using Social Media to Analyze Public Concerns and Policy Responses to COVID-19 in Hong Kong","authors":"Guanqing Liang, Jingxin Zhao, Helena Yan Ping Lau, C. Leung","doi":"10.1145/3460124","DOIUrl":"https://doi.org/10.1145/3460124","url":null,"abstract":"The outbreak of COVID-19 has caused huge economic and societal disruptions. To fight against the coronavirus, it is critical for policymakers to take swift and effective actions. In this article, we take Hong Kong as a case study, aiming to leverage social media data to support policymakers’ policy-making activities in different phases. First, in the agenda setting phase, we facilitate policymakers to identify key issues to be addressed during COVID-19. In particular, we design a novel epidemic awareness index to continuously monitor public discussion hotness of COVID-19 based on large-scale data collected from social media platforms. Then we identify the key issues by analyzing the posts and comments of the extensively discussed topics. Second, in the policy evaluation phase, we enable policymakers to conduct real-time evaluation of anti-epidemic policies. Specifically, we develop an accurate Cantonese sentiment classification model to measure the public satisfaction with anti-epidemic policies and propose a keyphrase extraction technique to further extract public opinions. To the best of our knowledge, this is the first work which conducts a large-scale social media analysis of COVID-19 in Hong Kong. The analytical results reveal some interesting findings: (1) there is a very low correlation between the number of confirmed cases and the public discussion hotness of COVID-19. The major public concern in the early stage is the shortage of anti-epidemic items. (2) The top-3 anti-epidemic measures with the greatest public satisfaction are daily press conference on COVID-19 updates, border closure, and social distancing rules.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"69 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113992138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Junye Li, Aryan Sharma, Deepak Mishra, Gustavo E. A. P. A. Batista, A. Seneviratne
During the COVID-19 pandemic, authorities have been asking for social distancing to prevent transmission of the virus. However, enforcing such distancing has been challenging in tight spaces such as elevators and unmonitored commercial settings such as offices. This article addresses this gap by proposing a low-cost and non-intrusive method for monitoring social distancing within a given space, using Channel State Information (CSI) from passive WiFi sensing. By exploiting the frequency selective behavior of CSI with a Support Vector Machine (SVM) classifier, we achieve an improvement in accuracy over existing crowd counting works. Our system counts the number of occupants with a 93% accuracy rate in an elevator setting and predicts whether the COVID-Safe limit is breached with a 97% accuracy rate. We also demonstrate the occupant counting capability of the system in a commercial office setting, achieving 97% accuracy. Our proposed occupancy monitoring outperforms existing methods by at least 7%. Overall, the proposed framework is inexpensive, requiring only one device that passively collects data and a lightweight supervised learning algorithm for prediction. Our lightweight model and accuracy improvements are necessary contributions for WiFi-based counting to be suitable for COVID-specific applications.
{"title":"COVID-Safe Spatial Occupancy Monitoring Using OFDM-Based Features and Passive WiFi Samples","authors":"Junye Li, Aryan Sharma, Deepak Mishra, Gustavo E. A. P. A. Batista, A. Seneviratne","doi":"10.1145/3472668","DOIUrl":"https://doi.org/10.1145/3472668","url":null,"abstract":"During the COVID-19 pandemic, authorities have been asking for social distancing to prevent transmission of the virus. However, enforcing such distancing has been challenging in tight spaces such as elevators and unmonitored commercial settings such as offices. This article addresses this gap by proposing a low-cost and non-intrusive method for monitoring social distancing within a given space, using Channel State Information (CSI) from passive WiFi sensing. By exploiting the frequency selective behavior of CSI with a Support Vector Machine (SVM) classifier, we achieve an improvement in accuracy over existing crowd counting works. Our system counts the number of occupants with a 93% accuracy rate in an elevator setting and predicts whether the COVID-Safe limit is breached with a 97% accuracy rate. We also demonstrate the occupant counting capability of the system in a commercial office setting, achieving 97% accuracy. Our proposed occupancy monitoring outperforms existing methods by at least 7%. Overall, the proposed framework is inexpensive, requiring only one device that passively collects data and a lightweight supervised learning algorithm for prediction. Our lightweight model and accuracy improvements are necessary contributions for WiFi-based counting to be suitable for COVID-specific applications.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123112122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The ability to quickly learn fundamentals about a new infectious disease, such as how it is transmitted, the incubation period, and related symptoms, is crucial in any novel pandemic. For instance, rapid identification of symptoms can enable interventions for dampening the spread of the disease. Traditionally, symptoms are learned from research publications associated with clinical studies. However, clinical studies are often slow and time intensive, and hence delays can have dire consequences in a rapidly spreading pandemic like we have seen with COVID-19. In this article, we introduce SymptomID, a modular artificial intelligence–based framework for rapid identification of symptoms associated with novel pandemics using publicly available news reports. SymptomID is built using the state-of-the-art natural language processing model (Bidirectional Encoder Representations for Transformers) to extract symptoms from publicly available news reports and cluster-related symptoms together to remove redundancy. Our proposed framework requires minimal training data, because it builds on a pre-trained language model. In this study, we present a case study of SymptomID using news articles about the current COVID-19 pandemic. Our COVID-19 symptom extraction module, trained on 225 articles, achieves an F1 score of over 0.8. SymptomID can correctly identify well-established symptoms (e.g., “fever” and “cough”) and less-prevalent symptoms (e.g., “rashes,” “hair loss,” “brain fog”) associated with the novel coronavirus. We believe this framework can be extended and easily adapted in future pandemics to quickly learn relevant insights that are fundamental for understanding and combating a new infectious disease.
{"title":"SymptomID: A Framework for Rapid Symptom Identification in Pandemics Using News Reports","authors":"Kang Gu, Soroush Vosoughi, T. Prioleau","doi":"10.1145/3462441","DOIUrl":"https://doi.org/10.1145/3462441","url":null,"abstract":"The ability to quickly learn fundamentals about a new infectious disease, such as how it is transmitted, the incubation period, and related symptoms, is crucial in any novel pandemic. For instance, rapid identification of symptoms can enable interventions for dampening the spread of the disease. Traditionally, symptoms are learned from research publications associated with clinical studies. However, clinical studies are often slow and time intensive, and hence delays can have dire consequences in a rapidly spreading pandemic like we have seen with COVID-19. In this article, we introduce SymptomID, a modular artificial intelligence–based framework for rapid identification of symptoms associated with novel pandemics using publicly available news reports. SymptomID is built using the state-of-the-art natural language processing model (Bidirectional Encoder Representations for Transformers) to extract symptoms from publicly available news reports and cluster-related symptoms together to remove redundancy. Our proposed framework requires minimal training data, because it builds on a pre-trained language model. In this study, we present a case study of SymptomID using news articles about the current COVID-19 pandemic. Our COVID-19 symptom extraction module, trained on 225 articles, achieves an F1 score of over 0.8. SymptomID can correctly identify well-established symptoms (e.g., “fever” and “cough”) and less-prevalent symptoms (e.g., “rashes,” “hair loss,” “brain fog”) associated with the novel coronavirus. We believe this framework can be extended and easily adapted in future pandemics to quickly learn relevant insights that are fundamental for understanding and combating a new infectious disease.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130323601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The dynamic monitoring of home and workplace distribution is a fundamental building block for improving location-based service systems in fast-developing cities worldwide. Inferring these places is...
{"title":"Leveraging Individual and Collective Regularity to Profile and Segment User Locations from Mobile Phone Data","authors":"Yan Leng, Jinhuan Zhao, H. Koutsopoulos","doi":"10.1145/3449042","DOIUrl":"https://doi.org/10.1145/3449042","url":null,"abstract":"The dynamic monitoring of home and workplace distribution is a fundamental building block for improving location-based service systems in fast-developing cities worldwide. Inferring these places is...","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"15 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132435418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Edla, Shubham Dodia, Annushree Bablani, Venkatanareshbabu Kuppili
Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field ...
脑机接口是人类大脑和一种利用大脑信号控制人类行为的设备的合作。脑机接口的应用领域各不相同……
{"title":"An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals","authors":"D. Edla, Shubham Dodia, Annushree Bablani, Venkatanareshbabu Kuppili","doi":"10.1145/3458791","DOIUrl":"https://doi.org/10.1145/3458791","url":null,"abstract":"Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field ...","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128953887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Past years have seen rising engagement among caregivers in online health communities. Although studies indicate that this caregiver-generated online health information benefits patients, how such information can be perceived easily and correctly remains unclear. This study aims to fill this gap by exploring mechanisms to improve the perceived helpfulness of online health information. We propose a multi-method framework, including a novel Medical-Enriched DEep Learning (MEDEL) feature extraction method, econometric analyses, and a randomized experiment. The results show that when the medical language of health information is informal, the senior care information is more helpful. Our findings provide a theoretical foundation to understand the influence of language formality on many other business communications. Our proposed multi-method approach can also be generalized to investigate research questions involving complex textual features. Forum sites could leverage our proposed approach to improve the helpfulness of online health information and user satisfaction.
{"title":"Write Like a Pro or an Amateur? Effect of Medical Language Formality","authors":"Jiaheng Xie, Bin Zhang, Susan A. Brown, D. Zeng","doi":"10.1145/3458752","DOIUrl":"https://doi.org/10.1145/3458752","url":null,"abstract":"Past years have seen rising engagement among caregivers in online health communities. Although studies indicate that this caregiver-generated online health information benefits patients, how such information can be perceived easily and correctly remains unclear. This study aims to fill this gap by exploring mechanisms to improve the perceived helpfulness of online health information. We propose a multi-method framework, including a novel Medical-Enriched DEep Learning (MEDEL) feature extraction method, econometric analyses, and a randomized experiment. The results show that when the medical language of health information is informal, the senior care information is more helpful. Our findings provide a theoretical foundation to understand the influence of language formality on many other business communications. Our proposed multi-method approach can also be generalized to investigate research questions involving complex textual features. Forum sites could leverage our proposed approach to improve the helpfulness of online health information and user satisfaction.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116618203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the age of IoT, collection of activity data has become ubiquitous. Publishing activity data can be quite useful for various purposes such as estimating the level of assistance required by older adults and facilitating early diagnosis and treatment of certain diseases. However, publishing activity data comes with privacy risks: Each dimension, i.e., the activity of a person at any given point in time can be used to identify a person as well as to reveal sensitive information about the person such as not being at home at that time. Unfortunately, conventional anonymization methods have shortcomings when it comes to anonymizing activity data. Activity datasets considered for publication are often flat with many dimensions but typically not many rows, which makes the existing anonymization techniques either inapplicable due to very few rows, or else either inefficient or ineffective in preserving utility. This article proposes novel multi-level clustering-based approaches using a non-metric weighted distance measure that enforce ℓ-diversity model. Experimental results show that the proposed methods preserve data utility and are orders more efficient than the existing methods.
{"title":"Anonymization of Daily Activity Data by Using ℓ-diversity Privacy Model","authors":"Pooja Parameshwarappa, Zhiyuan Chen, Güneş Koru","doi":"10.1145/3456876","DOIUrl":"https://doi.org/10.1145/3456876","url":null,"abstract":"In the age of IoT, collection of activity data has become ubiquitous. Publishing activity data can be quite useful for various purposes such as estimating the level of assistance required by older adults and facilitating early diagnosis and treatment of certain diseases. However, publishing activity data comes with privacy risks: Each dimension, i.e., the activity of a person at any given point in time can be used to identify a person as well as to reveal sensitive information about the person such as not being at home at that time. Unfortunately, conventional anonymization methods have shortcomings when it comes to anonymizing activity data. Activity datasets considered for publication are often flat with many dimensions but typically not many rows, which makes the existing anonymization techniques either inapplicable due to very few rows, or else either inefficient or ineffective in preserving utility. This article proposes novel multi-level clustering-based approaches using a non-metric weighted distance measure that enforce ℓ-diversity model. Experimental results show that the proposed methods preserve data utility and are orders more efficient than the existing methods.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127977055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Advanced information technologies have enabled Massive Open Online Courses (MOOCs) , which have the potential to transform higher education around the world. Why are some institutions eager to embrace this technology-enabled model of teaching, while others remain reluctant to jump aboard? Applying the theory of absorptive capacity, we study the role of a university's educational IT capabilities in becoming an early MOOC producer. Examining the history of MOOC offerings by U.S. colleges and universities, we find that prior IT capabilities, such as (1) the use of Web 2.0, social media and other interactive tools for teaching and (2) experience with distance education and hybrid teaching, are positively associated with the early exploration of MOOCs. Interestingly, we also find that the effect of educational IT capabilities is moderated by social integration mechanisms and activation triggers. For example, when instructional IT supporting services are highly decentralized, educational IT capabilities have a greater impact on the probability of a university offering a MOOC. In addition, for colleges facing an adverse environment, such as those experience a decline in college applications, the effect of IT capabilities on the exploration of MOOCs is much stronger.
{"title":"Early Exploration of MOOCs in the U.S. Higher Education: An Absorptive Capacity Perspective","authors":"Peng-hsu Huang, H. Lucas","doi":"10.1145/3456295","DOIUrl":"https://doi.org/10.1145/3456295","url":null,"abstract":"\u0000 Advanced information technologies have enabled\u0000 Massive Open Online Courses (MOOCs)\u0000 , which have the potential to transform higher education around the world. Why are some institutions eager to embrace this technology-enabled model of teaching, while others remain reluctant to jump aboard? Applying the theory of absorptive capacity, we study the role of a university's educational IT capabilities in becoming an early MOOC producer. Examining the history of MOOC offerings by U.S. colleges and universities, we find that prior IT capabilities, such as (1) the use of Web 2.0, social media and other interactive tools for teaching and (2) experience with distance education and hybrid teaching, are positively associated with the early exploration of MOOCs. Interestingly, we also find that the effect of educational IT capabilities is moderated by social integration mechanisms and activation triggers. For example, when instructional IT supporting services are highly decentralized, educational IT capabilities have a greater impact on the probability of a university offering a MOOC. In addition, for colleges facing an adverse environment, such as those experience a decline in college applications, the effect of IT capabilities on the exploration of MOOCs is much stronger.\u0000","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131402627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Blockchain records transactions with various protection techniques against tampering. To meet the requirements on cooperation and anonymity of companies and organizations, researchers have developed a few solutions. Ring signature-based schemes allow multiple participants cooperatively to manage while preserving their individuals’ privacy. However, the solutions cannot work properly due to the increased computing complexity along with the expanded group size. In this article, we propose a Multi-center Anonymous Blockchain-based (MAB) system, with joint management for the consortium and privacy protection for the participants. To achieve that, we formalize the syntax used by the MAB system and present a general construction based on a modular design. By applying cryptographic primitives to each module, we instantiate our scheme with anonymity and decentralization. Furthermore, we carry out a comprehensive formal analysis of our exemplified scheme. A proof of concept simulation is provided to show the feasibility. The results demonstrate security and efficiency from both theoretical perspectives and practical perspectives.
{"title":"Anonymous Blockchain-based System for Consortium","authors":"Qin Wang, Shiping Chen, Yang Xiang","doi":"10.1145/3459087","DOIUrl":"https://doi.org/10.1145/3459087","url":null,"abstract":"Blockchain records transactions with various protection techniques against tampering. To meet the requirements on cooperation and anonymity of companies and organizations, researchers have developed a few solutions. Ring signature-based schemes allow multiple participants cooperatively to manage while preserving their individuals’ privacy. However, the solutions cannot work properly due to the increased computing complexity along with the expanded group size. In this article, we propose a Multi-center Anonymous Blockchain-based (MAB) system, with joint management for the consortium and privacy protection for the participants. To achieve that, we formalize the syntax used by the MAB system and present a general construction based on a modular design. By applying cryptographic primitives to each module, we instantiate our scheme with anonymity and decentralization. Furthermore, we carry out a comprehensive formal analysis of our exemplified scheme. A proof of concept simulation is provided to show the feasibility. The results demonstrate security and efficiency from both theoretical perspectives and practical perspectives.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129429232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lin Qiu, Sruthi Gorantla, Vaibhav Rajan, Bernard C. Y. Tan
Multi-Disease Predictive Analytics (MDPA) models simultaneously predict the risks of multiple diseases in patients and are valuable in early diagnoses. Patients tend to have multiple diseases simul...
{"title":"Multi-disease Predictive Analytics: A Clinical Knowledge-aware Approach","authors":"Lin Qiu, Sruthi Gorantla, Vaibhav Rajan, Bernard C. Y. Tan","doi":"10.1145/3447942","DOIUrl":"https://doi.org/10.1145/3447942","url":null,"abstract":"Multi-Disease Predictive Analytics (MDPA) models simultaneously predict the risks of multiple diseases in patients and are valuable in early diagnoses. Patients tend to have multiple diseases simul...","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124545656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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