This paper presents a novel token-based recall communication system, which integrates Enterprise Resource Planning (ERP) systems and blockchain technology to enhance recall communication and cooperation between manufacturers and customers. We employed a design science research methodology to develop a set of design principles and features that support the interoperable system. Our findings demonstrate that we can significantly improve recall coordination, traceability, and co-value creation between involved parties. By focusing on the integration potential of traditional technologies like ERP systems with blockchain and token techniques, we reveal innovative synergies for both social and technical subsystems. The study explores the implications of the proposed system for both theory and practice, offering insights into the advantages and challenges of such integration. The evaluation conducted with industry experts demonstrates high reusability of the design principles.
{"title":"From Dissonance to Dialogue: A Token-Based Approach to Bridge the Gap Between Manufacturers and Customers","authors":"Norman Pytel, Christian Ziegler, Axel Winkelmann","doi":"10.1145/3639058","DOIUrl":"https://doi.org/10.1145/3639058","url":null,"abstract":"This paper presents a novel token-based recall communication system, which integrates Enterprise Resource Planning (ERP) systems and blockchain technology to enhance recall communication and cooperation between manufacturers and customers. We employed a design science research methodology to develop a set of design principles and features that support the interoperable system. Our findings demonstrate that we can significantly improve recall coordination, traceability, and co-value creation between involved parties. By focusing on the integration potential of traditional technologies like ERP systems with blockchain and token techniques, we reveal innovative synergies for both social and technical subsystems. The study explores the implications of the proposed system for both theory and practice, offering insights into the advantages and challenges of such integration. The evaluation conducted with industry experts demonstrates high reusability of the design principles.","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139384508","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}
Moufida Aouachria, Abderrahmane Leshob, A. R. Ghomari, M. Aouache
Business process integration (BPI) allows organizations to connect and automate their business processes in order to deliver the right economic resources at the right time, place, and price. BPI requires the integration of business processes and their supporting systems across multiple autonomous organizations. However, such integration is complex and can face coordination complexities that occur during the resource exchanges between the partners’ processes. This paper proposes a new method called Process Mining for Business Process Integration (PM4BPI) that helps process designers to perform BPI by creating new process models that cross the boundaries of multiple organizations from a collection of process event logs. PM4BPI uses federated process mining techniques to detect incompatibilities before the integration of the partners’ processes. Then, it applies process adaptation patterns to solve detected incompatibilities. Finally, organizations’ processes are merged to build a collaborative process model that crosses the organizations’ boundaries. Adapt ({}_{WF_Net} ) , an extension of a Petri net, is used to design inter-organizational business processes and adaptation patterns. An integrated care pathway is used as a case study to assess the applicability and effectiveness of the proposed method.
业务流程集成（BPI）使企业能够将其业务流程连接起来并实现自动化，以便在合适的时间、地点和价格提供合适的经济资源。业务流程集成要求在多个自治组织之间集成业务流程及其支持系统。然而，这种整合是复杂的，在合作伙伴的流程之间进行资源交换时可能会面临复杂的协调问题。本文提出了一种名为 "业务流程集成流程挖掘"（PM4BPI）的新方法，通过从流程事件日志集合中创建跨越多个组织边界的新流程模型，帮助流程设计人员执行业务流程集成。PM4BPI 使用联合流程挖掘技术，在整合合作伙伴的流程之前检测不兼容性。然后，它应用流程适应模式来解决检测到的不兼容性问题。最后，合并各组织的流程，建立一个跨组织边界的协作流程模型。Adapt （{}_{WF_Net} ）是 Petri 网的扩展，用于设计组织间业务流程和适应模式。一个综合护理路径被用作案例研究，以评估所建议方法的适用性和有效性。
{"title":"A Process Mining Method for Inter-organizational Business Process Integration","authors":"Moufida Aouachria, Abderrahmane Leshob, A. R. Ghomari, M. Aouache","doi":"10.1145/3638062","DOIUrl":"https://doi.org/10.1145/3638062","url":null,"abstract":"Business process integration (BPI) allows organizations to connect and automate their business processes in order to deliver the right economic resources at the right time, place, and price. BPI requires the integration of business processes and their supporting systems across multiple autonomous organizations. However, such integration is complex and can face coordination complexities that occur during the resource exchanges between the partners’ processes. This paper proposes a new method called Process Mining for Business Process Integration (PM4BPI) that helps process designers to perform BPI by creating new process models that cross the boundaries of multiple organizations from a collection of process event logs. PM4BPI uses federated process mining techniques to detect incompatibilities before the integration of the partners’ processes. Then, it applies process adaptation patterns to solve detected incompatibilities. Finally, organizations’ processes are merged to build a collaborative process model that crosses the organizations’ boundaries. Adapt ({}_{WF_Net} ) , an extension of a Petri net, is used to design inter-organizational business processes and adaptation patterns. An integrated care pathway is used as a case study to assess the applicability and effectiveness of the proposed method.","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138959171","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}
{"title":"Introduction to the Special Issue on IT-enabled Business Management and Decision Making in the (Post) Covid-19 Era","authors":"Xin Li, Juhee Kwon, B. Padmanabhan, Pengzhu Zhang","doi":"10.1145/3627995","DOIUrl":"https://doi.org/10.1145/3627995","url":null,"abstract":"","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138584385","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}
Knowledge graphs can be used to enhance text search and access by augmenting textual content with relevant background knowledge. While many large knowledge graphs are available, using them to make semantic connections between entities mentioned in the textual content remains to be a difficult task. In this work, we therefore introduce contextual path generation (CPG) which refers to the task of generating knowledge paths, contextual path , to explain the semantic connections between entities mentioned in textual documents with given knowledge graph. To perform CPG task well, one has to address its three challenges, namely path relevance, incomplete knowledge graph, and path well-formedness. This paper designs a two-stage framework the comprising of the following: (1) a knowledge-enabled embedding matching and learning-to-rank with multi-head self attention context extractor to determine a set of context entities relevant to both the query entities and context document, and (2) a non-monotonic path generation method with pretrained transformer to generate high quality contextual paths. Our experiment results on two real-world datasets show that our best performing CPG model successfully recovers 84.13% of ground truth contextual paths, outperforming the context window baselines. Finally, we demonstrate that non-monotonic model generates more well-formed paths compared to the monotonic counterpart.
{"title":"Non-Monotonic Generation of Knowledge Paths for Context Understanding","authors":"Pei-Chi Lo, Ee-Peng Lim","doi":"10.1145/3627994","DOIUrl":"https://doi.org/10.1145/3627994","url":null,"abstract":"Knowledge graphs can be used to enhance text search and access by augmenting textual content with relevant background knowledge. While many large knowledge graphs are available, using them to make semantic connections between entities mentioned in the textual content remains to be a difficult task. In this work, we therefore introduce contextual path generation (CPG) which refers to the task of generating knowledge paths, contextual path , to explain the semantic connections between entities mentioned in textual documents with given knowledge graph. To perform CPG task well, one has to address its three challenges, namely path relevance, incomplete knowledge graph, and path well-formedness. This paper designs a two-stage framework the comprising of the following: (1) a knowledge-enabled embedding matching and learning-to-rank with multi-head self attention context extractor to determine a set of context entities relevant to both the query entities and context document, and (2) a non-monotonic path generation method with pretrained transformer to generate high quality contextual paths. Our experiment results on two real-world datasets show that our best performing CPG model successfully recovers 84.13% of ground truth contextual paths, outperforming the context window baselines. Finally, we demonstrate that non-monotonic model generates more well-formed paths compared to the monotonic counterpart.","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135618435","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}
Industrial Internet of Things (IIoT) networks (e.g., a smart grid industrial control system) are increasingly on the rise, especially in smart cities around the globe. They contribute to meeting the day-to-day needs (e.g., power, water, manufacturing, transportation) of the civilian society, alongside making societal businesses more efficient, productive, and profitable. However, it is also well known that IoT devices often operate on poorly configured security settings. This increases the chances of occurrence of (nation-sponsored) stealthy spread-based APT malware attacks in IIoT networks that might go undetected over a considerable period of time. Such attacks usually generate a negative first-party QoS impact with financial consequences for companies owning such IIoT network infrastructures. This impact spans (i.e., aggregates) space (i.e., the entire IIoT network or a sub-network) and time (i.e., duration of business disruption), and is a measure of significant interest to managers running their businesses atop such networks. It is of little use to network resilience boosting managers if they have to wait for a cyber-attack to happen to gauge this impact. Consequently, one of the questions that intrigues us is: can managers estimate this first-party impact prior to APT cyber-attack(s) causing financial damage to companies? In this paper, we propose the first computationally efficient and quantitative network theory framework to (a) characterize this first-party impact apriori as a statistical distribution over multiple attack configurations in a family of malware-driven APT cyber-attacks specifically launched on businesses running atop IIoT networks, (b) accurately compute the statistical moments (e.g., mean) of the resulting impact distribution, and (c) tightly bound the accuracy of worst-case risk estimate of such a distribution - captured through the tail of the distribution, using the Conditional Value at Risk (CVaR) metric. In relation to (a) above, our methodology extends the seminal Factor Analysis of Information Risk (FAIR) cyber-risk quantification methodology that does not explicitly account for network interconnections among system-risk contributing variables. We validate the effectiveness of our theory using trace-driven Monte Carlo simulations based upon test-bed experiments conducted in the FIT IoT-Lab. We further illustrate quantitatively that even if spread-based APT cyber-attacks induce a statistically light-tailed first-party cyber-loss distribution on an IIoT networked enterprise in the worst case, the aggregate multi-party cyber-risk distribution incurred by the same enterprise in supply-chain ecosystems could be heavy-tailed. This will pose significant market scale-up challenges to cyber-security improving commercial cyber (re-)insurance businesses. We subsequently propose managerial action items to mitigate the first-party cyber-risk exposure emanating from any given IIoT driven enterprise.
{"title":"How Should Enterprises Quantify and Analyze (Multi-Party) APT Cyber-Risk Exposure in their Industrial IoT Network?","authors":"Ranjan Pal, Rohan Xavier Sequeira, Xinlong Yin, Sander Zeijlemaker, Vineeth Kotala","doi":"10.1145/3605949","DOIUrl":"https://doi.org/10.1145/3605949","url":null,"abstract":"Industrial Internet of Things (IIoT) networks (e.g., a smart grid industrial control system) are increasingly on the rise, especially in smart cities around the globe. They contribute to meeting the day-to-day needs (e.g., power, water, manufacturing, transportation) of the civilian society, alongside making societal businesses more efficient, productive, and profitable. However, it is also well known that IoT devices often operate on poorly configured security settings. This increases the chances of occurrence of (nation-sponsored) stealthy spread-based APT malware attacks in IIoT networks that might go undetected over a considerable period of time. Such attacks usually generate a negative first-party QoS impact with financial consequences for companies owning such IIoT network infrastructures. This impact spans (i.e., aggregates) space (i.e., the entire IIoT network or a sub-network) and time (i.e., duration of business disruption), and is a measure of significant interest to managers running their businesses atop such networks. It is of little use to network resilience boosting managers if they have to wait for a cyber-attack to happen to gauge this impact. Consequently, one of the questions that intrigues us is: can managers estimate this first-party impact prior to APT cyber-attack(s) causing financial damage to companies? In this paper, we propose the first computationally efficient and quantitative network theory framework to (a) characterize this first-party impact apriori as a statistical distribution over multiple attack configurations in a family of malware-driven APT cyber-attacks specifically launched on businesses running atop IIoT networks, (b) accurately compute the statistical moments (e.g., mean) of the resulting impact distribution, and (c) tightly bound the accuracy of worst-case risk estimate of such a distribution - captured through the tail of the distribution, using the Conditional Value at Risk (CVaR) metric. In relation to (a) above, our methodology extends the seminal Factor Analysis of Information Risk (FAIR) cyber-risk quantification methodology that does not explicitly account for network interconnections among system-risk contributing variables. We validate the effectiveness of our theory using trace-driven Monte Carlo simulations based upon test-bed experiments conducted in the FIT IoT-Lab. We further illustrate quantitatively that even if spread-based APT cyber-attacks induce a statistically light-tailed first-party cyber-loss distribution on an IIoT networked enterprise in the worst case, the aggregate multi-party cyber-risk distribution incurred by the same enterprise in supply-chain ecosystems could be heavy-tailed. This will pose significant market scale-up challenges to cyber-security improving commercial cyber (re-)insurance businesses. We subsequently propose managerial action items to mitigate the first-party cyber-risk exposure emanating from any given IIoT driven enterprise.","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135729296","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}
Giovanni Pilato, Fabio Persia, Mouzhi Ge, Theodoros Chondrogiannis, D. D’Auria
Orienteering or itinerary planning algorithms in tourism are used to optimize travel routes by considering user preference and other constraints, such as time budget or traffic conditions. For these algorithms, it is essential to explore the user preference to predict potential Points-of-Interest (POIs) or tourist routes. However, nowadays, user preference has been significantly affected by COVID-19 since health concern plays a key trade-off role. For example, people may try to avoid crowdedness, even if there is a strong desire for social interaction. Thus, the orienteering or itinerary planning algorithms should optimize routes beyond user preference. Therefore, this paper proposes a social sensing system that considers the trade-off between user preference and various factors, such as crowdedness, personality, knowledge of COVID-19, POI features, and desire for socialization. The experiments are conducted on profiling user interests with a properly trained fastText neural network and a set of specialized Naïve Bayesian Classifiers based on the “Yelp!” data set. Also, we demonstrate how to approach and integrate COVID-related factors via conversational agents. Furthermore, the proposed system is in a modular design and evaluated in a user study; thus, it can be efficiently adapted to different algorithms for COVID-19-aware itinerary planning.
{"title":"A Modular Social Sensing System for Personalized Orienteering in the COVID-19 Era","authors":"Giovanni Pilato, Fabio Persia, Mouzhi Ge, Theodoros Chondrogiannis, D. D’Auria","doi":"10.1145/3615359","DOIUrl":"https://doi.org/10.1145/3615359","url":null,"abstract":"Orienteering or itinerary planning algorithms in tourism are used to optimize travel routes by considering user preference and other constraints, such as time budget or traffic conditions. For these algorithms, it is essential to explore the user preference to predict potential Points-of-Interest (POIs) or tourist routes. However, nowadays, user preference has been significantly affected by COVID-19 since health concern plays a key trade-off role. For example, people may try to avoid crowdedness, even if there is a strong desire for social interaction. Thus, the orienteering or itinerary planning algorithms should optimize routes beyond user preference. Therefore, this paper proposes a social sensing system that considers the trade-off between user preference and various factors, such as crowdedness, personality, knowledge of COVID-19, POI features, and desire for socialization. The experiments are conducted on profiling user interests with a properly trained fastText neural network and a set of specialized Naïve Bayesian Classifiers based on the “Yelp!” data set. Also, we demonstrate how to approach and integrate COVID-related factors via conversational agents. Furthermore, the proposed system is in a modular design and evaluated in a user study; thus, it can be efficiently adapted to different algorithms for COVID-19-aware itinerary planning.","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47914057","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}
Dazhong Shen, Hengshu Zhu, Keli Xiao, Xi Zhang, Hui Xiong
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.
{"title":"Exploiting Connections among Personality, Job Position, and Work Behavior: Evidence from Joint Bayesian Learning","authors":"Dazhong Shen, Hengshu Zhu, Keli Xiao, Xi Zhang, Hui Xiong","doi":"10.1145/3607875","DOIUrl":"https://doi.org/10.1145/3607875","url":null,"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.","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46869714","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}
Environmental, social and governance (ESG) considerations play an increasingly important role due to the growing focus on sustainability globally. Entities, such as banks and investors, utilize ESG ratings of companies issued by specialized rating agencies to evaluate ESG risks of companies. The process of assigning ESG ratings by human analysts is however laborious and time intensive. Developing methods to predict ESG ratings could alleviate such challenges, allow ESG ratings to be generated in a more timely manner, cover more companies, and be more accessible. Most works study the effects of ESG ratings on target variables such as stock prices or financial fundamentals of companies, but few works study how different types of company information can be utilized to predict ESG ratings. Previous works also largely focus on using only the financial information of individual companies to predict ESG ratings, leaving out the different types of inter-company relationship networks. Such inter-company relationship networks are typically dynamic, i.e., they evolve across time. In this paper, we focus on utilizing dynamic inter-company relationships for ESG ratings prediction, and examine the relative importance of different financial and dynamic network information in this prediction task. Our analysis shows that utilizing dynamic inter-company network information, based on common director, common investor and news event-based knowledge graph relationships, can significantly improve ESG rating prediction performance. Robustness checks over different time-periods and different number of time-steps in the future further validate these insights.
{"title":"On Predicting ESG Ratings Using Dynamic Company Networks","authors":"Gary (Ming) Ang, Zhiling Guo, E. Lim","doi":"10.1145/3607874","DOIUrl":"https://doi.org/10.1145/3607874","url":null,"abstract":"Environmental, social and governance (ESG) considerations play an increasingly important role due to the growing focus on sustainability globally. Entities, such as banks and investors, utilize ESG ratings of companies issued by specialized rating agencies to evaluate ESG risks of companies. The process of assigning ESG ratings by human analysts is however laborious and time intensive. Developing methods to predict ESG ratings could alleviate such challenges, allow ESG ratings to be generated in a more timely manner, cover more companies, and be more accessible. Most works study the effects of ESG ratings on target variables such as stock prices or financial fundamentals of companies, but few works study how different types of company information can be utilized to predict ESG ratings. Previous works also largely focus on using only the financial information of individual companies to predict ESG ratings, leaving out the different types of inter-company relationship networks. Such inter-company relationship networks are typically dynamic, i.e., they evolve across time. In this paper, we focus on utilizing dynamic inter-company relationships for ESG ratings prediction, and examine the relative importance of different financial and dynamic network information in this prediction task. Our analysis shows that utilizing dynamic inter-company network information, based on common director, common investor and news event-based knowledge graph relationships, can significantly improve ESG rating prediction performance. Robustness checks over different time-periods and different number of time-steps in the future further validate these insights.","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47044767","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 COVID-19 pandemic instigated the rapid shift to remote work and virtual interactions, constituting a new normal of professional interaction over information and communication technologies (ICT), such as videoconferencing platforms, email, and mobile devices. While ICT may provide many benefits for remote work, such as flexibility, reductions in travel time, and geographical interaction, ICT may also contribute to increases in job strain, reductions in social interactions, and the decline of mental health. While reliance on ICT for remote work interactions is becoming the new normal of organizational activity, scholarly appreciation of the stages in which employees, particularly educators, enact interactions over ICT is limited. Further, the intensity of the pandemic is unlike anything management scholars have studied before, and previous research into ICT use offers little insight into how ICT behaviours evolve over time. Our research explores how educators enact ICT interactions with students throughout the first two waves of the COVID-19 pandemic. We conducted 24 open-ended interviews with educators to learn about their experiences shifting to ICT for virtual classes. We found that ICT interactions between educators and students are enacted through two sequential, interrelated stages: divergent interaction behaviours and convergent interaction behaviours, with each stage corresponding to the first and second waves of the pandemic. We delineate the phases within each enacting stage of ICT interaction, and conjecture that future use of ICT may include iterative cycles of divergent and convergent interaction behaviours, particularly if educators explore how to leverage ICT in more creative ways. Our research presents a theorization of ICT interaction as an increasingly prevalent form of IT-enabled educational interactions and communication. We provide insight into how educators successfully shifted to ICT use for remote work, and offer implications for the facilitation of hybrid work arrangements in educational settings using ICT.
{"title":"ICT Interactions and COVID-19 – A Theorization Across Two Pandemic Waves","authors":"Jayson Killoran, Tracy A. Jenkin, J. Manseau","doi":"10.1145/3597938","DOIUrl":"https://doi.org/10.1145/3597938","url":null,"abstract":"The COVID-19 pandemic instigated the rapid shift to remote work and virtual interactions, constituting a new normal of professional interaction over information and communication technologies (ICT), such as videoconferencing platforms, email, and mobile devices. While ICT may provide many benefits for remote work, such as flexibility, reductions in travel time, and geographical interaction, ICT may also contribute to increases in job strain, reductions in social interactions, and the decline of mental health. While reliance on ICT for remote work interactions is becoming the new normal of organizational activity, scholarly appreciation of the stages in which employees, particularly educators, enact interactions over ICT is limited. Further, the intensity of the pandemic is unlike anything management scholars have studied before, and previous research into ICT use offers little insight into how ICT behaviours evolve over time. Our research explores how educators enact ICT interactions with students throughout the first two waves of the COVID-19 pandemic. We conducted 24 open-ended interviews with educators to learn about their experiences shifting to ICT for virtual classes. We found that ICT interactions between educators and students are enacted through two sequential, interrelated stages: divergent interaction behaviours and convergent interaction behaviours, with each stage corresponding to the first and second waves of the pandemic. We delineate the phases within each enacting stage of ICT interaction, and conjecture that future use of ICT may include iterative cycles of divergent and convergent interaction behaviours, particularly if educators explore how to leverage ICT in more creative ways. Our research presents a theorization of ICT interaction as an increasingly prevalent form of IT-enabled educational interactions and communication. We provide insight into how educators successfully shifted to ICT use for remote work, and offer implications for the facilitation of hybrid work arrangements in educational settings using ICT.","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43803192","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}
Xiao Zeng, David Ji, D. Thadani, Boying Li, Xiaodie Pu, Zhao Cai, Patrick Y. K. Chau
With the uncertain trajectory of COVID-19 conditions worldwide, there lies the potential for emergencies to arise, abruptly yielding mass social and economic disruption. Gaining insight into how digital technologies may be leveraged for effective emergency response is therefore pertinent. Emergency-induced needs may prompt citizens to organize mutual aid initiatives where people give what they can and get what they need in response. An increasingly prominent technology used for emergency response, online collaboration tools (OCTs), enables the appropriate match between the supply of aid and its relevant demand in mutual aid initiatives by mediating information and interactions between participants. Through analysis of mutual aid cases during the 2022 Shanghai COVID-19 lockdown, this study elucidates the benefits OCTs provide through the lens of affordance theory and identifies five key affordances of OCTs for emergency mutual aid: persistent accessibility, iterative modifiability, structured consolidation and retrieval, multisynchronous participation, and multichannel broadcasting. We illustrate how such affordances are actualized and how their enabling features work across information flow processes, specifically highlighting benefits of software minimalism with implications for practitioners and future software design in emergency situations. This study contributes to the body of knowledge on OCTs and affordances by disentangling its role in emergency responses.
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