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From Dissonance to Dialogue: A Token-Based Approach to Bridge the Gap Between Manufacturers and Customers 从分歧到对话:基于令牌的方法弥合制造商与客户之间的差距
IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-01-04 DOI: 10.1145/3639058
Norman Pytel, Christian Ziegler, Axel Winkelmann
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.
本文介绍了一种基于代币的新型召回沟通系统,该系统集成了企业资源规划(ERP)系统和区块链技术,以加强制造商和客户之间的召回沟通与合作。我们采用了设计科学研究方法,开发了一套支持互操作系统的设计原则和功能。我们的研究结果表明,我们可以显著改善召回协调、可追溯性以及相关方之间的共同价值创造。通过关注 ERP 系统等传统技术与区块链和代币技术的整合潜力,我们揭示了社会和技术子系统的创新协同效应。本研究探讨了拟议系统对理论和实践的影响,对这种整合的优势和挑战提出了见解。与行业专家进行的评估表明,设计原则具有很高的可重用性。
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引用次数: 0
A Process Mining Method for Inter-organizational Business Process Integration 组织间业务流程整合的流程挖掘方法
IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-12-19 DOI: 10.1145/3638062
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。
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引用次数: 0
Introduction to the Special Issue on IT-enabled Business Management and Decision Making in the (Post) Covid-19 Era 后)Covid-19 时代信息技术辅助的企业管理和决策特刊导言
IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-12-11 DOI: 10.1145/3627995
Xin Li, Juhee Kwon, B. Padmanabhan, Pengzhu Zhang
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引用次数: 0
Non-Monotonic Generation of Knowledge Paths for Context Understanding 上下文理解知识路径的非单调生成
Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-10-20 DOI: 10.1145/3627994
Pei-Chi Lo, Ee-Peng Lim
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.
知识图可以用来增强文本搜索和访问,通过增加文本内容与相关的背景知识。虽然有许多大型知识图可用,但使用它们在文本内容中提到的实体之间建立语义连接仍然是一项艰巨的任务。因此,在这项工作中,我们引入了上下文路径生成(CPG),它指的是生成知识路径,上下文路径,以解释具有给定知识图的文本文档中提到的实体之间的语义连接的任务。要很好地完成CPG任务,必须解决路径相关性、不完全知识图和路径格式良好性这三个挑战。本文设计了一个两阶段框架,包括:(1)基于知识的嵌入匹配和基于多头自关注上下文提取器的学习排序,以确定与查询实体和上下文文档相关的一组上下文实体;(2)基于预训练转换器的非单调路径生成方法,以生成高质量的上下文路径。我们在两个真实数据集上的实验结果表明,我们表现最好的CPG模型成功地恢复了84.13%的地面真实上下文路径,优于上下文窗口基线。最后,我们证明了非单调模型比单调模型产生更多的格式良好的路径。
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引用次数: 0
How Should Enterprises Quantify and Analyze (Multi-Party) APT Cyber-Risk Exposure in their Industrial IoT Network? 企业如何量化和分析其工业物联网网络中的(多方)APT网络风险暴露?
Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-10-19 DOI: 10.1145/3605949
Ranjan Pal, Rohan Xavier Sequeira, Xinlong Yin, Sander Zeijlemaker, Vineeth Kotala
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.
工业物联网(IIoT)网络(例如智能电网工业控制系统)正日益兴起,特别是在全球的智能城市中。它们有助于满足民间社会的日常需求(如电力、水、制造、交通),同时使社会企业更高效、更多产、更有利可图。然而,众所周知,物联网设备通常在配置不当的安全设置下运行。这增加了在工业物联网网络中发生(国家赞助的)基于传播的隐蔽APT恶意软件攻击的机会,这些攻击可能在相当长的一段时间内未被发现。此类攻击通常会对拥有此类IIoT网络基础设施的公司产生负面的第一方QoS影响和财务后果。这种影响跨越(即聚合)空间(即整个工业物联网网络或子网)和时间(即业务中断的持续时间),并且是在此类网络上运行业务的管理人员的重要利益衡量标准。如果管理者必须等到网络攻击发生后才衡量其影响,那么提高网络弹性对他们来说就没什么用了。因此,我们感兴趣的一个问题是:在APT网络攻击给公司造成财务损失之前,管理人员能否估计这种第一方影响?在本文中,我们提出了第一个计算高效和定量的网络理论框架,以(a)将这种第一方影响先验描述为专门针对运行在IIoT网络上的企业发起的恶意软件驱动的APT网络攻击系列中多个攻击配置的统计分布,(b)准确计算由此产生的影响分布的统计矩(例如,平均值)。(c)严格约束这种分布的最坏情况风险估计的准确性-通过分布的尾部捕获,使用条件风险值(CVaR)度量。关于上述(a),我们的方法扩展了开创性的信息风险因素分析(FAIR)网络风险量化方法,该方法没有明确考虑系统风险贡献变量之间的网络互连。我们使用基于FIT物联网实验室进行的试验台实验的轨迹驱动蒙特卡罗模拟验证了我们理论的有效性。我们进一步定量地说明,即使在最坏的情况下,基于传播的APT网络攻击导致IIoT联网企业的第一方网络损失分布在统计上是轻尾的,但同一企业在供应链生态系统中产生的多方网络风险分布可能是重尾的。这将对网络安全、改善商业网络(再)保险业务构成重大的市场规模挑战。随后,我们提出了管理行动项目,以减轻任何给定工业物联网驱动企业产生的第一方网络风险。
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引用次数: 0
A Modular Social Sensing System for Personalized Orienteering in the COVID-19 Era 面向新冠肺炎时代个性化定向运动的模块化社会传感系统
IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-09-06 DOI: 10.1145/3615359
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.
旅游中的定向或行程规划算法用于通过考虑用户偏好和其他约束(如时间预算或交通状况)来优化旅行路线。对于这些算法,探索用户偏好来预测潜在的兴趣点(poi)或旅游路线是至关重要的。然而,如今,用户偏好受到COVID-19的重大影响,因为健康问题起着关键的权衡作用。例如,人们可能会尽量避免拥挤,即使有强烈的社会互动的愿望。因此,定向或路线规划算法应该优化路线超越用户偏好。因此,本文提出了一种社会感知系统,该系统考虑了用户偏好与各种因素之间的权衡,如拥挤程度、个性、COVID-19知识、POI特征和社交欲望。实验使用经过适当训练的fastText神经网络和一组专门的Naïve贝叶斯分类器来分析用户兴趣。数据集。此外,我们还演示了如何通过会话代理处理和整合与covid相关的因素。此外,所提出的系统采用模块化设计,并在用户研究中进行评估;因此,它可以有效地适应不同的算法进行covid -19感知行程规划。
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引用次数: 0
Exploiting Connections among Personality, Job Position, and Work Behavior: Evidence from Joint Bayesian Learning 利用人格、工作岗位和工作行为之间的联系:来自联合贝叶斯学习的证据
IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-07-12 DOI: 10.1145/3607875
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.
性格一直被认为是工作投入的驱动因素,它显著影响人们在工作中的角色表现。虽然已有研究对人格特质与员工工作行为之间的关系提供了一些直观的认识,但仍然缺乏有效的量化工具来同时对人格特质、工作岗位特征和员工工作行为进行建模。为此,在本文中,我们引入了一种数据驱动的联合贝叶斯学习方法joint - pjb,从大量的个性和职位相关行为数据中发现可解释的联合模式。具体来说,Joint-PJB是在四象限行为模型DISC (Dominance, Influence, Steadiness, Conscientiousness)的知识指导下设计的。joint - pjb基于从一家高科技公司收集的真实数据,旨在从人格特征、工作职责和工作行为三个方面突出人格-工作-行为的联合模式。该模型可以根据员工的性格特征和工作岗位特征来衡量员工与其工作行为的匹配程度。我们发现这种匹配度与员工离职倾向之间存在显著的负相关。此外,我们还展示了如何利用已识别的模式来支持现实世界的人才管理决策。案例研究和定量实验都验证了联合pjb在理解不同工作情境下人格特质及其对工作行为的影响方面的有效性。
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引用次数: 0
On Predicting ESG Ratings Using Dynamic Company Networks 利用动态公司网络预测ESG评级
IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-07-08 DOI: 10.1145/3607874
Gary (Ming) Ang, Zhiling Guo, E. Lim
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.
由于全球对可持续性的日益关注,环境、社会和治理(ESG)考虑因素发挥着越来越重要的作用。银行和投资者等实体利用专门评级机构发布的公司ESG评级来评估公司的ESG风险。然而,人类分析师分配ESG评级的过程既费力又耗时。开发预测ESG评级的方法可以缓解这些挑战,使ESG评级能够更及时地生成,覆盖更多的公司,并且更容易获得。大多数工作研究ESG评级对目标变量(如公司股价或财务基本面)的影响,但很少有工作研究如何利用不同类型的公司信息来预测ESG评级。以前的工作也主要集中在只使用个别公司的财务信息来预测ESG评级,而忽略了不同类型的公司间关系网络。这种公司间关系网络通常是动态的,即它们随着时间的推移而演变。在本文中,我们专注于利用动态公司间关系进行ESG评级预测,并考察不同财务和动态网络信息在该预测任务中的相对重要性。我们的分析表明,利用基于共同董事、共同投资者和新闻事件的知识图关系的动态公司间网络信息,可以显著提高ESG评级预测性能。未来不同时间段和不同时间步数的稳健性检查进一步验证了这些见解。
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引用次数: 1
ICT Interactions and COVID-19 – A Theorization Across Two Pandemic Waves 信息通信技术互动与COVID-19——跨越两次大流行浪潮的理论分析
IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-05-23 DOI: 10.1145/3597938
Jayson Killoran, Tracy A. Jenkin, J. Manseau
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.
2019冠状病毒病大流行促使人们迅速转向远程工作和虚拟互动,形成了通过视频会议平台、电子邮件和移动设备等信息通信技术(ICT)进行专业互动的新常态。虽然信通技术可以为远程工作带来许多好处,例如灵活性、减少旅行时间和地理上的互动,但信通技术也可能导致工作压力增加、社会互动减少以及心理健康状况下降。虽然依赖信息通信技术进行远程工作互动正在成为组织活动的新常态,但对员工,特别是教育工作者,通过信息通信技术进行互动的各个阶段的学术评价是有限的。此外,大流行的强度不同于管理学者以前研究过的任何东西,以前对信息通信技术使用的研究几乎没有深入了解信息通信技术行为如何随时间演变。我们的研究探讨了在COVID-19大流行的前两波浪潮中,教育工作者如何与学生进行信息通信技术互动。我们对教育工作者进行了24次开放式访谈,以了解他们在虚拟课堂中转向ICT的经验。我们发现,教育者和学生之间的信息通信技术互动是通过两个连续的、相互关联的阶段制定的:发散的互动行为和趋同的互动行为,每个阶段对应于大流行的第一波和第二波。我们描述了ICT交互的每个制定阶段中的各个阶段,并推测ICT的未来使用可能包括发散和收敛交互行为的迭代循环,特别是如果教育者探索如何以更有创造性的方式利用ICT。我们的研究提出了一种理论,认为信息通信技术互动是一种日益普遍的信息通信技术教育互动和交流形式。我们深入了解了教育工作者如何成功地将信息通信技术用于远程工作,并提供了在使用信息通信技术的教育环境中促进混合工作安排的启示。
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引用次数: 0
Disentangling Affordances of Online Collaboration Tools for Mutual Aid in Emergencies: Insights from the COVID-19 Lockdown 解开紧急情况下互助在线协作工具的功能:来自COVID-19封锁的见解
IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-05-03 DOI: 10.1145/3593056
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.
由于COVID-19在世界范围内的发展轨迹不确定,有可能出现紧急情况,突然造成大规模的社会和经济混乱。因此,深入了解如何利用数字技术进行有效的应急反应是有意义的。紧急情况引起的需求可能促使公民组织互助行动,人们尽其所能,得到他们所需要的作为回应。用于应急反应的一项日益突出的技术是在线协作工具(oct),它通过协调参与者之间的信息和互动,使援助供应与其相关需求在互助倡议中得到适当匹配。本研究通过对2022年上海疫情封锁期间的互助案例分析,从功能可及性理论的角度阐述了应急互助的功能可及性,确定了应急互助的功能可及性主要有五个方面:持续可及性、迭代可修改性、结构化整合与检索性、多同步参与性和多通道广播性。我们说明了这些功能是如何实现的,以及它们的支持功能是如何跨信息流过程工作的,特别强调了软件极简主义的好处,以及在紧急情况下对从业者和未来软件设计的影响。本研究通过理清oct在应急响应中的作用,为oct及其能力提供了知识体系。
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引用次数: 0
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ACM Transactions on Management Information Systems
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