Digital Interoperability: In-Situ Adaptation in Natural Crisis Responses

2023 | International Conference on Information Systems | Citations: 0

Authors: Sandhyaduhita, Puspa; Tan, Chuan Hoo; Sutanto, Juliana

Abstract: Essential to effective and efficient collaboration among natural crisis respond ... Expand

Abstract: Essential to effective and efficient collaboration among natural crisis responders is the fast and ample crisis-related data, including victims' data (e.g., their locations and needs). However, the reality often falls short of it because the responders deployed by different crisis response agencies using various digital forms result in an incomplete and delayed view of crisis information. Attempts to enforce a unified digital form proposed by a leading agency or the pre-developed one co-created by several agencies had been made but failed. This research, based mainly on qualitative data collected, proposes an adaptability-by-bricolage approach of a digital interoperability solution for immediate digital collaboration among crisis responders during the crisis response. Preliminary findings that holistically define the solution are discussed. Collapse

Topics: sensemaking user experience computer supported cooperative work logistics management WhatsApp

Methods: qualitative interview design science design artifact constant comparative analysis personal interview

Theories: theory of affordance

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

2021 | Information Systems Frontiers | Citations: 5

Authors: Choudrie, Jyoti; Patil, Shruti; Kotecha, Ketan; Matta, Nikhil; Pappas, Ilias

Abstract: The pandemic COVID 19 has altered individuals’ daily lives across the globe. It ... Expand

Abstract: The pandemic COVID 19 has altered individuals’ daily lives across the globe. It has led to preventive measures such as physical distancing to be imposed on individuals and led to terms such as ‘lockdown,’ ‘emergency,’ or curfew’ to emerge in various countries. It has affected society, not only physically and financially, but in terms of emotional wellbeing as well. This distress in the human emotional quotient results from multiple factors such as financial implications, family member’s behavior and support, country-specific lockdown protocols, media influence, or fear of the pandemic. For efficient pandemic management, there is a need to understand the emotional variations among individuals, as this will provide insights into public sentiment towards various government pandemic management policies. From our investigations, it was found that individuals have increasingly used different microblogging platforms such as Twitter to remain connected and express their feelings and concerns during the pandemic. However, research in the area of expressed emotional wellbeing during COVID 19 is still growing, which motivated this team to form the aim: To identify, explore and understand globally the emotions expressed during the earlier months of the pandemic COVID 19 by utilizing Deep Learning and Natural language Processing (NLP). For the data collection, over 2 million tweets during February–June 2020 were collected and analyzed using an advanced deep learning technique of Transfer Learning and Robustly Optimized BERT Pretraining Approach (RoBERTa). A Reddit-based standard Emotion Dataset by Crowdflower was utilized for transfer learning. Using RoBERTa and the collated Twitter dataset, a multi-class emotion classifier system was formed. With the implemented methodology, a tweet classification accuracy of 80.33% and an average MCC score of 0.78 was achieved, improving the existing AI-based emotion classification methods. This study explains the novel application of the Roberta model during the pandemic that provided insights into changing emotional wellbeing over time of various citizens worldwide. It also offers novelty for data mining and analytics during this challenging, pandemic era. These insights can be beneficial for formulating effective pandemic management strategies and devising a novel, predictive strategy for the emotional well-being of an entire country’s citizens when facing future unexpected exogenous shocks. Collapse

Topics: Twitter pandemic covid 19 artificial intelligence Facebook

Methods: RoBERTa BERT machine learning deep learning natural language processing

Pandemic Containment With Digital Measures: Acceptance And Privacy Aspects Of Contact Tracing Apps

2021 | European Conference On Information Systems | Citations: 0

Authors: Reith, Riccardo; Fortagne, Marius Arved; Diel, Soeren; Buck, Christoph; Lis, Bettina; Eymann, Torsten

Abstract: In the unique context of an ongoing pandemic, this study aims to investigate th ... Expand

Abstract: In the unique context of an ongoing pandemic, this study aims to investigate the acceptance of contact tracing applications (CTA) proposed by the German government before its release in June 2020. The study develops and validates a research model based on established acceptance, privacy-, and healthrelated theories. The results of the structural equation model (N=584) indicate that performance expectancy, social influence, privacy concerns, and anxiety explain about 75% of the variance of the intention to use CTA. Most remarkably, privacy concerns represent the dominating barrier against using CTA, whereas anxiety could be confirmed as a pandemic-related driver of CTA use. In contrast to the established variables of UTAUT, effort expectancy did not reveal a significant impact on CTA acceptance. Consequently, the results both strengthen and contradict established theory, offering valuable insights for research and institutions in charge of distributing the app. Collapse

Topics: information privacy concern usage intention privacy pandemic social influence

Methods: survey chi squared test theory development archival research structural equation modeling

Theories: unified theory of acceptance and use of technology theory of planned behavior information systems theory privacy calculus theory dual-system theory

Development and Evaluation of Regional Information Sharing System (RISS) for Disaster Risk Reduction

2021 | Information Systems Frontiers | Citations: 1

Authors: Sasaki, Jun; Kitsuya, Makoto

Abstract: In Japan, there are many flood and tsunami disasters caused by typhoons, heavy r ... Expand

Abstract: In Japan, there are many flood and tsunami disasters caused by typhoons, heavy rains and earthquakes. In this case, the residents have an evacuation time to evacuate to shelters after evacuation alerts from a governmental office. However, some people don’t try to evacuate soon. As a result, it is problem that some victims of disasters are caused because without or late evacuation and supporting. In order to prevent such a damage, we developed Regional Information Sharing System (RISS) for disaster risk reduction. The RISS includes daily required life information from regional government and regional shops, as well as disaster information, shelters’ condition and the information of support required people and support teams. In this paper, we introduce the concept of RISS and its evaluation results on a field test and the effects in a regional disaster case by multi-agent simulation. Collapse

Topics: digital divide information exchange smartphone tsunami IT skill

Methods: experiment simulation agent based simulation case study design artifact

Exploring the role of deep neural networks for post-disaster decision support

2020 | Decision Support Systems | Citations: 0

Authors: Chaudhuri, Neha; Bose, Indranil

Abstract: Disaster management operations are information intensive activities due to high ... Expand

Abstract: Disaster management operations are information intensive activities due to high uncertainty and complex information needs. Emergency response planners need to effectively plan response activities with limited resources and assign rescue teams to specific disaster sites with high probability of survivors swiftly. Decision making becomes tougher since the limited information available is heterogenous, untimely and often fragmented. We address the problem of lack of insightful information of the disaster sites by utilizing image data obtained from smart infrastructures. We collect geo-tagged images from earthquake-hit regions and apply deep learning method for classification of these images to identify survivors in debris. We find that deep learning method is able to classify the images with significantly higher accuracy than the conventionally used machine learning methods for image classification and utilizes significantly lesser time and computational resources. The novel application of image analytics and the resultant findings from our models have valuable implications for effective disaster response operations, especially in smart urban settlements. Collapse

Topics: earthquake emergency management natural hazard database system decision support system

Methods: support vector machine machine learning deep learning literature study robustness check

Discovering event episodes from sequences of online news articles: A time-adjoining frequent itemset-based clustering method

2020 | Information & Management | Citations: 0

Authors: Lee, Yen-Hsien; Hu, Paul Jen-Hwa; Zhu, Hongquan; Chen, Hsin-Wei

Abstract: Firms perform environmental surveillance to identify important events and their ... Expand

Abstract: Firms perform environmental surveillance to identify important events and their developments. To alleviate the stringent information processing and analysis requirements, automated methods are needed to discover from online news articles distinct episodes (stages) of an important event. We propose a time-adjoining frequent itemset-based method that incorporates essential temporal characteristics of news articles for event episode discovery. With a corpus of 1468 news articles that pertain to 248 episodes of 53 different events, we empirically evaluate the proposed method and include several prevalent techniques as benchmarks. The results show that our method outperforms the benchmark techniques consistently and significantly, attaining the cluster recall, cluster precision, and F-measure values at 0.706, 0.593, and 0.584, respectively. Collapse

Topics: digital news hurricane earthquake armed conflict tsunami

Methods: cluster analysis machine learning nonparametric test Wilcoxon signed-rank test parametric test

Expecting the Unexpected: Effects of Data Collection Design Choices on the Quality of Crowdsourced User-Generated Content

2019 | Management Information Systems Quarterly | Citations: 0

Authors: Lukyanenko, Roman; Parsons, Jeffrey; Wiersma, Yolanda F.; Maddah, Mahed

Abstract: As crowdsourced user-generated content becomes an important source of data for o ... Expand

Abstract: As crowdsourced user-generated content becomes an important source of data for organizations, a pressing question is how to ensure that data contributed by ordinary people outside of traditional organizational boundaries is of suitable quality to be useful for both known and unanticipated purposes. This research examines the impact of different information quality management strategies, and corresponding data collection design choices, on key dimensions of information quality in crowdsourced user-generated content. We conceptualize a contributor-centric information quality management approach focusing on instance-based data collection. We contrast it with the traditional consumer-centric fitness-for-use conceptualization of information quality that emphasizes class-based data collection. We present laboratory and field experiments conducted in a citizen science domain that demonstrate trade-offs between the quality dimensions of accuracy, completeness (including discoveries), and precision between the two information management approaches and their corresponding data collection designs. Specifically, we show that instance-based data collection results in higher accuracy, dataset completeness, and number of discoveries, but this comes at the expense of lower precision. We further validate the practical value of the instance-based approach by conducting an applicability check with potential data consumers (scientists, in our context of citizen science). In a follow-up study, we show, using human experts and supervised machine learning techniques, that substantial precision gains on instance-based data can be achieved with post-processing. We conclude by discussing the benefits and limitations of different information quality and data collection design choices for information quality in crowdsourced user-generated content. Collapse

Topics: citizen science crowdsourcing data quality website systems design

Methods: machine learning experiment qualitative interview applicability check design science

Theories: cognitive theory

Content features of tweets for effective communication during disasters: A media synchronicity theory perspective

2019 | International Journal of Information Management | Citations: 20

Authors: Son, Jaebong; Lee, Hyung Koo; Jin, Sung; Lee, Jintae

Abstract: Users’ ability to retweet information has made Twitter one of the most prominent ... Expand

Abstract: Users’ ability to retweet information has made Twitter one of the most prominent social media platforms for disseminating emergency information during disasters. However, few studies have examined how Twitter’s features can support the different communication patterns that occur during different phases of disaster events. Based on the literature of disaster communication and Media Synchronicity Theory, we identify distinct disaster phases and the two communication types—crisis communication and risk communication—that occur during those phases. We investigate how Twitter’s representational features, including words, URLs, hashtags, and hashtag importance, influence the average retweet time—that is, the average time it takes for retweet to occur—as well as how such effects differ depending on the type of disaster communication. Our analysis of tweets from the 2013 Colorado floods found that adding more URLs to tweets increases the average retweet time more in riskrelated tweets than it does in crisis-related tweets. Further, including key disaster-related hashtags in tweets contributed to faster retweets in crisis-related tweets than in risk-related tweets. Our findings suggest that the influence of Twitter’s media capabilities on rapid tweet propagation during disasters may differ based on the communication processes. Collapse

Topics: Twitter situational awareness accounting sensemaking tsunami

Methods: topic model statistical hypothesis test natural language processing regression analysis method descriptive statistic

Theories: media synchronicity theory

Technology as Routine Capability

2019 | Management Information Systems Quarterly | Citations: 37

Authors: Swanson, E. Burton

Abstract: While technology is most commonly associated with material things, tools, or art ... Expand

Abstract: While technology is most commonly associated with material things, tools, or artifacts, it is also associated as a concept with routines, patterns of action that provide capabilities. Researchers have struggled to bring these interpretations together. Drawing from Schatzki's practice theory, we offer an overarching perspective that ties tools as devices to routines in a broader social context, yielding insights into what is termed technology as routine capability in the advancement of practices. How change in technology occurs both within and among practices is examined from this perspective. Four principal modes of change are identified: (1) design, in creating new devices and routines; (2) execution, in operating devices and performing routines; (3) diffusion, in spreading devices and routines to a population's members; and (4) shift, in adapting devices and routines to change among a world's practices. Change is seen to be closely intertwined among the modes, suggesting that future research examine cross-modal change, in particular, to gain a better understanding of how new technology advances practices. Overall, our new perspective provides a lens that ties together previous strands of research, allowing insights from multiple studies to accumulate in a way that both illuminates and motivates further work. Enlarging on current interpretations, it suggests that routines are integral to technology itself. Collapse

Topics: technological change enterprise resource planning online learning system tsunami telephone

Methods: theory development case study design science mixed method

Theories: social practice theory theory of affordance

Managing the e-commerce disruption with IT-based innovations: Insights from strategic renewal perspectives

2019 | Information & Management | Citations: 0

Authors: Xiao, Jinghua; Wu, Yao; Xie, Kang; Hu, Qing

Abstract: Transforming brick-and-mortar businesses into integrated online–offline models p ... Expand

Abstract: Transforming brick-and-mortar businesses into integrated online–offline models poses significant challenges for firms operationally and strategically. We conduct a comparative case study to investigate strategic renewals that result in the convergence of offline and online operations and strategies driven by information technology based innovations. We explore two strategic renewal paths used by our case firms: incremental resource-complementing renewal and discontinuous resource-substituting transformation. We illuminate the role of resource endowment in strategic renewal and its impact on the renewal path and explore the role of IT as operand or operant resource in strategic renewals and its impact on their success. Collapse

Topics: electronic commerce innovation management information technology capability disruptive innovation authorization

Methods: case study multiple case study qualitative interview qualitative coding theoretical contribution