Measuring the Big Data Readiness of Developing Countries – Index Development and its Application to Africa

2023 | Information Systems Frontiers | Citations: 0

Authors: Joubert, Anke; Murawski, Matthias; Bick, Markus

Abstract: The use of big data promises to drive economic growth and development and can th ... Expand

Abstract: The use of big data promises to drive economic growth and development and can therefore be a value-adding factor, but compared to private or public organisations, the country level is rarely investigated, and that is even more evident for developing countries. Another topic hardly ever considered in the big data research field is ‘big data readiness’, which means the level of preparation and willingness to exploit big data. We address these shortcomings in the literature and focus on the big data readiness of developing countries. Thus, the first research question is: what components are required for an index measuring big data readiness, and how can such an index be designed? We use a design science approach to develop the “Big Data Readiness Index” (BDRI), which is then applied to all African countries to answer our second research question: how do African countries perform in terms of the BDRI? Our analysis yields country rankings that show relatively high BDRI scores for coastal countries, such as South Africa, Kenya and Namibia, and for islands, such as Mauritius. Related implications for both research and policy are discussed. Collapse

Topics: big data missing data open data research and development telephone

Methods: design science cluster analysis hierarchical clustering literature study design process

Monitoring Cognitive Load with 3D-Printed EEG Headphones

2022 | International Conference on Information Systems | Citations: 0

Authors: Knierim, Michael; Zimny, Christian; Seitz, Julia

Abstract: Real-world cognitive load monitoring promises to be an essential foundation for ... Expand

Abstract: Real-world cognitive load monitoring promises to be an essential foundation for positive adaptive information systems that foster knowledge workers' productivity and wellbeing. Towards this goal, our research combines established EEG load monitoring principles with low-cost, 3D-printed headphones. This provides a means for continuous, unobtrusive, and real-time cognitive load detection. Results from two experiments document strong relationships of cognitive load reports with EEG frequency bands (Theta and Alpha), both around the ears and on top of the head. While limitations with Theta band power sensitivity are observed, Alpha band modulations are robust across sessions, task repetitions, and three tasks. Furthermore, short setup durations are found, and only minimal influences of hairstyle or glasses on setup times and load relationships. With a discussion of the remaining challenges for more naturalistic studies, this article documents the system's future potential for load sensing during knowledge work. Collapse

Topics: knowledge work open source IS discipline usability computer hardware

Methods: experiment electroencephalography survey self reported survey parametric test

Theories: flow theory

“Don’t Neglect the User!” – Identifying Types of Human-Chatbot Interactions and their Associated Characteristics

2022 | Information Systems Frontiers | Citations: 0

Authors: Nguyen, Thai Ha; Waizenegger, Lena; Techatassanasoontorn, Angsana A.

Abstract: Interactions with conversational agents (CAs) become increasingly common in our ... Expand

Abstract: Interactions with conversational agents (CAs) become increasingly common in our daily life. While research on human-CA interactions provides insights into the role of CAs, the active role of users has been mostly neglected. We addressed this void by applying a thematic analysis approach and analysed 1000 interactions between a chatbot and customers of an energy provider. Informed by the concepts of social presence and social cues and using the abductive logic, we identified six human-chatbot interaction types that differ according to salient characteristics, including direction, social presence, social cues of customers and the chatbot and customer effort. We found that bi-directionality, a medium degree of social presence and selected social cues used by the chatbot and customers are associated with desirable outcomes in which customers mostly obtain requested information. The findings help us understand the nature of human-CA interactions in a customer service context and inform the design and evaluation of CAs. Collapse

Topics: social presence customer service problem solving customer satisfaction conversational agent

Methods: chatbot theory development qualitative content analysis qualitative coding experiment

Establishing a frame of reference for measuring disaster resilience

2021 | Decision Support Systems | Citations: 1

Authors: Zobel, Christopher W.; MacKenzie, Cameron A.; Baghersad, Milad; Li, Yuhong

Abstract: Due to the increasing occurrence of disruptions across our global society, it ha ... Expand

Abstract: Due to the increasing occurrence of disruptions across our global society, it has become critically important to understand the resilience of different socio-economic systems, i.e., to what extent those systems exhibit the ability both to resist a disruption and to recover from one once it occurs. In order to characterize this ability, however, one must be able to quantitatively measure the relative level of resilience that a given system displays in response to a disruptive event. Such a measurement should be easily understandable and straightforward to implement, but it should also utilize a consistent frame of reference so that one can properly compare the relative performance of different systems and assess the relative effectiveness of different resilience investments. With this in mind, this paper presents an improved approach for measuring system resilience that supports better decision making by providing both consistency and flexibility across different contexts. The theoretical basis for the approach is developed first, and then its advantages and limitations are illustrated in the context of several different practical examples. Collapse

Topics: business continuity planning hurricane decision making systems design IT productivity

Methods: simulation descriptive statistic trade-off analysis method multiple criteria decision making

Rethinking Infrastructure Resilience Assessment with Human Sentiment Reactions on Social Media in Disasters

2020 | HICSS | Citations: 2

Authors: Fan, Chao; Farahmend, Hamed; Mostafavi, Ali

Abstract: The objective of this study is to propose and test a theoretical framework which ... Expand

Abstract: The objective of this study is to propose and test a theoretical framework which integrates the human sentiment reactions on social media in disasters into infrastructure resilience assessment. Infrastructure resilience assessment is important for reducing adverse consequences of infrastructure failures and promoting human well-being in natural disasters. Integrating societal impacts of infrastructure disruptions can enable a better understanding of infrastructure performance in disasters and human capacities under the stress of disruptions. However, the consideration of societal impacts of infrastructure disruptions is limited in existing studies for infrastructure resilience assessment. That is because an integrative theoretical framework for connecting the societal impacts to infrastructure resilience is missing. This study proposed a theoretical framework to examine the relationship between the societal impacts and infrastructure performance in disasters using social media data. Sentiments of human messages related to infrastructure systems are adopted as an indicator of societal impacts of infrastructure disruptions. A case study for electricity and transportation systems in Houston during the 2017 Hurricane Harvey was conducted to illustrate the application of the proposed framework. We find a relation between human sentiment and infrastructure status and validate it by comparing situational information from relevant tweets with official public sources. The findings enable a better understanding of societal expectations and collective sentiments regarding the infrastructure disruptions and improve the visibility of infrastructure performance in Smart City Digital Twin in the context of disasters. Collapse

Topics: Twitter hurricane social media power outage societal evaluation criteria

Methods: sentiment analysis theory development case study survey term frequency–inverse document frequency

Theories: social network theory

Assessing the Impact of a GIS for Improving Novice Crisis Decision-Making

2019 | Americas Conference on Information Systems | Citations: 0

Authors: Albina, Adam

Abstract: An increase in organizational emergencies warrants investigation into emergency ... Expand

Abstract: An increase in organizational emergencies warrants investigation into emergency management information systems that serve a novice crisis decision-maker. Small campus-based enterprises such as higher education, and corporate campuses, with no dedicated emergency management offices generally press novice decision-makers into emergency management roles. An investigation was conducted to assess the impact of an emergency management geographic information system (EMGIS) on the decision performance of novice crisis decision-makers through the use of a scenario-based simulation. A mixed method design was used to collect quasi-experimental data on decision time, decision accuracy and situational awareness. Qualitative analysis was also conducted. Statistical results indicated that decision accuracy is positively affected by the use of an EMGIS. Data Envelopment Analysis indicated that efficiency ratios of the EMGIS group outperform the traditional group. Geographic information systems hold much promise in providing systems that are easy to use, promote heightened levels of situational awareness and decision support. Collapse

Topics: emergency management decision making geographic information system situational awareness missing data

Methods: parametric test Student's t-test simulation experiment F-test

Expanding Awareness: Comparing Location, Keyword, and Network Filtering Methods to Collect Hyperlocal Social Media Data

2019 | HICSS | Citations: 0

Authors: Grace, Rob; Halse, Shane; Aurite, William; Tapia, Andrea

Abstract: Opportunities to collect real-time social media data during a crisis remain limi ... Expand

Abstract: Opportunities to collect real-time social media data during a crisis remain limited to location and keyword filtering despite the sparsity of geographic metadata and the tendency of keyword-based methods to capture information posted by remote rather than local users. Here we introduce a third, network filtering method that uses social network ties to infer the location of social media users in a geographic community and collect data from networks of these users during a crisis. In this paper we compare all three methods by analysing the distribution of situational reports of infrastructure damage and service disruption across location, keyword, and network-filtered social media data during a weather emergency. We find that network filtering doubles the number of situational reports collected in real-time compared to location and keyword filtering alone, but that all three methods collect unique reports that can support situational awareness of incidents occurring across a community. Collapse

Topics: Twitter social media situational awareness geographic information system social network

Methods: qualitative coding parametric test one-way ANOVA qualitative content analysis constant comparative analysis

CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing

2018 | Information Systems Frontiers | Citations: 9

Authors: Avvenuti, Marco; Cresci, Stefano; Del Vigna, Fabio; Fagni, Tiziano; Tesconi, Maurizio

Abstract: Natural disasters, as well as human-made disasters, can have a deep impact on wi ... Expand

Abstract: Natural disasters, as well as human-made disasters, can have a deep impact on wide geographic areas, and emergency responders can benefit from the early estimation of emergency consequences. This work presents CrisMap, a Big Data crisis mapping system capable of quickly collecting and analyzing social media data. CrisMap extracts potential crisis-related actionable information from tweets by adopting a classification technique based on word embeddings and by exploiting a combination of readily-available semantic annotators to geoparse tweets. The enriched tweets are then visualized in customizable, Web-based dashboards, also leveraging ad-hoc quantitative visualizations like choropleth maps. The maps produced by our system help to estimate the impact of the emergency in its early phases, to identify areas that have been severely struck, and to acquire a greater situational awareness. We extensively benchmark the performance of our system on two Italian natural disasters by validating our maps against authoritative data. Finally, we perform a qualitative case-study on a recent devastating earthquake occurred in Central Italy. Collapse

Topics: Twitter earthquake Wikipedia natural hazard big data

Methods: natural language processing word embedding case study support vector machine experiment

Using Social Sensors for Detecting Emergency Events: A Case of Power Outages in the Electrical Utility Industry

2017 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Bauman, Konstantin; Tuzhilin, Alexander; Zaczynski, Ryan

Abstract: This article presents a novel approach to detecting emergency events, such as po ... Expand

Abstract: This article presents a novel approach to detecting emergency events, such as power outages, that utilizes social media users as “social sensors” for virtual detection of such events. The proposed new method is based on the analysis of the Twitter data that leads to the detection of Twitter discussions about these emergency events. The method described in the article was implemented and deployed by one of the vendors in the context of detecting power outages as a part of their comprehensive social engagement platform. It was also field tested on Twitter users in an industrial setting and performed well during these tests. Collapse

Topics: Twitter power outage database system earthquake social media

Methods: machine learning random forest classification logistic regression naïve bayes topic model

Introduction to WITS 2015 Special Issue in TMIS

2017 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Bhattacharjee, Sudip; Jacob, Varghese; Jiang, Zhengrui (Jeffrey); Kumar, Subodha

Abstract: ... Expand

Abstract: Collapse

Topics: Twitter power outage microblog task productivity decision making

Methods: design principle computational algorithm machine learning method experiment