Collaborative Analytics and Brokering in Digital Humanitarian Response

Hellmann, Daniel; Maitland, Carleen; Tapia, Andrea H.

doi:10.1145/2818048.2820067

Cited by 19 publications

(11 citation statements)

References 32 publications

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“…Related to above, more distantly-related CSCW studies of data scientists working with open government data for social good [5] and crisis-response scenarios [21] also reveal the importance of broker roles in establishing common ground between collaborators. However, those study settings are more decentralized, often rely on part-time and volunteer workers, and involve a vast array of stakeholders, in contrast to the tightly-focused relationship between a data scientist and their client that we study in this paper.…”

Section: How Data Scientists Work With Clientsmentioning

confidence: 83%

Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia

Kross

Guo

2021

Proc. ACM Hum.-Comput. Interact.

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Data scientists often collaborate with clients to analyze data to meet a client's needs. What does the end-to-end workflow of a data scientist's collaboration with clients look like throughout the lifetime of a project? To investigate this question, we interviewed ten data scientists (5 female, 4 male, 1 non-binary) in diverse roles across industry and academia. We discovered that they work with clients in a six-stage outer-loop workflow, which involves 1) laying groundwork by building trust before a project begins, 2) orienting to the constraints of the client's environment, 3) collaboratively framing the problem, 4) bridging the gap between data science and domain expertise, 5) the inner loop of technical data analysis work, 6) counseling to help clients emotionally cope with analysis results. This novel outer-loop workflow contributes to CSCW by expanding the notion of what collaboration means in data science beyond the widely-known inner-loop technical workflow stages of acquiring, cleaning, analyzing, modeling, and visualizing data. We conclude by discussing the implications of our findings for data science education, parallels to design work, and unmet needs for tool development.CCS Concepts: • Human-centered computing → Human computer interaction (HCI).

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Section: How Data Scientists Work With Clientsmentioning

confidence: 83%

Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia

Kross

Guo

2021

Proc. ACM Hum.-Comput. Interact.

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“…When non-governmental organizations (NGOs) coordinate with the law-enforcement agencies or with transporters, each has different objectives; a better coordination will help in achieving the goal more easily (Akhtar et al, 2012;Balcik et al, 2010). In the humanitarian supply chain, there are limited human and capital resources (Tomasini and Van Wassenhove, 2009), which leads to the uncertainties regarding the coordination (Hellmann et al, 2016). Therefore, the duplication of resources and services should be avoided by synchronizing the common objectives.…”

Section: 27mentioning

confidence: 99%

Developing and analyzing framework to manage resources in humanitarian logistics

Ahmed

Najmi

Khan

et al. 2019

JHLSCM

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Purpose Humanitarian services usually perform in the face of uncertainty in which mobilization of resources in an efficient and effective manner is a big challenge. Sharing timely and correct information among logistics partners and workers is a key to drive rapid response logistics effectively. The purpose of this paper is to understand how coordinated effort effects resources management (RM). Design/methodology/approach This study uses quantitative research methodology and collected data from 82 humanitarian workers dealing with logistical activities from a densely populated city of Pakistan. Data were then statistically analyzed through partial least squares–structural equation modeling. Findings The results suggest that the success of humanitarian supply network depends upon the level of trust among the partners, which accelerates commitment through strong coordination. Information sharing reduces behavioral uncertainty and enhances swift trust (ST). ST then helps to improve coordination and commitment from all stakeholders in order to manage resources to lead effective relief operations. Practical implications The study guides the practitioners and relief operations’ policy makers to lay emphasis on distributing right and timely information flow among the partners, which can lead to effective, efficient and swift humanitarian relief operations. Originality/value This study on RM during humanitarian logistics is well timed in the context of developing country with high uncertain events, improper infrastructure and very limited resources.

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“…Sarcevic et al [54] examined emergency medical response teams' Twitter usage in the immediate aftermath of the 2010 Haiti Earthquake to understand the coordination challenges faced by personnel. Hellmann et al [27] studied relief practitioners' collaborative data management and analysis during the Ebola crisis in West Africa in 2014. Social media expanded the information pathways between emergency management organizations and the public from one-way communication to two-way communication [31].…”

Section: Icts and Crisis Responsementioning

confidence: 99%

Managing Uncertainty

Gui

Kou

Pine

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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Recently, diseases like H1N1 influenza, Ebola, and Zika virus have created severe crises, requiring public resources and personal behavior adaptation. Crisis Informatics literature examines interconnections of people, organizations, and IT during crisis events. However, how people use technology to cope with disease crises (outbreaks, epidemics, and pandemics) remains understudied. We investigate how individuals used social media in response to the outbreak of Zika, focusing on travel-related decisions. We found that extreme uncertainty and ambiguity characterized the Zika virus crisis. To cope, people turned to social media for information gathering and social learning geared towards personal risk assessment and modifying decisions when dealing with partial and conflicting information about Zika. In particular, individuals sought local information and used socially informed logical reasoning to deduce the risk at a specific locale. We conclude with implications for designing information systems to support individual risk assessment and decision-making when faced with uncertainty and ambiguity during public health crises.

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Collaborative Analytics and Brokering in Digital Humanitarian Response

Cited by 19 publications

References 32 publications

Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia

Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia

Developing and analyzing framework to manage resources in humanitarian logistics

Managing Uncertainty

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