Developing resilient community through collaborative knowledge transaction: Some examples from disaster management
This section explores how community participation, in the context of disaster management, can be enhanced through collaborative knowledge transaction, which eventually will lead to development of resilient community. Taking a cue from the Engagement-Participation-Empowerment model (Steiner &c Farmer, 2017) indicating three stages in transferring power from external actors to local communities, we show that the process of community resilience starts with engagement (Section 5.2) and follows with participation (Section 5.3) — both representing a precondition of community empowerment, leading to a resilient community (Section 5.4). To begin with we explore the role of knowledge management in combating disaster situations.
The role of knowledge management in disaster situations: An introduction
Responses to any disaster situation involve extensive cross-organizational coordination among several agencies. Multiple government and non-government agencies, including local volunteers/field workers, have to respond in a coordinated manner to handle a disaster situation. Therefore there is a serious need for both intra- and inter-organization information and knowledge transactions at various stages. In order to carry out situation analysis of a disaster- stricken region, to determine the need for various types of resources and to ensure fair and efficient allocation and distribution of those resources, all the agencies have to depend on coordinated and collaborative information and knowledge transactions. Since the scope of managing disasters is unstructured in nature, this is difficult to control centrally. Hence there is a need for a decentralized and hierarchy-independent approach, where even the end users (i.e., the disaster victims) are empowered to take quick and effective action.
Disaster management is inherently a knowledge-intensive (Ciccio et al., 2014) and collaboration-heavy process, involving all public safety-related organizations and affected community members (Abobakr & Majed, 2017). Therefore, managing knowledge becomes crucial in ensuring accessibility and usability of accurate and reliable disaster-related information whenever required and taking actionable decisions based on that (Seneviratne et ah, 2010). Since knowledge in any disaster, both during and after the incident, is inherently fragmented, there is a huge problem in integrating this fragmented knowledge, which would, in turn, lead to inefficient coordination and sharing of resource (Mohanty et ah, 2006; Seneviratne et ah, 2010). According to UNESCO (2005), we do have plentiful knowledge on risk and vulnerability to hazards, but its access and utilization at the community level has yet to reach its full potential. During the Asian tsunami, for example, “the lack of knowledge management resulted in re-inventing the wheel in terms of setting up and managing construction programs and projects within the tsunami recovery operation” (Koria, 2009).
Knowledge management systems (KMS) have been used extensively by several government agencies to collect and distribute disaster-related information (Hassan, 2011). For example, in India the National Disaster Risk Management Programme of the Ministry of Home Affairs developed a KMS portal to connect several government departments and selected non-government agencies to share disaster-related knowledge and information. Highlighting the example of
Hurricane Katrina, Murphy and Jennex (2006) concluded that KMS should be integrated with all disaster management activities. Another example is the use of the information management system for hurricane disasters (IMASH) (Iakovou & Douligeris, 2001). However, in most of the cases, the centralized, noil-interactive and static nature of these KMS make them ineffective.
Use of knowledge management practice to enhance community resilience has not been widely explored by academics and practitioners (Rahman et al., 2017). The members of a resilient community need to have the capacity to not only use and share existing knowledge but also to generate dynamically new knowledge for communitarian benefits. This would strengthen the capacity of all the members of the community individually as well as the community as a whole (Twigg, 2009). This also enables use of local perspectives and initiatives that increase the capacity of the community as a whole to recover from an adverse situation (Aldrich Sc Meyer, 2015). Although the concept of community resilience to disaster is complex and based on multiple factors, one of the specific characteristics that builds up community resilience is a focus on knowledge and education (Twigg, 2009).
Community resilience, thus, more particularly refers to how local communities develop their capacity to cope with disaster by (i) reusing local resources and (ii) mobilizing external resources by getting connected to external agencies for collaborative knowledge transaction. As illustrated in Rahman et al. (2017), resilience is “communities’ capacity to adapt to, reduce, manage, and recover from the worst impacts of hazards by utilizing available resources through appropriate, actionable decisions in pre-disaster, disaster, and post-event
phases____ Communities’ knowledge, both existing within the communities and
acquired from outside agencies, is central to ensuring community resilience.”
Emergency response processes are unpredictable, unrepeatable, complex, time-critical, knowledge-intensive, unstructured and very dynamic (Kushnareva et al., 2015). Therefore emergency response processes must be scalable, flexible and adaptable enough to enable a collaborative response. Community-sourcing of real-time data (as opposed to crowd-sourcing) from the entities who are physically present in an affected area is inevitably much more accurate than any other passive source and is better for appropriate resource allocation and effective planning of response operation. We will discuss the issues of community participation in disaster knowledge management in subsequent sections.
Use of social media to engage community members during disaster for situational information and knowledge transaction
Several researchers in the emergency management field believe that using social media will help build community disaster resilience. For example, White (2012, p. 187) states that “community resilience should include a grassroots effort where social media is utilized in a number of ways to support the safety of the community.” Dufty (2012) promotes the use of social media by emergency agencies to assist in “learning for disaster resilient communities”. The objective of this section is to discuss prospects and limitations of social media in this context.
Social media promote public participation in emergency management through knowledge and information transactions over various social media channels, such as Twitter, Facebook, different blogs and discussion forums, etc. This exhibits a new form of information-sharing behaviour where social media users share information not only to request assistance but to offer assistance to others (Palen 8c Liu, 2007). Through social media, community members provide multimodal information that would contribute to information required for analysing crisis situations (Hughes 8c Palen, 2012).
Social media facilitate formation of online communities where members share information during emergency situations (Wang, 2010). Hurricane Katrina in 2005, for example, has shown the potential of blogs and online forums where displaced US citizens could connect with members of their communities (Macias et al., 2009; Palen Sc Liu, 2007). Torrey et al. (2007) found that several citizens used online means to coordinate distribution of relief materials to help others. There are also evidences where social media is used to find missing persons as well as housing for victims (Macias et al., 2009; Palen 8c Liu, 2007).
During a major crisis, users’ participation in social media with crisis-related data contributes significantly in conducting situation analysis (Cameron et al., 2012; Vieweg et al., 2010). For example, analysis of tweets sent during the Oklahoma City fires and Red River floods, both in 2009, helped to improve situational awareness (for example, fire locations and flood levels) (Vieweg et al., 2010). Researchers also have attempted to develop natural language processing (NLP) tools to analyse social media text to help identify social media posts that would assist in a disaster situation (Corvey et al., 2012). However, the technology is not yet mature enough to derive meaningful insights from these free-formatted, multi-modal social media posts. In fact, the use of social media has become so widespread that during a major crisis the huge amounts of social media posts generated are difficult to monitor and analyse (Castillo, 2016). For example, the University of Colorado Boulder collected over 26 million tweets during Flurricane Sandy (Anderson Sc Schram, 2011). With the available technology it is very difficult to consolidate and derive actionable insights from this large amount of socially-generated data available during an emergency (Palen Sc Anderson, 2016). Real-time collection, analysis and interpretation of multi-modal, multi-lingual social media posts during a crisis situation still poses a challenge to the research community.
Of course, various social media channels can be potential tools to engage community members in crisis preparedness, response and recovery, which may enhance community resilience (Belblidia, 2010; Dufty, 2012). But, because of the limitations stated above and the informal and unregulated nature of social media data with low information credibility, social media adoption in formal emergency response has lagged behind that of public uptake (Hughes Sc Palen, 2012). Furthermore, traditional social media platforms may not generate any useful benefits to members of disempowered/rural communities who are not used to them because of access and/or language barriers (Cinnamon Sc Schuur- man, 2012). However, it is true that social media can provide an engagement platform for the member of the communities to interact with each other and other agencies during emergency situations.
Interactive community-sourcing: A participatory knowledge management practice during disaster
As indicated earlier, efficient management of disaster requires accurate and timely information from the disaster location so that the situation can be analysed, community needs can be assessed and, accordingly, resources can be mobilized to mitigate the effect of disaster. Traditional methods of information collection by the agencies involved in disaster management are time consuming and inaccurate, as they rely on survey results conducted by deployed volunteers/ agencies. Because of various constraints — such as anonymity of contributors, journalistic narratives, subjectivity of information, etc. — other media-generated data, including from social media, cannot be relied on to supply accurate and authentic situational information that can enable proper need assessment.
In a study we conducted on the use of Twitter during the Uttarakhand flood disaster in northern parts of India in 2013 (North India floods, n.d.), a total of 2,921 tweets spanning 19 days (from 14th June, 2013, to 3rd July, 2013) were collected. After data filtering, a total of 2,390 unique users were identified, of which 2,086 tweeted only once. With this evidence that most Twitter users tweeted only once in the crisis situation, the implication is that users tweeted out of sentiment or impulse, and not specifically to help or pass on some useful information. Some 54% of tweets were found to be 'retweets’ and these were posting news-URLs collected from other media such as television, newspapers and other websites. This reveals another pattern of Twitter use — to communicate or circulate the latest news headlines among the Twitter communities. Very few tweets were found that could be examples of Twitter providing useful and authentic information. Typically, 47% tweets in this dataset contained anguished comments on government or political parties, which corrupts the usefulness of the tweets as sources of information and makes it difficult to identify important pieces of information in the dataset through analytics.
This is where community-generated credible and useful information plays a significant role. With the help of social technologies it is possible not only to engage affected community members but also to encourage their participation in providing authentic situational information from identifiable users to help manage disaster. This can be termed community-sourcing, as opposed to crowdsourcing.
The term “crowd-sourcing”, coined by Jeff Howe (Howe, 2006), describes how businesses can involve “an undefined (and generally large) network of people” to carry out some tasks through an open call. Crowd-sourcing can also be used to describe a practice of obtaining task-based information or input from large numbers of people, either paid or unpaid, and typically via the internet. For example, Chen et al. (2014) used an automated question-answer system for spontaneous reporting of adverse drug reactions (ADR) in a crowd- sourced manner. They showed that information crowd-sourcing is an efficient way to track and discover cases of ADR. In the context of disaster management, social media, as discussed in the previous sub-section, use a crowd-sourced model to collect citizen-generated information in the context of disaster. In this context, Ushahidi is an effective example of collating and analysing citizen-generated data from affected locations to generate a crisis map.
On the other hand, community-sourcing can be defined as a way of getting a task done through a defined group of people who share a common interest and belong to a community. In crowd-sourcing, users are anonymous and there is no need for any affiliation to be engaged. The objective is to involve “everyone” with a hope that “someone” will contribute. Contrary to this, in community- sourcing, members are part of a defined community and, hence, identifiable; and they have a common interest in contributing, which satisfies not only the individual’s need but also the need of the community. The community-sourcing model, therefore, is more targeted, sustainable and effective for all concerned.
Since participants in community-sourcing are identifiable, they can be encouraged to interact and to participate in the process in a more intimate manner. This interaction model can be termed as interactive community- sourcing. This is derived from the notion of interactive crowd-sourcing, where the users in a crowd-sourcing process are made to interact to fulfil the purpose of crowd-sourcing. For example, in the context of designing innovative products through crowd-sourcing using a co-creation model, the participants from the crowd are made to interact and work closely together from the selection of the idea through to the production and marketing of the innovation (Djelassi & Decoopman, 2016). Basil et al. (2016) in their visionary paper introduced an interactive crowd-sourcing system that includes the human factor in interactive crowd-sourcing.
Using a similar notion, Das et al. (2016) and Basil et al. (2016) have illustrated how interactive community-sourcing can help to collate useful data and enable community participation in providing vital information related to damage assessment. Basil et al. (2016) conducted a field trial where a set of post-disaster situational awareness questions were put to people in a disaster- affected region. Their answers were collected with the help of 20 volunteers from a NGO in three remote villages in the Namkhana region of West Bengal, India, namely North Chandanpiri, South Chandanpiri and Haripur. Around 20 need-assessment questions, picked up from the need-assessment questionnaire normally used by disaster management authorities for situational data collection, were arranged in nine different categories (viz., affected area profile, health and medical infrastructure, food aid and nutrition, water and sanitation, education, and others). An automated system was developed for interactive community-sourcing to connect with the affected community members (disaster victims/volunteers/first responders in our case) and to collect interactive
SMS-based responses from them to help the system build a structured repository of situational information.
The field trial took place six months after the said region was affected by a flood in mid-2015 and was aimed at assessing the post-disaster “social continuity”. Social continuity can be defined as the process of returning to the originally prevailing socio-economic conditions of the local inhabitants after being disturbed by a disaster. The volunteers interacted with more than 150 inhabitants/responders of the villages and mobilized each of them to respond to around 20 interactive questions framed in the context of social continuity. Around 3,000 responses were collected (around 150 responses per question) through SMS-based interactive crowd-sourcing in order to gain actionable insight directly from the affected communities. Once the answers were collected, state-of-the-art text summarization algorithms were used to assess the answers and acquire situational awareness. Such summarized information will potentially assist the disaster management authorities in taking decisions regarding time-critical assessment of damage and needs. Interactive community-sourcing can supplement social media posts to generate effective insights.
Using a case study of the great deluge in Kerala, India, Ajay (2019) shows how mobile phone and social media in combination can be used as effective tools in rescue and relief operations. As part of a large digital volunteering team, the author collated social media posts from various platforms, and phone calls were made to the individuals who posted these online messages between 16th August and 18th August, 2018, with the objective to verify the authenticity of the messages and to collect more relevant details that might help the emergency service providers on the ground to provide specific support. Use of messaging apps (for example, WhatsApp) was found to be effective in this context, which provided a platform towards not only information sharing but also supporting interactions with affected members.