Abstract
Indonesia has significant expertise in disaster management due to its disaster geography. Collective expertise and knowledge are valuable resources for lowering disaster risk and enhancing disaster resilience. Additionally, in the current pandemic situation, a clearer understanding of COVID-19 is growing, which could make a difference in how effectively we respond to this and future pandemics. Therefore, it is crucial to record and maintain information related to the event in order to handle any crisis and COVID-19 pandemic appropriately. The goal of this study is to explore KM implementation approaches for handling disasters and the COVID-19 pandemic in Indonesia. In order to collect data for this study, 20 experts were interviewed and 30 experts participated in a Focus Group Discussion (FGD). SWOT analysis was utilised in this study to find different KM implementation strategies. The Analytic Network Process (ANP) was used to prioritize several previously discovered strategies. The study finds that the approach which must be prioritised is to ensure that knowledge products can be accessed by the public, and they must include the community (family) as subjects in establishing knowledge management methods (not only the government or institutions).
Keywords: KM strategy, SDGs, Sustainable cities, Disaster, Climate change, Health, Weighting, ANP
1. Introduction
Knowledge Management (KM) in the context of disasters encompasses not only identifying and documenting lessons learned or best practices, but also determining how to use the necessary knowledge at the proper time and place in order to create acceptable, accurate, and efficient actions [1,2]. According to the Sendai Framework for Disaster Risk Reduction (SFDRR), knowledge management is the foundation for identifying risk and developing solutions to aid the process. Some of these strategies include: 1) gathering, analysing, and using practical data and information; 2) information dissemination based on user needs; 3) compiling non-sensitive information that can be freely accessed; 4) the practice of sharing experiences; and 5) facilitating the science-policy interface for effective decision-making; 6) incorporate disaster risk knowledge into formal and non-formal education, as well as into community and professional training and education [3].
The availability of risk data and information, its conversion into knowledge, and its application to disaster risk reduction (DRR) activities have been found to be disparate in several research activities. Through an integrated and all-inclusive approach that includes all DRR actors at various levels, and all sorts of knowledge which must be transformed into action [4]. Regarding this issue, policymakers frequently do not employ research findings in their decision-making [5]. On the other hand, when conducting research, researchers can tend to neglect addressing the demands of policies and practices, resulting in conclusions that cannot be used by policymakers. As a result, there needs to be a paradigm change from the creation of risk information to the creation of shared risk knowledge that is accessible to many interested parties [6].
As a disaster-prone area, Indonesia offers rich disaster management knowledge and experience, the acquisition of which becomes a valuable asset in reducing disaster risk and increasing disaster resilience. This becomes achievable if information and expertise are adequately managed and communicated. Additionally, in the current pandemic, a common understanding of COVID-19 is growing, which could make a difference on how effectively we respond to this pandemic. To appropriately record and manage information related to handling this pandemic, knowledge management must be developed throughout the pandemic period.
In order for the knowledge and experience management process to be more effective and efficient, and for the learning transfer process to run smoothly, the National Disaster Management Agency (Badan Nasional Penanggulangan Bencana/BNPB) issued a regulation regarding this matter, namely, Regulation of the Head of the National Disaster Management Agency Number 21 of 2014 concerning Learning Mechanisms and Exchange of Knowledge (Knowledge Sharing) and Experience in Disaster Management [7]. In its implementation, this regulation still focuses on documentation and learning of disaster management practices, that have been carried out by BNPB and the mechanism for sharing such knowledge and experiences.
To ensure that the implementation of KM in disaster and pandemic situation in Indonesia works properly, it is necessary to develop a strategy that takes into account the SWOT framework of Strengths-S, Weaknesses-W, Opportunities-O, and Threats-T, and to determine which strategies are the most important for disaster and pandemic management. The goal of this study is to develop KM implementation solutions for handling disasters and the COVID-19 pandemic in Indonesia. The specific goals of this study are to: 1) Conduct a SWOT analysis for KM implementation in disaster and COVID-19 pandemic management; 2) Identify alternative strategies for KM implementation in disaster and COVID-19 pandemic management based on a combination of strengths, weaknesses, opportunities, and threats; and 3) Identify strategic priorities for KM implementation in disaster and COVID-19 pandemic management.
2. The role of knowledge management in disaster and the COVID-19 pandemic
In the event of a disaster or COVID-19 pandemic, it is very important to make timely and accurate decisions, starting from the prevention, mitigation, preparedness, and recovery phases [8]. However, to make the right strategic decisions, it is necessary to quickly acquire critical knowledge; therefore, it is important to develop a specific set of KM practices to deal with crises. Knowledge is the most valuable resource owned by individuals and organisations [9,10].
In order for the knowledge possessed by individuals and organisations to produce a rapid response, it is very important to implement the right knowledge management (KM) approach to activate and realise effective countermeasures [11]. KM can be seen as a process of identifying, capturing, storing, sharing, applying, and utilising collective knowledge to improve performance [9,10,12]. This can help in organising and coordinating management actions, including quickly identifying knowledge owners, and transferring the necessary knowledge to decision makers dealing with crises, or to the right location when needed [10,12].
Through the KM approach, key resources can be managed effectively to develop strategies to ensure welfare and survival, by reducing damage or loss that may result from disasters or pandemics [13]. In addition, information technology (IT) has an important role to play in facilitating the functions of disaster and pandemic management and KM, in order to achieve the goals that have been set [10,14].
Coordination and synergy are essential in generating knowledge about COVID-19, given the scarcity of expertise and the awareness of it. Each party ignores or even avoids learning about the knowledge of the other parties, because of the gap between scientists, policymakers with the knowledge to formulate policies, and health practitioners with operational experience [15]. A notion that can be used to filter the best information about COVID-19 and anyone with the authority to communicate that information must be e use of a knowledgeable management system. In that, anyone who lacks the capacity for an in-depth understanding of COVID-19 cannot impart knowledge about how to handle COVID-19.
3. Materials and methods
3.1. Instruments and data collection
In this study, focus group discussions (FGDs) and interviews were used to collect data. In order to adopt KM models for managing disasters and the COVID-19 pandemic, the FGDs were undertaken to identify strengths, weaknesses, opportunities, and threats, as well as to identify effective alternative solutions. The FGDs were held by inviting 30 professionals from different stakeholders, including government, non-governmental organisations, practitioners, academia, the commercial sector, and community members who are active in health, disaster risk reduction, and climate change adaptation activities. The FGDs were conducted on October 31, 2020. The guiding questions asked included: 1) What are KM practices in the context of disaster and COVID-19 in Indonesia? 2) What are the Strengths (S), Weakness (W), Opportunities (O), and Threats (T) of KM practices in increasing community resilience to disasters and the COVID-19 pandemic? and 3) What are the strategies for optimising the utilisation of KM in increasing community resilience to disasters and the COVID-19 pandemic? The interviews were conducted with 20 experts from November to January 2021 and they aimed to identify the priority or weight the alternative strategies for optimising the use of KM in increasing community resilience to disasters and the COVID-19 pandemic which had been identified during the FGDs.
The characteristics of the FGDs and interview respondents who took part in this study are shown in Table 1 .
Table 1.
Characteristics of FGDs and interview participants.
| Variable | FGD (N = 30) |
Interview (N = 20) |
||
|---|---|---|---|---|
| n | % | n | % | |
| Sex | ||||
| Male | 21 | 70 | 15 | 75 |
| Female | 9 | 30 | 5 | 25 |
| Category | ||||
| Government | 8 | 26.67 | 6 | 30 |
| Practitioner | 15 | 50 | 10 | 50 |
| Academics | 7 | 23.33 | 4 | 20 |
| Education | ||||
| Bachelor's | 6 | 20 | 6 | 30 |
| Master's | 15 | 50 | 9 | 45 |
| Doctorate | 9 | 30 | 5 | 25 |
| Expertise | ||||
| Disaster Risk Reduction | 27 | 90 | 18 | 90 |
| Climate Change Adaptation | 10 | 33.33 | 7 | 35 |
| Community Development | 10 | 33.33 | 9 | 45 |
| Health | 9 | 30 | 6 | 30 |
| Experience (years) | ||||
| <5 | 3 | 10 | 2 | 10 |
| 5 to 10 | 7 | 23.33 | 6 | 30 |
| >10 | 19 | 63.33 | 11 | 55 |
3.2. Data analysis
In this study, data analysis techniques such as SWOT analysis and the Analytic Network Process (ANP) were employed. SWOT analysis is a strategic planning and strategic management technique, and we use it here to identify different options for integrating knowledge management in disaster management, based on a framework of strengths, weaknesses, opportunities, and threats.
-
a.
SWOT analysis
SWOT analysis is a method of evaluating an industry, sector, firm, or organisation's strengths (S), weaknesses (W), opportunities (O), and threats (T) [16]. This SWOT analysis has two dimensions, namely four (4) components: internal elements of Strengths and Weaknesses, and external aspects of Opportunities and Threats.
The process for conducting a SWOT analysis is as follows: (1) SWOT analysis session, (2) Meeting overview and work explanation, (3) Identify strengths and weaknesses, as well as opportunities and threats, using brainstorming techniques, (4) Determine the steps that are necessary to move from strategy action and its consequences, (5) Develop the SWOT matrix and include the priority selection within it, (6) Compare with other internal and external factors and SO, WO, ST, WT strategies, (7) Determine the steps necessary to move from strategy action and its consequences, and (8) The SWOT matrix is updated at appropriate intervals.
Several potential strategies will be derived from a combination of internal and external factors as a consequence of the SWOT analysis. Because of the limits of different power sources, not all of these strategies can be implemented at the same time. To address this, a priority policy strategy that requires more attention, in order to achieve the defined goals, must be chosen. The Analytic Network Process (ANP) analysis is used to prioritize the strategic alternatives that have been determined through the use of SWOT in this study [17].
-
b.
Analytic Network Process (ANP)
The Analytic Network Process (ANP) is a mathematical theory that enables decision-makers to manage interconnected components (systems) and feedback in a methodical manner. The ANP is a decision-making process based on a set of criteria known as Multiple Decision-Making Criteria (MDMC) [18]. This method is a new way to perform qualitative research that is a progression of the previous method, the Analytic Hierarchy Process (AHP).
The process of extracting the findings from the raw data that are collected through the FGDs begins with the process of merging and standardising the data that are obtained from the recorded FGDs, which have been formatted into a verbatim transcription form. The data are then categorised into a matrix and coded according to the themes, namely, Strengths (S), Weaknesses (W), Opportunities (O), Threats (T), SO Strategies (Strengths-Opportunities), WO Strategies (Weakness-Opportunities), ST Strategies (Strengths-Threats), and WT Strategies (Weakness-Threats). Meanwhile, the interviews were aimed to weight the alternative strategies that are identified from the FGD results, and the results were processed with Super Decision® V 3.2 software.
4. Results
4.1. SWOT analysis results
In this study, we used the SWOT analysis to determine which strategies should be prioritised for KM implementation in disaster management. Comparing internal and external elements is the first stage in completing a SWOT analysis. The Internal Strategic Factor Analysis Summary (IFAS) is a matrix that lists internal factors, and the External Strategic Factor Analysis Summary (EFAS) matrix includes external components. Table 2, Table 3 show the IFAS and EFAS matrices, respectively, based on the results of the FGDs.
Table 2.
IFAS matrix of KM implementation in disaster and pandemic.
| STRENGTHS (S) | WEAKNESS (W) |
|---|---|
| S1: Many experiences and lessons from Indonesia S2: Various disaster stakeholders have carried out various KM practices in disaster management, including policy makers, practitioners, and academics S3: Good ICT infrastructure to support KM is already available at the National and Provincial levels S4: Excellent HR capacity to strengthen KM S5: The National Disaster Management Agency has initiated the building of KM in disaster management through the Education and Training Centre and the System and Strategy Division. |
W1: KM has not become a priority (lack of commitment), so many agencies, to date, have not factored this in to their budgets W2: The delivery method to the community is still not appropriate W3: KM activities generally still revolve around the national and provincial levels, and have not touched many lower administrative areas at the village level W4: Regulations related to KM already exist but they have not been applied optimally W5: The position and role of KM institutions in disaster management is currently lacking and, consequently there is no big impact W6: KM practices in mitigation and preparedness are still lacking W7: Lessons learned/outcomes from projects by development partners have not been well documented W8: Use of scientific language that is difficult for the target audience to accept W9: It is not quite understand how to produce knowledge (literacy issue) W10: Updating of data and information in KM has not run optimally, which has a direct impact on the quality of various important documents regarding DM. |
Table 3.
EFAS matrix of KM implementation in disaster and pandemic conditions.
| OPPORTUNITIES (O) | THREATS (T) |
|---|---|
| O1: The state of Indonesia is a country with a distinctive style of knowledge in the society O2: Projects from development partners produce many modules and other documents (e.g., disaster risk assessment) O3: Support from various Donor Institutions to apply KM practices in disaster management O4: The Indonesian ethnic group is very diverse, with great potential for tacit knowledge that can strengthen KM O5: There is great enthusiasm from disaster activists to use KM as an instrument for improving disaster management in the future. |
T1: The nature of the disaster (i.e., repeated and unpredictable occurrences) means that there is no time to manage knowledge T2: The manual KM process will always lag behind because explicit knowledge of disasters in Indonesia is growing fast, and tacit knowledge has not been well codified to date T3: The community has other, more important and pressing, issues to deal with T4: It is difficult for the community to follow the development of disaster-related knowledge T5: Knowledge of disasters can be lost by the community because of their influence is over a long period of time T6: The KM system is not yet robust and systematic T7: There is no effective strategy to motivate KM stakeholders in order that they carry out their respective roles. |
Based on the identification of internal and external factors, as described previously, 15 alternative strategies are formulated (see Table 4 ) which will then be analysed using the ANP to determine the priorities of the existing strategies.
Table 4.
SWOT analysis matrix for KM in disaster and pandemic Management.
| EFAS | IFAS |
|
|---|---|---|
| STRENGTHS | WEAKNESSES | |
| OPPORTUNITIES | STRATEGIES (S–O) | STRATEGIES (W–O) |
| SO1: Efforts for recording, documenting, institutionalising and transmitting KM (S1, O5) SO2: Increasing the role of NGOs regarding communication strategies for conveying KM practices to the community (S2, O3) SO3: Improving inter-institutional relations in KM practices in disaster situations (S2, O5) SO4: Commitment from regional leaders in implementing KM (S4, O4) |
WO1: Involvement of the community (family) as a subject in developing knowledge management strategies (not only government or institutions) (W2, O1) WO2: Conducting assessment and capacity building of village apparatus (including technology, skills, etc.) (W3, O2) WO3: Strengthening policies accompanied by simplification of “disaster language” so that it can be understood by various parties (W8, O1) WO4: Ensure knowledge products are accessible to the public (W9, O2) |
|
| THREATS | STRATEGIES (S-T) | STRATEGIES (W-T) |
| ST1: Production of balanced knowledge for each type of disaster and multi-hazard and also for each stage of disaster management (S1, T1) ST2: Management of the combination of explicit knowledge and tacit knowledge (S4, T2) ST3: Disaggregation in managing information data, input for decision making and management which is more permanent and long term (S3, T5). |
WT1: Consideration of 4 aspects: people, process, technology, and goal-oriented KM in KM policy and programming in disaster management (W1, W6, T6) WT2: Effort and innovation for knowledge management services with wider support and collaboration (W4, W5, W7, T3, T7) WT3: Increasing the role of universities to assist local governments in building knowledge hubs, repositories and knowledge sharing of best practices during disaster management (W9, T4) WT4: Standardisation in methodology so that the data obtained are valid (W10, T5). |
|
4.2. Priority of KM implementation strategy in disaster and pandemic situations
Weighting based on the Analytic Network Process (ANP) approach, with the use of Super Decision® V 3.2 software, is used to determine the priority order for alternative KM implementation options. The SWOT analysis matrix was used to build the ANP KM Implementation Strategy model prior to weighting.
The ANP model typically consists of four parts: 1) Goal (KM Implementation Strategy in Disaster Management); 2) Criteria (Strengths, Weaknesses, Opportunities, and Threats); 3) Sub-criteria (all items for each criterion, namely, five items for Strengths, ten items for Weaknesses, five items for Opportunities, and seven items for Threats); and 4) Alternatives (overall strategy items arranged based on sub-criteria). The ANP model created for the KM implementation method in disaster management is shown in Fig. 1 .
Fig. 1.
ANP model of KM implementation strategy.
After compiling the ANP model, the weighting is performed in two stages using pairwise comparison. The first stage involves comparing each criterion. The next step is to compare each item in each of the four alternatives: SO Strategies (Strengths-Opportunities), WO Strategies (Weakness-Opportunities), ST Strategies (Strengths-Threats), and WT Strategies (Weakness-Threats).
Table 5, Table 6, Table 7, Table 8, Table 9 show the results of weighting each criterion, and alternative use of the Super Decision® V 3.2 software. The average value of the weights is calculated by adding the findings of each resource's pairwise comparison assessment, and dividing them by the number of sources that were weighted. The findings of the analysis also indicate the Inconsistency values of each expert's evaluation for each weighting. Because the overall inconsistency score in this study is less than 0.1, it can be assumed that the judgment of informants, regarding the pairwise comparison on the criterion and alternative matrix, is consistent.
Table 5.
Criteria weighting analysis.
| Expert | Score Weight |
Inconsistency Value | |||
|---|---|---|---|---|---|
| S–O | S-T | W–O | W-T | ||
| 1 | 0.11 | 0.04 | 0.38 | 0.44 | 0.07 |
| 2 | 0.56 | 0.26 | 0.11 | 0.05 | 0.04 |
| 3 | 0.27 | 0.15 | 0.52 | 0.05 | 0.04 |
| 4 | 0.56 | 0.26 | 0.11 | 0.05 | 0.04 |
| 5 | 0.24 | 0.1 | 0.6 | 0.04 | 0.09 |
| 6 | 0.25 | 0.09 | 0.59 | 0.04 | 0.03 |
| 7 | 0.61 | 0.04 | 0.23 | 0.1 | 0.06 |
| 8 | 0.26 | 0.09 | 0.59 | 0.04 | 0.07 |
| 9 | 0.52 | 0.04 | 0.31 | 0.11 | 0.04 |
| 10 | 0.23 | 0.09 | 0.62 | 0.04 | 0.05 |
| 11 | 0.63 | 0.04 | 0.22 | 0.09 | 0.07 |
| 12 | 0.03 | 0.29 | 0.31 | 0.35 | 0.81 |
| 13 | 0.56 | 0.1 | 0.28 | 0.04 | 0.05 |
| 14 | 0.24 | 0.12 | 0.57 | 0.05 | 0.06 |
| 15 | 0.2 | 0.1 | 0.63 | 0.04 | 0.04 |
| 16 | 0.1 | 0.04 | 0.57 | 0.27 | 0.06 |
| 17 | 0.25 | 0.12 | 0.57 | 0.04 | 0.04 |
| 18 | 0.05 | 0.22 | 0.09 | 0.62 | 0.02 |
| 19 | 0.09 | 0.62 | 0.24 | 0.04 | 0.09 |
| 20 |
0.11 |
0.04 |
0.26 |
0.57 |
0.02 |
| Average | 0.3 | 0.15 | 0.4 | 0.15 | 1 |
Table 6.
Weighting Analysis of strategy S–O.
| Experts | Score Weight |
Inconsistency Value | |||
|---|---|---|---|---|---|
| SO1 | SO2 | SO3 | SO4 | ||
| 1 | 0.11 | 0.04 | 0.27 | 0.57 | 0.03 |
| 2 | 0.09 | 0.05 | 0.32 | 0.52 | 0.009 |
| 3 | 0.11 | 0.04 | 0.25 | 0.58 | 0.02 |
| 4 | 0.24 | 0.04 | 0.6 | 0.1 | 0.03 |
| 5 | 0.27 | 0.04 | 0.53 | 0.14 | 0.02 |
| 6 | 0.09 | 0.04 | 0.65 | 0.21 | 0.07 |
| 7 | 0.04 | 0.21 | 0.64 | 0.09 | 0.11 |
| 8 | 0.25 | 0.04 | 0.58 | 0.11 | 0.04 |
| 9 | 0.22 | 0.11 | 0.61 | 0.05 | 0.04 |
| 10 | 0.25 | 0.04 | 0.6 | 0.09 | 0.03 |
| 11 | 0.23 | 0.04 | 0.62 | 0.96 | 0.11 |
| 12 | 0.11 | 0.04 | 0.3 | 0.53 | 0.01 |
| 13 | 0.27 | 0.04 | 0.31 | 0.36 | 0.73 |
| 14 | 0.26 | 0.04 | 0.26 | 0.42 | 0.55 |
| 15 | 0.22 | 0.04 | 0.62 | 0.1 | 0.04 |
| 16 | 0.31 | 0.04 | 0.52 | 0.11 | 0.05 |
| 17 | 0.21 | 0.04 | 0.62 | 0.11 | 0.09 |
| 18 | 0.24 | 0.11 | 0.59 | 0.04 | 0.05 |
| 19 | 0.09 | 0.25 | 0.61 | 0.04 | 0.07 |
| 20 |
0.266 |
0.04 |
0.58 |
0.1 |
0.07 |
| Average | 0.19 | 0.05 | 0.5 | 0.26 | 1 |
Table 7.
Weighting Analysis of strategy W–O.
| Experts | Score Weight |
Inconsistency Value | |||
|---|---|---|---|---|---|
| WO1 | WO2 | WO3 | WO4 | ||
| 1 | 0.25 | 0.11 | 0.04 | 0.58 | 0.02 |
| 2 | 0.23 | 0.05 | 0.11 | 0.59 | 0.06 |
| 3 | 0.25 | 0.04 | 0.11 | 0.58 | 0.02 |
| 4 | 0.2 | 0.03 | 0.13 | 0.62 | 0.13 |
| 5 | 0.24 | 0.04 | 0.11 | 0.6 | 0.17 |
| 6 | 0.08 | 0.03 | 0.21 | 0.65 | 0.14 |
| 7 | 0.26 | 0.04 | 0.11 | 0.56 | 0.03 |
| 8 | 0.08 | 0.04 | 0.29 | 0.57 | 0.07 |
| 9 | 0.54 | 0.04 | 0.11 | 0.28 | 0.03 |
| 10 | 0.04 | 0.1 | 0.23 | 0.62 | 0.07 |
| 11 | 0.04 | 0.09 | 0.27 | 0.57 | 0.05 |
| 12 | 0.21 | 0.04 | 0.09 | 0.63 | 0.04 |
| 13 | 0.23 | 0.04 | 0.12 | 0.59 | 0.04 |
| 14 | 0.23 | 0.04 | 0.09 | 0.62 | 0.06 |
| 15 | 0.26 | 0.04 | 0.1 | 0.59 | 0.07 |
| 16 | 0.23 | 0.04 | 0.11 | 0.6 | 0.1 |
| 17 | 0.21 | 0.04 | 0.12 | 0.61 | 0.04 |
| 18 | 0.1 | 0.04 | 0.28 | 0.55 | 0.03 |
| 19 | 0.26 | 0.09 | 0.04 | 0.59 | 0.04 |
| 20 |
0.56 |
0.03 |
0.28 |
0.1 |
0.11 |
| Average | 0.22 | 0.05 | 0.15 | 0.58 | 1 |
Table 8.
Weighting Analysis of strategy S-T.
| Experts | Score Weight |
Inconsistency Value | ||
|---|---|---|---|---|
| ST1 | ST2 | ST3 | ||
| 1 | 0.08 | 0.73 | 0.18 | 0.06 |
| 2 | 0.1 | 0.63 | 0.25 | 0.03 |
| 3 | 0.06 | 0.71 | 0.21 | 0.17 |
| 4 | 0.07 | 0.7 | 0.22 | 0.05 |
| 5 | 0.07 | 0.67 | 0.25 | 0.15 |
| 6 | 0.19 | 0.74 | 0.05 | 0.2 |
| 7 | 0.07 | 0.7 | 0.22 | 0.22 |
| 8 | 0.08 | 0.68 | 0.23 | 0.1 |
| 9 | 0.27 | 0.66 | 0.06 | 0.04 |
| 10 | 0.32 | 0.58 | 0.08 | 0 |
| 11 | 0.05 | 0.69 | 0.24 | 0.13 |
| 12 | 0.08 | 0.73 | 0.18 | 0.06 |
| 13 | 0.64 | 0.27 | 0.08 | 0.05 |
| 14 | 0.07 | 0.7 | 0.22 | 0.22 |
| 15 | 0.24 | 0.68 | 0.06 | 0.11 |
| 16 | 0.22 | 0.7 | 0.07 | 0.05 |
| 17 | 0.07 | 0.72 | 0.2 | 0.28 |
| 18 | 0.07 | 0.32 | 0.6 | 0 |
| 19 | 0.19 | 0.06 | 0.73 | 0.09 |
| 20 |
0.05 |
0.76 |
0.18 |
0.25 |
| Average | 0.15 | 0.63 | 0.22 | 1 |
Table 9.
Weighting Analysis of strategy W-T.
| Experts | Score Weight | Inconsistency Value | |||
| WT1 | WT2 | WT3 | WT4 | ||
| 1 | 0.56 | 0.11 | 0.05 | 0.26 | 0.04 |
| 2 | 0.56 | 0.26 | 0.11 | 0.05 | 0.04 |
| 3 | 0.59 | 0.1 | 0.24 | 0.05 | 0.05 |
| 4 | 0.46 | 0.37 | 0.12 | 0.03 | 0.12 |
| 5 | 0.58 | 0.26 | 0.11 | 0.03 | 0.17 |
| 6 | 0.26 | 0.6 | 0.08 | 0.04 | 0.08 |
| 7 | 0.61 | 0.21 | 0.12 | 0.04 | 0.09 |
| 8 | 0.57 | 0.24 | 0.12 | 0.05 | 0.02 |
| 9 | 0.07 | 0.6 | 0.27 | 0.04 | 0.25 |
| 10 | 0.58 | 0.27 | 0.09 | 0.04 | 0.04 |
| 11 | 0.28 | 0.13 | 0.54 | 0.04 | 0.05 |
| 12 | 0.25 | 0.11 | 0.58 | 0.04 | 0.02 |
| 13 | 0.63 | 0.2 | 0.04 | 0.1 | 0.04 |
| 14 | 0.57 | 0.28 | 0.09 | 0.05 | 0.03 |
| 15 | 0.57 | 0.04 | 0.27 | 0.1 | 0.04 |
| 16 | 0.51 | 0.04 | 0.33 | 0.1 | 0.07 |
| 17 | 0.58 | 0.27 | 0.09 | 0.04 | 0.04 |
| 18 | 0.61 | 0.23 | 0.1 | 0.04 | 0.08 |
| 19 | 0.6 | 0.21 | 0.13 | 0.04 | 0.03 |
| 20 |
0.62 |
0.23 |
0.09 |
0.04 |
0.06 |
| Average | 0.5 | 0.25 | 0.18 | 0.07 | 1 |
Table 5 shows that the majority of informants assigned the W–O (Weakness-Opportunities) strategy items the most weight, with 45% of the weight values provided by the informants being larger than 0.5. Furthermore, the maximum weight value supplied by the informants for the W–O items is 0.62, the smallest weight value is 0.09, and the average weight is 0.4.
The item S–O (Strengths-Opportunities) has the second highest weight, with an average of 0.3, a maximum of 0.63, and a minimum of 0.05. Furthermore, the elements S-T (Strengths-Threats) and W-T (Weakness-Threats) have the same average weight value of 0.15, with 0.62 as the greatest value and 0.04 as the smallest value. The evaluations of the criteria items by the experts were also consistent, with inconsistency values ranging from 0.02 to 0.81 or <0.1. The priority of each strategy S–O, W–O, S-T, and W-T is then determined by weighting. Table 6, Table 7, Table 8, and Table 9 show the weighted analysis findings for each technique S–O, W–O, S-T, and W-T, respectively.
Table 6 shows that the order of priority in the S–O strategy is based on the average weight value, namely: SO3 (0.5), SO4 (0.26), SO1 (0.19), and SO2 (0.05). The results of the calculation of the average weight value for the W–O strategy (see Table 7), show the order of priority as follows: 1) WO4 (0.58); 2) WO1 (0.22); 3) WO3 (0.15); and 4) WO2 (0.05). The priority of the ST strategy is based on the average weight value shown in Table 8, namely: 1) ST2 (0.63); 2) ST3 (0.22); and 3) ST1 (0.15). Finally, the calculation of the average weighted value for the W-T strategy (see Table 9) shows the order of priority as follows: 1) WT1 (0.5); 2) WT2 (0.25); 3) WT3 (0.18); and WT4 (0.07).
The results of the recapitulation of the weighting of criteria and alternative strategies for implementing KM can be seen in Table 10 .
Table 10.
Recapitulation of weighting strategy for KM implementation.
| No | Strategy | Description | Weight |
|---|---|---|---|
| I |
W–O |
Weaknesses-Opportunities |
0.4 |
| 1 | WO4 | Ensure knowledge products are accessible to the public | 0.58 |
| 2 | WO1 | Involvement of the community (family) as a subject in developing knowledge management strategies (not only government or institutions) | 0.22 |
| 3 | WO3 | Strengthening policies accompanied by simplification of “disaster language” so that it can be understood by various parties | 0.15 |
| 4 | WO2 | Conducting assessment and capacity building of village apparatus (including technology, skills, etc.) | 0.05 |
| II |
S–O |
Strengths-Opportunities |
0.3 |
| 1 | SO3 | Improving inter-agency relations in KM practices | 0.5 |
| 2 | SO4 | Commitment from local leaders in implementing KM | 0.26 |
| 3 | SO1 | Efforts for recording, documenting, institutionalising, and transmitting KM | 0.19 |
| 4 | SO2 | Increasing the role of NGOs for KM practices in community outreach initiatives | 0.05 |
| III |
S-T |
Strengths-Threats |
0.15 |
| 1 | ST2 | Management of the combination of explicit knowledge and tacit knowledge | 0.63 |
| 2 | ST3 | Disaggregation in managing information data, input for decision making and management which is more permanent and long term | 0.22 |
| 3 | ST1 | Production of balanced knowledge for each type and multi-hazard, and also for each stage of disaster management | 0.15 |
| IV |
W-T |
Weakness-Threats |
0.15 |
| 1 | WT1 | Consideration of 4 aspects: people, process, technology, and goal-oriented KM in KM policy and programming in disaster management | 0.5 |
| 2 | WT2 | Effort and innovation for knowledge management services with wider support and collaboration | 0.25 |
| 3 | WT3 | Increasing the role of universities to assist local governments in building knowledge hubs, repositories and knowledge sharing of best practices during disaster management | 0.18 |
| 4 | WT4 | Standardisation in methodology so that the data obtained are valid | 0.07 |
Based on the weighting value, the W–O (Weakness-Opportunities) strategy has the highest weighted value, which is 0.45, or about half of the overall weighted value. As a result, it can be inferred that in order for KM to be implemented effectively in disaster management, strategies that include internal factors of weakness and take advantage of external opportunities must be considered. Alternative strategies for implementing KM in disasters and pandemic situations that should be prioritised include ensuring community access to knowledge products (58%) and incorporating the community (families) as subjects in designing knowledge management strategies (rather than only government or institutions) (22%).
5. Discussion
It is common knowledge that evaluating the validity of models that are generated in operational scientific studies is critical. However, it is clear that the validity of the theoretical foundation of the proposed ANP model [19] has been overlooked, and no concrete criteria for verifying it have been devised. Any study that uses the ANP approach [[20], [21], [22], [23], [24], [25], [26], [27]] will reveal this flaw. SWOT analysis has produced more realistic results owing to the capability of the ANP. The recommended model not only enabled us to develop the objective of our study, but it also demonstrated the viability of the model.
The current study, similarly to other studies, has some limitations and difficulty testing the validity of the proposed model. Firstly, the fact that the ANP model's factors are not quantitative. Secondly, the ANP pairwise comparison matrix is utilised to establish the priority value of the elements that were decided by expert judgment. However, assigning numerical measurements to elements in a decision problem is not always viable, and producing consistent outcomes is not always achievable. This is due to the fact that the data utilised in the pairwise comparison matrix can alter, depending on the subjective opinions of the experts. As a result, it is impossible to arrive at the same conclusion utilising data from multiple case studies. These restrictions, however, are inherent in the structure of the decision-making problem. Thirdly, the tested model has not been assessed using historical data due to the lack of historical data for the management scenario under consideration. This flaw, however, should not be considered as severe when assessing the model's validity. The input to the suggested model, the comparison matrix, is defined under known conditions. Because multiple pairwise comparison matrices can be obtained at different time points, different results can be obtained.
The consistency ratio (CR) of the pairwise comparison matrices is another parameter that validates the model's validity. In pairwise comparison matrices, the consistency ratio is determined using the consistency index and the random index [18]. The computed CR value using the ANP must be less than 0.10. Super Decision® was used to calculate the consistency ratio of the pairwise comparison matrices used in this investigation. All of the calculated consistency ratios are less than 0.10, as can be observed by inspecting the values. We can be confident in the validity of the pairwise comparison matrices utilised in this study, based on these consistency ratios.
Four S–O strategies, four W–O strategies, three S-T strategies, and four W-T strategies have been determined by the SWOT analysis. The ANP results showed that strategies should concentrate on internal aspects of weaknesses and external factors of opportunities to boost the effectiveness of KM implementation in controlling disaster and the COVID-19 pandemic. More precisely, this study suggests that in order to reduce risk in disaster and COVID-19 pandemic situations, KM implementation in managing disaster and pandemic situations has to ensure that knowledge products can be accessed by the community and there must be community involvement, especially at the family level.
Although it can be exceedingly challenging to transfer tacit and uncodified knowledge, families often have strong relationship links that serve as a powerful informal knowledge-sharing mechanism [28]. Effective informal knowledge-sharing methods can be built on a foundation of regular face-to-face interactions, a high degree of trust, and shared values. The family unit can be a valuable, uncommon, and unique force controlling assets or resources [29,30]. Sharing informal knowledge within the family is a practice that fosters unity and consensus on goals, beliefs, and decisions [31]. Families are able to create a setting that is characterised by trust and enduring relationships, where each family member feels a part of the family unit through unconventional and informal knowledge transfer processes. The sharing and use of tacit knowledge are made easier by social capital, which is a crucial resource for knowledge management [[32], [33], [34], [35], [36], [37], [38]].
The results of the current study also show that the KM implementation strategy also needs to be focused on building commitment from local leaders in implementing KM, and increasing effort and innovation for knowledge management services with wider support and collaboration. This is also in line with earlier studies which found that one of the most important organisational limitations was the reluctance of policymakers to appreciate the importance of disaster and pandemic mitigation [[39], [40], [41], [42]]. Additionally, organisational memory, a crucial component of the cognitive aspect of learning in disasters for regional self-governance units, has not been fully developed. Examples of its explicit forms include databases, manuals, rulebooks, and other written materials. Efforts and improvements for knowledge management services with greater support and collaboration are required to maximise this process [38,39].
When managing a pandemic, knowledge is a crucial strategic tool, especially with respect to minimising adverse health and socioeconomic effects. In contrast to disasters, pandemics have lengthy waiting periods during which they can be actively managed. The direction and effects of a pandemic can be altered by decisions which are made during such a complicated process [43,44]. Previous research demonstrates that the widespread application of KM aids in directing decision-making by the authorities for the full-scale control of the COVID-19 pandemic. KM implementation was successful in mitigating the COVID-19 wave and reducing the number of positive cases and death rates [43]. Although the crucial function of knowledge management in a pandemic, which educates and empowers decision-makers, has been acknowledged, studies on how to specifically implement KM in a pandemic are still somewhat limited [44].
6. Conclusion
Knowledge is an important factor in mitigating disaster and the COVID-19 pandemic. Without integrated knowledge management, it is impossible to transfer good knowledge about disaster and the COVID-19 pandemic to the community. KM in the context of the disaster and the COVID-19 pandemic can be said to be quite unique, considering that the priority for developing KM is not only for the internal interests of the organisation, but also for the benefit of the community as users of this knowledge, especially in building resilience against disasters and the COVID-19 pandemic. Key resources can be efficiently managed through the KM approach to create strategies to ensure welfare and survival by minimising damage or loss that may arise from disasters or pandemics. This is different from other organisations, especially in profit companies, where KM development is directed at internal needs in order to increase competitiveness.
To apply KM in disaster and COVID-19 pandemic management in Indonesia, a strategy must be developed that examines strengths, weaknesses, opportunities, and threats, as well as which strategies should be prioritised in terms of that management. Five strengths, ten weaknesses, five opportunities, and seven threats have been found in this study that can affect KM implementation in disaster and COVID-19 pandemic management. Future research could investigate the impact of any interdependencies among the SWOT sub-factors. In addition, fuzzy numbers can be used in the AHP or ANP methods to analyse cases, with more uncertainty in the pairwise comparison matrices, more effectively.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The first author is grateful for the opportunity and assistance she received from the Integrated Research on Disaster Risk (IRDR), Beijing, China, which recognized her as an IRDR Young Scientist. This publication would not have been feasible without financial assistance from Universitas Syiah Kuala and The Indonesian Ministry of Education, Culture, Research, and Technology.
Data availability
Data will be made available on request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data will be made available on request.