An alternative to coping with COVID-19 - knowledge management applied to the banking industry in Taiwan

Chang, Chih-Hsiung; Chang, Wu-Hua; Hsieh, Hsiu-Chin; Shih, Yi-Yu Article An alternative to coping with COVID-19 - knowledge management applied to the banking industry in Taiwan Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Chang, Chih-Hsiung; Chang, Wu-Hua; Hsieh, Hsiu-Chin; Shih, Yi-Yu (2022) : An alternative to coping with COVID-19 - knowledge management applied to the banking industry in Taiwan, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 15, Iss. 9, pp. 1-21, https://doi.org/10.3390/jrfm15090405 This Version is available at: https://hdl.handle.net/10419/274926 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: Chang, Chih-Hsiung, Wu-Hua Chang, Hsiu-Chin Hsieh, and Yi-Yu Shih. 2022. An Alternative to Coping with COVID-19— Knowledge Management Applied to the Banking Industry in Taiwan. Journal of Risk and Financial Management 15: 405. https:// doi.org/10.3390/jrfm15090405 Academic Editor: Thanasis Stengos Received: 2 August 2022 Accepted: 2 September 2022 Published: 12 September 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Journal of Risk and Financial Management Article An Alternative to Coping with COVID-19—Knowledge Management Applied to the Banking Industry in Taiwan Chih-Hsiung Chang 1,* , Wu-Hua Chang 1, Hsiu-Chin Hsieh 2and Yi-Yu Shih 1 1Department of Finance, I-Shou University, No.1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung City 84001, Taiwan 2Department of International Business, National Kaohsiung University of Science and Technology, No.415, China Kung Rd., Kaohsiung City 80778, Taiwan *Correspondence: [email protected] Abstract: This study seeks to find an alternative strategy to cope with the impact of COVID-19. Though various measures have been adopted to respond to the threat of the pandemic, the problem remains unchanged. Undoubtedly, COVID-19 is also a crisis of knowledge, so this study explores whether the banking industry in Taiwan can apply knowledge management (KM) and fight the catastrophe of the century successfully and effectively. This study adopts an actual case to analyze the relationship between KM implementation and the banking industry; applies consistent fuzzy preference relations (CFPRs) to evaluate influential criteria including computational simplicity and guarantee the consistency of decision matrices; illustrates a decision-making model with seven criteria; and conducts pairwise comparisons, which are utilized to determine the priority weights of influential criteria amongst the outcome rankings and to formulate accurate KM strategies. The results show that predictions of success probabilities are higher than those of failure probabilities among the seven influential criteria and, in particular, the headquarters system and human resources are the most important priority indicators for implementing KM successfully during the pandemic or post-pandemic. The conclusion suggests significant policy implications for policymakers within other industries or countries in coping with COVID-19. Keywords: COVID-19; knowledge management (KM); banking industry; fuzzy preference relations (CFPRs); post-pandemic 1. Introduction COVID-19 has damaged the international economy and banking systems worldwide. Hence, banks’ performance and profitability were influenced significantly (Gazi et al. 2022b). The results indicated that the volatility spillover index increased during the pandemic crisis (Mohamed and Eddin 2022). In other words, the banks were the main sector associated with volatility spillover (Chirilă2022). Therefore, commercial banks were also exposed to the pandemic, which had a negative impact on their efficiency and productivity (Ünlü et al. 2022). Accordingly, it was found that insured or uninsured depositors chose different banks due to the pandemic as a result of the effect of political and financial events (Ghouse et al. 2022). Facing the impact and challenges derived from the pandemic, some responsive measures needed to be adopted by the banking industry. Digitalization was the measure mentioned most often. Stefanovic et al. (2021) proposed that digitalization was an important factor and needed to be strengthened and included in bank strategies during the pandemic. Szili et al. (2022) argued that many factors could affect the choice made by banks because these factors might also change and speed up the digitalization of banks. Supari and Anton (2022) highlighted that it was necessary to intervene in small and medium enterprises and help them to increase their resilience by means of digitalization. Specifically, the J. Risk Financial Manag. 2022,15, 405. https://doi.org/10.3390/jrfm15090405 https://www.mdpi.com/journal/jrfm J. Risk Financial Manag. 2022,15, 405 2 of 21 financial digitalization technologies that were usually employed by the banking industry to respond to the pandemic included Fintech (Abdul-Rahim et al. 2022), online payment, a hybrid machine learning and swarm metaheuristic approach (Jovanovic et al. 2022), and big data from FinTech websites (Sakas et al. 2022). Similarly, Ar and Abbas (2021a) explored the application of information communication technology in the Pakistani government and also recognized the contributions to COVID-19, ICT, the e-government, and public–private collaboration. In addition, the corporate governance mechanism was thought to be effective in improving the financial performance of banks during the pandemic (El-Chaarani et al. 2022). Therefore, corporate governance had been regarded as the moderator of knowledge management, business strategy, and innovation capabilities, which are important to improving organizational effectiveness and competitiveness (Kien and That 2022). Furthermore , Kabbani et al. (2022) suggested that banks should encourage leaders to strengthen suitable behaviors and attitudes to create an ethical culture and improve employees’ willingness to get the COVID-19 vaccination. Additionally, Soemitra and Rahma (2022) proposed that the Micro Waqf Bank had played a role in empowering women to deal with the COVID-19 pandemic. However, few studies have addressed knowledge management applied to the banking industry to help cope with the pandemic, even though it was proven that the hospitality industry succeeded in implementing knowledge management post-pandemic (Hsieh et al. 2020). Due to several issues that have undoubtedly remained unsolved in the banking industry, the importance of knowledge management has quickly regained attention. Therefore, the purpose of this study was to first determine the influential criteria of implementing knowledge management effectively and to predict the probability of the successful implementation of knowledge management in the banking industry. Accordingly, this study reviewed the current literature, documents, and associated articles, along with experts and scholars specialized in the academic field of the banking. An actual case study was employed to interview 15 experts in Taiwan. Seven criteria were investigated, and the related primary data were summarized into two categories. One was to address the secondary data, whereas the other was to address the primary data collected from 18 to 24 March 2021 through the surveys and interviews of the experts. Based on the results of the interviews, this study employed consistent fuzzy preference relations (CFPRs) to describe an analytic hierarchical prediction model, aiming at helping the banking industry in Taiwan to overcome the pandemic. Then, to rate the best implementation of knowledge management, this study employed pairwise comparisons to calculate and rank the priority weights of the seven criteria, and two outcomes (success or failure) were presented. It was expected that the model could help the banking industry to identify which criteria were vital to implement knowledge management effectively and successfully in the post-pandemic era. 2. The Review of the Professional and Academic Literature Initially, the pandemic was a health emergency and eventually caused an unpredictably negative influence on the global economy. Unfortunately, no country could be regarded as exemplary in their response to the economic crisis caused by the pandemic (Mustafa et al. 2021). Amir et al. (2021) also asserted that education, communication, and information were the primary methods in the early phase of the pandemic before the vaccines. Akram et al. (2018) argued that the banking industry had to continue to review its explicit and implicit management strategies to meet changing consumer needs and its sustainable development to improve organizational performance because the impact on the banking industry, especially consumer finance, was immediately apparent. Mila et al. (2021) stressed that knowledge management played a vital role during the COVID-19 outbreak, although knowledge management strategies were different in domestic and foreign enterprises and had different impacts on the varying levels of organizations. Therefore, supported by successful sustainability performance and a competitive advantage, accurate knowledge J. Risk Financial Manag. 2022,15, 405 3 of 21 management could help organizations to survive future pandemics. This study aimed to investigate and determine some of the influential standards that are necessary for successful knowledge management. 2.1. Headquarters System In terms of headquarters, Laidroo and Ööbik (2013) investigated the disclosed quantity of corporate social responsibility and the transparency of banks’ headquarters and argued that the patterns of disclosed quantities were different unit by unit. Furthermore, Barnes and Newton (2019) directly focused on the headquarters of the National Provincial Bank of England and demonstrated that its national identity was impressive as it differed from its rivals. Specifically, the banking industry could benefit from knowledge management and use their resources sufficiently (Cebi et al. 2010). In other words, the headquarters system recognized the inherent nature of organizations, which could facilitate their successful transformation during the pandemic. Therefore, the headquarters had to play an important role in various kinds of incentives, including hiring, promoting, and punishing, as well as enforcing rules, discouraging dissonant views and manipulating data (Broad 2007). 2.2. Human Resources Due to the importance of the knowledge economy, human resources have been agreed upon as the most important resource for enterprises. In particular, corporate performance could be increased by improving human resources (Wu et al. 2022). Furthermore, the role of the human factor was essential in the implementation and application of the own normative system, which not only had the role of risk reduction, but also, in particular, the realization of the bank’s strategies and policies. It was seen that human resource management was already a priority element of the evolutionary strategies of a modern bank (Tomescu-Dumitrescu 2020). Similarly, D’Angelo et al. (2022) also argued that human resources had to play a leading role in developing human capital management on the basis of caring, evaluating, developing, and training. Additionally, the government was required to devote time to human resource management and reach the strategic goals of the banking industry (Van Hoa et al. 2022). Human resource management was even considered to be able to mitigate barriers and ensure effective and sufficient manpower in the Banking 4.0 era (Kuchciak and Warwas 2021). Additionally, human resource managers could apply human resource information systems to make strategic decisions with the help of timely and effective information (Mohamed et al. 2022). It is no wonder that Islamic financial principles needed to be supplemented with managerial skills. This was because human resources in Islamic finance had not their reached optimal level, especially in the banking industry (Firdiansyah 2021). 2.3. Corporate Image Nedelchev (2003) stated that corporate image could be translated into identifying the corporation and communicating with its stakeholders. Osman et al. (2015) argued that even Islamic banks needed to incorporate a corporate performance image. It could be seen that corporate image was highly important, as expected. Furthermore, it was recognized that the positive corporate image (CI) served as a major factor for the stability of the banking system (Nedelchev 2002) and that corporate image had a positive effect on financial services provided by banks (Awan et al. 2018). Ologbenla (2021) even proposed that corporate governance could be substituted by corporate image, which had a significant influence on banks’ customer loyalty. In other words, banks needed corporate image to improve their competitiveness because corporate image was evaluated as more important than reputation by customers (Szwajca 2018). Furthermore, Ar and Abbas (2021b) argued that enterprises could boost their image through supporting the government and society, which could help them overcome the damaged caused by sickness and disease. J. Risk Financial Manag. 2022,15, 405 4 of 21 2.4. Location Advantage Banks had to consider various problems when attempting to find a suitable location for their branches. For example, the distance from customers to the branches should be minimized. This factor determined the customers’ attraction to the banks (Talatahari et al. 2022). Heard et al. (2017) also proposed that location decisions were important for local banks, and suggested that past visits were more suitable for explaining recent visits. Taking foreign banks in Spain as an example, the location of offices might be positive or negative, depending on various types of variables (Corra-Ariass 2020). Similarly, foreign banks in China did not receive the same benefits as domestic banks. Therefore, they could not perform as well as domestic banks even if they were in the same location. As a result, the cost of the disadvantages of being a foreign bank exceeded the cost of location disadvantages (Liu et al. 2021). 2.5. Innovation and Transformation Innovation was important for banks to respond to COVID-19, which combined both knowledge management and business strategy and affected innovation capabilities (Kien and That 2022). Similarly, Edeh et al. (2022) proposed that knowledge management had an important impact on innovation in the banking industry. Therefore, knowledge management implemented by managers was proven to increase innovation capabilities, including marketing, producing, and processing. Furthermore, the innovation performance of the accounting system had a positive effect on the business performance when managers required performance evaluations (Gazi et al. 2022a). On the other hand, the implementation of financial innovation from nonfinancial firms also had to be supported to reduce various challenges and barriers (Błach 2020). It could be seen that innovation was an important factor to determine sustainable development and played a leading role from a financial perspective (Ku´s and Grego-Planer 2021). 2.6. Marketing Strategy Interactive marketing and database marketing were the dominant roles played by banks (Choudhury et al. 2022). Ndegwa (2022) proposed that banks should employ electronic marketing strategies to increase their competitiveness in the local or international market. Özkaynar (2022) argued that banks’ marketing strategies had to incorporate new technologies such as the Metaverse, Blockchain, and Cryptocurrency. Islam et al. (2022) indicated that banks could gain more from event marketing than traditional advertising because the former could generate more attention than the latter. Simultaneously, Uksumenko et al. (2017) asserted that banks needed to adapt themselves to the fast changes and made use of digital marketing services such as Internet banking, mobile banking, and ATM, to connect their customers, which could help banks promote their products effectively and reach their marketing goals (Nguru et al. 2017). 2.7. Crisis Management Banks were asked to contribute to the stability of the market and society during the pandemic crisis. Making access to credit easier or keeping rates low were among the options (Ordonez-Ponce et al. 2022). To adapt crisis management strategies to financial crises, banks had to pay more attention to crisis management, and anti-crisis tools were consequently developed to improve management performance (Sinyagovsky 2021). Additionally, with the deterioration of the financial conditions derived from COVID-19 and military aggression in Eastern Ukraine, the anti-crisis management of banks was employed more broadly to maintain the stability of the financial market (Drahan et al. 2021). In other words, it was believed that anti-crisis management was a dominant factor to overcome these challenges and ensure stability and sustainability in the development of banks (Rushchyshyn et al. 2022). Therefore, it could be recognized that anti-crisis management required a conceptual basis, which determined how they prioritized their functions to reduce and neutralize a crisis (Kopylyuk et al. 2019). Finally, central banks had to play a significant role in J. Risk Financial Manag. 2022,15, 405 5 of 21 implementing crisis management and processing crisis resolution, though the crises were caused by imperfect market functioning (Singh 2018). According to the objective, method, and findings, the seven criteria are summarized in Table 1. Table 1. Summary of the seven criteria. Criteria Objective Methods Findings Headquarters system Being the first criteria of knowledge management, it was proven by the studies that banks’ headquarters play an important initiative on coping with the financial crisis and pandemic. Document analysis combined with qualitative and quantitative analysis was applied to the studies, to prevent any bias from either of analyses. •The 2008 financial crisis is reflected in the CSR disclosure quantity and readability of banks’ headquarters and subsidiaries (Laidroo and Ööbik 2013). •The headquarters of National Provincial Bank of England directly impressed its national identity (Barnes and Newton 2019). • The headquarters system recognizes the inherent organizational nature which can make successful change sooner in the post-pandemic era (Cebi et al. 2010). • The headquarters has to play a central role in some incentives in hiring, promotion, and punishing, as well as selective enforcement of rules (Broad 2007). Human Resources Through the studies related to human resource management, knowledge management can be strengthened to overcome the problems from COVID-19. Document analysis combined with qualitative and quantitative analysis was applied to the studies, to prevent any bias from either of analyses. •Improving the level of human resource management can promote the improvement of corporate performance (Wu et al. 2022). •The human resource management is already a priority element of the evolutionary strategies of a modern bank (Tomescu-Dumitrescu 2020). •Human resources in the banking industry will need to play a leading role to develop human capital management (D’Angelo et al. 2022). •The government is required to set a requirement to focus on developing human resources of the banking industry to ensure the completion of strategic goals (Van Hoa et al. 2022). •Human resource management practices are a solution to mitigate challenges and the HRM roadmap for banks will become a major guide to ensure effective workforce management (Firdiansyah 2021). Corporate image Because the importance of the corporate image is indisputable in any organizations, these studies confirmed the positive relationship between knowledge management and the corporate image. Document analysis combined with qualitative and quantitative analysis was applied to the studies, to prevent any bias from either of analyses. •The corporate image is in fact translation of the corporate identity (Nedelchev 2003). •The importance of corporate image in any organization is indisputable including those from Islamic banks (Osman et al. 2015). •The positive corporate image (CI) serves as a major factor for the stability of the banking system and the corporate image creates positive brand attitude and intention to use banking services (Nedelchev 2002). •The corporate image creates positive brand attitude and intention to use banking services (Awan et al. 2018). •The corporate image management has significant impact on customer retention of the banks (Ologbenla 2021). •The banks, whose reputation is rated better by the customers, also have a better and more coherent image in their minds (Szwajca 2018). •Enterprises could step in and support the government and provide aids to societies through their own networks, which could help them in terms of boosting their corporate image (Ar and Abbas 2021b). Location advantage Based on the studies associated with the location of banks, it was proven that the location decisions have an influence on the implementation of knowledge management. Document analysis combined with qualitative and quantitative analysis was applied to the studies, to prevent any bias from either of analyses. •Determining the location of bank branches needs to be focused under competitive conditions considering different levels of customer attraction (Talatahari et al. 2022). • The location decisions are potentially important for competition in local banking markets (Heard et al. 2017). • Location of offices is a positive variable in relation to the size of the financial market but negative in terms of the amount of foreign trade for both subsidiaries and branches (Corra-Ariass 2020). • The cost of location-based disadvantages outweighed the cost of bank-specific disadvantages for foreign banks (Liu et al. 2021). J. Risk Financial Manag. 2022,15, 405 6 of 21 Table 1. Cont. Criteria Objective Methods Findings Innovation and transformation Exploring these studies involving in innovation, the objective is to construct the relationship between knowledge management and innovation, and the results proved that there were casual relationships between them. Document analysis combined with qualitative and quantitative analysis was applied to the studies, to prevent any bias from either of analyses. •Innovation is considered an important factor in banks’ effectiveness and competitive advantage (Kien and That 2022). •Knowledge management has significant positive effects on innovation capability (Edeh et al. 2022). •Performance evaluation has become an essential tool for managers in the banking sector to achieve sustainable balanced scorecard systems (Gazi et al. 2022a). • Financial innovations aimed to reduce the barriers and support the implementation of financial innovations by nonfinancial firms (Błach 2020). •Innovation is an extraordinarily important determinant of the sustainable development of economies across the world (Ku´s and Grego-Planer 2021). Marketing strategy According to these studies on marketing strategies, combining knowledge management, it’s more probable for banking sector to respond to the impact of COVID-19. Document analysis combined with qualitative and quantitative analysis was applied to the studies, to prevent any bias from either of analyses. • Interactive marketing, as well as database marketing strategies, plays dominant roles in the success of banking business (Choudhury et al. 2022). • International environments has forced banks to adopt electronic marketing strategies to gain competitive edge (Ndegwa 2022) •The banking sector focused on technologies such as Metaverse, Blockchain, and Cryptocurrency (Özkaynar 2022). •Event marketing makes the bank more profitable than other advertising strategies (Islam et al. 2022). •Pursuing effective client policy in the context of marketing concept of the commercial bank’s development strategy should be considered (Uksumenko et al. 2017). •Banking sector is adapting drastic changes in their marketing strategies by using digital marketing services (Nguru et al. 2017). Crisis management Facing the impact of COVID-19, banks needed to pay more attention to the crisis management. Either crisis management or anti-crisis management achieved knowledge management to solve the problems derived from the pandemic. Document analysis combined with qualitative and quantitative analysis was applied to the studies, to prevent any bias from either of analyses. •Banks are called to contribute to society by easing access to credit or keeping rates low (Ordonez-Ponce et al. 2022). •Banks needs to pay more attention to crisis management (Sinyagovsky 2021). •Anti-crisis management needs to be introduced because the anti-crisis management of the bank can be carried out to diagnose, prevent, neutralize and overcome crisis phenomena (Drahan et al. 2021). •Anti-crisis management is relevant and necessary to ensure an adequate level of sustainability (Rushchyshyn et al. 2022). •The priority of functioning of bank and its task in preventing and neutralizing a crisis is an approach based on the interconnection of resources, opportunities, competitive advantages and strategy (Kopylyuk et al. 2019) • If a crisis occurs, central banks play a significant role in efficient crisis management(Singh 2018). 3. The Research Methodology and Design This research applied the CFPR process to assess the criteria required to implement KM in the banking industry. Herrera-Viedma et al. (2004) suggested the CFPR research methodology to design pairwise comparison preference predictions in decision-making models from a number of alternatives. Additionally, the research examined the consistency of the decision-making process (Herrera-Viedma et al. 2004,2007;Wang et al. 2016b). This study presents a number of descriptive definitions and propositions as follows. 3.1. Fuzzy Preference Relations Definition 1. According to fuzzy preference relations P, the alternative X was expressed using a positive preference relations matrix P ⊂ X × X belonging to the function: αp : X × X →[ 0, 1 ] . Furthermore, pij = αp ( xi , xj ) interpreted the degree of the preference intensity of the alternative xi over xj . If pij = ∑n i=1pij , the intimated indifference between xi and xj ( xi ~ xj ), pij = 1, denoted that xi was absolutely preferred to xj ; pij = 0 indicates that xj was absolutely preferred to xi ;and J. Risk Financial Manag. 2022,15, 405 7 of 21 pij > 1 2 showed that xi was preferred to xi ( xi > xj ). In this research, the preference matrix P was an additive reciprocal (Wang and Chang 2007;Chiclana et al. 2001;Herrera et al. 2001): pij +pji =1∀i,j ∈{1, 2, . . . n}(1) Proposition 1. Considering a set of alternatives, X = { xi , . . . , xn } were related to the reciprocal multiplicative preference relations A= (aij) with aij ∈ [ 1 9 , 9]. In addition, the parallelism of the reciprocal additive fuzzy preference relations P = ( pij ) with pij ∈ [ 0, 1 ] was associated with the formula for A, which is stated as follows: pij =gaij=1 21+log9aij(2) 3.2. Consistency of the Fuzzy Preference Relations Proposition 2. Let A = ( aij ) be a consistent multiplicative preference relation in which the parallel reciprocal additive fuzzy preference relation P = g (A) proved the additive transitivity property. Proof. For A = ( aij ) to be consistent, aij·ajk = aik ∀ i, j, k, or equivalently, aij·ajk·aki = 1 ∀ i, j, k. By assuming logarithms on both sides, log9aij +log9ajk +log9aki =0∀i, j . k. (3) Adding Equation (3) and dividing by Equation (2) on both sides gives: 1 2(1+log9aij) + 1 2(1+log9ajk) + 1 2(1+log9aki) = 3 2∀i, j,k (4) The fuzzy preference relations P=g(A), where pij =1 2(1+log9aij)affirms: pij +pjk +pik =3 2∀i, j,k (5) Undoubtedly, P= g(A) verifies the additive transitivity property. This research also considered the definition of CFPRs.  Definition 2. A reciprocal additive fuzzy preference relations P = (pij) is consistent if: pij +pjk +pki =3 2∀i,j,k =1, . . . ,n (6) 3.3. Additive Transitivity Consistency of the Fuzzy Preference Relations This research used the term “additive consistency” to refer to the consistency of the fuzzy preference relations of the additive transitivity property. Proposition 3. For a reciprocal fuzzy preference relation P = ( pij ), the following statements were equivalent: pij +pjk +pki =3 2∀i,j,k (7) pij +pjk +pki =3 2∀i<j<k (8) Proposition 4. A fuzzy preference relations P = (pij) is consistent if: pij +pjk +pki =3 2∀i≤j≤k (9) J. Risk Financial Manag. 2022,15, 405 8 of 21 Proposition 5. The equation for a reciprocal additive fuzzy preference relation P = ( pij ) is as follows: pij +pjk +pki =3 2∀i<j<k (10) pi(i+1)+p(i+1)(i+2)+. . . +p(j−1)j+pji =j−i+1 2∀i<j (11) The research established pairwise comparison matrices for ncriteria ( Ci ,i= 1, 2, . . . , n) in a hierarchical system. This provided the essential equations and decision matrices as follows: Ak= c1c2. . . cn−1cn c1 c2 . . . cn−1 cn         1ak 12 . . . × × ×1ak 23 × × . . .. . ........ . . × × . . . 1 ak (n−1)n × × . . . ×1         (12) The preference value ak ij in pk ij utilized an interval scale [0, 1] . Then, the preserved pk ij that was derived relied on the reciprocal transitivity property, as follows: Ak1 2(1+log9aij) ⇒=pk c1c2. . . cn c1 c2 . . . cn      0.5 pk 12 × × 1−pk 12 0.5 pk 23 × . . . 1 −pk 12 . . .. . . × × . . . 0.5      (13) The transformation function (x) was stated as follows (Herrera-Viedma et al. 2004): f:[−a,1 +a]→[0,1] f:[−a,1 +a]→[0,1] f(x) = x+a 1+2a The transformation function was formulated as follows (Wang et al. 2016a): fpk ij=pk ij +a 1+2a (14) The notation for the average integrated values of mevaluators was: pij =1 mp1 ij +p2 ij +. . . +pm ij (15) Normalized fuzzy preference relation matrix qij was aggregated to present the normalized fuzzy preference value for each criterion as follows: qij =1 n n ∑ i=1 pij (16) The number of influential criteria and priority of each criterion could be defined as follows: ωi=qij n ∑ i=1qij (17) J. Risk Financial Manag. 2022,15, 405 15 of 21 C5innovation and transformation ( 0.1307 )>C3 corporate image (0.1305) > C4 the location advantage (0.1003). The results indicated that the three most important influential criteria identified by the experts were the headquarters system (0.1861), human resources (0.1668), and crisis management (0.1515), and the four least important criteria were marketing strategy (0.1340), innovation and transformation (0.1307), corporate image (0.1305), and location advantage (0.1003). 4.3.2. The Influential Criteria were Calculated to Acquire Weights for Possibilities of Outcomes Each influential criterion in the banking sector was evaluated for its ability to implement KM and increase the probability of success (Yeung et al. 2016). The linguistic terms used by experts to determine the priority weight matrix of possible outcomes for each criterion are shown in Table 2. The prediction values of the two possible outcomes were analyzed as follows: (1) To assess the real-world situation for the banking sector during the pandemic period, the 15 experts were interviewed and asked to evaluate which influential criteria were most essential. Table 11 indicates the selections made by the 15 experts in terms of their preference for increasing the probability of success according to each influential criterion. Table 11. Linguistic variables assigned to the priority weights of the two possible outcomes in Taiwan. E1E2E3E4E5E6E7E8E9E10 E11 E12 E13 E14 E15 FFFFFFFFFFFFFFF C1 s HF VHG VH H H H H H F VHG H VHG VH VHG VHG C2 s VHG VHG HFH VHG VHG H VHG HF LHF VHG VH H VHG C3 s H VHG VH LH H LHF H HF LVH VHG HHHHH C4 s F VHG HF VHG VHG F F H VHG F LHF H VHG LH H C5 s VHG LHF H F LHF H H VHG HF F VHG VHG H HF H C6 s VHG HHHHF VHG VHG VHG VHG VHG H VHG HF H C7 s VH H VHG F HF H H HF LVHG VHG H VH F LVHG VHG Note: S and F indicate the abbreviated success and failure, respectively. (2) The process for translating the linguistic variables into parallel numbers is illustrated in Table 2. The function iqk uv =1 21+log5ibk uv was applied to transform the values on a scale of [ 1 5 , 5] into the interval [0, 1]. The preference data were transformed into the possible outcomes of success, as shown in Table 12. Table 12. Transformed preference weight for possible outcome of “success” in Taiwan. E1E2E3E4E5E6E7E8E9E10 E11 E12 E13 E14 E15 qSF F F F F FFFFFFFFFFFTotal Average C1 s 0.7153 0.9307 1.0000 0.8413 0.8413 0.8413 0.8413 0.8413 0.5000 0.9307 0.8413 0.9307 1.0000 0.9307 0.9307 12.9165 0.8611 C2 s 0.9307 0.9307 0.8413 0.5000 0.8413 0.9307 0.9307 0.8413 0.9307 0.7153 0.2847 0.9307 1.0000 0.8413 0.9307 12.3799 0.8253 C3 s 0.8413 0.9307 1.0000 0.1587 0.8413 0.2847 0.8413 0.7153 0.0000 0.9307 0.8413 0.8413 0.8413 0.8413 0.8413 10.7505 0.7167 C4 s 0.5000 0.9307 0.7153 0.9307 0.9307 0.5000 0.5000 0.8413 0.9307 0.5000 0.2847 0.8413 0.9307 0.1587 0.8413 10.3360 0.6891 C5 s 0.9307 0.2847 0.8413 0.5000 0.2847 0.8413 0.8413 0.9307 0.7153 0.5000 0.9307 0.9307 0.8413 0.7153 0.8413 10.9292 0.7286 C6 s 0.9307 0.8413 0.8413 0.8413 0.8413 0.5000 0.9307 0.9307 0.9307 0.9307 0.9307 0.8413 0.9307 0.7153 0.8413 12.7779 0.8519 C7 s 1.0000 0.8413 0.9307 0.5000 0.7153 0.8413 0.8413 0.7153 0.0693 0.9307 0.8413 1.0000 0.5000 0.0693 0.9307 10.7266 0.7151 (3) The reciprocal additive transitivity property was applied, and the opposite comparison was made for failure, as shown in Table 13. J. Risk Financial Manag. 2022,15, 405 16 of 21 Table 13. Opposite comparison matrix for possible outcomes of “failure” in Taiwan. E1E2E3E4E5E6E7E8E9E10 E11 E12 E13 E14 E15 qFS ss s s sssssssssssTotal Average C1 F 0.2847 0.0693 0.0000 0.1587 0.1587 0.1587 0.1587 0.1587 0.5000 0.0693 0.1587 0.0693 0.0000 0.0693 0.0693 2.0835 0.1389 C2 F 0.0693 0.0693 0.1587 0.5000 0.1587 0.0693 0.0693 0.1587 0.0693 0.2847 0.7153 0.0693 0.0000 0.1587 0.0693 2.6201 0.1747 C3 F 0.1587 0.0693 0.0000 0.8413 0.1587 0.7153 0.1587 0.2847 1.0000 0.0693 0.1587 0.1587 0.1587 0.1587 0.1587 4.2495 0.2833 C4 F 0.5000 0.0693 0.2847 0.0693 0.0693 0.5000 0.5000 0.1587 0.0693 0.5000 0.7153 0.1587 0.0693 0.8413 0.1587 4.6640 0.3109 C5 F 0.0693 0.7153 0.1587 0.5000 0.7153 0.1587 0.1587 0.0693 0.2847 0.5000 0.0693 0.0693 0.1587 0.2847 0.1587 4.0708 0.2714 C6 F 0.0693 0.1587 0.1587 0.1587 0.1587 0.5000 0.0693 0.0693 0.0693 0.0693 0.0693 0.1587 0.0693 0.2847 0.1587 2.2221 0.1481 C7 F 0.0000 0.1587 0.0693 0.5000 0.2847 0.1587 0.1587 0.2847 0.9307 0.0693 0.1587 0.0000 0.5000 0.9307 0.0693 4.2734 0.2849 (4) The rating of possible outcomes was synthetically acquired by applying Equation (20) , as shown in Table 14. Equations (21) and (22) could then be applied to synthesize and normalize the fuzzy preference ratings of the possible outcomes, relying upon the seven influential criteria. The normalized values and priority weights are listed in Table 15. Table 14. Normalized values and priority weights of possible outcomes relying on seven criteria in Taiwan. Success Failure Total Average c1Success 0.7826 0.6327 1.4152 0.7076 Failure 0.2174 0.3673 0.5848 0.2924 c2Success 0.7411 0.6227 1.3638 0.6819 Failure 0.2589 0.3773 0.6362 0.3181 c3Success 0.6383 0.5891 1.2274 0.6137 Failure 0.3617 0.4109 0.7726 0.3863 c4Success 0.6166 0.5795 1.1961 0.5980 Failure 0.3834 0.4205 0.8039 0.4020 c5Success 0.6482 0.5930 1.2412 0.6206 Failure 0.3518 0.4070 0.7588 0.3794 c6Success 0.7714 0.6301 1.4016 0.7008 Failure 0.2286 0.3699 0.5984 0.2992 c7Success 0.6370 0.5885 1.2255 0.6128 Failure 0.3630 0.4115 0.7745 0.3872 Table 15. Prediction of “success” and “failure” probabilities in Taiwan. C1C2C3C4C5C6C7Prediction Probability Rank 1 2 6 7 5 4 3 priority Weight 0.1861 0.1668 0.1305 0.1003 0.1307 0.1340 0.1515 1.0000 Success 0.7076 0.6819 0.6137 0.5980 0.6206 0.7008 0.6128 0.6534 Failure 0.2924 0.3181 0.3863 0.4020 0.3794 0.2992 0.3872 0.3466 4.3.3. Determining the Prediction Values of Priority Weight Appling Equation (23), two possible outcomes were calculated by multiplying the priority weight, and the prediction weights determined the probabilities of success and failure from KM implementation, as shown in Table 14. For instance, the prediction weight was calculated as follows: Zsuccess = (0.1861 ×0.7076) + (0.1668 ×0.6819) + (0.1305 ×0.6137) + (0.1003 ×0.5980) + (0.1307 ×0.6206) + (0.1340 ×0.7008) + (0.1515 ×0.6128) = 0.6534, Zfailure = (0.1861 ×0.2924) + (0.1668 ×0.3181) + (0.1305 ×0.3863) + (0.1003 ×0.4020) + (0.1307 ×0.3794) + (0.1340 ×0.2992) + (0.1515 ×0.3872) = 0.3466 J. Risk Financial Manag. 2022,15, 405 17 of 21 5. Discussion and Implications of the Study 5.1. Discussion The empirical results showed that C1 the headquarters system (0.1861), C2 the human resource advantage (0.1668), and C7 crisis management (0.1515) were the three most important indicators for the banking industry to facilitate knowledge management and increase the probability of business success. The less important indicators were marketing strategy C6 (0.1340), C5 innovation and transformation (0.1307), C3 corporate image (0.1305), and C4 location advantage (0.1003) (see Tables 9and 14). However, after analyzing the predictions obtained from the questionnaires of experts and scholars, it was also found that the seven indicators that affected the successful implementation of the KM model were C1 the headquarters system (0.7076), C2 human resources (0.6819), C6 the marketing strategy (0.7008), C5 innovation and transformation (0.6206), C3 corporate image (0.6137), C7 crisis management (0.6128), and C4 the location advantage (0.5980). Among them, C1 the headquarters system (0.7076), C6 the marketing strategy (0.7008), and C2 human resources (0.6819) were more influential than the other four indicators to implement KM successfully (see Table 14). On the contrary, the seven index values that were expected to cause the implementation of the knowledge management model to fail were C1 the headquarters system (0.2924), C2 human resources (0.3181), C3 corporate image (0.3863), C4 the location advantage (0.4020), C5 innovation and transformation (0.3794), C6 the marketing strategy (0.2992), and C7 crisis management (0.3872) (Table 14). Again, it is worth noting that the four indicators included— C4 location advantage (0.4020), C7 crisis management (0.3872), C3 corporate image (0.3863), and C5 innovation and transformation (0.3794)—probably failed to implement knowledge management successfully. Based on Table 14, it was proven that the successful indicators for the banking industry to facilitate KM implementation were different, with or without multiplying the priority weight and predicting weights of the probabilities of success and failure from knowledge management implementation. However, the predictions of success probabilities (0.6354) were higher than those of failure probabilities (0.3466) among the seven influential criteria. In other words, the predictions of success probabilities (0.6354) were higher than those of failure probabilities (0.3466) among the seven influential criteria. Therefore, predictions of success probabilities needed to be emphasized to implement knowledge management within the banking industry in Taiwan after weighing the probabilities of success and failure. Interestingly, though C7 crisis management was listed as an important priority indicator to conduct knowledge management, it was not listed in either the priority indicator of the successful implementation of the knowledge management model or the failure of the implementation of knowledge management. The result implied that, compared to other countries, the banking industry in Taiwan was more conscious of the threat of the pandemic and understood how to respond to it. 5.2. Implications of the Study The results of the above discussion showed that the probability of the successful implementation of knowledge management was higher than that of failure to do so. More specifically, both C1 the headquarters system and C2 human resources were regarded as the most important indicators among the seven criteria when considering the probability of the successful implementation of knowledge management. This study contributes to various industries and policymakers theoretically and practically because the results explicitly revealed that there was an alternative to the traditional countermeasures when coping with the unprecedented pandemic effectively and efficiently, especially when they were also knowledge-intensive industries. J. Risk Financial Manag. 2022,15, 405 18 of 21 6. Conclusions and Limitations 6.1. Conclusions Facing the challenges derived from COVID-19, the banking industry in Taiwan has adopted various measures to respond to the threat of the pandemic, but it seemed that the problem had not changed or improved obviously. Based on knowledge management and applying consistent fuzzy preference relations (CFPRs), it was found that the results of the study can practically or theoretically contribute to other industries in Taiwan. According to the earlier studies in the paper, the most commonly used countermeasures were financial digitalization technology, including Fintech online payment and big data from the FinTech website, and corporate governance, which had been revealed as the moderator of knowledge management, business strategy, and innovation capabilities. It is clear that these studies focused on partial countermeasures, such as headquarters, human resources or innovation, but did not employ knowledge management combined with the seven criteria to respond to the pandemic. In other words, in contrast to the practical measures to respond to the pandemic, such as adopting social distancing, isolation periods, and vaccinations, the study highlighted the importance of implementing knowledge management through seven influential criteria applied in the banking industry in Taiwan, which were found to have made significant contributions, as follows. First, the initial result showed that the headquarters system, human resource advantage, and crisis management are the three most important indicators for the banking industry to facilitate knowledge management (see Table 9). Second, considering the prediction of success probabilities, the headquarters system, the marketing strategy, and human resources are more influential than the other four indicators for the successful implementation of knowledge management. Conversely, the headquarters system and human and crisis management are more influential than the other indicators when considering the probability of failure to implement knowledge management (see Table 14). Third, the predictions of success probabilities are higher than those of failure probabilities among the seven influential criteria (see Table 14). This means that predictions of success probabilities need to be emphasized to implement knowledge management if the banking industry in Taiwan wants to survive or succeed in their business operations. Specifically, according to the analysis in the Discussion section and its results, it is concluded that the headquarters system and human resources are the most important priority indicators among the seven influential criteria, which could help reduce the gap in knowledge because they both show the ability to ensure the successful implementation of knowledge management in the banking industry in Taiwan. Furthermore, due to Taiwan’s outstanding performance in managing the pandemic in its earlier stage, crisis management was not prominent in the criteria of knowledge management, which also represents another gap in the knowledge. This implied the significant policy implications within the banking industry and other industries because it has been proven that knowledge management can be employed as an alternative to the traditional tools of financial technologies or corporate governance to cope with the pandemic effectively and efficiently. 6.2. Limitations and Future Work In spite of the study’s substantial contributions, future research is needed to face several limitations. First, this study aimed to explore the banking industry in Taiwan during the pandemic period. As such, it is suggested that future studies should be expanded to compare other kinds of countries or industries. Second, owing to the pandemic, this study conducted 15 expert surveys. Therefore, it is suggested that future research should increase the sample size to promote the survey’s level representativeness. Third, though the pandemic situation has improved gradually in some countries, knowledge management still faces some difficulties and uncertainty. In other words, the financial industry, especially the banking industry, lacks successful knowledge management practices, which restricts business performance. Knowledge management researchers must recognize this phenomenon in order to build sustainable performance and competitive advantages within J. Risk Financial Manag. 2022,15, 405 19 of 21 the banking industry or service sectors. Since the findings revealed successful knowledge management strategies for the banking industry, it is clear that they should also be considered within other industries as a mechanism to improve competitiveness. This study encourages researchers to carry out more in-depth studies in similar fields. Author Contributions: Conceptualization, C.-H.C., W.-H.C., H.-C.H. and Y.-Y.S.; methodology, H.- C.H.; software, H.-C.H.; validation, C.-H.C., W.-H.C., H.-C.H. and Y.-Y.S.; formal analysis, C.-H.C., W.-H.C., H.-C.H. and Y.-Y.S.; investigation, C.-H.C. and H.-C.H.; data curation, C.-H.C., W.-H.C. and H.-C.H.; writing—original draft preparation, C.-H.C.; writing—review and editing, C.-H.C. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. References Abdul-Rahim, Ruzita, Siti Aisah Bohari, Aini Aman, and Zainudin Awang. 2022. Benefit–Risk Perceptions of FinTech Adoption for Sustainability from Bank Consumers’ Perspective: The Moderating Role of Fear of COVID-19. Sustainability 14: 8357. 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