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Article

Assessing the Role of Financial Literacy in FinTech Adoption by MSEs: Ensuring Sustainability Through a Fuzzy AHP Approach

by
Nargis Mohapatra
1,
Mousumi Das
1,
Sameer Shekhar
1,
Rubee Singh
2,
Shahbaz Khan
3,
Lalit Mohan Tewari
4,
Maria João Félix
5,* and
Gilberto Santos
5
1
School of Economics and Commerce (KSEC), Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar 751024, India
2
Institute of Business Management, GLA University, Mathura 281406, India
3
Department of Industrial Engineering, University of Tabuk, Tabuk 71491, Saudi Arabia
4
Department of Botany, DSB Campus, Kumaun University, Nainital 263001, India
5
Design School, Polytechnic Institute Cavado Ave, Campus do IPCA, 4750-810 Barcelos, Portugal
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(10), 4340; https://doi.org/10.3390/su17104340
Submission received: 11 April 2025 / Revised: 7 May 2025 / Accepted: 8 May 2025 / Published: 11 May 2025
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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Abstract

:
The transformation from traditional financial methods to the adoption of new financial technologies is a key challenge for micro and small business enterprises in ensuring sustainability. Therefore, the purpose of this study is to identify the key factors of financial literacy and examining their significance in adoption of FinTech by MSEs in Odisha that leads to sustainability of the firm. The study has been conducted using the MCDM technique integrating grey Delphi for relevant factor identification and Fuzzy AHP for prioritizing the factors of financial literacy. In total, 33 financial literacy factors were initially identified through extensive literature review, which were reduced to 18 factors using grey Delphi. Further, these factors were prioritized on the level of their significance towards FinTech adoption, using fuzzy AHP. The results of the study highlighted that financial knowledge holds greater significance among all four criteria of financial literacy, i.e., financial knowledge, financial behaviour, financial attitude, and financial training, towards adoption of FinTech ensuring sustainability in MSEs. The prioritization of the factors further revealed that knowledge of current products and services, evaluation of FinTech products, expenditure behaviour, assurance for the future security, and financial education have the most significant impact on the adoption of FinTech services by entrepreneurs. The study is innovative as it shows the importance of financial literacy factors in influencing the adoption of FinTech by micro and small businesses, which helps in ensuring firms’ sustainability. The results of the study have come up with some key factors, which need to be considered at priority basis if an MSE is willing to adopt FinTech services for its operations to ensure sustainable practices.

1. Introduction

In recent years, the financial services sector has witnessed a massive digital transformation with the advent of financial technology, abbreviated as ‘FinTech’ [1], which is being regarded as a flag bearer for ensuring sustainability in the service sector [2]. FinTech is the integration of prevailing digital technologies with financial products and services to alleviate the complex operations of financial institutions and provide better consumer experience [3,4]. It offers various innovative technologies like digital payments [5,6], cryptocurrencies [7], crowdfunding [8], robo-advisors [9], InsurTech [10], and stock trading applications [11] protected by the blockchain technologies [12], which enable greater responsiveness and transparency [13]. The adoption of FinTech services enhances a firm’s growth prospects by having better financial access via digital lending platforms [14]. This is an excellent path to achieve financial sustainability as the integration of FinTech enhances a firm’s operational capabilities and makes it competent in the market [15].
The FinTech growth rate keeps increasing in India as many individuals as well as organizations actively adopt the services offered by it. The major revenue generating sectors adopt FinTech services to enhance their money management mechanisms [16,17]. Micro and small enterprises (MSEs) are considered as the key business players, as they contribute towards a country’s innovation, employment, and growth [16]. The adoption of FinTech by MSEs is helping those enterprises to become innovative which drives them towards having sustainable business models that address social and environmental issues [18]. In the Indian context, the economy of the country is being upheld by the micro, small, and medium enterprises (MSMEs) through their noteworthy contribution towards the industrial development [19]. Among these enterprises, MSEs have started businesses that use intensive capital, for which technology acquisition and advancement act as a major enabler for its growth and survival, as it faces aggressive competition from the medium sector because of its operating scale [17].
The technological upgradation in MSEs is largely influenced by the financial knowledge and financial capability of the entrepreneurs that ensure sustainable practices [17]. Financial literacy is the culmination of investors’ and consumers’ knowledge of financial concepts and products, along their capacity and assurance to recognize opportunities and risks in the financial world to enhance financial decision-making and management for progressive well-being and optimum utilization of resources [20,21,22]. Inadequate financial literacy of MSE entrepreneurs acts as a major obstacle in acquiring relevant financial services provided by the financial institutions which leads to the downfall of the enterprises [23]. Thus, the success of these enterprises relies greatly upon the entrepreneurs’ financial literacy. Financial literacy of an entrepreneur refers to their knowledge about the financial matters and their skill to mitigate the risks and make appropriate investment decisions, ensuring sustainable business practices [24]. Ali et al. [25] stated that the entrepreneurs need to have basic financial education and knowledge on accounting, budgeting, costing, and basic economic indicators like interest rates, inflation rates, and exchange rates. Additionally, sound financial literacy among the entrepreneurs can help the MSEs in taking smart financial decisions, enhancing risk management and enabling resource optimization, thereby ensuring sustainability [26].
As of 2019, Odisha had a financial literacy rate of only 11% and stood amongst the states having the lowest rates in India [27]. Although Odisha has been actively taking initiatives to increase its financial literacy rate, no official survey has yet been conducted to provide validation with updated figures. Thus, it is important to study the impact financial literacy factors on FinTech adoption by MSEs in Odisha. Therefore, the study focuses on identifying the key financial literacy factors that affect the entrepreneurs’ decision and ability to adopt FinTech services offered by various financial institutions to implement them in MSEs in Odisha and ensure sustainability. Further, each identified factor might not have the same impact on the adoption of FinTech, so it is important to prioritize the factors as most significant, moderately significant, and least significant, particularly for MSEs. Thus, the study also aims at prioritizing the factors of financial literacy that influence the adoption of FinTech by MSEs towards sustainability in Odisha.

1.1. Research Questions: The Study Has Been Conducted Around the Following Research Questions

  • What are the different financial literacy factors influencing FinTech adoption by MSEs, ensuring sustainability?
  • Which of the financial literacy factors can be prioritized for FinTech adoption by MSEs, ensuring sustainability?

1.2. Research Objectives: The Following Objectives Have Been Undertaken in the Study

  • To identify the relevant financial literacy factors influencing FinTech adoption in MSEs ensuring sustainability.
  • To examine the significance and develop a hierarchy reflecting prioritization of each financial literacy factor influencing FinTech adoption by MSEs ensuring sustainability.
To achieve the results for the developed research objectives, the following sections include the literature review, methodology, results and discussions, and conclusion. The literature review reveals the financial literacy factors identified for the study followed by the methodology, which includes sub-sections describing the procedure for grey Delphi, used to finalize factors and fuzzy AHP, used to prioritize the finalized factors. The results and discussion section gives a detailed insight about the results obtained through grey Delphi and fuzzy AHP analysis. Finally, the conclusion section includes the concluding remarks, limitations and implications of the study.

2. Literature Review

Financial literacy level of the entrepreneurs significantly affects the growth of MSEs and a study in Punjab reveals that successful MSEs have higher financial literacy rate than other manufacturing factors, and entrepreneurs having higher education and knowledge have higher level of financial literacy [24]. Further, the Bank of Japan conducted a survey and developed a financial literacy index relating to financial decision-making skills and financial knowledge, which provided empirical evidence that higher financial literacy is positively associated with higher usage of FinTech services [28]. Entrepreneurs’ basic financial knowledge on costing, budgeting and accounting is a crucial growth and survival factor for MSEs [20]. Further, studies reveal that financial literacy acts as a moderating factor for influencing digital transformation effects on FinTech adoption [29]. A study on SMEs in Malaysia reveals that i-FinTech adoption is significantly influenced by levels of financial literacy and digital financial literacy [30]. AlSuwaidi and Mertzanis [31] conducted a study on the association of financial literacy and FinTech growth across 114 countries using panel data regression and implied that higher financial literacy level can act as an impetus to expand the FinTech market with financial knowledge having a vital role in promoting the adoption and utilization of FinTech services. Additionally, trust, safety, and financial literacy are found as significant determinants for FinTech adoption [32]. Inder et al. [33] used fuzzy AHP and considered financial knowledge, financial behaviour, financial training, financial attitude. and financial culture as the five major criteria to assess the financial literacy among B-school students, among which financial training was found to be the most significant and financial culture as the least significant criteria. Further, Sharma [34] found the enablers to financial literacy using DEMATEL and suggested that in the perspective of government agencies, money management is the key factor in determining financial literacy. Agrawal et al. [35] revealed the cause-and-effect relationship among financial literacy enablers in SMEs using DEMATEL, where they found top management commitment, policies, and willingness to change as cause variables, whereas, economic benefits to employees, and skill factors are regarded as effect variables. Further, another study using fuzzy AHP prioritizes knowledge and skills of the individuals for the SMEs to be ready for digitalization [36]. Andreou and Anyfantaki [37] highlight a positive statistical relationship between level of financial knowledge and internet banking usage frequency. Further, the factors related to financial literacy, i.e., financial literacy awareness and financial literacy competency were found having a positive impact on sustainable growth in North India, using PLS-SEM [38]. Seraj et al. [39] conducted a study on the entrepreneurs in Saudi and used PLS-SEM which revealed a significant positive effect of financial literacy on entrepreneurial competency and entrepreneurial resilience.
The interlinkage among FinTech adoption, financial literacy and corporate sustainability is investigated in a study, proving that FinTech adoption and financial literacy both are essential for the sustainability in business practices as they enhance environmental performance, economic performance and social performance [40]. A firm’s FinTech usage might drive environment protection since it can enhance environmental investments by reducing carbon emissions and improving resource efficiency [41]. Further, adoption of FinTech can improve firms’ capability building, which enables them to compete and thrive in the industry [42]. Moreover, the integration of FinTech and knowledge of finance and sustainability significantly influence the Indian garment sector in promoting and attaining environmental, economic, and social sustainability achievements through the amplification of information flow [43]. Studies reveal that financial literacy has an impact on utilization of FinTech services and the sustainability of MSMEs is indirectly influenced by financial literacy, as financially literate entrepreneurs can utilize FinTech more efficiently [44,45]. Thus, the study focuses on determining the financial literacy factors that affect the adoption of FinTech in MSEs ensuring sustainability.
Several studies have been conducted to measure the significance of financial literacy, assessing the business performance and investment decision as well as their significance in adoption of FinTech by business organizations from which 33 financial literacy factors have been identified for the study, which are summarized below in Table 1.
Table 1 lists out the financial literacy factors that are identified through an extensive literature review along with a brief description of each factor. A total of 33 factors have been identified that are divided into four main criteria, which are financial knowledge, financial behaviour, financial attitude, and financial training. Further, as these factors are the initially identified factors, they are abbreviated with codes having initials ‘IF’, which is used while conducting grey Delphi analysis in the forthcoming section.

Research Gap

The literature reveals that most of the studies pertaining to financial literacy and FinTech have been conducted separately [17,25,26,30,32,36,53]. Several studies have mentioned the financial literacy as a catalyst to the success of the business enterprise, and many research have studied the impact of FinTech on business and organizational performance. Few studies have taken these two aspects together where Financial Literacy and FinTech both have been taken as predictor to the business performance and success. One study has also been found analyzing the impact of financial literacy on FinTech adoption by small and medium enterprises (SMEs). But none of the studies have considered the assessment of financial literacy factors on FinTech adoption by micro and small enterprises. In a country like India where the micro and small enterprise sector contributes around 30–35 percent to the GDP and around 40 percent to India’s total export, the use of FinTech becomes quite rational. Therefore, it becomes essential to study the significance of financial literacy factors, which are held largely responsible for ensuring the adoption of FinTech by MSEs. With these aspects in mind, the present study was undertaken.

3. Methodology

The study is exploratory in nature which has been conducted along structured and integrated methodology comprising an extensive literature survey to identify the initial set of financial literacy factors, grey Delphi method to finalize the relevant factors influencing FinTech adoption, and Fuzzy AHP to prioritize the finalized factors. A comprehensive methodological framework has been presented in Figure 1, reflecting the chronological order of the study design.
In the 1st phase of the study, an extensive literature survey was conducted that helped in identification of 33 factors of financial literacy. This was followed by phase 2 research design under which grey Delphi analysis has been used for finalizing the list of the relevant financial literacy factors influencing adoption of FinTech by MSEs. As all 33 identified factors might not have significant influence on the adoption of FinTech particularly in MSEs, experts’ opinions have been taken into account using grey Delphi to determine the factors. Thereafter, the 3rd phase of the framework reveals that the responses collected from experts have been analyzed using the Fuzzy AHP method that ultimately resulted in the prioritization of the financial literacy factors influencing FinTech adoption ensuring sustainability of MSEs in Odisha.

3.1. Selection of Experts

The study has been conducted along the MCDM technique that seeks expert opinion on the factors of financial literacy and their relevance in the adoption of FinTech by MSEs. Different studies have suggested different number of experts as a minimum sample size for conducting grey Delphi and Fuzzy AHP [72,73]. Though there are few studies [74,75] which confirm the minimum number of samples for conducting grey Delphi and fuzzy AHP may be 8–10 and 8–16, respectively. However, Jorm [76] states that no specified minimum number is required for the size of a panel of experts and that higher number of experts will provide more consistent outcomes. Therefore, this study has taken a sample of 14 panel experts to conduct grey Delphi and Fuzzy AHP. To have a comprehensive and rational opinion from different perspectives, the panel comprises 10 professionals at the top-level management from MSEs in Odisha with more than 5 years of experience in handling financial technology at a firm level, and 4 academicians specialized in finance and management with more than 10 years of experience. The experts have been selected through purposive sampling based on the accessibility and feasibility. The responses of the experts were collected by personal visit and online platforms (email and Linkedin) through structured questionnaires. The details about the experts have been provided in Appendix A Table A1.
Further, the grey Delphi and Fuzzy AHP have been applied to reach the inferences along well-defined mechanism which have been presented under the appendices section. The grey Delphi process has been presented in Appendix B, and the Fuzzy AHP methodology adopted as presented in Appendix C.

4. Results and Discussions

To derive the results for the formulated research objectives, grey Delphi and fuzzy AHP analysis have been conducted. The initial financial literacy factors were identified through extensive literature review, which were finalized using grey Delphi analysis. Further, the F-AHP approach was adopted for prioritization of the finalized factors. The results derived from the analysis were discussed in this section.

4.1. Finalization of Factors Using Grey Delphi

Following the steps of grey Delphi, the responses from 14 experts are collected through questionnaires as discussed earlier in Section 3.1. The responses collected from the panel of experts in linguistic scale, depicting the level of significance of each identified financial literacy factor towards the adoption of FinTech by MSEs ensuring sustainability, are mentioned in Appendix D Table A4. These responses were then converted into grey values using the scale of conversion given in Appendix B Table A2; the responses with converted grey data are shown in Appendix D Table A5. Further, the overall grey weights were obtained by using Equation (A1), and these weights were then whitened by using Equation (A2). The calculated crisp weights obtained after whitening the grey numbers are shown in Table 2. The decision related to selection and rejection of the factors for the study is made by evaluating the crisp weight of each factor.
Previous studies on grey Delphi suggested that the threshold value for crisp weight should be 3.5 [77,78,79]. Thus, the factors having crisp weight greater than or equal to 3.5 were selected as the relevant factors for further study. Table 2 lists out the 19 factors finalized for the study along with those factors which were rejected. The result of the grey Delphi analysis was again shared with the experts for confirmation and validation. Further, based on the experts’ suggestions, two factors—‘knowledge about risk management’ and ‘risk & return’—were merged to form one factor, i.e., knowledge about risk and return. The names of the finalized factors were also modified as per the experts’ suggestion to make them more appropriate for the study based on the objectives framed earlier. Further, the selected financial literacy factors were segregated into two categories, based on their permanent and circumstantial influence on the adoption of FinTech. However, two of the factors, i.e., knowledge about risk and return and evaluation of financial products can have both permanent as well as situational influence on the adoption of FinTech. The segregation of the factors based on their influence have been presented in Table 3.
Based on the grey Delphi, the list of 18 finalized factors under four criteria of financial literacy, i.e., financial knowledge, financial behaviour, financial attitude, and financial training has been shown in Figure 2, along with the abbreviations for each factor, which are used while conducting F-AHP analysis.

4.2. Prioritization of Factors Using Fuzzy AHP

To prioritize the finalized financial literacy factors for adoption of FinTech services by MSEs ensuring sustainability, fuzzy AHP approach has been used. The experts’ responses were collected through questionnaires prepared along the 18 finalized factors as mentioned earlier. These responses were used to establish relationship among the criteria as well as for the sub-factors under each criterion. The responses from the experts who were part of the study were collected using the Likert’s comparative scale, which were later converted into triangular fuzzy numbers (TFN) based on the conversion scale presented in Table A6. The response of each expert was first structured in a pair-wise matrix to check the consistency of each matrix. Further, the data collected from the responses of all experts are aggregated using geometric mean to form the aggregated pair-wise comparison matrix, and then the final consistency ratio of each matrix is calculated, after which extent analysis method is used to rank the factors.
The matrix of pair-wise comparisons for the main criteria, which are financial knowledge, financial behaviour, financial attitude, and financial training, is shown in Table 4.
To calculate the consistency ratio of Table 4, first the TFN are de-fuzzified using the Equation (A14) to obtain the Fuzzy Crisp Matrix (FCM), which is presented in Table 5.
Thereafter, the largest eigenvalue (λmax) of FCM is calculated by adding the values derived by multiplying column sum of FCM with the respective Priority Vector Weight (PVW) of the matrix using Equation (A17). To obtain the PVW, a normalized matrix has been developed by dividing each element of FCM by the column sum of the matrix and the results derived are shown in Table 6.
The calculated λmax for the main criteria matrix is 4.1008. For four-dimension FCM, the given Random Consistency Index (RI) in Appendix C Table A3 is 0.9. Thus, the Consistency Index (CI) of the matrix using Equation (A15) was found as 0.0336, and the calculated Consistency Ratio (CR) along the CI using Equation (A16) has been obtained as 0.0373, which is less than 0.10. Hence, the pair-wise comparison matrix formed for the main criteria has been found consistent and acceptable for further calculation. In the similar process, the CR for each criteria matrix having their sub-factors has been calculated. Table 7, Table 8, Table 9 and Table 10 show the pair-wise comparison matrix of each criterion followed by the calculated CR of each matrix.
Table 7 shows the pair-wise comparison matrix for the criterion ‘financial knowledge’. It also shows that the CI for the matrix is 0.0378 and the calculated CR is 0.0338, which is less than 0.10 and thus acceptable for further calculation.
Table 8 shows the pair-wise comparison matrix for the criterion ‘financial behaviour’. It also shows that the CI for the matrix is 0.0464, and the calculated CR is 0.0516 which is less than 0.10, and thus, acceptable for further calculation.
Table 9 shows the pair-wise comparison matrix for the criterion ‘financial attitude’. It also shows that the CI for the matrix is 0.0778 and the calculated CR is 0.0695, which is less than 0.10 and thus acceptable for further calculation.
Table 10 shows the pair-wise comparison matrix for the criterion ‘financial training’. It also shows that the CI for the matrix is 0.0395 and the calculated CR is 0.0439, which is less than 0.10 and thus acceptable for further calculation.
Further, to calculate the criteria weight, first the criteria synthetic extent values were found by using Equation (A4). Then, the degree of possibility (V) of each criterion was determined by using Equation (A8), and the result is shown in Table 11.
As the lowest value determined for each criterion should be considered as weight vector, the values taken for further calculations are 1, 0.7170, 0.8932, 0.8062. Then, these values are normalized and the final criterion weight is derived, and criteria ranks are given, based on their weights, as mentioned in Table 12.
The similar method of calculation was adopted to calculate the local weights of the sub-factors under each criterion matrix. These local weights are multiplied with the criteria weight to derive the global weight of each factor, which is shown in Table 13. Thereafter, based on these global weights, the factors are given global ranks which show the level of importance of each factor, where 1 signifies as the most important.
Table 13 reveals the final prioritization of the financial literacy factors that influence FinTech adoption by MSEs. The factors are chronologically presented in Figure 3 on the basis of their priority.
Figure 3 reveals that knowledge about current product and services, evaluation of FinTech products, expenditure behaviour, assurance for future security, financial education, professional financial assistance, and portfolio and diversification behaviour are ranked as the seven most prioritized factors for FinTech adoption by MSEs having the global weights more than 0.08. An entrepreneur’s awareness about the different services offered by FinTech and his skill to evaluate the benefits attached to these services are prioritized as these help the entrepreneurs to take rational financial decisions and acts as a catalyst to adopt FinTech services, which enhances the firm’s future performance and reduces inefficiencies. This aligns with the previous findings as studies reveal that financial awareness and attitude ensure sustainability of a firm [80]. The willingness of the entrepreneur to spend money for the acquisition of new products significantly affects FinTech adoption, and thus the expenditure behaviour pattern is prioritized as the third factor, which aligns with previous studies showing financial behaviour as a vital factor ensuring sustainability in MSEs [81]. Assurance for future security of the enterprise ensures long-term operationality of the firm and motivates the entrepreneurs to adopt new technologies and find an optimum value for investments. Financial education and professional financial assistance have much less difference in prioritization as they both refer to the acquired knowledge about the finance mechanism through education or training from professionals and thus both have almost equal impact on the adoption of FinTech. The next 3 factors, training on use of FinTech, investment behaviour, and influence of competitors have moderate significance for the adoption of FinTech, with global weights less than 0.08 but more than 0.06. This can be confirmed as previous studies disclose that FinTech training enhances digital literacy and builds confidence among entrepreneurs to adopt different FinTech tools [82]. Further, sound investment decisions can guide MSEs toward sustainable investment decisions and contribute to economic sustainability [83] and the competitors influence can motivate MSEs to adopt FinTech. Self-efficacy, knowledge about risk and return, perception and opinion, making use of debit/credit cards, hard and soft skills and knowledge about data security and privacy are found to have less priority towards the FinTech adoption by MSEs with global weights less than 0.06 and more than 0.05. Although these factors are essential for FinTech adoption, their influence on MSEs can be weak. Sometimes, an entrepreneur’s ability to use FinTech might not be supported by institutional infrastructure and readiness [84]. Similarly, the knowledge about risk and return and data security might lack practicality in real scenarios. Perception and opinion are subject to change and are often led by misinformation, which makes it an unstable predictor [85], and the regular use of debit and credit cards does not necessarily equate to deep FinTech integration in firms. The readiness to adopt new technologies and knowledge about inflation have the least priority with the global weights less than 0.05. The readiness to adopt new technologies has a low priority as the MSEs focus more on catering to the customers’ needs, irrespective of their readiness. Similarly, inflation knowledge, although helpful to understand economic indicators, appears to be abstract and less actionable compared to practical FinTech benefits.

5. Conclusions

The purpose of this study is to identify the factors of financial literacy that influence the micro and small enterprises to adopt the FinTech services ensuring sustainability. The identified financial literacy factors were finalized through grey Delphi analysis and the factors were prioritized by fuzzy AHP analysis. The fuzzy AHP analysis revealed that financial knowledge holds most significant rank, followed by financial attitude, financial training, and financial behaviour. The hierarchy developed based on the global weights of all financial literacy factors presented that, knowledge about current products and services is most important factor in FinTech adoption by MSEs in Odisha ensuring sustainability. The subsequent factors in the hierarchy having significant influence on FinTech adoption were—evaluation of FinTech products, expenditure behaviour, assurance for the future security, and financial education, which were a combination of factors from each criterion of financial literacy. Financial knowledge held the most significance as it helps the MSEs to understand the market situation with innovative products and maintain the profit of the firm, which enables the firms to sustain in the market and perform well globally with the advanced technology. To create awareness and financially educate people, Odisha Grameen Bank operates nine financial literacy centres across various districts in the state [86]. This has developed the knowledge about different FinTech services and financial behaviour among the entrepreneurs by training and encouraging them to expand their business, which enhances sustainability of their businesses. Additionally, Odisha has also started the Mission Shakti financial inclusion initiative, which links self-help groups with banks and provides SHGs with access to institutional credit, thereby promoting sustainable livelihoods [87]. The expenditure pattern and budget planning of entrepreneurs to achieve more profit in business also influence the adoption of new financial technologies. Therefore, the government of Odisha also provides education on financial behaviour patterns so that entrepreneurs can channel their funds towards enhancing the operations of their firms. The adoption of FinTech shifts the MSEs towards a paper-less economy, which enhances the environmental sustainability. It was also found that the entrepreneurs who have received training in finance evaluate the digitally available financial services more efficiently, which signifies that financial training has a positive impact on FinTech adoption. Further, readiness to adopt new technology had a very low impact in adoption of FinTech services. This indicates that MSEs in Odisha adopt FinTech to cater to customer requirements, irrespective of their readiness.

5.1. Implication

The study has analyzed the financial literacy factors which are most relevant in ensuring the FinTech adoption by MSEs, and the result clearly shows that the factors related to financial knowledge and few of the financial training related factors have been found having highest weights in the top ten factors. It clearly suggests that the micro and small enterprises should focus on taking knowledge and training related factors into account on priority basis; therefore, financial training, specifically for evaluation of the FinTech products and receiving advice from the professionals who can better analyze the FinTech alternatives, becomes quite important. The results also find policy implications for the government to design such training programmes and develop the academic syllabi for vocational courses that impart knowledge specifically on different dimensions of FinTech adoption, which would encourage micro and small businesses in India to adopt the modern-day Financial Technology. The study also floats academic implication by defining the set of financial literacy factors refined through the grey Delphi method relevant for adoption of FinTech at the micro and small enterprise level. These factors may be further used for conducting research along other methodologies.

5.2. Limitations

The study was conducted with special reference to entrepreneurs of the MSE sector in Odisha; therefore, the results of the study cannot be generalized in the global context. Similar study may be conducted in other regional contexts or in the global context. The study excludes medium enterprises which contribute significantly to the Indian economy, indicating that the width of the study is a bit narrowed in its approach, as the medium enterprises certainly execute financial services along extensive use of technology as compared to MSEs. Thus, similar studies can be conducted taking into account medium enterprises as well. Further, the study focused on the factor prioritization using Fuzzy AHP; however, there is also room for assessing the cause–effect relationship between financial literacy factors and FinTech. Therefore, it is suggested that similar studies be conducted using DEMATEL for examining the cause–effect relationship.

Author Contributions

N.M. and S.S. have developed the idea of the study. M.D., R.S. and S.K. have worked on extensive LR and identification of the constructs of financial literacy. M.D., S.K. and G.S. developed the research design. M.D., R.S. and L.M.T. worked on grey Delphi. N.M., S.S. and R.S. conducted the Fuzzy AHP analysis. N.M., M.D. and S.S. worked on the results’ interpretation. M.J.F., S.K. and L.M.T. contributed to writing the implications of this study. G.S. contributed to this study as the project supervisor. All authors have read and agreed to the published version of the manuscript.

Funding

The project has not received funding from any agency or organization.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be made available on the request.

Conflicts of Interest

The study has no such conflict of interest as no part of the study has been submitted for publication or for any other purpose.

Appendix A

Table A1. Experts Profile in Brief.
Table A1. Experts Profile in Brief.
Sl. No.Expert’s DesignationType of
Organization		LocationQualificationExperienceFinancial Training
1Managing DirectorStainless steelRourkelaMBA17 years2
2Managing DirectorHandicraftPuriPhD19 years4
3Managing DirectorGlass and CeramicAngulPhD18 years6
4Managing DirectorHandicraftCuttackM.Com7 years1
5Managing DirectorHandicraftBhubaneswarMA in
Economics		6 years1
6CEOPlastic and Fibre IndustryAngulPhD13 years2
7CEOFood processingCuttackB. Com8 years1
8CEOCoir productsBhubaneswarBBA10 years3
9CEOTextilePuriMA in
Economics		7 years2
10CEOFisheries
production		JagatsinghpurMBA14 years5
11Associate ProfessorUniversityBhubaneswarPost Doctorate13 years3
12Associate ProfessorUniversityBhubaneswarPhD10 years2
13ProfessorUniversityCuttackPhD30 years6
14Associate ProfessorUniversityCuttackPhD12 years4
Source: Authors’ Compilation.

Appendix B

Grey Delphi:
The Delphi method was popularized by Dalkey and Helmer [88] that helps in refinement and identification of factors pertaining to specific reference. It is a well-known survey methodology that combines the views of experts on a particular issue to achieve mutual acceptance [89,90]. The grey Delphi method integrates the principles of the Delphi method with the grey set theory. The grey Delphi analysis is conducted along the steps suggested by [88] which have been adopted by other studies [77,91].
Step 1: Factors identification from literature review.
In this step, possible critical factors of financial literacy which play important role in adoption of FinTech services were identified through the literature review. Initially 33 financial literacy factors were identified with their respective categories. Along the identified factors, a questionnaire was developed to collect the experts’ opinion.
Step 2: By using linguistic scale, responses were collected.
The responses were collected from the experts on linguistic scale. The collected data on the scale is further converted into quantified significance value based on their respective assigned grey numbers which have been presented in Table A2.
Table A2. Linguistic Scales and their respective assigned grey numbers.
Table A2. Linguistic Scales and their respective assigned grey numbers.
Linguistic ScaleGrey Numbers
Very low Significance (VLS)[0, 1]
Low Significance (LS)[1, 2]
Medium Significance (MS)[2, 3]
High Significance (HS)[3, 4]
Very High Significance (VHS)[4, 5]
Source: [90].
Step 3: Demonstrate the grey numbers
The collected responses were converted into assigned grey numbers using Appendix B Table A2. As total 14 experts were selected for grey Delphi analysis, the opinion of the experts on each factor’s significance was evaluated by using Equation (A1) as used in the previous studies, who have theoretically considered total number of experts as ‘k’, which is 14 for this study [92,93]:
⊗ G i = ⊗ G i 1 + ⊗ G i 2 + … + ⊗ G i 10 + … + ⊗ G i 14 14
where ⊗ G i = overall evaluation of factor importance
⊗ G i 14 = 14 t h expert’s evaluation of factor i.
Step 4: Whitening the grey numbers
To calculate the general interval grey number ⊗ G i = [G, Ḡ] = [G’ € G|G ≤ G’≤ G], ⊗ is considered as the whitened value. Thereafter, the whitening can be performed by using Equation (A2), when the distribution of ⊗ is unknown [93,94,95].
⊗ = α · G + (1 − α) · Ḡ, α = [0, 1]
If α coefficient is 0.5, ⊗ is known as equal weight mean whitenisation. It is a frequently utilized value for α [93,94].
Step 5: Finalize the factors by setting a threshold value.
The last step is to finalize the factors by selecting a threshold value. The factors were finalized by comparing the overall score of each factor with the threshold value. The selection of the factors was based on the condition that ⊗ should be greater than or equal to the threshold value, i.e., 3.5.

Appendix C

Fuzzy AHP:
After finalization financial literacy factors, Fuzzy AHP method has been used to prioritize the factors according to their significance in the adoption of FinTech by MSEs ensuring sustainability. Fuzzy AHP is a method used for solving multi-criterion decision-making (MCDM) problems [96,97,98]. It has been adapted and modified over the years in various studies [99,100,101,102,103]. With the final set of 18 factors, a questionnaire on comparative Likert scale was developed, categorized under 4 criteria of financial literacy (i.e., financial knowledge, financial behaviour, financial attitude, and financial training), and was administered to all the 14 experts who were participants under grey Delphi method [104,105]. Thereafter, the responses collected from the experts have been analyzed using Fuzzy AHP method using the following steps:
  • Arrange the complex decision problem in hierarchy structure.
  • Determine the weight of the criterion using pairwise comparison at each level of the hierarchy.
  • To determine the final ranks, compute the normalized weights.
In a problem of prioritization, X = { x 1 , x 2 , … x n } represents the primary category elements as an object set and U = { u 1 , u 2 , … u n } represents the components of every category as a predetermined aim. Each item is taken into account, and in-depth analysis is carried out for every goal (gi) [100,101]. As a result, (m) extent analysis values can be derived for each item using Equation (A3):
T g i 1 ,   T g i 2 , … , T g i m ,   i = 1,2 … ,   n
where all T g i j , (j = 1, 2, …, m) are triangular fuzzy number.
The Steps of extent analysis method are mentioned below [100,101]:
Step 1: The Equation (A4) is utilized to show how valuable fuzzy synthetic extent is in relation to ith object.
S i = ∑ j = 1 m T j g i ⊗ ∑ i = 1 n ∑ j = 1 m T g i j − 1
To find out the expression ∑ j = 1 m T g i j , fuzzy addition operation of m extent analysis is performed, such as:
∑ j = 1 m T g i j = ( ∑ j = 1 m   t g i l , ∑ j = 1 m   t g i m , ∑ j = 1 m   t g i r )
To obtain the expression [ ∑ i = 1 n ∑ j = 1 m T g i j ] − 1 , the process of fuzzy addition is used with
T g i j (j = 1, 2, …, m) value, as follow:
∑ i = 1 n ∑ j = 1 m T g i j = ( ∑ i = 1 n t i l ,   ∑ i = 1 n t i m ,   ∑ i = 1 n t i r )
And finally, the inverse of the vector has been calculated with the help of Equation (A7),
[ ∑ i = 1 n ∑ j = 1 m T g i j ] − 1 = ( 1 ∑ i = 1 n t i l ,   1 ∑ i = 1 n t i m ,   1 ∑ i = 1 n t i r )
Step 2: Given that T1 and T2 are two fuzzy triangles, the likelihood that,
T 2 = ( t l , t m , t 1 r ) ≥ T 1 = ( t 1 l , t 1 m , t 1 r ), is explained as follows.
V   T 2 ≥   T 1 = s u p [ min ⁡ µ T 1 x ,           µ T 2 x ]
The Equation (A8), can be defined similarly as follows:
V ( T 2 ≥ T 1 ) = h g t ( T 1 ∩   T 2 ) = µ T 2 ( d ) = 1                                                                           i f   t 2 m ≥ t 1 m 0                                                                               i f   t 1 l ≥ t 2 r t 1 l − t 2 r t 2 m   ≥   t 1 r − t 2 m   ≥   t 1 l   O t h e r w i s e
The values of V 1 ( T 2 ≥ T 1 ) and V 2 ( T 2 ≥ T 1 ) are compulsory to calculate the values of P 1 and P 2 .
Step 3: To determine the convex fuzzy number’s overall degree of possibility as well as the convex fuzzy number’s other values T i (i = 1, 2, …, k) can be explained as followed:
V   ( T ≥ T 1 , T 2 , T 3 , … ,   T k ) = m i n   V   ( T   ≥   T i )
Assuming that,
d’ (Ti) = min V (Ti ≥ Tk)
for k = 1, 2, …, n; k ≠ i.
In Equation (A12), the weight vector was calculated by using Equation (A11).
W ’     ( d ’   ( T 1 ) ,   d ’   ( T 2 ) ,   d ’   ( T 3 ) , … d ’   ( T n ) ,   ) T
where, T i (i = 1, 2, …, n) are n separate components.
Step 4: Finally, by using Equation (A13), the weight vector was normalized, and the outcome is a non-fuzzy number that indicates the criterion’s priority weight.
W = ( d ( T 1 ) ,   d ( T 2 ) ,   d ( T 3 ) , … d ( T n ) ) T
where, W is a non-fuzzy number.
Step 5: It is important to verify the consistency of the pairwise matrices [77]. Thus, to de-fuzzify the matrix, the graded mean integration approach was used, in which the triangular fuzzy number P = (l, m, u) could be refined to a precise figure as follows:
P c r i s p = ( 4 m + l + u ) 6
Additionally, it is important to assess the pairwise comparison matrix’s consistency following de-fuzzification, which can be performed by calculating the consistency index (CI) values and consistency ratio (CR) by using Equations (A15) and (A16) correspondingly.
C I = λ m a x − n n − 1
CR = C I R I
where, λ m a x   , indicates the largest eigenvalue
n, indicates number of factors taken for pairwise comparison matrix.
RI, is the random index of consistency given in Appendix CTable A3
λ m a x can be calculated by using Equation (A17)
λ m a x = Σ   ( [ Σ C j ]   ×   { W } )
where,  Σ Cj = aggregate of the columns of Matrix [C],
W = weight vector
The consistency of the matrix is deemed satisfactory if the computed value of CR is <0.10. If not, it is crucial to carry out the pairwise comparison method again.
Table A3. Random Consistency Index (RI) with relation to the matrix size.
Table A3. Random Consistency Index (RI) with relation to the matrix size.
Matrix Size12345678910
RI000.580.91.121.241.321.411.451.49
Source: [99].
Considering these 17 steps of Fuzzy AHP the analysis has been performed resulting in prioritization of the factors, which has been presented in detail under the results and discussions section.

Appendix D

Table A4. Experts’ responses for initial factors.
Table A4. Experts’ responses for initial factors.
Initial FactorsE1E2E3E4E5E6E7E8E9E10E11E12E13E14
IF1HSVHHSHSVHVHHSHSHSVHMSHSVHHS
IF2VHVHHSVHVHHSHSVHHSHSHSMSHSMS
IF3MSMSLSMSLSLSMSVLVLLSLSVLVLLS
IF4LSLSLSLSVLLSLSVLLSLSVLVLLSVL
IF5LSLSLSLSMSLSMSLSVLVLLSMSMSVL
IF6HSVHHSHSVHHSVHVHVHHSLSMSHSHS
IF7VHVHHSHSHSVHVHVHHSHSMSLSMSMS
IF8HSHSVHVHHSVHHSHSVHHSMSVHHSVH
IF9LSLSLSLSLSMSLSMSVLLSLSVLMSLS
IF10LSLSMSLSMSMSLSMSLSVLLSMSHSVL
IF11HSHSVHHSMSHSHSVHHSVHMSHSHSHS
IF12MSMSLSLSLSLSLSLSHSMSVLVLLSVL
IF13VHVHVHHSVHVHHSVHVHVHHSMSHSVL
IF14VHVHHSVHVHHSVHVHHSHSVHVHMSVL
IF15HSHSVHHSVHHSHSVHVHHSHSVHHSHS
IF16MSLSLSLSMSLSLSLSLSVLVLMSLSMS
IF17LSLSVLLSVLLSVLLSMSLSMSVLVLLS
IF18HSHSHSHSHSHSVHMSHSVHVHVHHSVH
IF19VLLSVLVLVLLSVLLSVLVLMSLSMSLS
IF20LSVLLSMSLSLSVLMSLSMSLSMSVLVL
IF21LSLSVLLSMSLSLSVLLSLSVLLSMSLS
IF22HSHSHSMSMSHSHSHSHSVHHSVHHSHS
IF23LSLSLSLSLSMSMSLSVLVLVLMSVHLS
IF24VHVHHSHSVHVHVHHSHSMSVLVHVHHS
IF25VHHSHSHSMSVHVHHSHSVHVHVLMSHS
IF26VHVHVHVHHSHSVHMSVHMSVLHSVHHS
IF27LSLSVLLSLSMSLSVLLSVLVLMSHSVL
IF28HSHSHSMSMSMSHSHSHSVHHSMSHSHS
IF29VHHSHSVHVHVHVHHSVHHSVHVHMSHS
IF30HSHSVHHSHSVHHSHSHSHSVHVHVHVH
IF31VLVLLSLSLSLSVLVLVHHSHSHSVHHS
IF32VHHSHSHSHSHSVHVHHSHSMSMSMSHS
IF33HSHSVHMSVHHSHSVHHSVHHSVHVHMS
Source: Authors’ Compilation.
Table A5. Grey data for conducting Delphi.
Table A5. Grey data for conducting Delphi.
IFsE1E2E3E4E5E6E7E8E9E10E11E12E13E14
IF1[3, 4][4, 5][3, 4][3, 4][4, 5][4, 5][3, 4][3, 4][3, 4][4, 5][2, 3][3, 4][4, 5][3, 4]
IF2[4, 5][4, 5][3, 4][4, 5][4, 5][3, 4][3, 4][4, 5][3, 4][3, 4][3, 4][2, 3][3, 4][2, 3]
IF3[2, 3][2, 3][1, 2][2, 3][1, 2][1, 2][2, 3][0, 1][0, 1][1, 2][1, 2][0, 1][0, 1][1, 2]
IF4[1, 2][1, 2][1, 2][1, 2][0, 1][1, 2][1, 2][0, 1][1, 2][1, 2][0, 1][0, 1][1, 2][0, 1]
IF5[1, 2][1, 2][1, 2][1, 2][2, 3][1, 2][2, 3][1, 2][0, 1][0, 1][1, 2][2, 3][2, 3][0, 1]
IF6[3, 4][4, 5][3, 4][3, 4][4, 5][3, 4][4, 5][4, 5][4, 5][3, 4][1, 2][2, 3][3, 4][3, 4]
IF7[4, 5][4, 5][3, 4][3, 4][3, 4][4, 5][4, 5][4, 5][3, 4][3, 4][2, 3][1, 2][2, 3][2, 3]
IF8[3, 4][3, 4][4, 5][4, 5][3, 4][4, 5][3, 4][3, 4][4, 5][3, 4][2, 3][4, 5][3, 4][4, 5]
IF9[1, 2][1, 2][1, 2][1, 2][1, 2][2, 3][1, 2][2, 3][0, 1][1, 2][1, 2][0, 1][2, 3][1, 2]
IF10[1, 2][1, 2][2, 3][1, 2][2, 3][2, 3][1, 2][2, 3][1, 2][0, 1][1, 2][2, 3][3, 4][0, 1]
IF11[3, 4][3, 4][4, 5][3, 4][2, 3][3, 4][3, 4][4, 5][3, 4][4, 5][2, 3][3, 4][3, 4][3, 4]
IF12[2, 3][2, 3][1, 2][1, 2][1, 2][1, 2][1, 2][1, 2][3, 4][2, 3][0, 1][0, 1][1, 2][0, 1]
IF13[4, 5][4, 5][4, 5][3, 4][4, 5][4, 5][3, 4][4, 5][4, 5][4, 5][3, 4][2, 3][3, 4][0, 1]
IF14[4, 5][4, 5][3, 4][4, 5][4, 5][3, 4][4, 5][4, 5][3, 4][3, 4][4, 5][4, 5][2, 3][0, 1]
IF15[3, 4][3, 4][4, 5][3, 4][4, 5][3, 4][3, 4][4, 5][4, 5][3, 4][3, 4][4, 5][3, 4][3, 4]
IF16[2, 3][1, 2][1, 2][1, 2][2, 3][1, 2][1, 2][1, 2][1, 2][0, 1][0, 1][2, 3][1, 2][2, 3]
IF17[1, 2][1, 2][0, 1][1, 2][0, 1][1, 2][0, 1][1, 2][2, 3][1, 2][2, 3][0, 1][0, 1][1, 2]
IF18[3, 4][3, 4][3, 4][3, 4][3, 4][3, 4][4, 5][2, 3][3, 4][4, 5][4, 5][4, 5][3, 4][4, 5]
IF19[0, 1][1, 2][0, 1][0, 1][0, 1][1, 2][0, 1][1, 2][0, 1][0, 1][2, 3][1, 2][2, 3][1, 2]
IF20[1, 2][0, 1][1, 2][2, 3][1, 2][1, 2][0, 1][2, 3][1, 2][2, 3][1, 2][2, 3][0, 1][0, 1]
IF21[1, 2][1, 2][0, 1][1, 2][2, 3][1, 2][1, 2][0, 1][1, 2][1, 2][0, 1][1, 2][2, 3][1, 2]
IF22[3, 4][3, 4][3, 4][2, 3][2, 3][3, 4][3, 4][3, 4][3, 4][4, 5][3, 4][4, 5][3, 4][3, 4]
IF23[1, 2][1, 2][1, 2][1, 2][1, 2][2, 3][2, 3][1, 2][0, 1][0, 1][0, 1][2, 3][4, 5][1, 2]
IF24[4, 5][4, 5][3, 4][3, 4][4, 5][4, 5][4, 5][3, 4][3, 4][2, 3][0, 1][4, 5][4, 5][3, 4]
IF25[4, 5][3, 4][3, 4][3, 4][2, 3][4, 5][4, 5][3, 4][3, 4][4, 5][4, 5][0, 1][2, 3][3, 4]
IF26[4, 5][4, 5][4, 5][4, 5][3, 4][3, 4][4, 5][2, 3][4, 5][2, 3][0, 1][3, 4][4, 5][3, 4]
IF27[1, 2][1, 2][0, 1][1, 2][1, 2][2, 3][1, 2][0, 1][1, 2][0, 1][0, 1][2, 3][3, 4][0, 1]
IF28[3, 4][3, 4][4, 5][2, 3][2, 3][2, 3][4, 5][3, 4][3, 4][4, 5][3, 4][2, 3][3, 4][3, 4]
IF29[4, 5][3, 4][3, 4][4, 5][4, 5][4, 5][4, 5][3, 4][4, 5][3, 4][4, 5][4, 5][2, 3][3, 4]
IF30[3, 4][3, 4][4, 5][3, 4][3, 4][4, 5][3, 4][3, 4][3, 4][3, 4][4, 5][4, 5][4, 5][4, 5]
IF31[0, 1][0, 1][1, 2][1, 2][1, 2][1, 2][0, 1][0, 1][4, 5][3, 4][3, 4][3, 4][4, 5][3, 4]
IF32[4, 5][3, 4][3, 4][3, 4][3, 4][3, 4][4, 5][4, 5][4, 5][4, 5][2, 3][2, 3][2, 3][3, 4]
IF33[3, 4][3, 4][4, 5][2, 3][4, 5][3, 4][3, 4][4, 5][3, 4][4, 5][3, 4][4, 5][4, 5][2, 3]
Source: Authors’ Compilation.

Appendix E

Table A6. Triangular Fuzzy Conversion scale.
Table A6. Triangular Fuzzy Conversion scale.
Linguistic ScaleTriangular Fuzzy ScaleTriangular Fuzzy Reciprocal Scale
Just Equal (JE)(1, 1, 1)(1, 1, 1)
Equally Important (EI)(0.5, 1, 1.5)(0.7, 1, 2)
Weakly Important (WI)(1, 1.5, 2)(0.5, 0.7, 1)
Strongly More Important (SMI)(1.5, 2, 2.5)(0.4, 0.5, 0.7)
Very Strongly More Important (VSMI)(2, 2.5, 3)(0.3, 0.4, 0.5)
Absolutely More Important (AMI)(2.5, 3, 3.5)(0.2, 0.3, 0.4)
Source: [100,101].

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Figure 1. Diagrammatic presentation of the research framework.
Figure 1. Diagrammatic presentation of the research framework.
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Figure 2. Graphical hierarchical structure of the financial literacy factors.
Figure 2. Graphical hierarchical structure of the financial literacy factors.
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Figure 3. Priorities of the finalized financial literacy factors.
Figure 3. Priorities of the finalized financial literacy factors.
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Table 1. Initial factors identified through extensive review of the literature.
Table 1. Initial factors identified through extensive review of the literature.
CriteriaFactorsCodeDescriptionReferences
Financial KnowledgeKnowledge about current products and ServicesIF1It relates to the awareness among the users about the digital products and services available in the market that offers a variety of facilities to the users.[46,47,48,49]
Financial educationIF2Financial education helps in assessing the products and services provided by FinTech more efficiently through knowledge and skills regarding the management of personal finances, financial decisions, and financial stability.[46,50,51,52]
Basic Knowledge about financeIF3The understanding about the basics of finance such as interest rate, inflation rate, tax knowledge, exchange rate, etc., leads to better perception of FinTech.[34,51,53]
Knowledge about Money ManagementIF4It includes budgeting, debt management, tax planning, retirement planning, credit management, etc., ensuring financial planning and management of FinTech solutions.[34,47,53]
Knowledge about Savings and InvestmentIF5It is the awareness regarding the different types of savings and investment opportunities available in the market with the adoption of FinTech and having the skill to extract the most benefits out of it.[34,47,53]
Knowledge about Risk ManagementIF6It is the skill to assess and averse the risk related to management of financial products and services acts as a driving force for FinTech adoption.[34,46,53]
InflationIF7It is the knowledge about the inflation and its effect on the FinTech products and services. Inflation can drive both individuals and organizations to FinTech solutions because they offer cost savings, higher efficiency, and better financial management.[37,47,48,52,54]
Risk and ReturnIF8It is the knowledge regarding perceived risk attached to financial products and services and the return to be extracted from it acts as a driving force for FinTech adoption.[33,52,55,56]
Interest ratesIF9It is the knowledge and skill of calculating the interest rates of the products and services offered by FinTech to assess which yields better return. It includes calculation of simple interest, compound interest, etc.[37,47,48,52]
DiversificationIF10It is the knowledge regarding diversification of business opportunities boosts FinTech adoption as it leads to risk reduction, new market accessibility, fosters innovation, cost efficiency, and improves user experience.[47,52,54,55]
Knowledge about data security and privacyIF11It shows the customer’s knowledge about data security and privacy concerns that are associated with the adoption of FinTech services. [57]
Financial
Behaviour		Basic Money ManagementIF12The money management trend and practice followed by an individual or a business leads to adoption of FinTech solutions.[47,54,58]
Savings behaviourIF13The behavioural savings pattern of individuals and businesses shows their constraint and perception towards availing the benefits of FinTech solutions.[46,55,58]
Investment behaviourIF14The behavioural investment pattern shows the willingness and capacity of an individual or business to explore the available financial products and services which leads to decision of FinTech adoption.[33,47,54,55]
Portfolio and diversificationIF15A diversified financial portfolio shows the willingness to explore different aspects of the market which leads to effective FinTech adoption.[59,60]
Financial participationIF16It is the participation of individuals in different financial activities with the help of Financial Technology.[59,61]
Making payments on timeIF17Individuals make payments of loans and bills and better understand the fraud associated with the financial transactions with the adoption of FinTech.[33,59]
Making use of debit/credit cards IF18This is the awareness about the digital debit and credit cards, loan facilities, insurance, and mutual funds with the FinTech adoption, which promotes cashless economy, ultimately connected to sustainable development goals 7 and 9.[33,47]
Household budgets managementIF19Household finance such as payments, lending, and investment decisions can be made with the adoption of FinTech.[33,35,47]
Financial
Attitude		Influence of parentsIF20Traditional thoughts shared by parents in adoption of FinTech services can influence the decision-making process.[33,50,51]
Financial responsibilityIF21Understanding of individuals’ responsibility towards financial management with adoption of FinTech plays an important role in FinTech adoption.[47,51]
Perception and OpinionIF22It refers to the opinion and perception about the consequences of adoption of FinTech services.[47,54]
Importance of savingsIF23It refers to the long-term perception of savings with the help of financial technology.[33,47,54]
Readiness to adopt new technologyIF24It is an individual’s or organizational preference to adopt FinTech services over traditional services, which drives financial inclusion through digital literacy.[62,63]
Importance of preparing for futureIF25It refers to secured financial planning for future with the adoption of FinTech assets.[47,49,54]
Influence of friends and peersIF26The degree of influence received from surrounding friends and peers to use FinTech influences the acceptance of service.[50,51,64]
Perceived DangerIF27Possibility of loss such as anxiety towards opportunity cost, danger, and uncertainty faced by the consumers affects the use of FinTech.[64]
Self-EfficacyIF28It is the trust or the individual’s belief in his or her capacity on successfully performing task with FinTech using different devices.[54,64]
Financial
Training		Received training in finance IF29It refers to specific financial training programmes for the individuals and organizations to access FinTech services and skills.[65,66,67,68]
Evaluating financial productsIF30Financial training given to the individuals to evaluate the financial products for usage and investment and help to achieve the better profit can influence FinTech adoption and ensure sustainability by choosing eco-friendly products and making smart investments plans.[65,69,70]
Investment related newsIF31Regularly reading news make the individual to understand the current market situation and risk management in financial technology.[33,71]
Taking professional adviceIF32It refers to seeking financial advice to access financial services and support for innovative start-ups in FinTech Sector.[33,70]
Hard and soft skillsIF33Soft skill training programmes provided by higher education in usage of FinTech can be helpful for FinTech adoption.[66,68]
Table 2. Results of grey Delphi analysis.
Table 2. Results of grey Delphi analysis.
CriteriaFactorsOverall Grey WeightCrisp WeightDecision
Financial KnowledgeKnowledge about current products and Services[3.29, 4.29]3.79Selected
Financial education[3.21, 4.21]3.71Selected
Basic Knowledge about finance[1, 2]1.5Rejected
Knowledge about Money Management[0.64, 1.64]1.14Rejected
Knowledge about Savings and Investment[1.07, 2.07]1.57Rejected
Knowledge about Risk Management[3.14, 4.14]3.64Selected
Inflation[3, 4]3.5Selected
Risk and Return[3.36, 4.36]3.86Selected
Interest rates[1.07, 2.07]1.57Rejected
Diversification[1.36, 2.36]1.86Rejected
Knowledge about data security and privacy[3.07, 4.07]3.57Selected
Financial BehaviourBasic Money Management[1.14, 2.14]1.64Rejected
Investment behaviour[3.29, 4.29]3.79Selected
Savings behaviour[3.29, 4.29]3.79Selected
Portfolio and diversification behaviour[3.36, 4.36]3.86Selected
Financial participation[1.14, 2.14]1.64Rejected
Making payments on time[0.79, 1.79]1.29Rejected
Making use of debit/credit cards and borrowing[3.29, 4.29]3.79Selected
Household budgets management[0.64, 1.64]1.14Rejected
Financial AttitudeInfluence of parents[1, 2]1.5Rejected
Financial responsibility[0.93, 1.93]1.43Rejected
Perception and Opinion[3, 4]3.5Selected
Importance of savings[1.21, 2.21]1.71Rejected
Readiness to adopt new technology[3.21, 4.21]3.71Selected
Importance of preparing for future[3, 4]3.5Selected
Influence of friends and peers[3.14, 4.14]3.64Selected
Perceived Danger[0.93, 1.93]1.43Rejected
Self-Efficacy[2.93, 3.93]3.43Selected
Financial TrainingReceived training in finance[3.5, 4.5]4Selected
Evaluating financial products[3.43, 4.43]3.93Selected
Reading news relating to investments[1.71, 2.71]2.21Rejected
Taking professional advice[3.14, 4.14]3.64Selected
Hard and soft skills[3.29, 4.29]3.79Selected
Source: Authors’ calculation.
Table 3. Division of factors based on influence on FinTech adoption.
Table 3. Division of factors based on influence on FinTech adoption.
Factors Having Permanent Influence on
FinTech Adoption		Factors Having Circumstantial Influence on
FinTech Adoption
  • Financial Education
  • Knowledge about Risk and Return
  • Knowledge about Inflation
  • Knowledge about Data Security and Privacy
  • Received Training in Finance
  • Evaluation of Financial Products
  • Hard and Soft Skills
  • Knowledge about Current Product and Services
  • Knowledge about Risk and Return
  • Expenditure Behaviour
  • Investment Behaviour
  • Portfolio and Diversification Behaviour
  • Making Use of Debit/Credit Cards
  • Perception and Opinion
  • Readiness to Adopt New Technology
  • Assurance for Future Security
  • Influence of Competitors
  • Self-Efficacy
  • Evaluation of Financial Products
  • Professional Financial Assistance
Source: Authors’ compilation.
Table 4. Pair-wise comparison matrix of financial literacy factors criteria.
Table 4. Pair-wise comparison matrix of financial literacy factors criteria.
FKFBFAFT
FK1, 1, 10.78, 1.16, 1.490.87, 1.39, 1.90.79, 1.31, 1.83
FB0.68, 0.87, 1.281, 1, 10.58, 0.86, 1.360.52, 0.69, 1.1
FA0.46, 0.63, 0.950.71, 1.16, 1.721, 1, 11.04, 1.5, 1.97
FT0.55, 0.77, 1.250.94, 1.41, 1.90.51, 0.69, 0.971, 1, 1
Source: Authors’ calculation.
Table 5. Fuzzy crisp matrix for financial literacy factors criteria.
Table 5. Fuzzy crisp matrix for financial literacy factors criteria.
FKFBFAFT
FK11.15531.38981.3112
FB0.905110.89680.7295
FA0.65491.177211.5016
FT0.80951.41350.70571
Source: Authors’ calculation.
Table 6. Normalized matrix of financial literacy factors criteria.
Table 6. Normalized matrix of financial literacy factors criteria.
FKFBFAFTPriority Vector Weight
FK0.29680.24340.34810.28870.2943
FB0.26860.21070.22460.16060.2161
FA0.19440.2480.25050.33060.2559
FT0.24020.29780.17680.22020.2338
λmax = 4.1008, CI = 0. 0.0336, CR = 0. 0373, Source: Authors’ Calculation.
Table 7. “Financial Knowledge” criteria pair-wise comparison matrix.
Table 7. “Financial Knowledge” criteria pair-wise comparison matrix.
FK1FK2FK3FK4FK5
FK11, 1, 10.7, 1.01, 1.31.4, 1.91, 2.411.24, 1.76, 2.271.54, 2.06, 2.57
FK20.77, 1, 1.41, 1, 10.92, 1.22, 1.681.91, 2.41, 2.921.07, 1.59, 2.11
FK30.43, 0.55, 0.780.59, 0.83, 1.111, 1, 11.49, 2.05, 2.590.5, 1, 1.5
FK40.4, 0.56, 0.770.32, 0.42, 0.540.36, 0.49, 0.671, 1, 10.46, 0.6, 0.92
FK50.38, 0.49, 0.670.47, 0.64, 0.950.7, 1, 21.11, 1.66, 2.181, 1, 1
λmax = 5.1511, CI = 0.0378, CR = 0.0338, Source: Authors’ Calculation.
Table 8. “Financial Behaviour” criteria pair-wise comparison matrix.
Table 8. “Financial Behaviour” criteria pair-wise comparison matrix.
FB1FB2FB3FB4
FB11, 1, 10.61, 0.84, 1.330.46, 0.63, 0.880.83, 1.38, 1.91
FB20.73, 1.21, 1.671, 1, 10.83, 1.35, 1.861, 1.54, 2.06
FB31.15, 1.65, 2.160.55, 0.76, 1.221, 1, 10.89, 1.36, 1.89
FB40.53, 0.73, 1.220.49, 0.66, 1.020.53, 0.76, 1.131, 1, 1
λmax = 4.1391, CI = 0.0464, CR = 0.0516, Source: Authors’ Calculation.
Table 9. “Financial Attitude” criteria pair-wise comparison matrix.
Table 9. “Financial Attitude” criteria pair-wise comparison matrix.
FA1FA2FA3FA4FA5
FA11, 1, 10.67, 1, 1.920.48, 0.68, 0.980.56, 0.78, 1.071.16, 1.66, 2.17
FA20.52, 1, 1.561, 1, 10.4, 0.5, 0.70.3, 0.4, 0.520.42, 0.58, 0.82
FA31.03, 1.52, 2.061.49, 1.99, 2.491, 1, 11.47, 2, 2.510.82, 1.34, 1.84
FA40.94, 1.34, 1.781.94, 2.45, 2.950.42, 0.53, 0.751, 1, 10.56, 0.78, 1.09
FA50.46, 0.62, 0.881.26, 1.73, 2.340.55, 0.78, 1.220.89, 1.34, 1.781, 1, 1
λmax = 5.311, CI = 0.0778, CR = 0.0695, Source: Authors’ Calculation.
Table 10. “Financial Training” criteria pair-wise comparison matrix.
Table 10. “Financial Training” criteria pair-wise comparison matrix.
FT1FT2FT3FT4
FT11, 1, 10.5, 0.67, 1.070.68, 1, 1.960.78, 1.12, 1.41
FT20.95, 1.5, 2.031, 1, 10.8, 0.94, 1.130.98, 1.52, 2.04
FT30.51, 1, 1.530.91, 1.09, 1.271, 1, 11.09, 1.6, 2.1
FT40.72, 0.9, 1.280.49, 0.66, 1.050.48, 0.65, 0.931, 1, 1
λmax = 4.1184, CI = 0.0395, CR = 0.0439, Source: Authors’ Calculation.
Table 11. V value results for criteria.
Table 11. V value results for criteria.
FKFBFAFT
V (FK ≥ ..) 111
V (FB ≥ ..)0.7170 0.81570.8992
V (FA ≥ ..)0.89321 1
V (FT ≥ ..)0.806210.9119
Source: Authors’ calculation.
Table 12. Results of criteria weight calculation.
Table 12. Results of criteria weight calculation.
CriteriaCriteria Weight (CW)Criteria Rank
FK0.29271
FB0.20994
FA0.26142
FT0.23603
Source: Authors’ calculation.
Table 13. Fuzzy local and global weight of financial literacy factors and their criteria.
Table 13. Fuzzy local and global weight of financial literacy factors and their criteria.
CriteriaCWFactorsLocal WeightLocal RankGlobal WeightGlobal Rank
FK0.2927FK10.304610.08921
FK20.284320.08325
FK30.201230.058912
FK40.029950.008818
FK50.180140.052716
FB0.2099FB10.230730.06759
FB20.300410.08793
FB30.283520.08307
FB40.185440.054314
FA0.2614FA10.192240.056313
FA20.079950.023417
FA30.298710.08744
FA40.226420.066310
FA50.202730.059311
FT0.2360FT10.234630.06878
FT20.300510.08802
FT30.283920.08316
FT40.181040.053015
Source: Authors’ calculation.
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Mohapatra, N.; Das, M.; Shekhar, S.; Singh, R.; Khan, S.; Tewari, L.M.; Félix, M.J.; Santos, G. Assessing the Role of Financial Literacy in FinTech Adoption by MSEs: Ensuring Sustainability Through a Fuzzy AHP Approach. Sustainability 2025, 17, 4340. https://doi.org/10.3390/su17104340

AMA Style

Mohapatra N, Das M, Shekhar S, Singh R, Khan S, Tewari LM, Félix MJ, Santos G. Assessing the Role of Financial Literacy in FinTech Adoption by MSEs: Ensuring Sustainability Through a Fuzzy AHP Approach. Sustainability. 2025; 17(10):4340. https://doi.org/10.3390/su17104340

Chicago/Turabian Style

Mohapatra, Nargis, Mousumi Das, Sameer Shekhar, Rubee Singh, Shahbaz Khan, Lalit Mohan Tewari, Maria João Félix, and Gilberto Santos. 2025. "Assessing the Role of Financial Literacy in FinTech Adoption by MSEs: Ensuring Sustainability Through a Fuzzy AHP Approach" Sustainability 17, no. 10: 4340. https://doi.org/10.3390/su17104340

APA Style

Mohapatra, N., Das, M., Shekhar, S., Singh, R., Khan, S., Tewari, L. M., Félix, M. J., & Santos, G. (2025). Assessing the Role of Financial Literacy in FinTech Adoption by MSEs: Ensuring Sustainability Through a Fuzzy AHP Approach. Sustainability, 17(10), 4340. https://doi.org/10.3390/su17104340

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