India is often listed among the countries with the highest adoption rates of FinTech globally, which is impressive. Recent reports citing Union Minister of Finance Nirmala Sitharaman states that the adoption rate is 87%, significantly higher than the global average of approximately 64-67%. UPI has been a game-changer, revolutionizing digital payments in India; with billions of transactions per month, it has made digital payments easy, accessible and widely accepted across a variety of societal segments, including rural areas. The National Payments Corporation of India (NPCI) introduced the Unified Payments Interface (UPI), which is a key component in India's digital payments revolution. UPI is a ground-breaking technology that consists of a collection of open-architecture, standard APIs that enable users to move money instantly across accounts. One of the main factors contributing to UPI's exponential growth in transaction volume and value is how simple it is to incorporate into any consumer tech platform, making digital payments very accessible across the country. UPI's global expansion to nations like the United Arab Emirates, Singapore and France further demonstrates its success. As more people grow accustomed to digital technologies, they are more inclined to adopt FinTech solutions for a range of financial needs. The swift growth of financial technology, or Fintech, is a major factor in the significant change that the modern financial sector is experiencing. For advancements in the area, particularly in Punjab, this dynamic climate sets a strong example. The Indian fintech industry is anticipated to expand at a compound annual growth rate (CAGR) of 30.55%, with projections suggesting that it might reach a value of $180–200 billion by 2029 and some estimates suggesting a possible market size of USD 421.48 billion by the same year. Although state-specific FinTech adoption figures for Punjab are not publicly available in the materials under review, data from the capital city and surrounding regions shows a strong desire for digital financial involvement. Chandigarh, a Union Territory that is the capital of both Punjab and Haryana, leads the market in this area with an average of over 38 digital payment transactions per person.

Financial technology, or fintech, is an acronym for cutting-edge digital solutions that provide a wide variety of financial services, mostly via online channels. These services include lending, investment management, typical banking operations and a range of payment options. The primary dependence of Fintech enterprises on technology and cloud services, contrasted to the physical branch networks that traditionally characterized financial institutions, is one of their distinctive features. This technology underpinning radically alters how people handle and engage with their finances, impacting procedures like saving, borrowing, investing, transferring, paying, and safeguarding money.

"The personal influence of opinion leaders is more powerful than mass media when it comes to persuading individuals."— Katz & Lazarsfeld, 1955, "Personal Influence

Opinion leaders are those whose decisions, thoughts and actions have significant effects on other people's decisions. They interpret and disseminate information to less engaged customers, acting as crucial intermediaries between the public and the mass media. Their impact extends beyond just disseminating information; it also includes influencing attitudes and directing behaviour within their social networks. A wide range of opinion leaders, each with a different effect through different channels, contribute to the Indian fintech ecosystem. These consist of financial experts, celebrity endorsers, social bloggers and digital influencers.

"In technology adoption, opinion leaders function as the bridge between innovators and the majority."— Wejnert, B. (2002). "Integrating models of diffusion of innovations."

The endorsements and recommendations offered by these opinion leaders possess an immense capacity to exert a profound impact on the extent to which FinTech solutions are adopted and utilized. Internet bloggers serve as disseminators of information and influence the behavior of their followers. The prominent position of opinion leaders is associated with status. Bloggers may not only facilitate the acquisition of new information but also serve as behavioral role models. Sometimes, people are eager to believe opinion leaders since they are the personification of positive ideals in the eyes of followers (H.Zheng, 2021).

As the market for FinTech services expands, there are several risks to these services. In order to create a safe and convenient service, it is necessary to establish the requirements and security issues for mobile Fintech payment services. Numerous mobile payment and security research are being carried out in order to safely offer such mobile payment services. By surveying and questioning mobile payment security issues from the customer's perspective, Linck et al. proposed a security guideline that satisfies the customer, while Kadhiwal et al. defined security methods that can be applied to mobile payments according to type and summarised security properties.

Opinion leaders are sometimes perceived as biased by the public who think that they are promoting the fintech for their personal benefits. Also, some people prefer their own independent financial decisions instead of relying on someone. The consumers sometimes get confused and reluctant to trust any one source due to information overload and contradicting advices from the various opinion leaders. The problem is not only with the opinion leaders, but sometimes people hesitate to use the fintech due to financial risks, limited digital literacy and regulatory issues as well.  So, there are different kinds of barriers that make consumers hesitate to use the FinTech with the recommendations of opinion leaders. The other significant obstacle is the accessibility of opinion leaders that may be over accessibility or under accessibility of the opinion leaders. Despite their general tech-savviness, opinion leaders may lack the specialized financial literacy required to fully grasp the intricacies and benefits of various FinTech innovations, such as blockchain-based solutions or algorithmic lending. This is coupled with resistance to change and entrenched traditional financial habits, as existing banking relationships and familiar processes often fulfill their current needs adequately, diminishing the perceived urgency to switch to new solutions.

Therefore, this paper focuses on examining the barriers due to which consumers are reluctant to use FinTech services through opinion leaders. The issues and barriers related to FinTech services like perceived financial risk, accessibility of opinion leaders, lack of trust, inefficient opinion leaders and uncertain regulatory concerns are examined in this paper that will depict why the consumers are not being easily convinced by Opinion Leaders. 

The data is derived from the fintech users of Punjab. Primary data was collected through an online survey distributed to 400 fintech users in urban and rural areas of Punjab. Secondary data was also included from sources such as journals, newspapers, reports, books and magazines etc. Purposive and snowball sampling techniques were used based upon the FinTech usage history of the respondents. All the items of the constructs other than demographic were measured with 5- point likert scale where 1 as Strongly Disagree, 2 as Disagree, 3 as Neutral, 4 as Agree and 5 as Strongly Agree. After adequately verifying the reliability and validity of the scale, data was collected from the structured questionnaire considering the pre-defined scale. The instrument (questionnaire) designed is divided into two parts; the first part covers the demographics of the respondents like age, gender, education and income etc. whereas the second part covers the statements related to different types of opinion leaders. To ensure reliability of the instrument, a pilot survey was conducted with 40 participants. The Cronbach’s alpha score of 0.891 indicated high internal consistency. To study these constructs, Factor analysis (EFA) was applied using the software like SPSS (25), AMOS (25) and MS Excel. Five different barriers related to opinion leaders like perceived financial risk, accessibility of opinion leaders, lack of trust, inefficient opinion leaders and uncertain regulatory concerns are examined in this paper. The hypotheses created for the study are:

• H1: Perceived financial risk is associated with the lower likelihood to adopt FinTech services through opinion leaders.
• H2: Lack of trust in FinTech and opinion leaders have significant impact on the consumers for resisting FinTech through opinion leader recommendations.
• H3: Uncertain regulatory concerns resist the consumers to adopt FinTech services.
• H4: Lack of expertise and inefficient opinion leaders is a barrier to adopt FinTech services.
• H5: Under/Over Accessibility of the opinion leaders has significant impact on the consumers for resisting FinTech through opinion leaders.

BARRIERS THAT RESIST INDIVIDUALS TO ADOPT FINTECH SERVICES

                                                              H1                                                                                  H4

                                                              H2

                                                                                                                                                       H5

                                                              H3

Research Model for Barriers related to Opinion leaders

Demographic profile of the respondents

The Table 1 shows that male respondents (56.5%) are more as comparison to female respondents (43.5%). The age distribution of the respondents shows that 20% are under 25 years old, 36.3% fall within the 25 to 34 age range, 30% are between 35 to 44 years, and 13.8% are aged 45 and above. The study on FinTech adoption with reference of opinion leaders included a well-balanced mix of age groups. Furthermore, the table depicts that most of the respondents are businessman (31.8%) and in education, maximum of the respondents are graduates (43.3%). Married respondents (60.3) are more as compared to the single (39.8%). The respondents are from the different regions of Punjab like majha, malwa and doaba. There is not much difference between their frequencies i.e. 33.3%.  Lastly, the income of the respondents is also measured, maximum of the respondents were between the income groups of 10000-25000, i.e. 33%.

Table 1.Demographic profile of respondents

 EXPLORATORY FACTOR ANALYSIS (EFA)

Factor analysis technique is applied that finds all the commonalities among the set of variables and divide a group of variables into fewer components. Another name for this technique is Data reduction. Exploratory factor analysis was conducted to check the face validity, reliability and the adequacy of the statements conducted as a means of data reduction, to see if the face validity of the items is held (Pallant, 2001). Since there are various approaches to do factor analysis, this study employed Principal Component Analysis (PCA) in conjunction with varimax rotation to achieve its objective. Prior to EFA, the appropriateness of data is assessed by using the Bartlett's Test of Sphericity, the Kaiser-Meyer-Olkin (KMO) measure, and the correlation matrix. A scale was developed to assess the elements that reflected the barriers of opinion leaders due to which consumers hesitate to follow their advice and resist using the FinTech services. The relevant literature was reviewed and the variables were chosen to determine the different types of barriers. The final of 18 items were chosen for the survey and the respondents were supposed to rate the items on five point Likert scale. A Likert scale (1 = Strongly Disagree to 5 = Strongly Agree) is used to measure responses of Fintech users.

The item-wise reliability analysis was done on few selected variables in order to determine which scale items should be retained and which should be eliminated in order to develop a reliable scale. In order to assess the reliability, uni-dimensionality has been examined through the validity and reliability. So the refined and purified scale was generated for this objective. During the reliability study, inter-item correlations and Cronbach's Alpha statistics were used to determine the degree to which the items were related to the set of items being studied.  The value of the Cronbach's alpha of the scale is 0.929 which is above the 0.6 i.e. the threshold limit. Therefore, it is the good indication (Cronbach, 1990). It is important to note that the inter-item correlation value is > 0.3 and corrected-item-total correlation is >0.5 both of which are acceptable for the reliability of the scale (Hair et al., 2009). According to Table 4.19, by using the Principal component analysis, the communality value of the constructs is >0.5 ranging from 0.649 to 0.847 which is sufficient for the justification of the constructs (Hair et al., 2009). Thus, all these values show the high correlations among the items which mean all the requirements for the validity, reliability and uni-dimensionality are fulfilled.

The Kaiser-Meyer-Olkin (KMO) value is used as an index for assessing the sampling adequacy of the data. The KMO measured value is 0.904 being excellent and exceeding the threshold limit of 0.5 as specified by Dimanche,F. et.al.(1991) and also provided a range for the level of adequacy, like <0.90 being excellent, 0.80-0.90 being best, 0.70-0.80 being good, and 0.50-0.70 being fair enough to apply further the factor analysis. The concept that the correlation matrix is an identity matrix is examined by using the Barlett's test of sphericity. Identity matrix implies the variables are not related and are not appropriate for the factor analysis if it has identity correlation matrix. Barlett's test suggests that the value should be less than 0.05 (Field, 2009). In this research, the Barlett's test results are significant with chi-square of 4774.773 (p-value <0.01) i.e. .00. Therefore, both the findings indicate that the factor analysis is adequate for the data set. (Table 4.20).

Correlation Analysis

Pearson Correlation coefficients were applied to check the correlation between all the variables. The main focus of the correlation study was to determine whether the eighteen significant factors were independent of each other or not. As a thumb rule, there is a weak relationship between the variables if the value of correlation coefficient (r) lies from 0 to 0.2. It is considered moderate if r values between 0.3 and 0.6 while 0.7 to 1 are strong (Dancey and Reidy, 2007). As per the scale used if all the 18 items get a rating of 5 each, the total score would be 90. The correlations between the various items in this study were strong and significant.

Table 2- Correlation Matrix

Source: Compiled from primary data

INTER-ITEM CORRELATION: Mean= 0.423, Minimum= 0.211, Maximum= 0.787,

                                                    Range= 0.576, Max/Min= 3.728, Variance= .015,

                                                    N=18

The correlation matrix is calculated as indicated in Table 4.21. Mean score of respondents was 64.68. The mean correlation was 0.423 and it varies from 0.211 to 0.787 with the range 0.576. There is a sufficient correlation to move forward with component analysis. The Factor analysis is conducted by using Varimax rotated and Principal Component Analysis.

Total Variance explained

To figure out the barriers due to which consumers are reluctant to use fintech services through opinion leaders, eighteen statements were grouped into five factors to reach to the specified objective. The outcome of rotated sums of squared loading indicates that the total variance explained by five factors was 75.995 which are seemed to be good for the effective implementation of the factor analysis. Additionally, Table 4.22 displays the eigen value which ranges from 2.232 to 3.207 and individual as well as cumulative % of variance of each factor is also given in the table.

Table 3. Total Variance explained

Source: Compiled from primary data

Factor Loadings and Cronbach's Alpha Results

Exploratory Factor analysis is used to analyze the data and to decide whether the data set is useful for the further study or not. If the findings fall within the set standards then the factor analysis is found satisfactory. For this purpose the factor loadings are examined and the factor reliability is checked with the help of Cronbach's Alpha value of each variable. The value of Cronbach's alpha of five factors was found above the threshold limit of 0.70 (Nunnally and Bernstein 1994). Hair et. al.(2006) state that the average factor loadings of all the items should be above 0.5.  As the data shown in the table, factor loading ranged from 0.658 to 0.867 which is above 0.5 and found satisfactory.

Table 4. Rotated Component Matrix

Extraction Method: Principal Component Analysis.   Rotation Method: Varimax with Kaiser Normalization.       Source: Compiled from primary data

First Factor- Accessibility of Opinion Leaders (AC)

It includes the statements that explain the barriers related to availability of opinion leaders. Multiple opinion leaders and unavailability of opinion leader can result in the resistance to adopt fintech services. The statements are; I felt overwhelmed or bombarded by opinions of multiple Opinion leaders, I feel pressured or coerced by opinion leaders to adopt any fintech service over influence of social norms or expectations, Non Accessibility/ availability of opinion leaders when required is one of the barriers in adoption of Fintech services, I seek the personal benefit of opinion leaders behind promoting the FinTech services. This factor explains the highest value of the % of total variance explained i.e. 17.818. The factor loadings of this factor range from 0.780 to 0.867 and its Cronbach's alpha is 0.910. The inter item correlation ranges from 0.659 to 0.744 and item to total correlation ranges from 0.772 to 0.807. It contains the highest Eigen value of 3.207. 

Second Factor- Perceived Risk (PR)

Fear of financial loss, concerns about hidden charges and risk of being misled by the opinion leaders make the consumers to repel the use of fintech services. This factor includes the statements; I am afraid of the transactions failure and hidden or fraudulent charges, Opinion leaders are reluctant to endorse/promote FinTech product due to perceived risk, I worry about the abuse of my financial (transaction and private information) when I use Fintech, Using Fintech is associated with high level of risk. This factor explains the 16.674% of the total variance explained. The factor loadings of this factor range from 0.744 to 0.839 and its Cronbach's alpha is 0.873. The inter item correlation ranges from 0.551 to 0.721 and item to total correlation ranges from 0.649 to 0.772. It contains the Eigen value of 3.001.

Third Factor- Inefficient Opinion Leaders (IE)

If opinion leaders are inefficient, it means if they are unable to provide accurate, clear and unbiased guidance then they can act as a barrier to fintech adoption rather than a catalyst for adoption. This factor includes the statements; Opinion Leader having lack of evidence and data to support recommendation result as barrier to adoption, I am resistant to change and prefer to stick with traditional financial service, Opinion leaders with lack of communication skills and influencing power are often failed to convince the customers to adopt FinTech, The lack of awareness/knowledge of opinion leaders can result in resistance to adopt the FinTech service. This factor explains the 15.286% of the total variance explained. The factor loadings of this factor range from 0.658 to 0.779 and its Cronbach's alpha is 0.846. The inter item correlation ranges from 0.480 to 0.639 and item to total correlation ranges from 0.608 to 0.724. It contains the Eigen value of 2.751.

Fourth Factor- Regulatory Concerns (RC)

Strict and unclear regulations related to the fintech and opinion leaders also affect the fintech users. This factor includes the statements; I am afraid of non-refund of failed payments/poor grievance redressal mechanism, There are many complicated legal/regulatory concerns associated with FinTech, I experience slow FinTech performance due to lack of infrastructure like slow internet, bug in software etc. This factor explains the 13.817% of the total variance explained. The factor loadings of this factor range from 0.796 to 0.837 and its Cronbach's alpha is 0.897. The inter item correlation ranges from 0.713 to 0.787 and item to total correlation ranges from 0.762 to 0.820. It contains the Eigen value of 2.487.

Fifth Factor- Trust Issues (TR)

Peer opinion leaders those have fintech knowledge (e.g. family, friends, colleagues etc.) play a crucial role in adoption of Fintech services through word of mouth recommendations. This factor includes the statements; My personal autonomy and decision making does not allow me to seek advice of opinion leaders, The negative experience with the FinTech (recommended by opinion leaders) affects my willingness to use and recommend this service to others, I think Opinion leaders give more attention in gaining followers and popularity than giving genuine advice. This factor explains the 12.400% of the total variance explained. The factor loadings of this factor range from 0.725 to 0.799 and its Cronbach's alpha is 0.836. The inter item correlation ranges from 0.595 to 0.680 and item to total correlation ranges from 0.659 to 0.721. It contains the Eigen value of 2.232.

From the above data, it has been concluded that an Accessibility/Availability of opinion leaders has a major impact on the consumers in resisting FinTech services. Accessibility of opinion leaders has two aspects- one is over-accessibility and another is under-accessibility. In case of under-accessibility, people might face problems to get personalized recommendations and lack of advice. This information gap can slow down the trust and adoption of FinTech services. On the other hand, over-accessibility and too many opinion leaders also lead to skepticism and mistrust. Consumers might think that opinion leaders are promoting the services for their own benefits or they are paid to do so. Also, this component creates confusions for the consumers to know which opinion leaders and FinTech app is more trustworthy and reliable. So, in both the situations the consumers always hesitate to follow the recommendations of the opinion leaders and avoid using FinTech. Therefore, hypothesis H5 is given more weightage as comparison to other barriers that affect the perception of consumers in adoption of FinTech services.

The findings imply that consumers are become more conscious of marketing tactics and are becoming resistant to overt attempts at persuasion, particularly from those who are perceived as having personal motives. Individual beliefs, habits, past knowledge and lifestyle are examples of personal traits that are important in moderating the influence of opinion leaders. Opinion leaders lose power when they don't fit these personal standards or come out as dishonest. Opinion leaders are often seen as reliable and genuine but when their messaging is considered as biassed or out of step with objective facts, consumers become sceptical. Opinion leaders' impact may be weakened by elements including perceived lack of credibility, excessive commercialisation and deviation from consumer expectations, particularly on platforms where viewers yearn for vulnerability and authenticity. Additionally, consumers are more likely to be convinced by regular users or review bloggers who appear to be less influenced by business interests. Opinion leaders must be seen as reliable, informed, and really concerned about the well-being of customers in order to be effective. Customers may ignore opinions or even react adversely when trust is compromised, for example, by excessive promotional content or disparities between endorsements and actual product performance. Therefore, the necessity for regular, genuine and transparent involvement that connects with customers' actual experiences and beliefs limit opinion leaders' actual persuasive ability.

Future studies should examine how various opinion leaders such as financial experts, social media influencers, community leaders, and tech-savvy peers differ in their capacity to get over the obstacles that have been found. It may be possible to determine when and for whom opinion leaders are most successful in marketing online financial services by conducting comparative research across demographic groupings, cultural contexts and degrees of digital maturity.

FinTech companies can use the research data to create more relevant strategies. The businesses can identify key opinion leaders who are seen as reliable by particular demographics and can engage them as brand ambassadors to promote new features and to emphasize on the usability. It guarantees that FinTech develops in a way that is inclusive, user-centric and responsive to real needs. The accessibility of the opinion leaders can bridge the gap between the FinTech innovations and the population excluded from the FinTech services. This study will help the government in reaching the marginalized communities of Punjab through opinion leaders where the poor, elderly people and the rural areas are unable to gain the access to the tools like digital payments, savings etc. Research findings contribute to the fact that opinion leaders with the strong reputations can have more impact on the society as their endorsements carry more weight. The government and the FinTech companies can cooperate with high-reputation opinion leaders to reach their target audiences more effectively. In this way the adoption rate can be increased in underprivileged and skeptical communities as well. Research results will enhance academic knowledge of consumer behavior, technological adoption and marketing. This information may be disseminated to researchers, educators and students by enhancing academic programs and encouraging intellectual expansion.

Conflict of interest

The authors declare that they have no conflict of interest.

Declarations

Ethics approval and consent to participate: Not applicable

Consent for publications: Not applicable

Competing interests: The authors declare that they have no competing interests.

Funding: This research received no specific grant from public, commercial, or not-for-profit funding agencies.

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