Fintech Adoption and Dispositional Innovativeness in E-Gold Investment: Evidence from India

Abstract

In the digital age, investing in e-gold is increasing in popularity. This study’s objective is to assess the moderating role of dispositional innovativeness between fintech adoption and the intention to invest in e-gold, as well as to understand investors’ behavioral intentions. This study uses the theory of planned behavior model to analyze the data. We prepared a structured questionnaire to collect data from Maharashtra, a state in India, and used PLS-SEM for analytical purposes. We also used focus group interviews to validate the findings from PLS-SEM. Our evidence shows that attitude, subjective norms, and perceived behavioral control significantly impact fintech adoption and the intention to invest in e-gold. This study also confirmed that dispositional innovativeness moderates the relationship between fintech adoption and the intention to invest in e-gold. This study implies that policymakers can redesign the regulation of digital assets to promote transparency, security, and faith in the fintech platform by recognizing the interest rate in e-gold.

1. Introduction

Gold is a valuable asset with diverse uses. It is a safe investment option, offering protection against market volatility and economic uncertainty [1,2]. The gold investment landscape has expanded, offering investors choices such as physical gold, gold exchange-traded funds (ETFs), gold mutual funds, mining stocks, and sovereign gold bonds [3]. Gold investment has entered the digital age with the rise of e-gold, or digital gold. E-gold is a digital representation of gold that can be bought, sold, and stored online. E-gold offers fractional ownership, global access, lower costs, liquidity, security, transparency, integration, and real-time information through fintech [4].

Fintech offers a platform for the convenient buying, selling, and trading of digital assets, which is appealing to modern investors. Several factors drive the rising adoption of fintech, including its democratization of investing, ease of access, and preference for digital assets. Fintech platforms have experienced considerable user growth due to their convenience and accessibility [5]. Many investors are interested in digital assets for diversification and their lower costs. Awareness campaigns can boost e-gold investment by increasing understanding and driving fintech adoption [6,7,8,9]. However, the probability of fraud is also high on fintech platforms [10].

Various researchers have investigated behavioral intentions toward digital asset adoption using the theory of planned behavior (TPB) and unified theory of acceptance and use of technology (UTAUT-2) 2 models [11,12,13,14,15,16,17]. Although these studies have explored different digital assets, no research has specifically applied the TPB to e-gold investments. This observation suggests a gap in understanding investor intentions in this context. The TPB provides a framework for understanding how attitude, subjective norms, and perceived behavioral control influence behavioral intentions [18,19]. Specifically, attitude reflects an individual’s positive or negative evaluation of a behavior [20], subjective norms capture perceived social pressure to engage or not engage in the behavior [21,22], and perceived behavioral control assesses an individual’s belief in their ability to perform the behavior [18]. The TPB model was employed to study the behavioral intentions of investment in digital gold by [23,24]. Given these precedents, TPB is a valuable tool for analyzing e-gold investment intentions. By applying TPB to the context of e-gold investment, we can analyze these factors to understand and predict investor behavior better.

Dispositional innovativeness measures an individual’s inclination to adopt new ideas, products, or technologies. Thus, understanding investor responses to e-gold investments is crucial. Antony (2020) and Hoffmann and Broekhuizen (2010) have demonstrated the significance of dispositional innovativeness in investment behavior and product innovation [25,26]. Dispositional innovativeness acts as a moderator in the fintech adoption of e-gold because it influences the strength or direction of the relationship between various factors like the perceived ease of use, perceived usefulness, trust, and social influence, which affects the decision to adopt e-gold. Antony (2020) conducted a similar study, finding dispositional innovativeness to be an indispensable factor that influenced investor intention and behavior toward investment [25]. Although investors were aware of digital and new financial instruments, they continued to prefer traditional investment avenues. Hence, this finding aligns with our study, as an investor had negative views regarding the adoption of fintech with e-gold investment [27]. In this context, dispositional innovativeness does not directly affect adoption. Instead, it changes how other variables influence a person’s likelihood to adopt e-gold based on that person’s inherent tendency to embrace new technologies. It is considered a moderator for several reasons. First, dispositional innovativeness strengthens the impact of perceived usefulness and ease of use. Second, the impact on dispositional resistance can also be evaluated using emotional exhaustion, which helps speed up both the internal factor, such as perceived organizational support and informational team climate, and the external factor, such as the role of trust and security, interactions with social influence, adoption speed and engagement, risk perception, and tolerance [28]. Hence, we can infer that dispositional innovativeness acts as a moderator in adopting e-gold within fintech because it influences how other factors affect an individual’s decision to adopt it. Instead of directly driving adoption, it shapes the strength of the connection between these factors and the likelihood of adoption, with individuals who are more innovative being more responsive to these adoption-related factors compared to those who are less innovative [29].

This study’s objective is to understand investors’ behavioral intentions by evaluating the moderating effect of dispositional innovativeness between fintech adoption and the intention to invest. We propose two research questions to guide the investigation:

RQ1: Does attitude, subjective norms, and perceived behavior control lead to fintech adoption and the intention to invest in e-gold?

RQ2: Does dispositional innovativeness moderate between fintech adoption and the intention to invest in e-gold?

This study extends the technology acceptance model (TAM) and the theory of planned behavior (TPB) to explain fintech adoption, particularly e-gold investments. It highlights the role of dispositional innovativeness as a moderator, aligning with innovation diffusion theory [30]. This study also underscores the importance of e-gold literacy in shaping investment decisions, contributing to understanding how knowledge influences the adoption of new financial technologies. Literacy and knowledge about e-gold contribute to understanding how the knowledge and understanding of financial concepts can influence the adoption of new financial technologies.

This study comprises the following sections. Section 2 deals with the theoretical background, literature review, and hypothesis development; Section 3 outlines the research methodology; Section 4 provides the findings and analysis; Section 5 addresses the implications; and Section 6 provides limitations and the future scope of this study. Section 7 offers our conclusions.

2. Theoretical Background, Literature Review, and Hypothesis Development

In this section, we present the theoretical background, review of related research, and hypothesis development.

2.1. The Theory of Reasoned Action (TRA)

According to the theory of reasoned action (TRA), two main factors influence an individual’s intention to act: (1) the person’s attitude toward the behavior and (2) subjective norms. One’s attitude toward a behavior reflects how positively or negatively the individual evaluates the behavior. Meanwhile, subjective norms represent the perceived social pressure regarding engaging or not engaging in the behavior [31,32]. Intention serves as an indicator of a person’s readiness to carry out a specific behavior and is seen as the immediate precursor to a behavior. Contrasting with the TRA, Ajzen (1985) expanded the theory of planned behavior (TPB) by introducing a third crucial factor influencing intention and behavior: perceived behavior control [33]. This factor relates to individuals’ beliefs about their ability to perform the behavior considering control factors that may facilitate or impede the behavior.

2.2. The Theory of Planned Behavior (TPB)

Ajzen (1985) developed the TPB, which is a cognitive framework that links beliefs to actions [33]. It expands on the TRA by incorporating perceived behavioral control as a key determinant of behavioral intentions [34,35]. According to the TPB, three factors influence an individual’s intention to engage in a specific behavior: (1) attitude, which reflects personal evaluations of the behavior; (2) subjective norms, which capture social pressures and expectations; and (3) perceived behavioral control, which represents the individual’s belief in one’s ability to perform the behavior. The TPB posits that behavioral intentions serve as strong predictors of actual behavior, making the model widely used in understanding decision-making across various domains, including finance, health, and consumer behavior.

Some other models, such as the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), explain the acceptance and use of technology. The UTAUT is particularly relevant for studying technology adoption. The extended UTAUT, known as UTAUT-2, incorporates additional constructs such as hedonic motivation, price value, and habit. Although the original UTAUT model primarily focuses on predicting technology adoption and acceptance behavior in organizational settings, UTAUT-2 extends this framework to examine individual users’ behavioral intentions toward technology adoption in broader contexts. In our study, we chose the TPB instead of UTAUT-2. The TPB is advantageous because it goes beyond technology adoption and is applicable to a wider range of behavioral contexts. This theory not only addresses intentional behavior but also incorporates key psychological factors that influence action. Moreover, the TPB’s structure allows for flexible adaptation to new domains with minimal complexity, making it a suitable and robust framework for our research objectives.

2.3. Attitude and Fintech Adoption

Attitudes encompass an individual’s overall evaluation of investment activities, shaped by beliefs about the outcomes of investing and the perceived desirability of these outcomes [35]. Positive attitudes toward investing are often characterized by the belief that investments can lead to wealth accumulation, financial security, and long-term growth, which in turn can increase one’s propensity to engage in investment activities [36]. Investors with a positive attitude toward risk are more likely to participate in the stock market, seeking higher returns despite potential volatility [37]. Conversely, individuals with negative attitudes, perhaps due to past losses or a general aversion to risk, might prefer safer, more conservative investment options such as bonds or savings accounts [38].

Behavioral beliefs are the underlying factors that shape attitudes. These beliefs are formed based on past experiences, available information, and individual perceptions. The TPB (Ajzen, 1991) and TAM (Davis, 1989) both consider attitude as a major component that impacts the behavior or desire to adopt certain technologies [39,40]. Thus, we frame our first hypothesis as follows:

H₁: 

A significant association exists between investors’ attitudes and fintech adoption.

2.4. Subjective Norms and Fintech Adoption

Subjective norms influence investment behavior through the role of normative beliefs, which are perceptions about whether important referents approve or disapprove of specific behaviors [39]. These beliefs shape individuals’ perceptions of social pressure, affecting their investment decisions. For example, if investors believe that their peers or financial advisors endorse investing in green energy, they are more likely to feel compelled to follow this trend.

Normative beliefs are often formed through direct communication or observations of behaviors within one’s social circle. Family, friends, and professional advisors serve as key influencers, providing cues and reinforcing expectations about appropriate investment behaviors [34]. The media also plays a crucial role in shaping normative beliefs. Coverage of successful investment strategies and endorsements by financial gurus can establish societal norms that drive individual behavior [41]. Thus, assuming that people require some form of fundamental social reinforcement and taking into consideration social rewards associated with the use of fintech, we frame our second hypothesis:

H₂: 

A significant association exists between subjective norms and fintech adoption.

2.5. Perceived Behavioral Control and Fintech Adoption

Perceived behavioral control (PBC) is a crucial determinant in investment decision-making, referring to how individuals perceive their ability to perform a given behavior [39]. It encompasses both the perceived ease or difficulty of performing the behavior and one’s control over factors that might facilitate or hinder investment activities. Past experiences and anticipated obstacles influence PBC. Investors with higher PBC are more likely to participate in the market [18]. For instance, those with extensive financial knowledge and experience may perceive fewer barriers to investing, leading to more proactive investment behaviors.

External factors, such as access to financial resources, availability of information, and supportive infrastructure, also affect PBC. For example, easy access to online trading platforms and educational resources can enhance how individuals perceive their control over their investments [42,43]. Conversely, perceived complexities in understanding market mechanisms or a lack of capital can diminish PBC, deterring investment actions.

Control beliefs are the beliefs about the presence of factors that may facilitate or hinder the performance of a behavior. Various factors, including financial literacy, access to information, and market conditions, can influence these beliefs. For instance, investors who feel well informed and equipped with the necessary tools and resources are likely to exhibit high PBC, thereby increasing their likelihood of making investment decisions. Thus, we frame the third hypothesis as follows:

H₃: 

A significant association exists between perceived behavioral control and fintech adoption.

2.6. Dispositional Innovativeness on E-Gold Investment Intention

Dispositional innovativeness refers to an individual’s inherent tendency to adopt new ideas, technologies, or products. In the context of e-gold investment intention, dispositional innovativeness plays an important role in shaping investors’ attitudes and behaviors toward this innovative financial instrument. Regarding the diffusion of innovations theory, Rogers (1961) suggests that individuals with a high level of dispositional innovativeness are more likely to embrace novel concepts and technologies earlier than their counterparts [44]. They exhibit a curiosity and openness to new experiences, which can positively influence their perception of e-gold as a modern and innovative investment option.

Investors with high dispositional innovativeness may view e-gold as a convenient and technologically advanced way to invest in e-gold, bypassing the traditional barriers associated with physical gold ownership. Their willingness to explore and adopt new financial instruments aligns with the characteristics of e-gold, such as the ease of access, transparency, and security offered by fintech platforms. In a past study, individuals with higher dispositional innovativeness were more likely to express interest in investing in electronic financial services due to their comfort with technology and inclination toward novelty [45]. Thus, we frame the fourth hypothesis as follows:

H₄: 

A significant association exists between dispositional innovativeness and e-gold investment intention.

2.7. Fintech Adoption and E-Gold Investment Intention

Fintech adoption is the integration of financial technologies into daily operations, enhancing efficiency and access to financial services [46]. Fintech adoption in investment involves leveraging digital platforms and tools to enhance portfolio management, streamline transactions, and provide data-driven insights, thereby increasing accessibility and efficiency in the investment process [47]. The consumers’ trust, perceived ease of use, and customer innovation in fintech services substantially impact their attitude toward adoption and their behavioral intention to use the fintech online platform [48]. Thus, we frame the fifth hypothesis as follows:

H₅: 

A significant association exists between fintech adoption and e-gold investment intention.

2.8. Dispositional Innovativeness on Fintech Adoption

Dispositional innovativeness influences fintech adoption. Individuals with high dispositional innovativeness are more likely to embrace new fintech solutions due to their inherent openness to novel technologies and ideas. This openness leads them to explore and integrate emerging financial tools and platforms more readily [49]. The fintech platform offers many different innovations, which attracts more investors [50]. In this study, we use dispositional innovativeness as a subject with e-gold as one of the investment categories and as a moderator. We considered innovation as a method and then applied SEM analysis, taking innovation as a moderator. Moreover, dispositional innovativeness is contextually specific, as this study pertains to Maharashtra only. Thus, we frame the sixth hypothesis as follows:

H₆: 

A significant association exists between fintech adoption and dispositional innovativeness.

2.9. E-Gold Literacy on E-Gold Investment Intention

Literacy plays a crucial role in shaping investment intention. Individuals with higher financial literacy are better equipped to understand investment options, assess risks, and make informed decisions, which increases their likelihood of investing. Knowledge of financial concepts and market dynamics enhances confidence and reduces perceived risks, thereby fostering a stronger intention to invest. Previous research highlights that financial literacy is often synonymous with financial awareness and has a positive correlation with investment behavior, as informed investors are more proactive in managing and growing their portfolios [42,43,51,52,53,54]. Educated investors are more likely to recognize the advantages of digital gold over physical gold, such as ease of transaction and security, thus increasing their investment intention [55]. Thus, we frame the seventh hypothesis as follows:

H₇: 

A significant association exists between e-gold literacy and e-gold investment intention.

We analyzed seven factors, such as attitude, subjective norms, and perceived behavioral control, that contribute to fintech adoption. Dispositional innovativeness was considered as a moderator as it indirectly influences investors’ intentions to invest in e-gold, with e-gold literacy playing a role as well, and we identified these factors based on a literature review. We developed the proposed research framework based on these factors and the moderator, as depicted in Figure 1.

3. Research Methodology

We discuss this study’s research methodology below.

3.1. Sampling Process and Method of Data Collection

This study involves people from the state of Maharashtra. A demat (dematerialized) account is an account used to store and trade financial securities, such as stocks, bonds, mutual funds, and other assets, in electronic form rather than as physical certificates. This approach helps to eliminate the necessity for paper-based transactions, streamlining the process and enhancing security and efficiency. All participants had demat accounts with a depository (i.e., National Securities Depository Ltd. (NSDL) or Central Depository Services Ltd. (CDSL)).

As previously noted, a demat account is a digital repository for holding and transacting financial securities in an electronic format [56]. We intentionally chose retail equity investors due to their familiarity with other investment-related matters. In many cases, one of the prerequisites for e-gold investments is having a demat account. Therefore, we viewed this group as a relevant and appropriate target population for this research. Based on the data available on 31 October 2024, the total number of active demat accounts in Maharashtra, India, was approximately 3.8 crores. This became the population (N) for this study. To determine a representative and statistically valid sample, we applied Slovin’s formula as follows:

$$\mathrm{n}={\displaystyle \frac{N}{1+N{e}^2}}$$

(1)

where n = sample size, N = population size (3.8 crore), and e = margin of error (0.05 for a 95% confidence level).

Applying this formula yielded a minimum required sample size of 385 respondents. Examining the perspectives of demat account holders becomes crucial at the intersection of traditional financial markets and emerging digital assets. We developed a list of 3.8 crore active demat account holders from the NSDL and CDSL databases and collected responses. Nurbarani and Soepriyanto (2022) also used the Slovin formula at the 5% significance level and 95% confidence level to determine their sample size [57]. We distributed the questionnaires using simple random sampling to 385 demat account holders via email.

We provided a first reminder call after 15 days, followed by additional reminders every 15 days. After six months, we received 380 questionnaires. After examining all the questionnaires, we decided to include 371 questionnaires in our study. Previously conducted studies have confirmed that the sample size should be at least five times greater than the number of items used to evaluate a specific construct [58,59]. However, we followed a more widely accepted rule of thumb in estimating the sample size, which recommends 10 times the number of items (22 × 10 = 220), because researchers consider it more appropriate for structural equation modeling (SEM) analysis [60,61,62,63]. Therefore, our sample size of 371 exceeds the recommended range of 10 times the number of items and satisfies the standard requirements for SEM analysis.

Table 1. Respondent profile.

Classification	Category	Frequency	Percentage
Gender	Male	235	63.34
	Female	136	36.66
Age	<20 years	49	13.21
	20 < 25 years	170	45.82
	25 < 30 years	35	9.43
	30 < 35 years	49	13.21
	≥35 years	68	18.33
Education	Lower than High School	31	8.36
	High School	39	10.51
	Undergraduate	172	46.36
	Postgraduate	91	24.53
	Doctorate	38	10.24
Income	<2.5 lacs	41	11.05
	2.5 < 5 lacs	52	14.02
	5 < 7.5 lacs	78	21.02
	7.5 < 10 lacs	68	18.33
	10 < 12.5 lacs	32	8.63
	12.5 < 15 lacs	49	13.21
	≥15 lacs	51	13.75
Occupation	Services	152	40.77
	Business	101	27.22
	Housewife	57	15.36
	Professional	21	5.66
	Others	40	10.78

includes a respondent profile.

3.2. Research Instrument

This study used a five-point Likert scale to collect responses through a structured questionnaire. We developed a measurement scale based on the existing literature that ranged from 1 (Strongly Disagree) to 5 (Strongly Agree). We took a total of 22 measurement items from the past literature for analysis, as shown in the questionnaire attached in Appendix B.

We used structural equation modeling (SEM) within SPSS AMOS (Analysis of Moment Structures) to examine the proposed hypotheses. SEM is a robust statistical method for analyzing complex relationships between observed and latent variables, allowing researchers to test and refine theoretical models by estimating multiple regression equations simultaneously. It is widely applied in fields such as social sciences, economics, and psychology due to its ability to examine intricate relationships among variables. SEM incorporates methods like factor analysis, path analysis, and regression analysis to assess causal pathways between variables and evaluate the overall model fit.

We used SPSS AMOS-24.0 to investigate the hypothesized relationships among the variables of interest [64,65]. SPSS AMOS, designed for covariance-based SEM, offers robust features, seamless integration with SPSS statistics, and a user-friendly interface. We conducted a pilot survey with 70 responses to ensure the validity and reliability of the measurement scale, providing insights that contributed to the refinement of the questionnaire. To conduct SEM in AMOS, we started by defining our model specification and outlining construct relationships and their direction based on theory. Next, we visually represented this model as a path diagram within AMOS. We then prepared the data by importing them to use in the software. After this, we proceeded to model estimation by selecting our method and running the analysis. Our next step was to evaluate the model’s fit by comparing it with the relevant p-value. If it was less than 0.05, we considered that the model had an appropriate fit. Finally, we interpreted the results and reported the significant findings within our research context.

3.3. Common Method Bias

Podsakoff et al. (2003) proposed measurement techniques to eliminate common method bias [66]. One method involved pretesting and revising the survey instrument to reduce complexity and prevent respondent confusion. Another method involved instructing respondents to answer all questions honestly and assuring them that there were no right or wrong answers.

3.4. Focus Group Discussion

We also conducted an in-depth focus group discussion (FGD) to learn the attitudes toward and the role of fintech adoption, which enhanced investment in e-gold from the expert respondents. We applied this qualitative approach to gain an in-depth understanding of e-gold investment. This method aimed to obtain data from a purposely selected group of individuals rather than from a statistically representative sample of a broader population [67]. As our study is emerging, this technique was sufficiently appropriate to obtain all the relevant information we needed [68]. We interviewed nine people who regularly invested in e-gold. Appendix A and Appendix C presents details involving these individuals and the questions we asked them respectively.

FGD can be conducted with 6 to 8 or 5 to 10 participants [69]. The majority of the group must agree to analyze the data [70]. FGDs provide rich qualitative data by encouraging open-ended discussion and exploring nuances missed in structured responses. The interactive group dynamic generates new insights as participants build on each other’s ideas [71,72]. Flexible and adaptable, FGDs allow moderators to probe deeper into emerging themes. Real-time feedback, including non-verbal cues, reveals true feelings [73]. Cost-effective and efficient, FGDs illuminate the sociocultural context, shaping opinions and generating hypotheses and research tools [74,75]. They also offer a comfortable space for discussing sensitive topics [76].

4. Analysis and Results

Data analysis helped us to determine different results from the collected data and to conclude whether support exists for this study’s hypotheses. The analysis covered SEM, descriptive analysis, the psychometric properties of the measures, discriminate validity, the reliability of variables, and Cronbach’s alpha. The model helped us study the impact of subjective norms and domain knowledge on impulsive behavior and self-control, respectively. Additionally, we checked the impact of impulsive behavior and self-control on speculative investment decisions in cryptocurrency, which was likely to result in negative consequences.

4.1. Construct Reliability

Social science researchers often use Cronbach’s alpha as a tool to measure internal consistency [77,78]. Any score above 0.7 shows that the measure of the constructs has good reliability. Our results show that each construct has the desired level of Cronbach’s alpha, which shows internal reliability.

We also found that the composite reliability for all the constructs exceeded 0.9, demonstrating the constructs’ reliability. Further, the calculated average variance extracted (AVE) exceeded 0.5, which helped to establish convergent validity.

Table 2. Psychometric properties of measures.

Constructs	Items	Mean	Standard Deviation	Factor Loading	AVE	CR	Cronbach’s Alpha
Attitude	AT1	4.110	0.934	0.759	0.529	0.771	0.966
	AT2	4.090	0.928	0.718
	AT3	4.079	0.956	0.704
Subjective Norms	SN1	3.248	1.006	0.704	0.523	0.767	0.856
	SN2	3.527	0.936	0.718
	SN3	3.542	1.154	0.747
Perceived Behavioral Control	PBC1	3.404	1.326	0.550	0.598	0.811	0.918
	PBC2	3.601	1.030	0.872
	PBC3	3.670	1.087	0.854
Fintech Adoption	FA1	3.263	1.493	0.826	0.590	0.811	0.929
	FA2	3.708	1.352	0.697
	FA3	3.683	1.384	0.777
E-gold Literacy	EL1	3.384	1.075	0.917	0.804	0.925	0.957
	EL2	3.563	1.126	0.871
	EL3	3.496	1.102	0.902
Dispositional Innovativeness	DI1	2.864	1.176	0.778	0.577	0.845	0.721
	DI2	2.463	1.193	0.730
	DI3	3.169	1.195	0.815
	DI4	2.537	1.131	0.712
Intention to Invest in e-gold	IN1	3.194	0.949	0.803	0.494	0.739	0.928
	IN2	3.327	0.931	0.754
	IN3	3.432	0.877	0.519

Note(s): AVE is average variance extracted, and CR is composite reliability.

presents all the computed scores of factor loading, AVE, composite reliability, Cronbach’s alpha, the mean, and the standard deviation.

4.2. Kaiser–Meyer–Olkin Test

To perform the factor analysis, we needed to check the correctness of the data using the Kaiser–Meyer–Olkin test (KMO test). Some underlying causes might explain the proportion of common variance. As a sample adequacy indicator, KMO helps to measure such variance. The constructs were abbreviated as attitude (AT), subjective norms (SN), perceived behavioral control (PBC), fintech adoption (FA), e-gold literacy (EL), dispositional innovativeness (DI), intention to invest in e-gold (IN), and Kaiser–Meyer–Olkin test (KMO test), as depicted in

Table 3. Discriminant validity.

Constructs	KMO	AT	SN	PBC	FA	EL	DI	IN
AT	0.715	(0.727)
SN	0.709	0.586 **	(0.723)
PBC	0.729	0.598 **	0.549 **	(0.773)
FA	0.686	0.564 **	0.618 **	0.716 **	(0.768)
EL	0.773	0.710 **	0.431 **	0.455 **	0.404 **	(0.896)
DI	0.751	−0.084	−0.291 **	−0.084	−0.144 **	0.044	(0.759)
IN	0.729	0.573 **	0.704 **	0.481 **	0.548 **	0.365 **	0.010	(0.702)

Notes: ** p < 0.01; n = 371.

. Table 3 shows the different KMO values as computed for each construct. These values exceed 0.7, indicating the appropriateness of the data for factor analysis.

The diagonal parenthesis scores in Table 3 show the square root of the AVEs of the individual constructs. Cross-construct squared correlations represent the non-diagonal values.

To verify whether each element showcases a different dimension, we used the discriminant validity of the scales. We used the homogeneous linear or covariance correlation to correlate the attributes. Since the presumed values were far from 1, the computed values depicted discriminant validity indices between the different aspects studied. Table 3 shows that the correlation confidence interval between any pair of attribute values was not equal to 1, indicating the dependence of each construct.

We analyzed the square root of the AVE for each construct and the squared correlations among all the elements to compute discriminant validity. The AVE value should be higher than the computed correlation. As Table 3 indicates, the AVE’s square root was higher as compared to the correlations of the cross-attributes.

4.3. Hypothesis Testing

We used SEM to analyze the impact of independent attributes on the dependent variables, as shown in Figure 2.

Table 4. Structural model results.

Hypothesized Relationships	Beta	SE	t-Value	p-Value	Result
H1: FA ← AT	0.140	0.047	2.973	0.003	Supported
H2: FA ← SN	0.415	0.047	8.849	0.001	Supported
H3: FA ← PBC	0.618	0.04	15.465	0.001	Supported
H4: DI ← FA	−0.039	0.016	−2.486	0.013	Supported
H5: IN ← DI	0.176	0.1	1.761	0.078	Not Supported
H6: IN ← FA	0.321	0.031	10.238	0.001	Supported
H7: IN ← EL	0.132	0.034	3.921	0.0001	Supported

represents the SEM outcomes and the hypothesis testing results to explain whether support exists for the hypotheses.

p-values less than 0.05 support the corresponding proposed hypothesis and p-values greater than 0.05 do not. Our findings support H₁, H₂, H₃, H₄, H₆, and H₇, as their p-values were less than 0.05. Only H₅ lacked support because its p-value exceeded 0.05. Since the p-values for four of the hypotheses are below 0.05, this indicates that these constructs are statistically significant, suggesting a good model fit. This good fit confirms that the model is appropriate for our study. Our findings for these five hypotheses imply a significant relationship and dependency between the two variables.

5. Discussion

This study explores the factors influencing fintech adoption and the intent to invest in e-gold. It focuses on two key research questions. RQ1 addresses the relationship among attitude, subjective norms, and perceived behavioral control with investment in e-gold through fintech adoption. Our evidence shows that attitude, subjective norms, and perceived behavioral control positively affect fintech adoption regarding investing in e-gold. Support for H₁ implies a significant impact of attitude toward investment in e-gold and fintech adoption. One’s attitude toward investment in a digital asset such as an e-gold also indicated a favorable attitude toward fintech and access to technologies [79]. Additionally, those who perceived a strong social expectation from peers, family, or society were more likely to adopt fintech services and invest in e-gold [39]. While participating in the FGD, participant 5 stated the following:

I want to invest in e-gold because it drives significant investment by leveraging gold’s longstanding reputation as a top-tier asset. With its convenient and flexible investment options, I have the power to influence the gold investment landscape, even with small shifts in sentiment. I believe that maintaining a positive mindset is essential as it allows me to unlock e-gold’s potential and reshape the way I invest in gold.

Support for H₂ implies a significant association between the subjective norms concerning investment in e-gold and fintech adoption. Subjective norms only explain the user’s intention toward any activity performed regularly, such as exercise and investment [32]. Participant 1 of the FGD stated the following:

I rely heavily on my peers, family, and experts when making the decision to invest in e-gold. Their opinions and behaviors influence me significantly. When considering an investment, I often look to those around me for guidance. If my peers, family, and experts approve of e-gold, it reinforces my confidence in investing. […] The perception that those I trust support e-gold plays a crucial role in my investment decisions.

Participant 3 remarked:

I believe that influential behavior supports e-gold, making me more likely to invest. This social influence can generate strong momentum, encouraging wider adoption of e-gold. On the other hand, if I perceive disapproval or skepticism from others, it can discourage me from investing.

Participant 4 opined the following:

I find e-gold attractive, but if I perceive social disapproval, it can sometimes outweigh my interest. That’s why fostering positive subjective norms is crucial for e-gold’s growth. I believe that effective marketing campaigns and strong community-building efforts can help shape these norms, encouraging wider acceptance and driving more investment.

Support for H₃ means that a significant association exists between perceived behavioral control and fintech adoption. PBC refers to a person’s perception of how easy or hard it is for someone to perform a specific behavior. PBC was a vital construct of the TPB model used to measure behavioral intention in adopting any activity [80]. Hence, we conclude that PBC is a significant predictor of fintech adoption and e-gold investment intention. Individuals who believe they have the necessary resources, knowledge, and control over using fintech are more likely to adopt these technologies and invest in e-gold. This finding underscores the importance of enhancing users’ self-efficacy and providing the necessary support systems to foster fintech adoption.

While engaged in the FGD, participant 2 noted the following:

I believe that PBC strongly influences e-gold investment. When investing, I assess my ability to invest in and manage e-gold. Based on this assessment, I can say that a high PBC where I feel confident in navigating the platform, understanding the process, and controlling my investments encourages my participation.

Participant 5 of the group said that:

I strongly believe that user-friendly interfaces, transparent information, and accessible support improve my PBC. However, when I encounter low PBC due to complexity, lack of understanding, or difficulty accessing the platform, investing becomes more difficult. If I face too many barriers, I tend to hesitate, even if I have a favorable view of e-gold.

Another member of the panel, participant 8, expressed the following views:

I believe that information about e-gold should be prioritized on user-friendly platforms, supported by educational resources, and accessible to customer support. This situation would enhance my PBC, making it easier for me to invest with confidence and increase overall participation.

RQ2 addresses whether dispositional innovativeness plays a moderating role between fintech adoption and the intention to invest in e-gold. Support for H₄ confirms that dispositional innovativeness moderates the relationship between fintech adoption and the intention to invest in e-gold. Individuals with high dispositional innovativeness, who are inherently more open to new ideas and technologies, show a stronger link between adopting fintech services and the intention to invest in e-gold.

Participant 6 of the FGD remarked:

I am confident that advanced technology strengthens my e-gold investment by offering secure and user-friendly platforms. It enables me to monitor prices instantly, complete transactions effortlessly, and store my assets safely. These actions enhance my trust, minimize obstacles, and motivate me to engage more actively in the e-gold market.

This evidence suggests that while fintech adoption is crucial, the level of innovativeness further amplifies its impact on investment intentions. The lack of support for H₅ reveals that no significant association exists between the intention to invest in e-gold and dispositional innovativeness. Thus, we can infer that an individual’s behavior to adopt any technology is not related to the intention to invest in e-gold through fintech adoption.

Participant 4 of the FGD panel stated:

I perceive traditional gold and e-gold investments as distinct. My interest in physical gold does not necessarily translate to an interest in e-gold. When making investment decisions, I consider factors such as security, ease of access, and familiarity with the technology.

Support for H₆ implies that a moderate relationship existing between fintech adoption and dispositional innovativeness influences the intention to invest in e-gold. The adoption of fintech and one’s risk perception affects the investment decision [81,82]. E-gold literacy affects an investor’s ability to understand and use financial information to make informed decisions about investment. E-gold investment refers to the act and effort made by an investor to invest in digital assets such as e-gold and cryptocurrency. Thus, our study shows an association between e-gold literacy and e-gold investment. This finding adds to the existing literature by highlighting the role of individual traits in the adoption of financial innovations [30,49].

Participant 3 stated:

I emphasize that fintech adoption depends entirely on digital platforms, and my willingness to embrace fintech for investing in e-gold reflects my dispositional innovativeness and investment behavior.

5.1. Policy Implications

Policymakers should prioritize financial literacy, particularly regarding e-gold and associated technologies, to empower individuals to make informed investment decisions. Policymakers must implement focused and practical strategies to promote e-gold investment and accelerate fintech adoption. Enhancing financial literacy is essential and should be addressed through structured programs targeting digital investments such as e-gold. These initiatives can be delivered via educational institutes, banks, and community centers using mobile apps and multilingual content for a wider reach. The government and private collaborations should ensure platforms are intuitive and accessible, especially for rural users, with incentives for startups developing inclusive tools. Regulators such as the Reserve Bank of India and Securities Exchange Board of India must provide guidelines and transparency policies such as real-time updates, investment and operational updates, and transparent investment audits, to encourage investors to buy e-gold. Robust regulation and transparency are essential to build trust on a fintech platform. Clear, supportive regulations for e-gold investments are crucial for balancing investor protection and innovation.

A comprehensive campaign to educate investors about e-gold risks and benefits will foster informed decision-making. Investing in fintech research and development will enhance efficiency and accessibility. Targeting innovative individuals and leveraging early adopters’ influence can accelerate fintech adoption and e-gold investment. These policy measures will create a conducive environment for fintech growth, promoting financial inclusion and economic development.

5.2. Managerial Implications

Our study highlights the need for strategies that foster an innovative but friendly environment. By encouraging a culture of innovation and providing the necessary infrastructure and support for fintech development, institutions can boost the adoption of digital financial services.

Given the positive impact of dispositional innovativeness on fintech adoption, managers should identify and engage with innovative segments of their customer base [83]. A positive impact can be achieved through personalized offers or early-access programs that cater to the most forward-thinking customers, ultimately leading to higher rates of technology diffusion. Managers should also consider implementing customer support systems that improve users’ sense of control and knowledge regarding digital asset investment. Such actions could include in-app tutorials, interactive frequently asked questions, and personalized assistance, which would enhance perceived behavioral control, leading to higher adoption rates. Institutions need to invest in financial literacy programs that focus on digital asset investments, helping individuals make informed decisions about e-gold and other investments. Providing accurate and accessible information about these assets will enhance trust and the willingness to invest, especially among users who might perceive these investments as risky.

6. Limitations and Future Work

This study focused on e-gold and its adoption using fintech platforms. The fintech platform for the adoption of e-gold offers unique features such as tailored financial products, enhanced accessibility, and the use of innovative technologies. These unique features can offer a good opportunity for a niche market. However, the study of e-gold cannot be generalized for all digital assets. Previously, other researchers did not use the TPB model in the context of e-gold investment through fintech adoption. However, this model may not depict all the relevant variables that influence an investor’s intentions, such as risk perception, digital asset literacy, and other cultural factors affecting decision-making. This is a comprehensive study that shows the factors that influence e-gold investment and its adoption through the fintech platform considering only seven factors. This study emphasizes that its findings are limited to the mentioned factors. Factors other than these seven can be used in future research to generalize the findings. This study is confined to the state of Maharashtra, a state in India. The factors affecting e-gold adoption do not have equal weight, and all the factors are inter-linked to each other. This relative importance and their inter-linkages can be studied using social network analysis (SNA), as was studied by [84,85].

Future researchers should increase the sample size and diversify the data collection process to include other states to improve the generalization of the findings. Additionally, these researchers should add more variables such as risk perception [86,87,88], investor behavior, and the role of cultural/demographic factors for investing in e-gold through fintech platforms to increase the generalizability of the findings. Multi-criteria decision-making can be used to analyze the given seven factors, aligned with the study [89].

7. Conclusions

This study provides some novel insights regarding the decision-making process surrounding digital assets and factors shaping investor behavior, especially concerning e-gold. The TPB model used in this study helps to identify factors that influence an investment decision in e-gold through fintech adoption. The findings show that disposition innovativeness plays a moderating variable role between the investor’s intention to invest in e-gold and the adoption of fintech. Previously, researchers had not used the TPB model in the context of e-gold investment through fintech adoption. Hence, this study provides valuable insights into e-gold investment. We conclude that fintech can help to democratize access to financial assets. Additionally, analyzing investor intentions to invest can play a crucial role in increasing economic growth and enhancing investment in digital assets like e-gold.