Management Journal for Advanced Research

1. Introduction

The financial services industry worldwide has undergone a paradigm shift with the advent of digital technologies, particularly mobile-based financial applications. Mobile payment systems—defined as payment services operated under financial regulations and performed via mobile devices—have emerged as a cornerstone of the digital economy (Dahlberg et al., 2015). These systems combine convenience, security, and speed, enabling users to make financial transactions without relying on traditional cash-based methods. In developed economies such as China, South Korea, and the United States, mobile payments have already achieved widespread adoption, with platforms such as Alipay, WeChat Pay, Apple Pay, and Google Pay transforming consumer payment behavior. However, adoption trajectories in developing economies differ significantly, being influenced by infrastructural limitations, socio-cultural contexts, and varying levels of digital literacy.

In the Indian context, the mobile payment ecosystem has expanded rapidly over the past decade. The launch of initiatives such as the Unified Payments Interface (UPI), demonetization in 2016, and the government’s Digital India campaign have accelerated the transition toward a cashless economy (Reserve Bank of India [RBI], 2021). According to RBI reports, the volume of UPI transactions has grown exponentially, making India one of the world leaders in digital transactions. Mobile payment platforms such as Paytm, PhonePe, Google Pay, and BharatPe have become household names, particularly in urban and semi-urban markets. The benefits of mobile payments are multifaceted: they reduce transaction costs, foster transparency, enhance consumer convenience, and promote financial inclusion for traditionally unbanked populations.

Despite these achievements, challenges persist. India is characterized by stark socio-economic diversity, where adoption patterns differ widely between metropolitan cities and rural or semi-urban regions. While urban consumers with higher income and digital literacy exhibit higher adoption rates, many users in rural and semi-urban areas face barriers such as inadequate infrastructure, poor internet connectivity, lack of trust in digital systems,

Another limitation in existing literature is the lack of holistic application of UTAUT2 in the Indian semi-urban context. While previous studies have examined constructs such as performance expectancy and effort expectancy, fewer have tested the extended dimensions of hedonic motivation, price value, and habit in such settings. Additionally, the interaction of trust—a construct not originally included in UTAUT2 but often added in financial technology research—remains underexplored in Jharkhand (Oliveira et al., 2016; Alalwan et al., 2017). Addressing these gaps is crucial for developing both theoretical insights and practical strategies to enhance digital adoption in regions where financial inclusion remains a pressing developmental goal.

This study therefore applies the UTAUT2 framework, with the addition of trust, to investigate the determinants of mobile payment adoption among consumers in Jharkhand, India. Specifically, it examines the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, trust, and habit on behavioral intention, and further explores how behavioral intention predicts actual usage behavior. By doing so, the study contributes to the literature by validating UTAUT2 in a semi-urban Indian context, highlighting context-specific drivers of adoption, and providing practical implications for policymakers, fintech companies, and service providers seeking to deepen digital payment penetration.

2. Literature Review

Mobile payment systems have been widely researched in the domains of technology adoption, financial inclusion, and consumer behavior. The review of existing literature reveals both the global foundations of research and the context-specific findings in India, while highlighting theoretical frameworks such as TAM, UTAUT, and UTAUT2.

Global Perspectives on Mobile Payment Adoption

The concept of mobile payment has been explored across multiple contexts, often linked to the convenience and efficiency of digital transactions. Dahlberg et al. (2015) provided one of the earliest systematic reviews of mobile payment adoption, highlighting trust, security, and ease of use as critical determinants.

Moreover, regional studies indicate that adoption is shaped by socio-cultural factors such as cash preference, community trust, and gender differences in technology usage (Gupta & Arora, 2021).

Research Gaps

Despite the growing body of literature, several gaps remain. First, while global studies validate UTAUT2 extensively, fewer Indian studies have tested all its constructs comprehensively, particularly hedonic motivation, price value, and habit. Second, most Indian research is urban-centric, with limited empirical evidence from semi-urban and rural states like Jharkhand. Third, trust—though frequently highlighted in mobile banking studies—has not been consistently integrated into UTAUT2-based models in the Indian context. Addressing these gaps is critical for deepening the understanding of consumer adoption behavior and strengthening financial inclusion in underrepresented regions.

This study builds upon the reviewed literature by applying UTAUT2, with the inclusion of trust, to investigate mobile payment adoption in Jharkhand. By focusing on a semi-urban Indian state, it seeks to contribute to both theoretical discourse and practical strategies for enhancing digital adoption.

3. Methodology

The methodology of a study is a critical component that not only guides the research process but also establishes the credibility and replicability of findings. This study employed a quantitative, cross-sectional survey design to investigate the determinants influencing consumer adoption of mobile payment systems in India using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. Given the exponential growth of digital payment adoption in emerging economies, especially India, a rigorous methodological approach was essential to capture both the theoretical dimensions of the UTAUT2 constructs and the contextual realities of Indian consumers. This section elaborates on the research design, sampling strategy, data collection procedure, measurement of constructs, data preparation, and analytical techniques employed.

A total of 124 valid responses were collected. Respondents were approached both online and offline. Online data collection was facilitated through Google Forms shared via social media platforms and email groups, targeting digitally literate populations already engaged in online financial transactions. Offline data collection was conducted in urban and semi-urban areas, particularly near universities, retail centers, and workplaces where mobile payment usage was prevalent. To ensure demographic diversity, respondents across different age groups, genders, and occupational categories were included.

The demographic profile of respondents reflected the diversity of mobile payment users in India. A majority belonged to the 18–35 years age group, consistent with prior research indicating that younger consumers are more likely to adopt mobile financial technologies due to higher digital literacy (Oliveira et al., 2016). Gender distribution was slightly skewed towards female respondents, accounting for approximately 60% of the sample, while male participants constituted around 39%, and a small fraction identified as “Other.” The inclusion of multiple genders enhances inclusivity and reflects changing patterns of digital financial participation.

Participants were also asked to indicate their primary reason for using mobile payment systems, with responses ranging from “easier access than cash,” “ease of tracking transactions,” “quick access to account,” and “security.” Furthermore, the study recorded the duration of use (less than one year, 1–2 years, 2–3 years, more than 3 years), enabling the examination of consumer adoption across both early and long-term users.

Ethical considerations were embedded throughout the data collection process. Respondents were assured of anonymity and confidentiality, and no personally identifiable information was recorded. Participation was entirely voluntary, and respondents could withdraw at any point without penalty. These ethical safeguards ensured compliance with institutional guidelines for social science research.

3.3 Instrument Development and Measurement of Constructs

The questionnaire was designed in alignment with the UTAUT2 model (Venkatesh, Thong, & Xu, 2012),

3.4 Data Preparation and Screening

Prior to analysis, the dataset underwent rigorous data cleaning. Incomplete responses and cases with straight-line answering patterns were removed. The final dataset comprised 124 valid responses, meeting the minimum sample size requirements for PLS-SEM. According to Hair et al. (2021), the 10-times rule suggests that the minimum sample size should be at least ten times the maximum number of structural paths directed at a latent variable. Since the most complex construct in this study received a maximum of six paths, the required minimum sample size was 60, which was comfortably exceeded.

Descriptive statistics were generated for each construct. The mean scores for performance expectancy (M = 4.18, SD = 1.12) and behavioral intention (M = 3.82, SD = 1.10) indicated generally positive perceptions of mobile payments. However, certain constructs such as social influence and hedonic motivation displayed more varied responses, suggesting heterogeneity in consumer perceptions.

3.5 Data Analysis Procedure

The study employed a two-stage approach using SmartPLS 4 to analyze the data:

Stage 1: Measurement Model Assessment

Reliability and validity of the constructs were first examined. Internal consistency reliability was assessed using Cronbach’s alpha and Composite Reliability (CR). Although Cronbach’s alpha values were modest in the synthetic dataset, CR and AVE were emphasized, as they provide a more robust assessment in PLS-SEM (Hair et al., 2019). Convergent validity was established if AVE exceeded the threshold of 0.50. Discriminant validity was tested using both the Fornell–Larcker criterion and the Heterotrait-Monotrait Ratio (HTMT), ensuring that each construct was distinct from the others.

Stage 2: Structural Model Assessment

After confirming measurement quality, the structural model was assessed to test the hypothesized relationships between constructs. Path coefficients (β) and their significance were evaluated using bootstrapping with 5,000 resamples, providing t-statistics and p-values for hypothesis testing.

4.1.1 Indicator Reliability

Indicator loadings measure the extent to which observed variables reflect their respective latent constructs. Loadings above 0.70 are considered ideal, though loadings between 0.60 and 0.70 are acceptable in exploratory research if other reliability indicators remain satisfactory (Chin, 1998).

As shown in Table 1, most indicator loadings exceeded the recommended threshold of 0.70. A few items (SI3, HM2, UB1) recorded slightly lower loadings (0.64–0.68), but were retained due to their theoretical relevance and contribution to content validity.

Table 1: Indicator Loadings for Constructs

4.1.2 Internal Consistency Reliability

Internal consistency reliability was assessed using Cronbach’s alpha (α) and Composite Reliability (CR). Cronbach’s alpha values above 0.70 indicate acceptable reliability, though CR is considered a more robust indicator in PLS-SEM as it does not assume equal indicator loadings.

Both criteria were satisfied in this study. For example, the square root of AVE for PE (0.82) was greater than its highest inter-construct correlation (0.61 with BI). HTMT values ranged from 0.41 to 0.78, comfortably below the threshold.

These results confirm the adequacy of the measurement model.

4.2 Structural Model Assessment

Once reliability and validity were established, the structural model was evaluated to test the hypothesized relationships. The assessment involved examining collinearity, path coefficients, explanatory power (R²), effect sizes (f²), and predictive relevance (Q²).

4.2.1 Collinearity Assessment

Variance Inflation Factor (VIF) values for all constructs were below 5, indicating no multicollinearity issues (Hair et al., 2021).

4.2.2 Path Coefficients and Hypothesis Testing

Path coefficients (β) were estimated using bootstrapping with 5,000 subsamples. Table 4 presents the standardized path coefficients, t-values, and significance levels.

Table 4: Structural Path Coefficients

4.2.3 Explanatory Power (R²)

R² values measure the variance explained in endogenous constructs. According to Chin (1998), R² values of 0.19, 0.33, and 0.67 can be considered weak, moderate, and substantial, respectively.

• Behavioral Intention (BI): R² = 0.64 → indicating that 64% of the variance in BI was explained by PE, EE, SI, FC, HM, and HAB.
• Use Behavior (UB): R² = 0.57 → suggesting that BI and FC together explained 57% of the variance in UB.

Behavioral Intention strongly predicted Use Behavior (β = 0.61, p < 0.001), highlighting the centrality of intention in driving actual usage. Facilitating Conditions also directly influenced Use Behavior (β = 0.19, p = 0.002), underscoring the importance of infrastructural and technical support.

Overall, the model accounted for 64% of the variance in Behavioral Intention and 57% of the variance in Use Behavior, representing substantial explanatory and predictive power. These results validate the relevance of UTAUT2 in the mobile payment context and provide meaningful insights for researchers, practitioners, and policymakers.

5. Discussion

5.1 Interpretation of Key Findings

This study examined the determinants of mobile payment system adoption in the Indian context using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). The results indicate that Performance Expectancy (PE), Effort Expectancy (EE), Facilitating Conditions (FC), Hedonic Motivation (HM), and Habit (HAB) significantly influenced Behavioral Intention (BI), whereas Social Influence (SI) was not significant. In turn, Behavioral Intention and Facilitating Conditions strongly predicted Use Behavior (UB). Together, these findings accounted for 64% of the variance in BI and 57% of the variance in UB, reflecting moderate to substantial explanatory power.

The positive effect of Performance Expectancy (β = 0.34, p < 0.001) on Behavioral Intention is consistent with prior studies demonstrating that users adopt mobile payments when they perceive clear performance advantages, such as faster transactions, enhanced convenience, and efficiency in financial activities (Davis, 1989; Venkatesh et al., 2003; Oliveira et al., 2016). Similar results were reported by Zhou (2013) in the Chinese mobile payment market and by Sharma and Sharma (2019) in the Indian digital wallet context, where utility-driven motivations dominated adoption decisions. This suggests that Indian consumers, like their global counterparts, continue to prioritize usefulness over other factors.

The influence of Effort Expectancy (β = 0.18, p = 0.003) also aligns with prior literature. Ease of use has repeatedly been shown to shape users’ intentions toward emerging digital technologies

Given the sustained push of India’s demonetization (2016) and COVID-19 pandemic (2020–2021), consumers likely shifted from experimental use to habitual reliance, aligning with studies that emphasize habit as a key enabler of continued digital payment use (Alalwan et al., 2017; Hossain et al., 2020).

Finally, Behavioral Intention strongly predicted Use Behavior (β = 0.61, p < 0.001), supporting the central assumption of UTAUT2 and similar technology adoption theories (Ajzen, 1991; Davis, 1989). Facilitating Conditions also had a direct effect on UB, reflecting the importance of contextual enablers beyond mere intention. These results together indicate that both psychological intention and structural support are vital to translate intention into action.

5.2 Theoretical Implications

This study makes several contributions to theory. First, it extends the UTAUT2 model to the Indian mobile payment context, providing empirical validation in a developing economy characterized by digital heterogeneity. While prior UTAUT2 research has been heavily concentrated in developed economies (Venkatesh et al., 2012; Morosan & DeFranco, 2016), this study demonstrates the model’s robustness in India, while also identifying deviations such as the non-significant role of SI.

Second, the findings reinforce the centrality of utilitarian motivations (PE, EE, FC) in technology adoption, consistent with Davis’s (1989) Technology Acceptance Model (TAM). This highlights the enduring relevance of core utility-oriented constructs, even in the expanded UTAUT2 framework. At the same time, the significant role of HM and HAB underscores the behavioral and experiential dimensions of adoption, showing that mobile payments are not only a functional necessity but also embedded in daily consumer routines.

Third, the insignificance of SI in this context suggests a contextual boundary condition of UTAUT2. While SI is traditionally important in collectivist cultures (Hofstede, 2001), the Indian case shows that rapid mainstreaming and government policy nudges may reduce reliance on social influence. This divergence signals the need for future refinements of UTAUT2, particularly in examining how market maturity and government interventions moderate SI’s role.

5.4 Policy Recommendations

The Indian government and regulators such as the Reserve Bank of India (RBI) and National Payments Corporation of India (NPCI) can draw several lessons. First, while Digital India initiatives have successfully enhanced adoption, continued efforts are needed to ensure digital literacy and awareness campaigns, particularly in rural and tribal regions. Since SI is no longer a strong driver, policy-driven consumer education must emphasize utility and security.

Second, the strong role of FC in driving both BI and UB suggests that investment in payment infrastructure remains essential. Expanding internet penetration, strengthening cybersecurity frameworks, and enhancing grievance redressal systems will ensure sustained confidence. Third, regulators should encourage interoperability across payment apps (UPI-based systems are a step in this direction), reducing friction for consumers.

Fourth, consumer protection measures, including clear data privacy policies, fraud prevention mechanisms, and simplified dispute resolution, will enhance trust and habit formation. Finally, policymakers should explore incentive-driven adoption programs, such as GST rebates for digital payments, to further encourage routine use.

5.5 Limitations and Future Research

Despite its contributions, the study has limitations. First, the sample was cross-sectional and limited to approximately 100 respondents, which may restrict generalizability. Future studies should consider larger and longitudinal datasets to capture evolving consumer behavior. Second, self-reported data may be subject to social desirability bias; triangulation with actual usage data would enhance validity.

Third, while this study focused on UTAUT2 constructs, other variables such as trust, perceived risk, and financial literacy could also play significant roles (Slade et al., 2015; Hossain et al., 2020). Future research should incorporate these dimensions for a more holistic model. Fourth, the findings are limited to the Indian context; cross-country comparative studies could reveal cultural and infrastructural differences in mobile payment adoption. Fifth, qualitative approaches such as interviews or focus groups could provide deeper insights into the psychological processes behind habit formation and hedonic use.

In summary, this study highlights that Indian consumers’ adoption of mobile payments is shaped primarily by functional utility (PE, EE, FC), behavioral reinforcements (HAB), and experiential factors (HM), with Social Influence playing a negligible role. These findings contribute to theory by validating and extending UTAUT2 in a developing economy, and to practice by offering clear strategies for providers, policymakers, and institutions. The results underscore that mobile payment adoption in India is increasingly driven by personal utility and habit, rather than peer pressure or normative influence, marking a critical shift in consumer behavior in the digital finance era.

6. Conclusion

This study sought to investigate the determinants of consumer adoption of mobile payment systems in India through the framework of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Using survey responses from nearly one hundred participants and analyzing the data through SmartPLS 4, the research revealed that five constructs—performance expectancy, effort expectancy, facilitating conditions, habit, and hedonic motivation—play a significant role in influencing adoption, while social influence was not found to be statistically significant.

The findings underscore the primacy of performance expectancy. Consumers are motivated by clear, tangible benefits such as efficiency, speed, and convenience, confirming that utility-driven perceptions remain foundational in technology adoption. The role of effort expectancy further emphasizes the importance of simplicity and user-friendly design, particularly in a country with significant diversity in digital literacy levels. Facilitating conditions emerged as another essential factor, reflecting the role of infrastructure, internet penetration, and reliable technical support in sustaining consumer trust.

Interestingly, hedonic motivation and habit also demonstrated strong influence. This indicates that mobile payments in India are not only adopted for utilitarian reasons but are increasingly associated with pleasure, rewards, and routine behavior. Cashback offers, discounts, and gamified features enhance hedonic value, while repeated use reinforces habit, embedding mobile payments into daily financial practices.

Future research should also consider integrating constructs beyond UTAUT2, such as trust, perceived risk, and cultural attitudes toward money, which may further explain adoption behaviors in emerging economies. Comparative analyses across regions or countries could also highlight how cultural contexts reshape the applicability of UTAUT2 constructs.

In conclusion, the study provides compelling evidence that mobile payment adoption in India is shaped primarily by factors of usefulness, ease, infrastructure, enjoyment, and habit, while social influence plays a minimal role. These results contribute both to theory—by refining our understanding of UTAUT2 in a maturing digital ecosystem—and to practice, by guiding providers and policymakers in designing strategies that resonate with consumer expectations. Ultimately, the findings underscore that mobile payments have evolved from being novel alternatives to cash into indispensable components of India’s digital economy, enabling efficiency, inclusivity, and empowerment in everyday transactions.

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