Mohamed Soliman*
| Jeganathan Gomathi Sankar | Dina Darwish | Ahmed Nousir | Reham Adel Ali
© 2026 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
This research investigates the implementation of digital payments in Egypt and India, two developing economies, utilizing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) paradigm. Utilizing performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and perceived risk (PR), we examine their effects on behavioral intention (BI) through structural equation modeling based on survey data from 130 Egyptian and 307 Indian students. Findings indicate that FC and EE serve as universal motivators in both contexts, although PE has a more pronounced impact in Egypt, which is at an early-adoption phase. SI substantially influences Indian users, mirroring cultural standards, although it does not impact Egyptians. Public relations have an adverse impact on adoption in Egypt, although it has a negligible impact in India, suggesting a lack of trust. The findings highlight the importance of addressing contextual elements in digital maturity and policy, offering valuable insights for regulators and fintech companies to enhance infrastructure, usability, and risk mitigation for financial inclusion in emerging nations.
digital payment adoption, Unified Theory of Acceptance and Use of Technology model, cross country comparison, financial inclusion, fintech adoption, technology adoption, perceived risk, emerging economies
In the financial services industry, Information technology revolution has been encouraged by the emergence of internet, which eventually changed how banking services are provided [1]. Digital finance such as mobile banking, internet banking, and Unified Payment Interface (UPI) use witnessed significant improvements with the help of online financial services [2]. Digital finance presents new opportunities to offer financial services to populations that were previously not aware of this. The growing number of smartphone subscriptions-over six billion globally-is driving demand for digital banking services [3]. Financial institutions now enable customers to perform transactions remotely via mobile devices [4].
Adoption of electronic funds is not just found in advanced nations. For example, During the COVID-19 outbreak, 33% of Indians said they used digital payments more frequently [5]. Mobile wallets have gained attention for their potential to simplify payments and offer new income streams for service providers such as financial institutions, telecom operators, and e-commerce platforms. Key factors influencing their adoption include perceived usefulness, facilitating conditions, security, ease of use, social influence, and regulatory support. The rapid evolution of smartphones, in tandem with information technology, has transformed them into essential tools of daily life [6].
Mobile wallets offer services such as instant payment through QR codes, digital receipt storage, reduced waiting time, promotional discounts, and increased customer satisfaction. Their popularity surged during COVID-19, which altered consumer habits and heightened interest in contactless payments to minimize virus transmission [7, 8]. The design of mobile payments supported social distancing and facilitated transactions without physical contact.
Despite their advantages, mobile wallets are not yet universally adopted. Studies report skepticism and resistance to new technologies as barriers [9, 10]. Inadequate government support and regulatory challenges further hinder progress toward cashless societies. Scholars have highlighted the need to comprehend customer behavior in order to promote adoption [11]. These observations are appropriate, for developing countries like Egypt and India. This kind of research can provide suggestions to policy makers in creating policies to promote digital payment, as well as support service providers in developing effective marketing campaigns.
Despite significant efforts to boost financial inclusion, many people in developing countries still struggle to access digital payments and transaction accounts. As of 2021, only 71% of youngsters in these regions had maintained a bank account, compared to 76% globally. Also, just 57% of adults in developing countries engaged in online payments, whereas the worldwide average reported 64% [12]. Some of the hurdles to adoption include informality [13], tax benefit [14], and poor internet or mobile connectivity [15].
Payment digitization has boosted financial inclusion and identified to boost financial development [16], reduce poverty [17], and close the disparities in education [18] and gender [19]. Mobile financial applications have made it possible for consumers to send money over long distances, which has increased household financial stability and enhanced tax collection [20, 21]. Yet, mobile money adoption remains uneven. In contrast, bank-based digital transfers have grown rapidly, requiring a closer examination of cross-country differences and drivers.
It was identified that acceptance of fast payment systems (FPS) is higher when central banks operate them, when non-bank entities are allowed to participate, and when there are diverse use cases and cross-border linkages [22]. Instant payments-real-time credit transfers processed 24/7 represent a shift from traditional payment methods that take hours or days to settle [23]. Drivers include enhanced digital infrastructure, supportive regulation, and growing consumer demand for speed and convenience [24].
Unlike batch-processed credit transfers, instant payments eliminate the float period during which funds are locked in the system. This allows immediate access to money for both consumption and business use and reduces risks for merchants who would otherwise rely on delayed payment guarantees [25]. By providing instant confirmation and settlement, instant payments function similarly to cash but in digital form.
This study uses Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as a theoretical framework to examine the factors that influence intention to adopt digital payment differ between Egypt and India. The present work analyzes user behavior, policy environments, and system-level factors to discuss their implications for financial institutions, digital service providers, and policy regulators. The findings affirmed that marketing strategies and regulatory policies aimed at promoting digital transaction in Egypt and India.
This study not only provides cross country evidence but also extends the UTAUT2 framework by extending its perspective in two nations with different levels of digital payment ecosystem maturity. This study compares Egypt (where the digital payment ecosystem is still evolving) with India (where it is more mature) to investigate determinants of digital adoption. The study goes beyond applying the UTAUT2 model by refining its relevance for emerging market contexts.
Mobile wallets and digital payment platforms have appeared as transformative tools, particularly in emerging markets. The first online transaction using electronic payment occurred in 1994 [26]. Last decade has witnessed accelerated innovation through platforms such as PayPal (1998), Google Wallet (2011), Apple Pay (2014), and Samsung Pay (2015). More recently, financial ecosystems have expanded to include Buy Now Pay Later (BNPL) services and pilot implementations of Central Bank Digital Currencies (CBDCs). Mobile payment service providers now include banks, telecom operators, fintech firms, and e-commerce companies [27]. These platforms enable transactions anytime and anywhere, making them a vital component of modern financial ecosystems [28].
Mobile payment is especially beneficial in emerging economies, where smartphones are ubiquitous and financial inclusion is a policy priority. According to Statista (2022a), there are over six billion smartphone subscriptions worldwide [6]. The rise in smartphone penetration and internet usage has contributed to the popularity of digital payment apps and mobile banking [3]. Financial institutions now facilitate remote transactions via mobile devices, enhancing convenience [4].
The mobile payment adoption has been significantly increased at the time of COVID – 19 pandemics. With the World Health Organization [8] advocating contactless payments to reduce virus transmission, mobile wallets gained prominence due to their touch-free transaction capabilities [29]. The pandemic shifted consumer behavior globally, prompting increased reliance on digital payments in both developed and emerging economies [30]. Studies have identified that 33% of Indian users confirmed that the usage of digital payment was increased at the time of COVID-19 [31]. Despite these advantages, adoption is still unequal, and user resistance is fueled by perceived complexity, lack of awareness, and trust difficulties [10].
In the field of marketing literature, the emergence of technology adoption has extensively been conferred. Technology adoption is a important area of study for both academics and practitioners. However, conceptual difficulties have arisen due to the lack of clarity in defining “adoption,” limiting operational definitions necessary for scale development [32]. Scholars describe consumer adoption as the process by which individuals decide to accept or reject an innovation [33, 34]. This was further explained by [35], who described adoption as a multi-phase process that starts with awareness and ends with the decision to adopt. Important groups within this adoption spectrum include the factors of diffusion of innovation theory. Technology adoption is consumer’s interest to engage with new technological innovation [36]. Recent studies have also explored the adoption of AI-powered tools and intelligent platforms among university students, highlighting the role of configurational and behavioral factors in technology acceptance.
Researchers had developed various models over the years to study technology acceptance. Among the models, Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and UTAUT’s later extensions are widely adopted by the academicians [37]. These models had postulated to find out the factors that affect the customer to accept new technology particularly in digital transaction. Based on behavioural theories, TAM was developed by [38]. Before that Ajzen and Fishbein [39] introduced the Theory of Reasoned Action (TRA) to study the individual behavioural intentions. Despite, its popularity and wide application, TAM has been criticized for its narrow focus [40] and has ignored social, personal, and institutional factors [41]. Various extensions TAM had been introduced to overcome the limitations. Nevertheless, UTAUT [41] has become extensively employed model for technology acceptance research. UTAUT integrates eight previous theories: diffusion of innovation, TRA, TAM, social cognitive theory, model of PC utilization, motivation theory, TPB, and combined TAM-TPB [41]. Recent empirical studies have extended these models into emerging domains such as ERP systems in higher education, demonstrating both enabling and inhibiting factors influencing adoption decisions. Venkatesh et al. [42] further expanded the UTAUT framework by adding price value, hedonic motivation, and habit and named UTAUT2. This modification is said to improve the model’s descriptive power.
Trust was not a construction of UTAUT or UTAUT2. But researchers have integrated trust in the existing UTAUT and UTAUT2 model. Literature suggested that trust had significantly influenced the adoption of digital technology in banking industry [43, 44]. It has also been affirmed that trust help mitigate perceived risks while using digital financial services [45]. Additional constructs such as security and privacy had been included in digital payment studies in developing nation [46].
Prior studies have confirmed that UTAUT and UTAUT2 have been widely used with integrating various factors in accordance with research context. It offers a widely employed framework for analysing cross-national comparisons where diverse socioeconomic factors play a significant role on digital payment adoption. Table 1 details the previous digital payment adoption studies with variables used and model employed in that study.
Table 1. Summary of digital payment adoption studies
|
Author(s) (Year) |
Study Context |
Variables Examined |
Model Used |
|
Davis [47] |
Technology Acceptance Model (TAM) |
PU, PEU, A, BI |
TAM |
|
Venkatesh et al. [41] |
Unified Theory of Acceptance and Use of Technology (UTAUT) |
PE, EE, SI, FC, BI, UB |
UTAUT |
|
Lin [48] |
Mobile Banking Adoption |
Ease of use, Perceived relative advantage, Compatibility |
DOI |
|
Venkatesh et al. [42] |
UTAUT2: Extended UTAUT Model |
PE, EE, SI, FC, HM, PV, H |
UTAUT2 |
|
Slade et al. [49] |
Mobile Payment adoption in UK |
PE, EE, SI, FC, PR, T |
UTAUT |
|
Oliveira et al. [50] |
Mobile payment, Portugal |
Compatibility, Security, Innovativeness, PE, SI |
UTAUT2 & DOI |
|
Alalwan et al. [51] |
Mobile Banking, Jordan |
T, HM, PV, H |
Meta UTAUT2 |
|
Kumar et al [52] |
Mobile Wallet Usage |
PU, PEU, A, T |
TAM |
|
Chhonker et al. [53] |
m-commerce technology adoption |
PE, EE, SI, FC |
TAM, TAM2, TAM3, UTAUT2, TRA & TPB |
|
Sivathanu [54] |
Digital Payment Adoption in India |
PU, PEU, T, Awareness |
UTAUT 2 & Innovation resistance theory |
|
Patil et al. [55] |
Mobile payment, India |
PE, EE, T, Personal innovativeness, Anxiety, Grievance Redressal |
Meta UTAUT |
|
Zhong & Moon [56] |
Mobile QR-Code Payment in China |
PE, PU, PR |
TAM, UTAUT |
|
Sahi et al. [57] |
Digital Payment Adoption: A Review |
PE, PU, PR, T |
TAM, UTAUT, DOI |
|
Kapoor et al. [58] |
Mobile wallet adoption |
Perceived critical mass, Perceived values, Promotional benefits |
SOR |
|
Razi-ur-Rahim and Uddin [59] |
Adoption of UPI among Indian users |
PE, SI, EE, FC, T, A, Personal innovations, Anxiety |
Meta UTAUT |
|
Kar [60] |
Mobile payment satisfaction |
PV, PU, T, SI, Credibility, Information privacy, Responsiveness |
Digital Service Usage Satisfaction Model |
|
Haritha [61] |
Mobile payment adoption |
PU, PEU, A |
TAM |
|
Saha & Kiran [62] |
UPI payment adoption by baby boomers |
PE, SI, EE, PR Ubiquity, Perceived security |
Meta UTAUT |
|
Banerji & Singh [63] |
Mobile wallet adoption in India |
PU, PEU, Compatibility, Observability, Trialability |
TAM & DOI |
|
Lee & Chen [64] |
Acceptance of Mobile-Banking Applications |
PE, EE, SI, FC |
UTAUT |
|
Shahid [65] |
UPI adoption in India |
Relative advantage, Complexity, Trialability Observability |
DOI |
|
Kirmani et al. [66] |
Intentions to continue UPI in India |
Perceived value, PU, PR |
TAM |
|
Thakkar & Thakkar [67] |
UPI usage among women in India |
PE, EE, SI, FC |
UTAUT |
|
Al-Sabaawi et al. [68] |
Electronic Payment Adoption in developing nations |
PE, EE, SI, FC, T, PR |
Meta UTAUT |
|
Guhan & Nigma [69] |
UPI usage during COVID-19 |
PE, EE, SI, FC |
UTAUT |
|
Jegerson & Hussain [70] |
Mobile payment adoption |
PE, EE, SI, FC |
UTAUT |
|
Wu & Liu [71] |
Mobile payment: Cross country adoption |
PE, SI, FC, EE, HM, PV, and H. |
UTAUT2 |
|
Soormo et al. [72] |
Adoption of QR payments |
PE, EE, SI, FC |
UTAUT |
|
Alam et al. [73] |
QR code payment methods |
PE, EE, SI, FC Perceived values, Perceived critical mass, Promotional benefits |
UTAUT & SOR |
|
Razi-ur-Rahim et al. [59] |
UPI adoption in India |
PE, EE, SI, FC, A, UB |
Meta-UTAUT |
|
Tang & Tsai [74] |
Mobile payment after COVID-19 |
PE, HM, PV, EE, SI, FC, H. |
UTAUT2 |
|
Usman et al. [75] |
Digital Payment usage |
Subjective Norms, Attitude, Financial Literacy, Perceived Behavioral Control |
TPB |
|
Davis [47] |
Technology Acceptance Model (TAM) |
PU, PEU, A, BI |
TAM |
|
Venkatesh et al. [41] |
Unified Theory of Acceptance and Use of Technology (UTAUT) |
PE, EE, SI, FC, BI, UB |
UTAUT |
|
Lin [48] |
Mobile Banking Adoption |
Ease of use, Perceived relative advantage, Compatibility |
DOI |
|
Venkatesh et al. [42] |
UTAUT2: Extended UTAUT Model |
PE, EE, SI, FC, HM, PV, H |
UTAUT2 |
|
Slade et al. [49] |
Mobile Payment adoption in UK |
PE, EE, SI, FC, PR, T |
UTAUT |
|
Oliveira et al. [50] |
Mobile payment, Portugal |
Compatibility, Security, Innovativeness, PE, SI |
UTAUT2 & DOI |
|
Alalwan et al. [51] |
Mobile Banking, Jordan |
T, HM, PV, H |
Meta UTAUT2 |
|
Kumar et al [52] |
Mobile Wallet Usage |
PU, PEU, A, T |
TAM |
|
Chhonker et al. [53] |
m-commerce technology adoption |
PE, EE, SI, FC |
TAM, TAM2, TAM3, UTAUT2, TRA & TPB |
|
Sivathanu [54] |
Digital Payment Adoption in India |
PU, PEU, T, Awareness |
UTAUT 2 & Innovation resistance theory |
|
Patil et al. [55] |
Mobile payment, India |
PE, EE, T, Personal innovativeness, Anxiety, Grievance Redressal |
Meta UTAUT |
|
Zhong & Moon [56] |
Mobile QR-Code Payment in China |
PE, PU, PR |
TAM, UTAUT |
|
Sahi et al. [57] |
Digital Payment Adoption: A Review |
PE, PU, PR, T |
TAM, UTAUT, DOI |
|
Kapoor et al. [58] |
Mobile wallet adoption |
Perceived critical mass, Perceived values, Promotional benefits |
SOR |
|
Razi-ur-Rahim and Uddin [59] |
Adoption of UPI among Indian users |
PE, SI, EE, FC, T, A, Personal innovations, Anxiety |
Meta UTAUT |
|
Kar [60] |
Mobile payment satisfaction |
PV, PU, T, SI, Credibility, Information privacy, Responsiveness |
Digital Service Usage Satisfaction Model |
A critical review of the studies summarized in Table 1 highlights several important trends and research gaps. Most of the studies employed TAM, UTAUT, and UTAUT2 models, showing performance expectancy, effort expectancy, social influence, and facilitating conditions are the important factors of behavioral intention. But perceived risk, trust, and habit were found to be inconsistent across nations. Prior studies focused on single country perspective and emphasized mobile wallet and UPI related with limited consideration of broader ecosystem-level differences. Cross country comparison remains dearth. These gaps indicate the need for a comparative study examining relationships among UTAUT2 framework across countries having different regulatory environments, infrastructure readiness, and levels of digital maturity.
UTAUT2 serves as a widely employed adoption framework of online payment adoption. The UTAUT2 model, integrated with important factors to the first UTAUT model [42], was employed in a plethora of studies in the field of financial technology. This study finds that performance expectancy, social influence, effort expectancy, facilitating conditions, and perceived risk demonstrates significant negative impact on customers intention to use digital payment. Digital payment refers to an economic environment where transaction is relying on electronic transfer [76]. According to reviewed literature and conceptual foundations of UTAUT2, the study hypotheses are framed”.
3.1 Performance expectancy
The degree to which the utilizing technology assists to accomplish the task is deemed as performance expectancy [77]. Users will adopt or ready to accept new technology once they think it would support to complete their task [41]. This construct identified to be a substantial influencer on digital adoption [44, 78-81]. Thus, the hypothesis below is framed.
H1: Performance expectancy is having positive influence on digital payment adoption.
3.2 Effort expectancy
The ease of handling technology is known as effort expectancy [42]. When people consider the process is simple to learn, they would consider adopting. Effort expectancy ascertained to be an imperative construct of digital payment [2, 79]. Thus, the hypothesis below is framed.
H2: Effort expectancy is having positive influence on digital payment adoption.
3.3 Social influence
Individuals’ behaviour is being influenced by family, friends, and co-workers [80]. Social influence is the extent to which anyone thinks that family, friends, and colleagues impact on an individual’s behaviour [81]. Studies have confirmed the positive association between social influence and intention in the situation of digital payment [50]. Social influence affirmed to have a substantial influence on UPI adoption in India [82]. Thus, the hypothesis below is framed
H3: Social influence is having positive influence on digital payment adoption.
3.4 Facilitating conditions
The degree to which the utilizing technology assists to accomplish the task is deemed as performance expectancy [77]. Users will adopt or ready to accept new technology once they think it would support to complete their task [41]. This construct identified to be a substantial influencer on digital adoption [44, 78, 81]. Thus, the hypothesis below is framed
H1: Performance expectancy is having positive influence on digital payment adoption.
3.5 Performance expectancy
The infrastructure support given to an individual to assess the new technology is referred to as facilitation condition [41]. It is pertained to have required resources to access any technology [83]. Researchers have reported that facilitating conditions have predominantly influence individual to adopt digital payment, particularly in India [54, 83]. Thus, the hypothesis below is framed.
H4: Facilitating conditions is having positive influence on digital payment adoption.
3.6 Perceived risk
Risk is deemed to have an imperative explanatory power on digital payment adoption [84, 85], particularly in emerging fintech and Sharia-compliant financial contexts [84]. This is deemed to have negative influence on digital adoption [86]. In accordance with digital transactions, loss of money while transaction, data breach, account of fraud, payment delay are the possibility of risk [87]. Thus, the hypothesis below is framed.
H5: Perceived risk is having negative influence on digital payment adoption.
4.1 Measurement scales
Measurement instruments are developed from the scales of previous studies. It comprised six latent factors and 18 observed variables. To confirm the face validity of the instrument, an English form of the questionnaire was initially presented to three experts, one was engaged in digital marketing and two were from fintech research. These experts included one digital marketing practitioner with industry experience in digital payment systems and two academic researchers specializing in fintech and digital finance. Their review focused on face validity, clarity, and contextual relevance of the measurement items. The questionnaire was translated into Arabic for collecting data from Egypt and Hindi for collecting data from India by a bilingualist. The translator was proficient in both English and the target language and familiar with survey-based research terminology to ensure conceptual equivalence rather than literal translation. After the translation both versions were reviewed further to confirm the accuracy. Although a formal back-translation procedure was not employed, the translated versions were independently reviewed by experts to verify semantic consistency, readability, and the absence of ambiguity across language versions. Experts were requested to examine the readability and ambiguity of the measurement instrument. The suggested modifications primarily involved simplifying sentence structure and adjusting terminology related to perceived risk and facilitating conditions to improve respondent comprehension. Results of this process are highly consistent with Dillman’s [88] recommendations. A pilot study was led with 30 samples to verify the instrument’s validity and reliability. The pilot respondents confirmed that the questionnaire items were clear and easy to understand across both language versions. The responses were analysed to authenticate the items’ reliability. Cronbach’s alpha was used to assess internal consistency reliability, and all constructs exceeded the recommended threshold value.
4.2 Sampling and data collection
Cochran’s [89] method was used to measure the sample size. Thus, 385 was decided as a minimum required sample. Consequently, 425 sample size was adequate. The survey was conducted online with students in Egypt and India, who have been using any form of digital transaction for buying goods or services. The participants were considered for data collection after confirming that they have been using digital payment for sales transactions.
Higher education students were selected as the study population. Students apparently have high smartphone usage with digital literacy and high engagement with financial services. Which make them likely early adopters of emerging new technologies. In addition to that, their high digital social interactions make them suitable for this research context. As young consumers are believed to shape the future of digital payment ecosystems in emerging economies, their behavioral intentions give valuable insight into evolving adoption patterns.
There was no easily accessible sampling frame that could be used to count the population of digital payment users, so convenience sampling method employed for data collection. Convenience sampling is the process of selecting respondents according to their approachability, closeness, availability at given point of time, and willingness to participate [90]. While this approach facilitated data collection from digitally active respondents, it may limit the generalizability of findings beyond the sampled population. Previous studies in related fields have effectively employed this technique under similar constraints [91, 92].
We used a Google Forms-created web-administrated questionnaire to conduct an online survey. Under these conditions, online questionnaires were simple to use. We properly detailed the objective and ensured that their data would remain confidential and that it was being done solely for scholarly purposes. Following Hair et al. [93], 437 valid responses remained in the final analysis after 39 responses with insufficient or missing data were eliminated. Of the 437 completed surveys that were distributed, 307 came from India and 130 from Egypt. This suggests a response rate of about 79% and is higher than the typical regional average of 51% in similar survey-based studies. Responses displaying significant missing values or discrepancies were removed, and data screening procedures were conducted in compliance with Hair et al. [93] recommendations. The final sample, which was deemed suitable for additional statistical analysis, consisted of 437 valid responses after these instances were eliminated.
5.1 Demographic details
Table 2 presents the demographic profile of respondents from India (N = 307) and Egypt (N = 130). In India, the sample consisted of 52% males and 48% females, with the majority aged between 18-21 years (55%), followed by 22-25 years (42%). All Indian respondents reported daily usage of digital payment applications. In contrast, the Egyptian sample comprised 62% males and 38% females, with most respondents aged between 18-21 years (58%) and 22-25 years (42%). Digital payment usage frequency in Egypt varied across daily (29%), weekly (29%), monthly (24%), and rarely (18%) categories. The uniformly high daily usage observed in the Indian sample reflects the characteristics of the surveyed population, which primarily consisted of urban university students highly engaged with mobile-based payment platforms.
Table 2. Participants information
|
|
Egypt |
India |
||
|
Construct |
Characteristics |
Percentage |
Characteristics |
Percentage |
|
Gender |
Male |
62 |
Male |
160 |
|
Female |
38 |
Female |
147 |
|
|
Age |
<18 |
0 |
<18 |
0 |
|
18-21 |
58 |
18-21 |
170 |
|
|
22-25 |
42 |
22-25 |
130 |
|
|
26-30 |
0 |
26-30 |
0 |
|
|
Educational Qualification |
High School |
31 |
High School |
0 |
|
Graduate |
35 |
Graduate |
169 |
|
|
Postgraduate |
33 |
Postgraduate |
138 |
|
|
PhD |
1 |
PhD |
0 |
|
|
Frequency of Usage |
Daily |
29 |
Daily |
307 |
|
Weekly |
29 |
Weekly |
0 |
|
|
Monthly |
24 |
Monthly |
0 |
|
|
Rarely |
18 |
Rarely |
0 |
|
|
Preferred App |
InstaPay |
|
Google Pay |
209 |
|
Vodafone Cash |
15 |
PhonePe |
98 |
|
|
Meeza |
20 |
Paytm |
0 |
|
|
PayPal |
11 |
BHIM |
0 |
|
|
Apple Pay |
1 |
|
|
|
|
Google Pay |
39 |
|
|
|
5.2 Exploratory results and measurement model (Egypt)
To investigate the framed hypotheses, structural equation modelling was employed. First step was to perform confirmatory factor analysis to analyse the measurement model, as shown in Figure 1. Null model and the empirical model were compared to ascertain the model fit. Literature recommended that it is necessary to attain CMIN/DF less than 3. In our model it is reported that 1.908. The Tucker-Lewis Index (TLI), Incremental Fit Index (IFI), and Comparative Fit Index (CFI) reported as 0.904, 0.926, and 0.925 respectively and greater than the suggested range of 0.90. Root Mean Square Error of Approximation (RMSEA) is found to be 0.08, as suggested. Fit measures were satisfied with the recommended limit of Hair et al. (2018). All factors confirmed acceptable Composite Reliability (CR) values ranging from 0.767 to 0.932, which is greater than level of 0.70 as suggested [93]. The Average Variance Extracted (AVE) of the factors are listed in Table 2 and reported above the suggested range of 0.50 [93].
Figure 1. Measurement model Egypt
The AVE was measured to assess the convergent validity. The factors AVE scores surpassed the 0.5 threshold; thus, CFA convergent validity has been confirmed. Discriminant validity was also verified by determining whether the AVEs of the factors surpass the corresponding inter-construct correlations. The results show in Table 3, confirm that the AVE as square root of all factors is greater than intercorrelation. Thus, discriminate validity of the factors has been confirmed. As per the standards suggested by Fornell and Larcker [94], these findings validate the construct validity of the measurement model. All these results support the measurement model’s validity in the Egyptian context.
Table 3. CR and AVE (Egypt)
|
Construct |
CR |
AVE |
|
Performance Expectancy (PE) |
0.860 |
0.674 |
|
Effort Expectancy (EE) |
0.767 |
0.525 |
|
Facilitating Condition (FC) |
0.932 |
0.821 |
|
Social Influence (SI) |
0.906 |
0.764 |
|
Perceived Risk (PR) |
0.804 |
0.580 |
|
Behavioural Intention (BI) |
0.882 |
0.715 |
5.3 Structural model (Egypt)
The structural model was examined to assess the proposed hypotheses between the latent variables, as shown in Figure 2. According to Hair et al. [93], the model fit indices showed an adequate level of fit: χ² = 228.958, df = 120, resulting in a CMIN/df = 1.908, well under the suggested criterion of <3.0 for a satisfactory fit. With a 90% confidence range between 0.067 and 0.100 (PCLOSE = 0.001), the RMSEA was 0.084, indicating a manageable error of approximation for large-sample social science research.
Figure 2. Structural model Egypt
Model adequacy is further supported by several fit indices: TLI = 0.904, IFI = 0.926, and CFI = 0.925 all surpassed the minimal suggested cut-off of 0.90. Both AGFI and GFI were within acceptable limits, albeit marginally below optimal, at 0.760 and 0.832, respectively. An acceptable fit in the Egyptian setting was further supported by NFI = 0.857 and RFI = 0.818. According to Henseler and Fassott [95], the model has moderate explanatory power, accounting for 40.2% of the variance in digital payment adoption (R2 = 0.402).
Most of the proposed hypotheses of the conceptual model were strongly supported statistically as results shown in Table 4. In particular, it was discovered that intention to adopt was considerably and favorably influenced by Performance Expectancy (PE) (β = 0.325, p = 0.001), Effort Expectancy (EE) (β = 0.243, p = 0.010), and Facilitating Conditions (FC) (β = 0.341, p < 0.001). The results are consistent with prior studies [42, 51], confirming that individuals are persuaded to accept digital payments when they consider the system is practical, user-friendly, and has sufficient technical support.
Table 4. Discriminant validity (Egypt)
|
|
R |
BI |
EE |
SI |
FC |
PE |
|
R |
0.762 |
|
|
|
|
|
|
BI |
0.034 |
0.845 |
|
|
|
|
|
EE |
0.240 |
0.377 |
0.725 |
|
|
|
|
SI |
0.402 |
0.358 |
0.194 |
0.874 |
|
|
|
FC |
0.226 |
0.581 |
0.293 |
0.457 |
0.906 |
|
|
PE |
0.152 |
0.558 |
0.188 |
0.416 |
0.517 |
0.821 |
On the contrary, behavioral intention has been significantly impacted by perceived risk but negatively (β = –0.189, p = 0.041), indicating that psychological hurdles to adoption are still present due to worries about financial loss, data breaches, or transaction failure. Studies like Zhou [96] and Featherman and Pavlou [97] emphasize the importance of risk perceptions in influencing technology-related behavioral outcomes, particularly in financial contexts, are supported by this conclusion.
Nevertheless, no substantial impact of Social Influence (SI) on behavioral intention (β = 0.096, p = 0.320). The widely accepted view in UTAUT-based research that societal norms and peer suggestion significantly impact adoption behavior, mainly in collectivist societies like Egypt and India is called into question by this non-significant finding. One possible explanation is the increasing normalcy and personalization of digital payment use, especially among student populations that use technology frequently. As digital payments become more common, especially in India’s advanced UPI ecosystem, users may no longer rely heavily on social approval or peer influence when adopting them. This result is same as Venkatesh et al. [42] research and the recent research of Al-Qudah et al. [98]. Both the studies have ascertained the marginal influence of social impact on customers’ technological adoption behaviour. But this interpretation is inferential and not directly tested within the present study; therefore, future research employing qualitative or mixed-method approaches is needed to further validate this explanation.
5.4 Exploratory results and measurement model (India)
A model fit between hypothetical model and the empirical model of Indian respondents affirms the good model fit, as shown in Figure 3. Literature recommended that it is necessary to have CMIN/DF less than 3. In our model it is reported that it is 2.912. TLI, IFI, and CFI reported as 0.938, 0.954, and 0.954 respectively and greater than the threshold range of 0.90. RMSEA is exactly as a recommended level of 0.08. For this study, fit measures were within the recommended limit of Hair et al. [93]. All factors confirmed acceptable Composite Reliability values ranging from 0.774 to 0.990, which is higher than suggested level of 0.70 [93]. AVE of the factors are listed in Table 5 and reported over the suggested value of 0.50 [93].
Figure 3. Measurement model India
Table 5. Path coefficient and hypothesis test result (Egypt)
|
Hypotheses |
Path |
Estimate |
S. E |
P Value |
Remarks |
|
H1e |
PE àBI |
0.325 |
0.098 |
0.001 |
Supported |
|
H2e |
EE àBI |
0.243 |
0.114 |
0.010 |
Supported |
|
H3e |
FC àBI |
0.341 |
0.085 |
0.000 |
Supported |
|
H4e |
SI àBI |
0.096 |
0.106 |
0.320 |
Not Supported |
|
H5e |
PR àBI |
-0.189 |
0.148 |
0.041 |
Supported |
Table 6. CR and AVE (India)
|
Construct |
CR |
AVE |
|
Performance Expectancy (PE) |
0.860 |
0.674 |
|
Effort Expectancy (EE) |
0.767 |
0.525 |
|
Facilitating Condition (FC) |
0.932 |
0.821 |
|
Social Influence (SI) |
0.906 |
0.764 |
|
Perceived Risk (PR) |
0.804 |
0.580 |
|
Behavioural Intention (BI) |
0.882 |
0.715 |
The AVE scores (Table 6) for all constructs surpassed 0.5, thus affirming convergent validity. Discriminant validity was also verified by determining whether the AVEs of the factors surpass the corresponding inter-construct correlations. The results shown in Table 7, confirms AVE as square root of all factors is greater than intercorrelation. Thus, discriminate validity of the factors has been confirmed. As the standards suggested by Fornell and Larcker [94], these findings validate the construct validity of the measurement model. All these results support the measurement model’s validity in the Indian context.
Table 7. Discriminant validity (India)
|
|
R |
BI |
EE |
SI |
FC |
PE |
|
R |
0.753 |
|||||
|
BI |
0.030 |
0.849 |
||||
|
EE |
0.093 |
0.274 |
0.646 |
|||
|
SI |
0.143 |
0.650 |
0.155 |
0.834 |
||
|
FC |
0.172 |
0.512 |
0.213 |
0.600 |
0.985 |
|
|
PE |
0.084 |
0.648 |
0.179 |
0.728 |
0.426 |
0.969 |
5.5 Structural model (India)
To examine the postulated relationship between the factors, path analysis has been performed, as shown in Figure 4. The structural model was considered to be fit as the CMIN/df = 2.633, less than the suggested value of 3 [93]. GFI = 0.900 and AGFI = 0.858, although the AGFI value (0.858) is slightly below the conventional 0.90 threshold, it exceeds the minimum recommended level of 0.80 for complex models as suggested by Hair et al. [93]. Parsimony indices like the Parsimony Comparative Fit Index (PCFI = 0.756) and the Parsimony Normed Fit Index (PNFI = 0.739) demonstrated the model’s balance between fit and simplicity. Acceptable generalizability was suggested by the Expected Cross-Validation Index (ECVI = 1.366), which was near to that of the saturated model (1.118). Besides, the sample size was adequate to produce stable model estimates, as evidenced by the Hoelter’s Critical N values of 142 at the 0.05 significance level and 154 at 0.01. The RMSEA was 0.074, which is less than the threshold level of 0.08. Based on the abovementioned fit indices, the structural model for the Indian sample ascertained to be appropriate for further analysis.
Structed path analysis revealed that UTAUT2 factors influenced adoption intention in different ways for the Indian sample, as shown in Table 8. Facilitating Conditions (FC) was the important predictor (β = 0.360, p < 0.001), emphasizing the significance of infrastructure support of adoption. Behavioral intention and SI also showed a strong positive connection (β = 0.280, p = 0.001), showing the significance of social and peer impact in influencing user attitudes toward digital payments. The modest and significant effect of Effort Expectancy (EE) (β = 0.184, p = 0.002) confirmed that EE is a crucial facilitator of adoption.
Table 8. Path coefficient and hypothesis test result (India)
|
Hypotheses |
Path |
Estimate |
S. E |
P Value |
Remarks |
|
H1i |
PE àBI |
0.179 |
0.079 |
0.023 |
Supported |
|
H2i |
EE àBI |
0.133 |
0.043 |
0.002 |
Supported |
|
H3i |
FC àBI |
0.290 |
0.059 |
0.000 |
Supported |
|
H4i |
SI àBI |
0.257 |
0.079 |
0.001 |
Supported |
|
H5i |
PR àBI |
-0.072 |
0.040 |
0.072 |
Not Supported |
PE had a significant but lesser influence on intention (β = 0.115, p = 0.036), indicating that ease, support, or social norms had a greater influence in this situation than perceived usefulness, despite PE’s relevance. Interestingly, the path coefficient for the relationship between intention and perceived risk in the Indian sample was statistically non-significant (p = 0.072) and negative (β = -0.072).
Figure 4. Structural model India
The Indian sample validated each of the hypotheses (H1 through H5) that were put out. To increase user trust and acceptance, the results confirm that UTAUT2 structures are applicable to the acceptance of digital payments in emerging areas, with a emphasis on reducing perceived risks.
This research employed UTAUT2 to examine the uptake of digital payments in two rising economies: Egypt and India. In both countries, facilitating conditions are ascertained to be a significant factor of behavioral intention. This affirms the most significant predictor of behavioral intention was Facilitating Conditions. This shows the importance of technical infrastructure, compatibility of the devices used, customer support from the provider and availability of internet to influence digital payment adoption. These results are consistent with prior studies that ascertained the influence of supporting infrastructure on technology adoption in different nations [37, 98-100]. Although facilitating conditions were found to have high influence on adoption in both the countries. Digital payment adoption in Egypt is still growing. Enabling reliable internet access, secure transactions, and merchant acceptance are significant for promoting digital adoption. In contrast, India’s digital ecosystem is deemed to be mature. Transaction time, reliable system and proper customer services are the influencing factors of digital financial transactions. While facilitating conditions are common across Egypt and India, their operational priorities are different based on the digital market maturity. Effort expectancy is another factor which shows constant results in both nations. Digital payment users in Egypt and India were apparently having a same perspective about digital transactions. They prefer to use digital payment applications if they are user friendly. Prior studies also witnessed a significant impact on digital adoption of user-friendly applications in low-income and middle-income countries [51, 98]. Another factor which was found to have influence on digital payment adoption was performance expectancy. But the influence of performance expectancy was higher in Egypt than India. Snice Egypt population are in early adoption stage in digital payment; users continuously evaluate the performance. On other hand, in India government has taken initiatives Digital India, UPI, and Jan Dhan Yojana. These initiatives made digital payment as a new normal, so performance expectation has considered as a standard factor rather than differentiator. Previous studies have supported this finding, social or psychological facilitators are the possible reason to adopt digital eco system [54, 101].
The impact of Social Influence on the digital adoption reported differently in both the countries. In India, it was ascertained to have significant influence on adoption. However, it was found insignificant in Egypt. Importance of peer group is a part of Indian culture, thus, social influence considered as an important factor of adoption intention. Scholars have witnessed the impact of family members and friends on digital adoption [102, 103]. Egyptian population is more independent as compared to Indian population; thus, social influence did not identify to be a important predictor of digital adoption. Digital maturity and cultural context could be the reasons for the difference between India and Egypt. This variation can also be interpreted through Hofstede’s cultural dimensions framework, which suggests that collectivist orientations tend to amplify the role of peer norms and social approval in shaping behavioral intentions. In more collectivist contexts, individuals may rely more heavily on social networks when evaluating new technologies, whereas in relatively autonomy-oriented settings, adoption decisions may be more individually driven. Thus, cultural value orientations may function as contextual boundary conditions influencing the strength of social influence within the UTAUT2 model.
Risk is another factor which is found to have different impact on digital adoption in both countries. It was ascertained to be influencing negatively in Egypt. However, in India it was not fount be significant predictor of digital adoption. It indicates that data privacy, cybersecurity, and financial theft were predominant factors of perceived risk in Egypt. Zhou [96] and Alalwan et al. [51] have found that trust factor significantly influences the customers adoption in financial innovations in Arab countries. In accordance with Indian respondents, the path coefficient is statistically insignificant. India has witnessed increasing usage of digital transactions as government encouraged people to adopt Digital India mission. Regulatory measures such as real time alert and two factor authentication have increased the user confidence in accordance with digital transactions.
In the Indian sample, the path coefficients for perceived risk and behavioral intention were negative and statistically insignificant. This result indicates that digital fraud, transaction failure, and data privacy do not have any influence on their digital payment adoption. Growth of digital payment in India due to government supported UPI applications, two factor authentication, and real-time transaction alerts are the reasons for the abovementioned results. Prior studies have also ascertained the results [54, 104], this indicated that perceived benefits and ease of use apparently outweigh apprehensions about potential. As a result, risk is certainly no longer a factor that prevent to adopt digital payments among the respondents. In mature digital ecosystem, convenience, usability, and social norms are the influencing factors of adoption as compared to perceived risk. This is consistent with technology adoption life cycle, where user priorities shift from safety to convenience as technology adoption moves from early adopters to late majority stage. These findings affirm the imperative role contextual factors in users’ digital adoption behavior. Infrastructure facilities and usability are found to be common across nations. Factors such as perceived risk and social impact are highly varied and influenced by cultural norms.
6.1 Theoretical contributions
This study contributes to technology adoption literature by providing cross-country comparison by examining digital payment adoption in emerging economies. It give empirical evidence for the framework’s evolution and integration by exhibiting difference across nations [42], highlighting the significance of culturally sensitive models [105, 106]. The result indicates that perceived risk was found to have less impact on digital payment adoption in India. This affirms that trust in digital adoption grows over time, as users are less concerned about adoption barriers.
6.2 Practical and policy implications
6.2.1 Implications for regulators
Policymakers in emerging economies should recognize that infrastructural readiness and institutional trust as they certainly influence digital adoption.
6.2.2 Implications for fintech providers
Service providers should focus on enhancing interface, value added services, and customer support to sustain behavioral intention.
This finding suggests that digital payment adoption intention in emerging economies such as Egypt and India appears to be influenced by contextual factors. Which emphasizes different strategies and regulatory policies to be implemented rather than uniform policies and strategies.
Although the required sample size was determined by William G. Cochran’s (1953) formula, the participants were selected through convenience sampling. Higher education students in India and Egypt were the population of this study. Therefore, the findings reflect digitally active students’ population and may not fully represent the population in emerging economies. As higher education students are digitally literate, technology-oriented, this finding may overestimate behavioral intention. This may differ with less literate and older population, and the non-probability sampling also restricts external validity.
This study considered behavioral intention not actual usage behavior. The findings give insights into the intention to adopt digital payment and may not completely assess usage patterns. Future research may include usage behavior in the postulated model.
Structured questionnaire was used to collect responses with a five-point Likert scale. In-spite of statistical rigor, this method may not reflect subjective experiences. Future studies could employe qualitative interviews, especially conducting cross-country research.
This research conducted in Egypt and India, both the countries are deemed as emerging economies. Low-income countries also need to be included in the future studies to extend the comparative framework. This could give thorough view of digital payment adoption perspective.
We thank the Prince of Songkhla University (PSU) and Faculty of Islamic Sciences (FaiS) for any support provided.
The authors declare that present study was independently carried out without institutional or commercial sponsorship that might have influenced its interpretation or result.
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