A Comparative Analysis of GCash and PayPal E-Wallets in Online Shopping Platforms

cognizancejournal.com Fabro, Mikylla B. et al, Cognizance Journal of Multidisciplinary Studies, Vol.5, Issue.10, October 2025, pg. 301-308 (An Open Accessible, Multidisciplinary, Fully Refereed and Peer Reviewed Journal) ISSN: 0976-7797 Impact Factor: 5.183 Index Copernicus Value (ICV) = 92.57 ©2025, Cognizance Journal, ZAIN Publications, Fridhemsgatan 62, 112 46, Stockholm, Sweden, All Rights Reserved 301 A Comparative Analysis of GCash and PayPal E-Wallets in Online Shopping Platforms Fabro, Mikylla B.; Lacao, Henrich Kem R.; Nogalo, Raymond Lei.; Rabino, Mico T.; Joan C. Mag-isa Electrical and Allied Department, Technological University of the Philippines, Taguig, Taguig City, Philippines Bachelor of Science in Information Technology [email protected]u.ph; [email protected]; raymo[email protected]u.ph; [email protected]u.ph; j[email protected]u.ph DOI: 10.47760/cognizance.2025.v05i10.028 Abstract: One of the greatest innovation in modern technology is electronic or digital wallets or e-wallets. Through these kinds of sites, individuals are able to conduct cashless operations such as payments, mobile credit reloads, money transfers and savings with their devices. Nevertheless, despite their convenience, e-wallets also have issues of their own particularly in terms of security of user personal data and money stored. The aim of the paper will be to compare two ewallets that are favored in the Philippines, i.e. GCash and PayPal in regards to features, usage, and general user experience and effects. The GCash mobile payment platform that is locally designed and is commonly considered more stable and trusted among Filipinos. PayPal, in its turn, as a globally recognized alternative is not as widespread in the Philippines but still can be viewed as a viable alternative to a digital wallet. It compares it with the existing literature and user feedback and the other studies available to maintain a balanced perspective. By comparing these two platforms, the study will not only be trying to draw users attention to the one that is safer and more trustworthy but to emphasize the increased importance of online safety in the modern cashless environment. The results indicate that GCash has better adoption, greater usage frequency, and user perceptions than PayPal. Its localized ecosystem and the way it matches the needs of Filipino users in the demographic terms provide GCash with a distinct competitive edge in the adoption of ewallets. Keywords: E-wallets, User Feedback, Cashless Environment, Usage Frequency, Perception I. INTRODUCTION GCash was initially introduced in October 2004, by Globe Telecom as an SMS based money-transfer service designed to serve financially marginalized Filipinos. Customers were able to put money into their e-wallets through retail stores at a low price. The service launched a specific smartphone application in 2012, becoming a fully-digitalized model of a wallet. Additional features were added to GCash over the years: QR-code payments, online bill presentment, barcode-based cash-in and instant interbank transfers via InstaPay. In the COVID-19 era, value-added services like GSave savings accounts and GCredit credit cognizancejournal.com Fabro, Mikylla B. et al, Cognizance Journal of Multidisciplinary Studies, Vol.5, Issue.10, October 2025, pg. 301-308 (An Open Accessible, Multidisciplinary, Fully Refereed and Peer Reviewed Journal) ISSN: 0976-7797 Impact Factor: 5.183 Index Copernicus Value (ICV) = 92.57 ©2025, Cognizance Journal, ZAIN Publications, Fridhemsgatan 62, 112 46, Stockholm, Sweden, All Rights Reserved 302 lines became possible with the help of the regulatory assistance of the Bangko Sentral ng Pilipinas and a strategic alliance with CIMB Bank Philippines, which solidified the position of GCash in leading financial inclusion in the country [1]. The financial inclusion of unbanked and underbanked Filipinos has also been increased through GCash, as this financial institution makes it possible to involve them in digital transactions. In adoption studies, trust, reliability, and easily used are factors to drive the use of GCash among consumers and businesses [2] [3]. Peter Thiel and Max Levchin founded PayPal in 1998 as Confinity and then acquired X.com by Elon Musk in 2000. As of 2013, it was a large global payment system, processing 180 billion in 26 currencies in 193 markets, with 6.6 billion in revenues and approximately 143 million active accounts. PayPal utilized by eBay continues to bring nearly half of the revenue and a quarter of its profits (41 and 36 percent, respectively), even though the percentage of transactions linked to eBay has decreased over the years: in 2008, it was 51 percent, and by 2013, it had dropped by nearly half to one-third [4]. It is widely utilized in e-commerce dealings, freelance payments and money transfers across borders and is therefore a favorite of both business dealings and consumers. It has the capability to protect buyers and sellers, allowing one-click checkout, sending crossborder remittances with currency conversion and integrating with popular online stores like eBay, Shopify, and WooCommerce. Security, side resolve and fraud prevention have gained PayPal a reputation as a secure choice in online payments around the world, though transaction fees and currency conversion costs still pose issues to certain users. E-wallets like GCash and PayPal have contributed greatly to the evolution of e-commerce because this provides a quick, secure and convenient payment system that is a pleasant experience to the client and improves the digital financial inclusion. It is projected that more than 52 percent of all retail transaction in the Philippines in 2023 are transacted using digital payment; this reflects that the trend of cashless business is growing [5]. Against the fame and the mentioned advantages, the lack of studies comparing the directly GCash and PayPal in terms of functionality, security, and impact on the Internet shopping settings is a thorn in the flesh. The proposed research will, thus, endeavor to address this gap by a comparative analysis of the two ewallets that will serve to furnish information that will be applied in achieving the objective of having a deeper insight into their position within the new e-commerce ecosystem. II. METHODOLOGY Phase 1: Research Design Our present research is a cross-sectional survey that uses a quantitative design, it’s based on Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology. In both systems, the ease of use is noted as one of the major factors that determine the perceived usability, security and also trust to the adoption of digital payment systems. The paper uses ease of use as the predictor of user intention as the primary factor as posited by Davis in the TAM [6], trust and security being the external variables as was reported in previous e-payment studies [7] [8]. We adopt the same construct definitions and Likert-scale instruments used in our original pilot (n = 101) and compare them under two sampling regimes: an original convenience sample versus a benchmark sample (n = 394). Notably, the benchmark data come from Sanchez & Tanpoco (2023) [11], who conducted a TAM-based online survey of Filipino mobile-wallet users; their instrument included scales for perceived ease-of-use, security, and trust (termed “trustworthiness” in their study) [11]. Thus, our hypotheses and measurements directly parallel TAM/UTAUT theory (e.g., higher ease-of-use, trust, and security predict greater continuance intention) and prior findings in mobile payment contexts [7] [8]. cognizancejournal.com Fabro, Mikylla B. et al, Cognizance Journal of Multidisciplinary Studies, Vol.5, Issue.10, October 2025, pg. 301-308 (An Open Accessible, Multidisciplinary, Fully Refereed and Peer Reviewed Journal) ISSN: 0976-7797 Impact Factor: 5.183 Index Copernicus Value (ICV) = 92.57 ©2025, Cognizance Journal, ZAIN Publications, Fridhemsgatan 62, 112 46, Stockholm, Sweden, All Rights Reserved 303 Figure 1. Illustration of the data collection instrument. Figure 2. Flowchart of the research methodology process, showing survey design, administration, and analysis. Phase 2: Data Collection Data collection for the original pilot sample (n = 101) and the benchmark sample (n = 394) was conducted via structured questionnaires. The survey instrument featured 5-point Likert scales for each construct (usability/ease-of-use, perceived security, trust, social influence) and for behavioral intention, adapted from standard TAM items [6]. Respondents also reported demographic information (age, gender, education, etc.). For the original sample, participants were recruited opportunistically (e.g., intercepts, class networks). Critically, for the benchmark component we did not conduct any new primary survey. Instead, we used the existing dataset from Sanchez & Tanpoco (2023) [11]. Sanchez & Tanpoco’s study collected data via an online Google Form distributed through social media and email lists (network sampling), yielding n = 394 mobile-wallet users [11]. In our methodology, this means all analysis of the “benchmark sample” is based on secondary data only. Figure 3. Demographic comparison of the original pilot sample (n = 101) and the benchmark sample from Sanchez & Tanpoco (2023) (n = 394). cognizancejournal.com Fabro, Mikylla B. et al, Cognizance Journal of Multidisciplinary Studies, Vol.5, Issue.10, October 2025, pg. 301-308 (An Open Accessible, Multidisciplinary, Fully Refereed and Peer Reviewed Journal) ISSN: 0976-7797 Impact Factor: 5.183 Index Copernicus Value (ICV) = 92.57 ©2025, Cognizance Journal, ZAIN Publications, Fridhemsgatan 62, 112 46, Stockholm, Sweden, All Rights Reserved 304 It compares key demographics of our pilot sample versus the Sanchez & Tanpoco (benchmark) sample: both are predominantly young, mixed-gender Filipino users, with the benchmark group on average slightly younger and more educated [5]. (For example, a majority of both samples are age 18–24 [5].) By using the Sanchez dataset as provided, our Phase 2 ensures consistency with the benchmark study without additional data collection, while enriching our analysis with a much larger comparison sample. Phase 3: Data Processing and Preparation Raw responses from both samples were compiled into datasets (one respondent per row, items in columns) and identically processed. All categorical variables were coded numerically (e.g., gender, age groups) and Likert responses recorded on a 1–5 scale. We screened for missing or invalid entries, applying listwise deletion when respondents skipped many items (under 5% of cases). Each multi-item construct scale was coded so that higher values represent more positive assessments, and composite scores were computed by averaging their items. We checked internal consistency via Cronbach’s alpha (targeting α ≥ 0.70) and reviewed any low-correlation items. Ultimately, the cleaned datasets retained approximately n = 101 and n = 394 cases, mirroring the original sample sizes [11]. These final datasets (our pilot and the benchmark secondary data) were then ready for analysis. No additional external data (beyond the original survey responses) were introduced at this stage; both datasets were treated in parallel to ensure a fair comparison. Figure 4. Flowchart of the data analysis procedure. Phase 4: Data Analysis We started by calculating descriptive statistics of all demographic and survey variables in the two samples including means, standard deviations and frequencies. To measure the reliability of the measurements, we evaluated the scale consistency and provided the exploratory factor analysis to make sure that every item was associated with the intended construct. After this we used inferential methods to test our hypotheses. A multiple linear regression was done with behavioral intention as the dependent variable and supposed usability, perceived security, social influence and trush as independent variables in each sample [7]. This mode of analysis is consistent with the technology acceptance model [6], in which usability and the corresponding constructs predict user intention and the methodology of Sanchez and Tanpoco. The regression assumptions, normality, homoscedasticity, and multicollinearity, were all checked. Independent-samples t-tests were employed in order to compare the differences in mean values between the two samples on the main constructs. We have conducted all statistical results at the 5% level of significance and provide both effect sizes (i.e., R2, b coefficients) and p-values [8]. cognizancejournal.com Fabro, Mikylla B. et al, Cognizance Journal of Multidisciplinary Studies, Vol.5, Issue.10, October 2025, pg. 301-308 (An Open Accessible, Multidisciplinary, Fully Refereed and Peer Reviewed Journal) ISSN: 0976-7797 Impact Factor: 5.183 Index Copernicus Value (ICV) = 92.57 ©2025, Cognizance Journal, ZAIN Publications, Fridhemsgatan 62, 112 46, Stockholm, Sweden, All Rights Reserved 305 Summary of analytical steps: Descriptive analysis: Standard deviation of each construct and means of each construct calculated. Scale validation: Cronbachs alpha was used to test reliability and factor analysis was used to test constructs. Regression analysis: Fitted behavioral intention versus ease of use, trust, security, etc as predicted by TAM [6]. Comparison testing ANOVA or t-tests compared the study and benchmark samples on constructs. Figure 5 below summarizes how the key constructs of interest are operationalized in each dataset. We ensured the same conceptual measures (Likert-scale items) underlie each construct, even if wording sources differed slightly between surveys. This alignment makes our cross-sample comparisons (e.g. of means and regression coefficients) valid. Figure 5. Measurement of Key Constructs in each Sample In Figure 5, “Original Pilot” refers to our convenience sample; “Benchmark Study” refers to the Sanchez & Tanpoco (2023) data. Each construct uses multiple Likert items so that higher scores indicate more positive evaluations. (For example, higher “Usability” means users find the e-wallet easier to use.) Both surveys’ measures are conceptually equivalent, supporting direct statistical comparison. Phase 5: Interpretation and Comparative Evaluation In this final phase, we interpret our results in the context of TAM/UTAUT theory and the benchmark findings. We directly compare key statistics (e.g. regression coefficients, means) between the original and benchmark samples. For instance, TAM predicts that supposed usability should have a strong optimistic effect on adoption intention [6]. Consistent with this theory, the benchmark sample showed a strong ease-of-use effect; we compare our estimated β coefficient to theirs. Likewise, we examine trust and security effects. For example, Sanchez & Tanpoco (2023) found both perceived security (β = 0.152, p < .001) and trustworthiness (β = 0.262, p < .001) to be significant predictors of continuance intention [9]. We compare these values to our pilot results to see if patterns hold or diverge. We also consider the relative precision: the larger benchmark sample gives smaller standard errors, whereas our pilot may have lower statistical power. When discussing the findings, we cite TAM principles and prior literature. For example, ease-of-use often emerges as the dominant factor [10]; we check if this holds in both samples. We also note any differences: if our pilot yields a smaller effect of trust than Sanchez’s sample, we consider factors like sampling variability. All interpretations are made with formal citation to the TAM literature and to Sanchez & Tanpoco (2023) [11] [9]. In summary, this comparative evaluation phase links our empirical results to established theory and the benchmark study, highlighting both consistencies (e.g. ease-of-use strongly predicts intention [10]) and discrepancies (e.g. trust effects may vary) between the original pilot and the larger online sample. cognizancejournal.com Fabro, Mikylla B. et al, Cognizance Journal of Multidisciplinary Studies, Vol.5, Issue.10, October 2025, pg. 301-308 (An Open Accessible, Multidisciplinary, Fully Refereed and Peer Reviewed Journal) ISSN: 0976-7797 Impact Factor: 5.183 Index Copernicus Value (ICV) = 92.57 ©2025, Cognizance Journal, ZAIN Publications, Fridhemsgatan 62, 112 46, Stockholm, Sweden, All Rights Reserved 306 Figure 6. Comparison of survey constructs and Likert-scale items. Table: Survey item structure by construct for the original convenience sample vs. the benchmark sample. Each construct is measured with multiple Likert items in both instruments, ensuring conceptual equivalence. This alignment allows direct comparison: for instance, both surveys measure ease-of-use and trust with similar item counts. In evaluating results, we note consistencies (e.g. ease-of-use strongly predicts intention [8]) and differences (e.g. trust effects might diverge due to sampling). Overall, the structured online sampling (n=394) yields more precise estimates (smaller standard errors) of TAM effects than the smaller convenience sample. Our comparative analysis highlights how methodological choices influence findings: for example, the benchmark study’s regression confirmed security and trust as significant predictors [7], whereas the original pilot’s power was limited. By interpreting both sets of findings in parallel, we assess robustness of conclusions about usability, trust, and security in e-payment adoption. III. RESULTS AND DISCUSSION Figure 7: Comparative Analysis of GCash and PayPal Usage The findings of the survey conducted on 103 participants in this Table provide a comparative analysis of the use of GCash and PayPal in online shopping, the adoption, frequency, security, convenience, satisfaction, and the problems that were reported. The number of users of GCash and PayPal was identified as 83 and 37 with a percentage of 80.6 and 35.9 respectively. These users had the highest frequency of use, with GCash users reporting 28.9% daily, 38.6% weekly, 18.1% monthly and 14.5% rarely and PayPal users reporting 13.5% daily, 32.4% weekly, 29.7% monthly and 24.3% rarely. The mean scores of perceived security (measured on a 1 being lowest to 5 being the highest scale) were 4.12 (SD=1.02) in case of GCash and 3.57 (SD=1.21) in case of PayPal with SD (standard deviation) representing the extent to which the means differ assuming that confidence in the security offered by GCash is more stable. The qualitative scales (Very convenient=5 to Very inconvenient=1) were used to transform into the convenience that had a mean of 4.24 (SD=0.89) in GCash and 3.76 (SD=1.14) in PayPal since GCash is typically more convenient to operate. Regarding the result on the 15 scale on the level of satisfaction, the result in the case of GCash was 4.24 (SD=0.92), whereas in the case of PayPal, the score was 3.59 (SD=1.28), with the higher and stronger mean in the first case. The 13.3% of users who reported problems with GCash with respect to fraud, unauthorized transactions etc, was lower than 18.9% of PayPal users. These measures, according to the table, indicate that more people adopt and utilize GCash more frequently and that people have better perceptions towards it as compared to PayPal. Based on the survey results, GCash is more likely to be adopted and considered by users in online shopping than PayPal, with 103 respondents, but further analysis of the demographic characteristics, type of purchases, and other decisionmaking factors that are not presented in the table are able to provide a wealth of information about the motivation factors behind cognizancejournal.com Fabro, Mikylla B. et al, Cognizance Journal of Multidisciplinary Studies, Vol.5, Issue.10, October 2025, pg. 301-308 (An Open Accessible, Multidisciplinary, Fully Refereed and Peer Reviewed Journal) ISSN: 0976-7797 Impact Factor: 5.183 Index Copernicus Value (ICV) = 92.57 ©2025, Cognizance Journal, ZAIN Publications, Fridhemsgatan 62, 112 46, Stockholm, Sweden, All Rights Reserved 307 these preferences. The demographic trait indicates that the sample is considerably young, with the largest segment of 51.5 percent of respondents 18-24 and 32 percent of respondents 25-34, which may suggest that the sample is tech-savvy and digitally active, and presumably, based in the Philippines, where GCash has become a significant part of the local digital environments. The occupational distribution is dominated by students (42.7%) and employed people (40.8%), which is in line with the high adoption rate of GCash (80.6% and including 83 users) and the regular use, especially daily (28.9%) and weekly (38.6%) payments. This age-based skew towards younger and urban users probably explains the dominance of GCash, which hosts mobile applications and ubiquitous acceptance of merchants serving daily, local transactions such as food delivery (58.3%) and digital subscriptions (35.0%), being common among students and young professionals. However, a less frequent payment (32.4% weekly, 29.7% monthly) and lower utilization (35.9% including 37 users) by PayPal might be also the result of being attractive to fewer individuals, especially international buying (23.3%) and gadgets or electronics (17.5%) purchases, which demand payment compatibility internationally. IV. CONCLUSION The comparative analysis of GCash and PayPal e-wallets in online shopping, conducted due to the survey with 103 respondents, shows that GCash has a definite advantage in terms of adoption, the frequency of usage, and the perceptions of the respondents in the studied population. The findings show that 80.6% of respondents, which included 83 users, used GCash, compared to 35.9% which included 37, used PayPal with more respondents most frequently using GCash executing a weekly transaction (38.6% weekly, 28.9% daily) and PayPal executing a weekly and monthly transaction (32.4% weekly, 29.7% monthly). On relevant perceptual measures, Gcash performed better than PayPal, with an average score of 4.24 being superior to 3.57 on security (4.24 vs. 3.57), convenience (4.24 vs. 3.76) and satisfaction (4.24 vs. 3.59), with reduced variability in the responses (SD=0.89 vs. 1.14) and fewer reported problems (13.3% vs. 18.9%) The following findings as discussed are informed by the fact that GCash fits the needs of a young, tech-proficient population (51.5% aged 18-24, 42.7% students) in a probable Philippine context, where the adoption by local merchants enables the high-frequency, low-cost transactions such as food delivery (58.3%), and digital subscriptions (35.0%). Factors of decision that favored security (28.2%), convenience (25.2%), and transaction speed (24.3%) further confirmed the suitability of GCash to the needs of the sample as opposed to PayPal, which only plays a niche role in international payments like traveling/bookings (23.3%). This paper has proposed the value of market unique characteristics and demographic fit in terms of e-wallet adoption where it is proposed that the localized ecosystem of GCash offers it with a competitive advantage in such scenario. Acknowledgement We wish to offer our thanks and recognition to our professors: Prof. Joan Mag-isa and Prof. Pops Madriaga, and our Organization, which assisted us in structuring this study with the IMRaD framework in addition to offering constructive criticism that helped tremendously in improving our work. The respondents and users are also valued that made the comparative analysis possible by sharing their experiences with GCash and PayPal generously. The literature and research articles that were used as valuable reference sources are also worth mentioning the authors. Lastly, we would like to acknowledge with humble gratitude our families and peers who have remained by our side and have helped us in the research process. REFERENCES 1. Afable, F. P. 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