APTISI Transactions on Technopreneurship (ATT) Vol.
No.
November 2025, pp.
687Oe700 E-ISSN: 2656-8888 | P-ISSN: 2655-8807.
DOI:10.
ye Driving Socialpreneurship and Diving into Digital Transformation to Enhance Donation Intentions in Indonesia Theodorus Sendjaja1* .
Didik J.
Rachbini2 .
Rina Astini3 .
Daru Asih4 1, 2, 3, 4 Faculty of Economics and Business.
Mercu Buana University.
Indonesia 1 tsendja@gmail.
com, 2 didik rachbini@mercubuana.
id, 3 rina astini@mercubuana.
id, 4 daru asih@mercubuana.
*Corresponding Author
Article Info
ABSTRACT
Article history:
This study investigates how digital transformation within the Catholic Church in Indonesia can enhance digital donation intentions by analyzing the influence of trust.
Perceived Ease of Use.
Perceived Usefulness, perceived risk, and perceived security on attitudes and engagement toward technology acceptance.
Employing a quantitative design with an explanatory and cross-sectional approach, data were gathered from 100 respondents across 10 archdioceses in Indonesia using stratified random sampling.
The analysis was carried out using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships between the variables.
The findings reveal significant effects of Perceived Usefulness.
Perceived Ease of Use, perceived risk, attitude, and engagement on digital donation intention.
These results offer practical insights into improving donation practices and accelerating digital transformation within the Catholic Church in Indonesia.
This study further proposes a novel conceptual framework that integrates trust.
Perceived Ease of Use.
Perceived Usefulness, perceived risk, and perceived security to explain attitudes and engagement in digital donations.
This model expands the Technology Acceptance Model (TAM) by incorporating additional factors relevant to donation behavior.
Moreover, the study addresses a gap in the literature by highlighting the decline in QRIS adoption following the COVID-19 pandemic-an issue rarely discussed in prior donation studies.
Digital donations in this context are also positioned as part of a broader social entrepreneurship movement that leverages technology to foster community involvement and ensure the financial sustainability of religious institutions.
Submission April 3, 2025 Revised April 21, 2025 Accepted August 28, 2025 Published September 4, 2025 Keywords:
Catholic Church Digital Donation Digital Transformation Intention Technology Acceptance Model This is an open access article under the CC BY 4.
0 license.
DOI: https://doi.
org/10.
34306/att.
This is an open-access article under the CC-BY license .
ttps://creativecommons.
org/licenses/by/4.
AAuthors retain all copyrights INTRODUCTION During the COVID-19 pandemic, digital transformation accelerated significantly across key sectors such as education, healthcare, and finance, fundamentally reshaping service delivery and user interaction through digital platforms powered by technologies like Artificial Intelligence (AI), cloud computing, big data, and the Internet of Things (IoT).
These technologies not only enhanced operational efficiency but also increased stakeholder value, encouraging organizations to adopt more agile, responsive, and innovative digital strategies.
In the financial sector, restrictions on physical interactions triggered a major shift from cash-based to digital Journal homepage: https://att.
id/index.
php/att ye
E-ISSN: 2656-8888 | P-ISSN: 2655-8807
transactions, leading to a rapid increase in the use of digital wallets, mobile payments, and online banking.
This transformation continues to evolve, redefining institutional priorities, streamlining services, and significantly improving user satisfaction and engagement .
In the philanthropic sector, digital donation platforms have streamlined the giving process, particularly for younger, tech-savvy donors.
With features like recurring payments, real-time impact tracking, and transparency, these platforms boost donor engagement and trust .
In Indonesia, the Catholic Church adopted QRIS (Quick Response Code Indonesian Standar.
during online Masses to facilitate digital donations .
However, after the pandemic, many congregants returned to cash donations, driven by emotional attachment to traditional practices.
Despite QRIS offering accessibility, efficiency, and transparency, security concerns remain, with Aysecurity biasAy leading some donors to perceive digital transactions as riskier than cash .
This study distinguishes itself by focusing on the unique cultural context of the Catholic Church in Indonesia, integrating QRIS digital donations into religious practices, and exploring attitudes and engagement, elements rarely addressed in previous studies.
The research highlights how digital transformation in religious institutions goes beyond mere technological adoption by embedding itself into communal and spiritual dimensions of giving .
Moreover, it underscores the role of social media in donation behavior, where impact depends on factors such as ease of use, trust, and emotional storytelling.
Additionally, negative post-donation emotions like guilt have been found to affect future donation intentions, further emphasizing the complexity of digital giving behavior .
This study fills a significant gap in the literature by being the first quantitative research to explore QRIS adoption in the Indonesian Catholic Church, offering empirical evidence and culturally grounded insights.
contributes theoretically by integrating trust, perceived risk, and perceived security into the Technology Acceptance Model (TAM) to explain donation intentions and behavior.
Practically, it offers actionable strategies for building inclusive, secure, and engaging digital donation platforms in religious settings.
The findings of this study can guide religious organizations, technopreneurs, and policymakers in leveraging digital tools to enhance community participation and financial sustainability, marking a critical step toward digital transformation within faith-based institutions .
LITERATURE REVIEW
The Technology Acceptance Model (TAM) is a widely used approach for understanding and predicting user behavior in adopting new technologies, emphasizing key constructs such as Perceived Ease of Use (PEOU).
Perceived Usefulness (PU), attitude, and behavioral intention .
This model explains how individuals accept and use technology, where ease of use and perceived benefits are the main factors influencing usersAo attitudes and intentions.
TAM was later expanded into the Unified Theory of Acceptance and Use of Technology (UTAUT), which extends the framework by incorporating elements such as performance expectancy, effort expectancy, social influence, and facilitating conditions, providing a more comprehensive perspective on technology adoption .
Subsequently.
UTAUT2 was adapted for consumer contexts by adding dimensions such as hedonic motivation, price value, and habitual use to capture a broader range of factors influencing technology acceptance in everyday life.
In the context of financial technologies and digital payment systems.
TAM has proven highly relevant for studying the adoption of the QRIS (Quick Response Code Indonesian Standar.
This model highlights that PEOU and PU play a critical role in shaping user acceptance of QRIS, as both directly influence the perceived convenience and practical benefits of using digital payments .
Prior research supports this, revealing that users are more likely to adopt QRIS when they find it easy to use and when it offers clear advantages over traditional payment methods .
In the realm of digital donations, these constructs are equally vital, as users willingness to adopt digital donation platforms hinges on their perceived usability and the value these platforms provide .
Furthermore, the integration of TAM and its extensions into the study of digital donations highlights the interplay of technological, social, and psychological factors that shape user behavior .
By focusing not only on the technology itself but also on user perceptions and attitudes, these models provide a robust framework for understanding the dynamics of digital transformation in sectors such as philanthropy and religious institutions .
This is particularly relevant in the Indonesian Catholic Church context, where QRIS adoption represents a convergence of technological advancement and cultural practice.
The model emphasis on PEOU.
PU, and behavioral intention thus offers a valuable lens for examining how digital solutions can be effectively implemented to enhance participation in digital giving .
APTISI Transactions on Technopreneurship (ATT).
Vol.
No.
November 2025, pp.
687Ae700 APTISI Transactions on Technopreneurship (ATT) ye Table 1.
Comparative Review of Constructs Used in Digital Donation and Technology Acceptance Studies Title Context Key Constructs Findings Trust has a positive Religiosity and Intention Crowdfunding influence on the to Participate in Donation-Based Belief.
Attitude in India intention to donate Crowdfunding through attitude Driving Factors Both PU and for the Implementation Adoption of Weapons.
PEOU influence of Smart Home Technology:
All of You user attitudes An Empirical Assessment and intentions Cybersecurity and Social Media Risk of reducing Networks for Donations:
Social media Perceived Risk, trust and intention An Empirical Investigation based donations Trust to donate online of Triads Empathy or Perceived Ease of use directly Credibility? An Empirical QRIS in religious influences the People Study of Muslim Donation desire to donate Behavior These three constructs Examining Intentions have a positive PU.
PEOU, to Use Crowdfunding Mobile donation influence on behavioral Trust Platforms-Expanded Technology Using the Civic Trust mediates Volunteering Model Digital financial to Compare Donation Faith.
PU increases intention Intentions in the US and India What Do We Mean PU and positive When We Talk About Millennial generation attitudes encourage Attitude.
PU Trust in Social Media? online donations donating behavior A Systematic Review Table 1 provides a comparative review of studies on digital donation behavior and technology acceptance across contexts like crowdfunding, social media.
QRIS in religious institutions, and millennial donation Using TAM and UTAUT frameworks, key constructs Perceived Usefulness (PU).
Perceived Ease of Use (PEOU), trust, attitude, and perceived risk are highlighted.
Findings consistently show that PU and PEOU positively influence attitudes and digital donation intentions .
Trust plays a crucial mediating or strengthening role, especially in crowdfunding and social media.
In QRIS contexts.
PEOU directly increases donation intention, while in cross-country donations.
PU and trust enhance user intentions.
Overall.
TAM constructs are pivotal for understanding technology acceptance in digital donation systems .
A Theoretical Framework This study applies the Technology Acceptance Model (TAM) to understand QRIS adoption for digital donations in the Indonesian Catholic Church, focusing on variables like trust.
PEOU.
PU, perceived risk, and perceived security, with attitudes and engagement as mediators.
Each hypothesis is now grounded in literature, clarifying cause-effect reasoning.
PEOU increases attitude by reducing cognitive load.
perceived risk reduces attitude by increasing uncertainty .
Trust plays a key role, as congregants must believe their donations are handled securely and transparently.
Trust has been shown to influence attitudes toward technologies such as mobile banking, supporting H1: Trust positively influences attitudes toward digital donation intention.
Perceived Ease of Use (PEOU) reflects a user confidence in navigating digital platforms easily, which is crucial for behavior in fast, intuitive systems, leading to H2: PEOU positively influences attitude.
Perceived Usefulness (PU) relates to whether QRIS improves donation effectiveness, leading to H3: PU positively influences attitude.
Perceived Risk, especially concerns around data breaches, can negatively impact technology adoption, ye
E-ISSN: 2656-8888 | P-ISSN: 2655-8807
supporting H4: Perceived risk negatively influences attitude.
Perceived Security can increase trust if users feel their transactions are safe due to features like encryption and third-party verification, leading to H5: Perceived security positively influences attitude.
A positive attitude toward QRIS boosts user engagement in religious donations, forming H6: Attitude positively influences engagement.
Increased engagement leads to stronger intentions to donate digitally, supporting H7: Engagement positively influences donation intention.
Finally, a favorable attitude toward technology significantly shapes intention, especially when acting as a mediator, resulting in H8: Attitude positively influences donation intention.
This framework ensures logical coherence by mapping psychological and technological factors from the introduction into measurable constructs.
As shown in Figure 1, the model consists of:
A Technology adoption (PEOU.
PU): users perceptions of ease and usefulness.
A Trust, risk, security: user confidence and concerns in digital donation platforms.
A Attitude and engagement as mediators influencing digital donation intentions.
Figure 1.
The Technology Adoption Model Theoretical Framework Figure 1 illustrates the theoretical framework of this study, integrating psychological and technological factors to explain digital donation intentions within the Catholic Church in Indonesia .
The model examines how trust.
Perceived Usefulness, ease of use, risk, and security influence user attitudes toward digital donation These attitudes then impact user engagement, which in turn affects donation intentions.
Additionally, attitude has a direct effect on intention.
The framework outlines the studyAos hypotheses: H1AeH5 .
actors influencing attitud.
H6 .
ttitude to engagemen.
H7 .
ngagement to intentio.
, and H8 .
ttitude to intentio.
This model offers a comprehensive view of how these factors interact in shaping digital donation behavior .
RESEARCH METHODS
This study employs a cross-sectional quantitative design with an explanatory approach, collecting data in December 2024 to examine factors influencing digital donation intention in the Indonesian Catholic Church.
Partial Least Squares Structural Equation Modeling (PLS-SEM) is used due to its strength in handling complex models with latent variables and predictive focus.
The sample includes 100 active Catholic donors aged 18 , from 10 Archdioceses, selected via stratified random sampling.
The survey was distributed through Google Forms via parish WhatsApp groups and bulletin boards .
Ethical measures ensured anonymity, confidentiality, voluntary participation, and informed consent.
The research instrument comprised a 22-item questionnaire measuring trust.
Perceived Ease of Use (PEOU).
Perceived Usefulness (PU), perceived risk, perceived security, attitude, engagement, and donation intention .
Validity and reliability tests confirmed instrument quality (AVE > 0.
CR > 0.
Cronbach alpha > 0.
APTISI Transactions on Technopreneurship (ATT).
Vol.
No.
November 2025, pp.
687Ae700 APTISI Transactions on Technopreneurship (ATT) ye QRIS usage frequency and digital transformation indicators .
ase of use, donation frequency, selfreported adoptio.
were included, measured on a 5-point Likert scale .
Digital transformation in this study is operationalized through QRIS integrated into online Mass platforms, mobile payment apps, and church-managed WhatsApp donation links.
Frequency of QRIS use was quantified via questionnaire items on donation behavior .
Sampling used stratified random sampling, with the total population of Catholic parishioners in 10 Archdioceses.
The survey was distributed via Google Forms through WhatsApp groups.
Instrument reliability and validity were ensured (AVE > 0.
CR > 0.
7, > 0.
The sample size of 100 respondents adheres to the widely accepted PLS-SEM 10-times rule, ensuring the adequacy of the structural model complexity .
These respondents, consisting of active church members, were carefully selected to represent the church community while accommodating logistical and technological limitations inherent in the study context .
This robust sampling design not only provides a comprehensive overview of QRIS adoption patterns but also offers deeper insights into the determinants influencing its acceptance within this unique religious environment, paving the way for more targeted digital transformation strategies tailored to the needs of faith-based communities .
Variables Trust (TR) Perceived Ease of Use (PE) Felt
Usage (PU) Perceived Risk (PR) Felt
Security (PS) Attitude (AT) Engagement
(INSIDE)
Intent (INSIDE) Table 2.
Variables.
Indicators.
Questionnaires Indicator Questionnaire Statement
Code
TR1
I believe QRIS can be used as a payment tool to replace cash
TR2
I believe QRIS does not contradict the Gospel of Matthew 6:3 I am sure that the funds collected through QRIS in the Church
TR3
will go into the Church account
PE1
I feel the ease in doing daily activities transactions using QRIS
PE2
I found it easy to collect in the Catholic Church using QRIS
PE3
I found it easy to find the QRIS barcode in the church for fundraising.
In my opinion, using QRIS provides advantages when making PU1
collections at Catholic Church
PU2
I feel using QRIS for billing is faster than using cash.
I think with the use of QRIS will increase the number
PU3
of collects at Mass in the Church
IAom worried that I made a mistake in filling the balance for the QRIS
PR1
collection during Mass at Church
I feel the benefits of using QRIS during Mass at Church
PR2
disturbing the solemnity of worship
IAom worried that I wonAot be able to collect during Mass at the Catholic PR3
Church with QRIS because there an error on my smartphone I am worried that the amount of funds I deposit via QRIS
PS1
will be known to other people I am worried that billing via QRIS will take more funds PS2 from my account.
PS3
I feel that using QRIS for billing is not safe In my opinion, the use of QRIS as a substitute for cash in collecting AT1 funds for Mass in the Catholic Church is good.
AT2
I feel comfortable using QRIS in fundraising during Mass at Church
EN1
I regularly update the QRIS application on my smartphone EN2
I regularly top up funds on the QRIS application on my smartphone I prefer to collect using QRIS rather than cash during INSIDE 1
Mass at the Catholic Church
DI2
I would recommend using QRIS to donate to Church Mass to others.
OF 3
I will use QRIS more often in my daily payment transactions Each construct in the study was measured using specific, carefully adapted items to ensure clarity and relevance to the context of digital donations within the Catholic Church.
For example.
Trust was assessed with the statement: AyI am sure that the funds collected through QRIS in the Church will go into the Church account,Ay ye
E-ISSN: 2656-8888 | P-ISSN: 2655-8807
reflecting the respondentAos confidence in the transparency and integrity of the donation process.
Meanwhile.
Perceived Ease of Use (PEOU) was measured by the statement: AyI found it easy to locate the QRIS barcode in the church for fundraising,Ay which captures the userAos perception of the systemAos accessibility and usability.
These measurement items were designed to reflect real user experiences and attitudes, thereby enhancing the validity and applicability of the research findings.
The variables were validated using several tests:
A Convergent Validity: This ensured constructs were well represented by their indicators, with Average Variance Extracted (AVE) values ranging from 0.
57 to 0.
94, indicating strong indicator-construct relationships.
A Discriminant Validity: Confirmed that constructs were distinct from one another, with HeterotraitMonotrait Ratio (HTMT) values below 0.
85 for most constructs.
A Reliability: Assessed via Composite Reliability (CR) and Cronbach Alpha (), both exceeding standard thresholds (CR > 0.
7, > 0.
, indicating high internal consistency.
Relationships among variables technology acceptance (PEOU.
PU), trust, perceived risk, perceived security, attitudes, and engagement toward digital donation intention were analyzed using PLS-SEM with SmartPLS software.
Constructs were measured by validated items adapted from prior studies, with examples like trust (AyI am confident funds collected via QRIS go to the ChurchA.
and PEOU (AyI find it easy to locate QRIS barcodes at churchA.
Full details appear in Table 1.
Table 3.
Respondents Age Respondent Age Number Amount The demographic profile in Table 3 (Respondent Ag.
shows that the majority of respondents are in the 41-60 age range, highlighting the potential for a focus group with diverse perspectives and experiences relevant to digital donation.
The absence of participants in the youngest age group .
may indicate factors influencing eligibility or willingness to participate.
Table 4.
Respondents Gender Gender Number Man Woman Amount Table 4 presents the gender distribution of the 100 respondents, consisting of 52% male and 48% female participants.
This nearly balanced sample, with a slight male majority, ensures a representative demographic composition, contributing to the generalizability and credibility of the study findings.
Instrument validation and reliability are critical components in ensuring the robustness and accuracy of the measurement model.
To this end, the study conducted comprehensive convergent validity tests aimed at evaluating the strength and coherence of relationships among the variables under investigation.
Specifically, these tests assessed:
A Independent variables such as trust.
Perceived Usefulness (PU).
Perceived Ease of Use (PEOU), perceived risk, and perceived security, which are posited to exert significant influence on attitudes toward using the system and the intention to donate.
APTISI Transactions on Technopreneurship (ATT).
Vol.
No.
November 2025, pp.
687Ae700 APTISI Transactions on Technopreneurship (ATT) ye A Mediating variables including attitude and engagement, which are shaped by the aforementioned independent variables and subsequently impact donation intention.
A The dependent variable, donation intention, which encapsulates respondent willingness and motivation to engage in digital giving through the system.
Through these validity assessments, the study confirms that the constructs and their indicators effectively capture the underlying theoretical concepts .
This rigorous validation process enhances the credibility and precision of the research findings, providing a reliable basis for understanding the adoption of QRIS within the context of church-based donation practices .
Table 5 presents the Average Variance Extracted (AVE) values for each indicator, all of which exceed the recommended threshold of 0.
This confirms the strength of the relationships between indicators and their respective constructs, thereby affirming the presence of convergent validity within the measurement model .
These robust validity tests, coupled with assessments of reliability including Cronbach Alpha and Composite Reliability confirm the overall accuracy and consistency of the measurement instruments used in this study.
As a result, the findings derived from this research can be considered credible and trustworthy, ensuring that the conclusions drawn regarding the determinants of QRIS adoption within the church community are both valid and reliable .
Moreover, by providing insights into the adoption of digital financial technology within a religious and community-based context, this study supports Sustainable Development Goals (SDG.
9 (Industry.
Innovation, and Infrastructur.
through promoting technological innovation.
SDG 16 (Peace.
Justice, and Strong Institution.
by fostering transparency and accountability in donation practices, and SDG 17 (Partnerships for the Goal.
by highlighting collaboration between religious institutions and digital service providers in advancing inclusive and sustainable financial practices .
Table 5.
Convergent Validity Test
Variables Indicator Track
Validity TR1
0,868 Legitimate Trust
TR2
0,880 Legitimate TR3
0,821 Legitimate PU1
0,870 Legitimate Felt
PU2
0,832 Legitimate Usage
PU3
0,931 Legitimate PEI1
0,892 Legitimate Felt
PEI2
0,941 Legitimate Ease of Use PEI3
0,895 Legitimate PR1
0,844 Legitimate Felt
PR2
0,881 Legitimate Risking PR3
0,822 Legitimate PS1
0,703 Legitimate Felt
PS2
0,738 Legitimate Security PS3
0,900 Legitimate AT1
0,943 Legitimate Attitude AT2
0,945 Legitimate EN1
0,939 Legitimate Engagement
EN2
0,930 Legitimate INSIDE 1 0,926 Legitimate Meaning DI2 0,927 Legitimate OF 3 0,874 Legitimate Discriminant validity test, using Heterotrait-Monotrait Ratio (HTMT), ensures that the measured variables differ in Structural Equation Modeling (SEM), such as Partial Least Squares (PLS).
HTMT assesses discriminant validity by comparing the average correlation between indicators in various constructs .
with those in the same construct .
onotrait-heterometho.
HTMT values below 0.
85 or 0.
indicate good discriminant validity.
Table 5 presents the results of the discriminant validity test .
E-ISSN: 2656-8888 | P-ISSN: 2655-8807
HTML
Table 6.
Discriminant Validity Test Construction-1 Construction-2 Trust Perceived Usefulness Trust Perceived Ease of Use Trust Perceived Risk Trust Perceived Security Trust Attitude Trust Engagement Trust Meaning Perceived Usefulness Perceived Ease of Use Perceived Usefulness Perceived Risk Perceived Usefulness Perceived Security Perceived Usefulness Attitude Perceived Usefulness Engagement Perceived Usefulness Meaning Perceived Ease of Use Perceived Risk Perceived Ease of Use Perceived Risk Perceived Ease of Use Perceived Security Perceived Ease of Use Attitude Perceived Ease of Use Engagement Perceived Ease of Use Meaning Perceived Risk Perceived Security Perceived Risk Attitude Perceived Risk Engagement Perceived Risk Meaning Perceived Security Attitude Perceived Security Engagement Perceived Security Meaning Attitude Engagement Attitude Meaning Engagement Meaning Table 6 presents the results of the discriminant validity test to assess the extent to which the constructs in the research model can be distinguished from each other.
Each row shows a pair of constructs (Construction-1anConstruction-.
along with the correlation coefficient (HTML).
Positive correlations indicate a direct relationship, while negative correlations indicate an inverse relationship .
The results show that Trust has a positive correlation with Perceived Usefulness .
Perceived Ease of Use .
, and Meani .
, indicating that user trust supports perceptions of usefulness, ease of use, and meaningfulness of the system.
Conversely.
PerceiveddeniedAt (-0.
and Engagement (-0.
, suggesting that perceived risk hinders usersAo positive attitudes and engagement.
The correlation values between constructs are lower than the correlations within constructs themselves, confirming good discriminant validity .
Thus, constructs such as Trust.
Perceived Usefulness.
Perceived Ease of Use.
Perceived Risk.
Perceived Security.
Attitude.
Engagement, and Meaning represent distinct concepts clearly, supporting the research model on technology acceptance in the context of digital donations .
HTMT values below 0.
indicate good discriminant validity, confirming constructs like trust versus PU and trust versus attitude are distinct .
Values between 0.
85 and 0.
rust vs.
PEOU) are acceptable but may need further review.
Negative HTMT values between trust and perceived risk or security suggest possible inverse relationships needing more examination .
Composite reliability (CR) assesses internal consistency, with values above 0.
7 indicating good reliability.
All constructs in this study exceed this threshold, showing consistent measurement .
Cronbach alpha, a traditional reliability measure, is acceptable above 0.
All variables meet this criterion, further confirming instrument reliability .
APTISI Transactions on Technopreneurship (ATT).
Vol.
No.
November 2025, pp.
687Ae700 APTISI Transactions on Technopreneurship (ATT) ye Table 7.
Composite Reliability and Cronbach Alpha Values Variables Composite Reliability Alfa Cronbach Reliability Trust
0,892
0,819
Reliable Perceived Usefulness 0,910 0,853 Reliable Perceived Ease of Use 0,935 0,896 Reliable Perceived Risk
0,886
0,812
Reliable Perceived Security 0,826 0,707 Reliable Attitude 0,942 0,877 Reliable Engagement
0,932
0,855
Reliable Meaning 0,935 0,895 Reliable Table 7 presents the study core findings from testing eight hypotheses regarding relationships affecting digital donation intentions.
A T-statistic above 1.
96 and a P-value below 0.
05 indicate statistical significance.
Results support six hypotheses: PEOU (H.
and PU (H.
positively influence attitudes.
perceived risk (H.
negatively influences attitude.
attitude (H.
positively affects engagement.
engagement (H.
positively influences donation intention.
and trust (H.
positively influences donation intention.
However, evidence does not support trust directly influencing attitude (H.
nor perceived security directly influencing attitude (H.
Table 8.
Results of T Statistic Calculation and P Value Average Standard Relationship Between Original Statistic T Example Deviation Variables Sample (O) (O/STDEV) (M) (STDEV) Belief ->Attitude 0,164 0,161 0,105 1,557 people Perceived Usefulness ->Attitude 0,285 0,284 0,116 2,465 years Perceived Ease of Use ->Attitude 0,350 0,350 0,088 3,952 people Perceived Risk ->Attitude -0,189 -0,185 0,078 2,417 years Perceived Security ->Attitude 0,032 0,022 0,069 0,461 Attitude ->Engagement 0,582 0,580 0,057 10,266 people Engagement >Intention 0,330 0,335 0,081 4,073 people Attitude ->Intention 0,543 0,540 0,073 7,479 years PValues 0,120 0,014 0,016 0,645 The R-squared values (RA) of each latent variable, which reflect the proportion of variance in the dependent variables explained by the model, are comprehensively presented in Table 8.
These values provide insights into the model ability to capture and explain the relationships among key constructs such as Attitude.
Engagement, and Meaning, thereby offering a quantitative measure of the model explanatory power .
Table 9.
R-square Test Results Variables R-squared Attitude 0,685 Engagement
0,339
Meaning 0,613 Table 9 shows RA values indicating the variance explained by the model: Attitude .
685 or 68.
5%) influenced by trust.
PU.
PEOU, perceived risk, and perceived security.
Engagement .
339 or 33.
9%) influenced by Attitude.
and Intention .
613 or 61.
3%) influenced by Attitude and Engagement.
The remaining variance is due to factors outside the study.
The model predictive power was assessed using QA, with a value of 0.
9%), indicating strong ability to explain variance in donation intention .
Based on Table 8.
Hypothesis 1 (Autrust positively affects attitude toward digital donation intentionA.
is rejected .
= 0.
T = 1.
, indicating that trust does not directly influence attitudes in this sample.
This may be due to the ChurchAos institutional credibility fostering implicit trust .
Recent studies also suggest that trustAos direct effect is less significant, with factors like Perceived Ease of Use, social influence, and emotional involvement playing stronger roles .
The narrative also highlights how Perceived Usefulness, ease of use, and engagement consistently explain donation intention .
E-ISSN: 2656-8888 | P-ISSN: 2655-8807
Hypothesis 2, that PU positively influences attitude, is accepted, supported by a p-value of 0.
014 and T-statistic of 2.
PEOU also positively influences attitudes, aligning with previous studies showing ease of use fosters positive attitudes and intentions to donate digitally.
Easy navigation helps users identify as generous donors, enhancing positive attitudes .
Hypothesis 3, that PEOU positively affects attitude toward digital donation, is strongly accepted .
OO 0.
T = 3.
This confirms PEOU as a key factor in user acceptance and donation intention, supported by TAM and prior research emphasizing simplicity and emotional engagement .
Hypothesis 4, stating perceived risk negatively influences attitude, is accepted .
= 0.
T = 2.
High perceived risk reduces positive attitudes toward digital donations, consistent with literature on banking and online services.
Concerns about donation effectiveness and security deter giving .
These findings emphasize the need to address perceived risk to improve attitudes and increase digital donations.
The study technopreneurial relevance lies in its potential to guide the development of faith-based fintech platforms that leverage trust, security, and engagement to encourage digital giving .
Hypothesis 5 (Auperceived security positively affects attitude toward digital donation intentionA.
is rejected, with a p-value of 0.
645 and T-statistic of 0.
Perceived security together enhance user trust in donation platforms, influencing attitudes .
Digital literacy and security awareness also play key roles in shaping positive attitudes and increasing participation.
Educating users and ensuring transparency are crucial for fostering engagement .
Hypothesis 6 (Auattitude positively affects involvement in digital donationA.
is accepted, with p=0.
and T = 10.
Positive attitudes increase engagement, supported by studies highlighting emotional connections, social media interactivity, and storytelling as motivators .
Hypothesis 7 (Auengagement positively affects digital donation intentionA.
is accepted .
= 0.
T =
High engagement fosters emotional bonds and social responsibility, boosting donation intentions .
Hypothesis 8 (Auattitude positively affects digital donation intentionA.
is accepted .
= 0.
T =
Positive attitudes toward technology correlate with stronger donation intentions.
However, positive attitude alone doesnAot guarantee behavior, as factors like perceived behavioral control and social norms also influence actual donations.
Overall, fostering positive attitudes, trust, and engagement is vital to increasing digital donation participation .
MANAGERIAL IMPLICATION
The findings suggest that unexamined factors such as habit, social influence, donation size, and leadership policies may significantly influence digital donation intentions in the Catholic Church.
While this study emphasizes psychological and technological constructs, behavioral and institutional factors also play a critical Church leaders and platform managers should implement strategies that address these elements to improve digital giving outcomes.
Cultivating a habit of digital donation can involve regular reminders, incorporating digital giving into weekly services, and offering easy to use systems that encourage consistent participation.
Social influence may be enhanced by engaging respected clergy, lay leaders, and active parish members to promote and normalize digital giving.
Providing transparency about donation size options and clearly communicating how contributions support church programs can increase trust and motivation.
Leadership policies that prioritize digital innovation through staff training, digital budgeting, and infrastructure support are vital for long-term adoption.
Future research should examine the roles of parish leadership and empirically analyze socioeconomic factors like income, education, and digital literacy to understand their impact on donation behavior.
These insights will help the Church develop more effective, inclusive, and sustainable digital donation strategies that align with its mission in an evolving digital society.
CONCLUSION
The results show that six of eight hypotheses are supported.
Perceived Ease of Use (H.
and Perceived Usefulness (H.
significantly influence attitudes toward digital donation, while perceived risk (H.
has a negative effect though reducing risk can enhance engagement.
Engagement (H.
and trust (H.
positively affect donation intention.
In contrast, the direct influence of trust on attitude (H.
and the indirect effect of perceived security (H.
are not significant, possibly due to the ChurchAos existing institutional credibility, which reduces the need for explicit trust.
APTISI Transactions on Technopreneurship (ATT).
Vol.
No.
November 2025, pp.
687Ae700 APTISI Transactions on Technopreneurship (ATT) ye These findings suggest that in the post-pandemic context, users are more driven by convenience and Perceived Usefulness than by trust alone.
The study demonstrates that digital transformation can strengthen donation intentions in the Indonesian Catholic Church by offering secure, accessible, and user friendly donation platforms.
Psychological constructs like trust.
Perceived Usefulness.
Perceived Ease of Use, and perceived security shape user attitudes and engagement, which drive donation behavior.
A proposed QRIS based mobile platform featuring donor dashboards, impact tracking, and secure transactions reflects practical implementation aligned with digital preneurship, technopreneurship, and socialpreneurship goals, enabling scalable and community oriented religious giving solutions.
Theoretically, this study extends the Technology Acceptance Model (TAM) by integrating trust, perceived risk, and security into a faith-based context.
Engagement emerges as a key mediator between attitude and donation intention, while ease of use and usefulness remain dominant predictors.
These insights provide a foundation for faith based digital entrepreneurship by encouraging the creation of fintech platforms that combine technology, trust, and transparency to foster greater participation and long-term sustainability in religious DECLARATIONS About Authors Theodorus Sendjaja (TS) Didik J.
Rachbini (DJ) https://orcid.
org/0000-0003-1239-6666 https://orcid.
org/0009-0000-0082-9466 Rina Astini (RA) https://orcid.
org/0000-0001-7632-4640 Daru Asih (DA) https://orcid.
org/0000-0002-1764-7439 Author Contributions Conceptualization: TS.
Methodology: DJ.
Software: RA.
Validation: DA and TS.
Formal Analysis:
DJ and RA.
Investigation: DA.
Resources: TS.
Data Curation: DJ.
Writing Original Draft Preparation: RA and DA.
Writing Review and Editing: TS and DJ.
Visualization: RA.
All authors.
TS.
DJ.
RA, and DA, have read and agreed to the published version of the manuscript.
Data Availability Statement The data presented in this study are available on request from the corresponding author.
Funding The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interest The authors declare that they have no conflicts of interest, known competing financial interests, or personal relationships that could have influenced the work reported in this paper.
REFERENCES