The Role of Social Influence in Alumni Engagement: Examining Behavioural Intention and Digital Platform Usage

The Role of Social Influence in Alumni Engagement: Examining Behavioural Intention and Digital Platform Usage

Suriani Jack^1*, Azlina Bujang² , Jati Kasuma Ali³ , Leviana Andrew⁴

^1,2School Of Information Science, College of Computing, Informatics and Mathematics, University Technology MARA Kampus Samarahan 2

^3,4Faculty Of Business and Management, University Technology MARA Kampus Samarahan

^*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90300106

Received: 28 January 2025; Revised: 14 February 2025; Accepted: 17 February 2025; Published: 03 April 2025

ABSTRACT

Alumni engagement has become a strategic priority for higher education institutions (HEIs) to strengthen institutional reputation, expand networking opportunities, and enhance financial support. Digital platforms serve as essential tools in fostering alumni connections; however, their effectiveness depends on user adoption and sustained participation. While prior research has extensively examined perceived usefulness (PU) and perceived ease of use (PEOU) within the Technology Acceptance Model (TAM), the role of social influence in shaping alumni engagement remains underexplored.  This study aims to examine the impact of social influence on behavioural intention and actual use of alumni engagement digital platforms. A quantitative research design will be employed, utilizing a structured survey to collect data from alumni of selected private universities in Malaysia. A purposive sampling technique will be used to recruit respondents. The survey instrument, adapted from established TAM-related studies, will measure social influence, behavioural intention, and actual use using a five-point Likert scale. Data will be analysed using Structural Equation Modeling (SEM) via SmartPLS to assess relationships among key constructs.  Findings from this study will contribute to the theoretical expansion of TAM by integrating social influence as a key determinant of alumni engagement. Additionally, the study offers practical implications for HEIs, providing insights into how social validation, peer influence, and digital engagement strategies can enhance alumni participation. By leveraging these insights, universities can develop more effective digital engagement strategies, fostering long-term alumni involvement and institutional support.

Keywords: Alumni Engagement, Social Influence, Behavioural Intention, Digital Platforms, Technology Acceptance Model (TAM), Higher Education

INTRODUCTION

Alumni engagement platforms serve as a bridge between universities and their graduates, fostering communication, networking, and institutional support (Rubejes-Silva, 2024). Universities increasingly rely on these digital platforms to maintain strong alumni relations, recognizing their potential in enhancing institutional reputation, securing financial contributions, and facilitating knowledge exchange (Malhotra et al., 2023).  However, despite their growing prevalence, the sustained use of alumni engagement platforms remains inconsistent, with many institutions struggling to encourage long-term participation among graduates (Hayt, 2023; Johnson, 2023).  Existing research on technology adoption in alumni engagement largely focuses on perceived usefulness (PU) and perceived ease of use (PEOU) as primary drivers, as outlined in the Technology Acceptance Model (TAM). While these factors are important, they do not fully explain why some alumni actively engage with these platforms while others do not (Politis et al., 2024; Malhotra et al., 2023).  Social influence—the impact of peers, institutional networks, and societal expectations—has been identified in broader technology adoption research as a key determinant of user behaviour (Leow et al., 2021). However, its role in shaping alumni engagement through digital platforms remains underexplored.  This study aims to bridge this gap by investigating the direct and indirect effects of social influence on alumni behavioural intention and actual use of digital engagement platforms. Understanding these dynamics is essential for both theoretical advancement and practical application. Theoretically, this study extends the TAM framework by incorporating social influence as a critical determinant, providing a more comprehensive model for explaining alumni engagement behavior. Practically, the findings will offer valuable insights for higher education institutions in designing and implementing more effective digital strategies to foster alumni participation. By leveraging social influence mechanisms—such as peer endorsements, social validation, and community-driven engagement—universities can enhance the long-term effectiveness of their digital platforms and strengthen alumni-institution relationships.

LITERATURE REVIEW

Alumni Engagement

Alumni engagement is an important part of the relationship between higher education institutions (HEIs) and their graduates.  Alumni engagement is defined as activities that foster long-term and mutually beneficial relationships between alumni and their alma mater (Alumni Engagement Metrics Task Force, Council for Advancement and Support of Education (CASE), 2018). It includes a variety of strategies aimed at increasing loyalty, financial support, and institutional reputation. These activities can include events, messaging, and projects that appeal to alumni, encouraging them to stay connected and involved with their university.  Traditional engagement tactics used by schools to build links with alumni include reunions, magazines, and newsletters (Johnson, 2023). HEIs strive to foster mutually beneficial, long-term relationships between alumni and institutions based on common affinities and affection (Johnson, 2024). According to research, effective alumni engagement techniques involve building strong communication and emotional links between alumni and institutions, as well as including alumni in institutional functions and decision-making processes (Veluvali & Surisetti, 2023; Nisar & Nasruddin, 2022). While the universities are trying to maintain permanent relationships with their alumni, innovative strategies have emerged that use technology, joint structure and personalized experiences. The HEIs increasingly used digital platforms to deal with their alumni community. Johnson (2024) suggested that the integration of modern technology, in particular the web-based alumni information systems, as the universities recognize the decisive role of alumni in the contribution to institutional success, as a decisive opportunity to promote and maintain these connections.  These systems offer alumni a platform to stay up to date through university news, events, employment opportunities and to deal with their Alma Mater.  Smith (2018) highlighted the critical role of web-based alumni information systems in streamlining the dissemination of information related to reunions, networking events, and career opportunities.  The study demonstrated that these systems positively influence alumni engagement, enabling graduates to maintain strong connections with their alma mater. Similarly, research by Anthony (2021) emphasized the importance of alumni information systems in supporting universities’ decision-making processes. His findings underscored how data collected through these platforms contribute to strategic planning, resource allocation, and the efficient management of human resources within academic institutions.

Existing literature suggests that such systems play a pivotal role in enhancing alumni engagement by facilitating seamless communication and offering valuable services to graduates as they transition into their professional careers. For instance, online learning platforms foster alumni connections through interactive features that promote continuous collaboration and peer support (Malhotra et al., 2023). Additionally, user-centered alumni portals enhance professional development and networking opportunities, thereby increasing alumni participation (Lacasandile et al., 2023). Engaged alumni, in turn, serve as institutional ambassadors, strengthening the university’s reputation and visibility. A well-connected alumni network also contributes to increased financial support through donations, which are vital for institutional sustainability and growth (Johnson, 2023). Furthermore, such networks facilitate professional networking opportunities, benefiting both recent graduates and established alumni by fostering a mutually supportive community (Smith, 2018).  Recognizing these benefits, universities are increasingly adopting dedicated digital platforms to facilitate alumni engagement.  Moving beyond traditional in-person reunions, higher education institutions now host virtual reunions to enhance alumni participation. For example, Lamar University introduced the “Lamar University Digital Alumni and Friends Gatherings” in 2020, while INSEAD launched its “Digital Alumni Reunion” in 2021, incorporating faculty talks and virtual networking rooms (Gunes, 2024). These digital strategies have proven to be cost-effective and highly practical, especially given the global dispersion of alumni communities.

Social Influence

Social influence, derived from the Theory of Planned Behaviour (TPB) and Unified Theory of Acceptance and Use of Technology (UTAUT), is the degree to which individuals perceive that important others believe they should use technology (Venkatesh, 2003). Social influence encompasses how individuals are affected by the opinions, behaviours, and actions of others when adopting new technologies.  It can manifest through various mechanisms, including:

Figure 1. Mechanisms of Social Influence (Sibtain et al., 2024)

Research indicates that social influence plays a significant role in technology adoption, often overshadowing individual preferences or perceived utility. However, existing studies have primarily focused on compliance-based interpretations, neglecting more nuanced forms of influence such as identification and internalization (Fraf-Vlachy & Buhtz, 2017).

Previous research suggests that individuals are more likely to adopt and engage with technologies when they perceive positive opinions and behaviours from their peers or significant others (Answer, Zaigham, Imran Rasheed, Pitafi, Iqbal & Luqman, 2020).  For example, a study found that social factors, such as peer recommendations and institutional reputation, significantly impact alumni’s intentions to engage with their alma mater through various digital platforms (Johnson, 2023).  The findings suggest that when alumni perceive a strong social endorsement for engaging with the institution, their likelihood of participation increases markedly.  Additionally, research has highlighted that alumni who have positive undergraduate experiences are more likely to remain engaged with their institutions (Smith & Tinto, 2024; Drezner & Pizmony-Levy, 2021).  Research has consistently demonstrated that social influence plays a crucial role in shaping alumni engagement behaviours. Alumni are more likely to remain connected with their alma mater when they perceive that their peers value and actively participate in such engagement (Johnson, 2023). This influence manifests in various forms, including participation in alumni events, contributions to fundraising initiatives, and involvement in mentorship programs (Delisa, 2022; Mullen, 2020). Strong alumni networks foster a sense of belonging and institutional loyalty, thereby increasing the likelihood of sustained engagement in alumni-related activities (Drezner et al., 2021).  Moreover, social media has emerged as a key enabler of social influence, providing institutions with effective tools to strengthen alumni connections. Universities that strategically utilize social media platforms can cultivate vibrant online communities where alumni share experiences, celebrate achievements, and promote institutional initiatives (Pringle & Fritz, 2019; Hall, 2016). These platforms not only enhance the visibility of alumni activities but also reinforce social norms that encourage participation and continued support for the institution.

Effects of Social Influence on Alumni Engagement Platforms

Research indicates that social media platforms can foster a sense of belonging among alumni. For example, platforms like Facebook allow alumni to engage with their university community, enhancing their feelings of connection and support. This sense of belonging is crucial for maintaining long-term engagement with the institution, especially in the context of challenges posed by events like the COVID-19 pandemic (White & Caccamo, 2023).  Alumni’s prior learning experiences significantly affect their current engagement levels. Studies show that alumni recalling negative challenges during their education—whether related to curriculum or social interactions—tend to have fewer positive attitudes toward lifelong learning and engagement with their alma mater. This suggests that institutions need to consider these past experiences when designing engagement strategies (Qi et al., 2024).  The influence of peers is another critical factor in alumni engagement. The presence of social support—both informational and emotional—can enhance engagement intentions. Alumni are more likely to participate in activities if they perceive that their peers are also involved, creating a cycle of increased participation driven by social influence (Qin et al., 2022).  Social media acts as a powerful tool for enhancing alumni engagement by providing platforms for interaction and communication. Sharing experiences, achievements, and events on these platforms keeps alumni informed and encourages them to engage more actively with their institutions (Gani et al., 2022).

The interplay between social influence and alumni engagement digital platform has become a pivotal area of study in the fields of information systems and technology management.  Understanding how social dynamics affect the adoption of new technologies can provide insights into user behaviour, enhance implementation strategies, and foster innovation.  This discussion will explore the effects of social influence.

Interplay in Alumni Engagement Digital Platforms

1. Perceived Usefulness and Social Validation

Alumni are more likely to engage with digital platforms that they perceive as beneficial for their professional networking and personal development. Social validation—seeing peers actively using and recommending a platform—enhances this perception, encouraging new users to adopt the technology (Almansoori, Al-Khateeri & Al-kfairy, 2024).

2. User Engagement Driven by Social Dynamics

The presence of active alumni communities can significantly enhance engagement levels. When alumni observe their peers interacting positively on a platform, it reinforces their own intention to participate. This aligns with TAM’s emphasis on perceived usefulness; if alumni see value in others’ experiences, they are more likely to view the platform as useful (Hardianto, 2023).

3. Feedback Mechanism

Digital platforms often incorporate feedback loops where users can share experiences and testimonials. This feedback not only serves as social proof but also influences potential users’ perceptions of the platform’s usefulness. Positive feedback from peers can enhance trust and encourage participation, reflecting both social influence and TAM principles (Sarder & Mustaqeem, 2024; Yin, 2024).

4. Community Building through Social Media

Platforms like LinkedIn have demonstrated how social media can facilitate community building among alumni. The ability to connect with former classmates and engage in discussions about career opportunities fosters a sense of belonging and enhances the perceived utility of these platforms (Brugliera, 2024; Velasco, 2024).

5. Challenges in Engagement

Despite the positive influences, challenges such as negative feedback or lack of engagement from peers can deter users from adopting these platforms. Understanding these dynamics is essential for platform developers aiming to improve user experience and retention (Almansoori et al., 2024; Sarder & Mustaqeem, 2024).

Technology Acceptance Model

The Technology Acceptance Model (TAM), introduced by Fred Davis in 1989, is a widely adopted theoretical model in the field of information systems research that seeks to explain and predict user acceptance of new technologies.  It posits those two primary beliefs – Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) – directly influence a user’s Attitude Towards Using (ATU) a particular technology, which in turn influences their Behavioural Intention to Use (BI), and ultimately their actual Use Behaviour (UB) (Legramante, Azevedo & Azevedo, 2023).   According to TAM, technology acceptance is a three-stage process, whereby external factors (system design features) trigger cognitive responses (perceived ease of use (PEOU) and perceived usefulness (PU)), which in turn, form an effective response (attitude toward using technology/intention), influencing use behaviour (Davis, 1993; Davis, 1989). TAM remains a fundamental framework for understanding the adoption of new technologies, particularly in educational and professional environments. By emphasizing key determinants such as perceived usefulness (PU) and perceived ease of use (PEOU), TAM offers valuable insights into users’ behavioural intentions and actual technology usage. Its extensive application across various contexts has highlighted diverse factors influencing technology adoption and associated outcomes (Dwivedi, Ismagilova, Rana & Raman, 2023; Singh, Sahni & Kovid, 2020).

Figure 1 Technology Acceptance Model (Davis, 1989) 

Behavioural Intention and Actual Use

In the Technology Acceptance Model (TAM), behavioural intention refers to the strength of an individual’s intent to engage in a specific behaviour, particularly the use of a given technology (Davis, 1989). It represents the likelihood that a person will adopt or utilize the technology based on their prior beliefs and attitudes (Liao, Hong, Wen & Pan, 2018; Davis, 1989). Serving as an intermediary between users’ attitudes and their actual behaviour, behavioural intention reflects the extent to which individuals are inclined to act upon their perceptions of usefulness and ease of use (To & Trinh, 2021; Davis, 1989). TAM posits that behavioural intention is the primary determinant of actual system usage (Davis, 1989). Once a strong intention to use a technology is established, it is likely to translate into actual adoption (Putra, 2018). This assumption is grounded in the notion that intention signifies an individual’s readiness to perform a behaviour and has been widely validated through empirical research (Wandira, Fauzi & Nurahim, 2024; Papakostas, Troussas, Krouska & Sgouropoulou, 2023; Humida, Al Mamun & Keikhosrokiani, 2022; Martín-García, Redolat & Pinazo-Hernandis, 2022; Mailizar, Burg & Maulina, 2021).  Understanding behavioural intention in TAM is critical for designing technology solutions that enhance user acceptance.  Organizations can improve user adoption by leveraging social influence by encouraging key influencers or peers to endorse the technology (Abdalla, 2024; Ngubelanga & Duffett, 2021), and focusing on user engagement to ensure that positive behavioural intentions are translated into sustained actual usage (Ngubelanga & Duffett, 2021).

While the Technology Acceptance Model (TAM) posits that behavioural intention strongly predicts actual system use, this relationship does not always hold in practice. Users may intend to adopt a technology but encounter external constraints such as time limitations, technical challenges, or organizational barriers, which can hinder actual usage (Kouhizadeh, Saberi & Sarkis, 2021; Al-Adwan, 2020; Choi, Chung, Seyha & Young, 2020; Chouki, Talea, Okar & Chroqui, 2020). Acknowledging these gaps is essential for enhancing the adoption process. Furthermore, initial technology adoption does not necessarily ensure sustained usage (Alshurideh, Al Kurdi, Salloum, Arpaci & Al-Emran, 2023; Yan, Filieri & Gorton, 2021). Users may engage with a system initially, but if they fail to perceive ongoing benefits or encounter usability challenges, their engagement may decline over time (Pozón-López, Higueras-Castillo, Muñoz-Leiva & Liébana-Cabanillas, 2021). Additionally, conventional metrics such as usage frequency or duration may not fully capture the quality of engagement (Alshurideh et al., 2023). For example, a user may interact with the system extensively but experience frustration due to interface complexities. Therefore, incorporating user satisfaction as a complementary measure to actual use provides a more comprehensive understanding of technology adoption (Kar, 2021; Pozón-López et al., 2021).

Conceptual Framework and Hypotheses Development

Figure 2 Proposed Conceptual Framework

The study proposes the following hypotheses:

• H1: Social influence has a positive impact on behavioural intention to use alumni engagement digital platforms.
• H2: Behavioural intention has a positive impact on the actual use of alumni engagement digital platforms.
• H3: Social influence has a direct positive impact on the actual use of alumni engagement digital platforms.

The conceptual framework integrates social influence as a key determinant, along with behavioural intention, to examine their effects on actual use.

RESEARCH METHODOLOGY

This study will adopt a quantitative research approach to examine the role of social influence in alumni engagement with digital platforms. By employing a structured survey method, the research will ensure the systematic collection of data, allowing for statistical analysis to validate the proposed relationships among key constructs. The methodology will be structured into three main components: research design, measurement of constructs, and data collection and analysis.

Research Design and Approach

This study will employ a survey-based quantitative approach, which is appropriate for testing relationships between constructs within the Technology Acceptance Model (TAM). The research will follow a cross-sectional design, collecting data at a single point in time to assess behavioural intentions and actual use of alumni engagement digital platforms. To ensure the quality and reliability of findings, established measurement scales will be adopted, and a pilot test will be conducted before full data collection.

 Measurement of Constructs

The study’s key constructs—social influence, behavioural intention, and actual use—will be measured using validated scales from prior research. All items will be rated on a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree) to capture respondents’ perceptions accurately.

1. Social Influence (SI): Adapted from Venkatesh et al. (2003), this construct will measure the extent to which alumni feel influenced by their peers, institutional networks, and social norms regarding the use of digital engagement platforms.
2. Behavioural Intention (BI): Adapted from Davis (1989), this construct will assess alumni’s willingness and intention to engage with digital platforms.
3. Actual Use (AU): Measured using self-reported frequency and duration of platform usage, based on previous studies on technology adoption (Venkatesh & Davis, 2000).

To ensure content validity, the survey items will be reviewed by academic experts in technology adoption and alumni engagement. A pilot study with 30 respondents will be conducted to test reliability using Cronbach’s alpha, ensuring internal consistency across constructs.

Data Collection and Analysis

A purposive sampling technique will be used to target alumni from selected private universities who actively engage with digital alumni platforms. The online survey will be distributed through official alumni engagement channels, including email, social media groups, and university portals.  Data analysis will be conducted using SPSS for descriptive statistics and Structural Equation Modeling (SEM) using SmartPLS to test hypotheses and examine the relationships between variables. SEM will be chosen due to its robustness in assessing latent constructs and complex relationships. The model fit indices, path coefficients, and effect sizes will be evaluated to ensure the findings’ reliability and validity.  By adopting a rigorous methodological approach, this study will ensure the credibility and generalizability of its findings, offering valuable insights for both academia and higher education institutions seeking to enhance alumni engagement through digital platforms.

Significance of the Study

This study holds both theoretical and practical significance in understanding the role of social influence in alumni engagement through digital platforms. By integrating social influence into the Technology Acceptance Model (TAM), this research contributes to advancing knowledge in technology adoption, alumni relations, and digital engagement strategies.

Theoretical Significance

1. Extension of TAM – While TAM has been widely used to explain technology adoption behaviours, its application in alumni engagement platforms remains underexplored. This study extends the model by incorporating social influence as a key determinant of behavioural intention and actual use, offering a more comprehensive framework for understanding alumni engagement in the digital age.
2. Bridging the Research Gap – Previous studies on alumni engagement have predominantly focused on perceived usefulness (PU) and perceived ease of use (PEOU). By highlighting the direct and indirect effects of social influence, this study fills a critical gap in literature on digital engagement and social-driven technology adoption.
3. Empirical Validation – By employing Structural Equation Modeling (SEM), the study provides empirical evidence on the relationships between social influence, behavioural intention, and actual use, strengthening existing theoretical foundations in information systems and higher education management research.

Practical Significance

1. Enhancing Digital Alumni Engagement Strategies – Findings from this study will help higher education institutions (HEIs) design more effective alumni engagement strategies by leveraging peer influence, social validation, and community-driven participation to increase platform adoption and sustained use.
2. Informing University Policies – Universities can use the insights to develop data-driven policies that foster long-term alumni engagement, benefiting from alumni contributions in networking, mentorship, and financial support.
3. Technology Implementation in HEIs – The study’s recommendations will assist universities in optimizing digital alumni platforms, ensuring they are user-friendly, socially engaging, and tailored to alumni needs, thereby improving overall institutional-alumni relations.

By addressing these theoretical and practical concerns, this study provides a valuable framework for both researchers and higher education practitioners in understanding and improving alumni engagement through digital technologies.

CONCLUSION

The role of social influence in alumni engagement remains an open question. By testing its impact on behavioural intention and actual use, this study provides insights that can inform both academic discussions and institutional strategies for digital alumni relations.

REFERENCES

1. Abdalla, R. A. (2024).  Examining awareness, social influence, and perceived enjoyment in the TAM framework as determinants of ChatGPT.  Personalization as a moderator.  Journal of Open Innovation:  Technology, Market, and Complexity, 10(3), 100327-100327.
2. Al-Adwan, A. S. (2020). Investigating the drivers and barriers to MOOCs adoption: The perspective of TAM. Education and information technologies, 25(6), 5771-5795.
3. Almansoori, L., Al-Katheeri, R., & Al-kfairy, M. (2024). Users’ Adoption of Social Media Platforms for Government Services: The Role of Perceived Privacy, Perceived Security, Trust, and Social Influence, Proceedings of the 11th European Conference on social media, ECSM 2024, 23-31.
4. Alshurideh, M., Al Kurdi, B., Salloum, S. A., Arpaci, I., & Al-Emran, M. (2023). Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms. Interactive Learning Environments, 31(3), 1214-1228.
5. Anthony Jr, B. (2021). Information flow analysis of a knowledge mapping-based system for university alumni collaboration: a practical approach. Journal of the Knowledge Economy, 12(2), 756-787.
6. Brugliera, P. (2024). The Effectiveness of Digital Learning Platforms in Enhancing Student Engagement and Academic Performance. Journal of Education, Humanities, and Social Research.
7. Choi, D., Chung, C. Y., Seyha, T., & Young, J. (2020). Factors affecting organizations’ resistance to the adoption of blockchain technology in supply networks. Sustainability, 12(21), 8882.
8. Chouki, M., Talea, M., Okar, C., & Chroqui, R. (2020). Barriers to information technology adoption within small and medium enterprises: A systematic literature review. International Journal of Innovation and Technology Management, 17(01), 2050007.
9. Davis, F.D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13 (3), 319.
10. Davis, F.D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioural impacts. International Journal of Man-Machine Studies, 38 (3), 475-487.
11. Delisa, M. (2022). Alumni and High-Impact Student Practices: A Phenomenological Exploration of the Factors that Influence Giving (Doctoral dissertation, City University of Seattle).
12. Drezner, N. D., & Pizmony-Levy, O. (2021). I belong, therefore, I give? The impact of sense of belonging on graduate student alumni engagement. Nonprofit and Voluntary Sector Quarterly, 50(4), 753-777.
13. Dwivedi, Y. K., Ismagilova, E., Rana, N. P., & Raman, R. (2023). Social media adoption, usage and impact in business-to-business (B2B) context: A state-of-the-art literature review. Information Systems Frontiers, 1-23.
14. Graf‐Vlachy, L., & Buhtz, K. (2017). Social Influence in Technology Adoption Research: A Literature Review and Research Agenda. ERN: Institutions.
15. Gunes, E. (2024). Best practices to improve alumni engagement.  Join It.  https://joinit.com/blog/alumni-engagement-guide.
16. Hardianto, H. (2023). Consumer Co-Creation and Its Impact on Brand Innovation in Digital Platforms. Journal of Social Commerce.
17. Hayt, C. (2023). Becoming a Learning Organization: Identifying Best Practices to Evaluate, Sustain, and Evolve Meaningful Engagement with Alumni (Doctoral dissertation, University of Pittsburgh).
18. Johnson, J. T. (2023).  A study of higher education alumni perceptions of digital or in-person engagement.  (Doctorate Thesis, University of Tennessee).
19. Johnson, J. K. (2024). Alumni Giving at a Christian Liberal Arts College That Originated as a Bible College (Doctoral dissertation, Southeastern University).
20. Kar, A. K. (2021). What affects usage satisfaction in mobile payments? Modelling user generated content to develop the “digital service usage satisfaction model”. Information Systems Frontiers, 23(5), 1341-1361.
21. Kouhizadeh, M., Saberi, S., & Sarkis, J. (2021). Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. International journal of production economics, 231, 107831.
22. Lacasandile, A. D., Altuna, R. B., Nova, A. C., Diaz, L. P., Gunay, K. R., & Balbuena, A. E. A. (2023, November). Fostering Alumni Involvement and Professional Advancement: A User-Centric Perspective on the National University Alumni Portal. In 2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) (pp. 1-6).
23. Leow, L. P., Phua, L. K., & Teh, S. Y. (2021). Extending the social influence factor: Behavioural intention to increase the usage of information and communication technology-enhanced student-centered teaching methods. Educational Technology Research and Development, 69(3), 1853-1879.
24. Liao, S., Hong, J. C., Wen, M. H., & Pan, Y. C. (2018). Applying technology acceptance model (TAM) to explore users’ behavioural intention to adopt a performance assessment system for E-book production. EURASIA Journal of Mathematics, Science and Technology Education, 14(10).
25. Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and information technologies, 26(6), 7057-7077.
26. Malhotra, R., Massoudi, M., & Jindal, R. (2023). An alumni-based collaborative model to strengthen academia and industry partnership: The current challenges and strengths. Education and Information Technologies, 28(2), 2263-2289.
27. Martín-García, A. V., Redolat, R., & Pinazo-Hernandis, S. (2022). Factors influencing intention to technological use in older adults. The TAM model application. Research on aging, 44(7-8), 573-588.
28. Nisar, N., Nasruddin, E., & Goh, Y. N. (2023). Strategizing alumni to encounter financial sustainability issue: insights from public higher education institutions of Pakistan. Journal of Applied Research in Higher Education, 15(2), 509-520.
29. Ngubelanga, A., & Duffett, R. (2021).  Modeling mobile commerce applications’ antecedents of customer satisfaction among millennials:  An extended TAM perspective.  Sustainability, 13, 1-29.
30. Politis, D., Aaboen, L., Eide, A. E., & Haneberg, D. H. (2024). Re-viewing entrepreneurial universities through alumni engagement. Small Business Economics, 63(2), 655-671.
31. Putra, I. D. G. R. D. (2018).  The evolution of technology acceptance model (tam) and recent progress on technology acceptance research in ELT: State of the art article.  Yavana Bhāshā: Journal of English Language Education, 1(2), 25-37.
32. Sarder, M. A. U., & Mustaqeem, K. M. The role of social media marketing in shaping educational institution branding.  Sarder, M.A., & Mustaqeem, K.M. (2024). The Role of Social Media Marketing in Shaping Educational Institution Branding. International Journal of Research and Innovation in Social Science, 4574-4588.
33. Pozón-López, I., Higueras-Castillo, E., Muñoz-Leiva, F., & Liébana-Cabanillas, F. J. (2021). Perceived user satisfaction and intention to use massive open online courses (MOOCs). Journal of Computing in Higher Education, 33, 85-120.
34. Qi, Z., Lyndgaard, S. F., Melkers, J. E., & Kanfer, R. (2024).  Toward sustainable lifelong learning:  Feedforward effects of challenge recollections on adult learning identity.  Applied Cognitive Psychology, 38(5), 1-13.
35. Qin, F., Le, W., Zhang, M., & Deng, Y. (2023). How perceived attributes of livestreaming commerce influence customer engagement: a social support perspective. Journal of Service Theory and Practice, 33(1)1, 1-22.
36. Rubejes-Silva, S. M. (2024). Bridging the Gap Between Universities and Alumni: A User-Centered Evaluation of a Digital Alumni Engagement Platform. Journal of Innovative Technology Convergence, 6(2).
37. Sibtain, M. M., Hashim, M., García Márquez, F. P., Baig, S. A., & Nazam, M. (2024). Role of social influence in adoption of energy-efficient household systems among Pakistani consumers: a quantitative study. International Journal of Housing Markets and Analysis.
38. Singh, S., Sahni, M. M., & Kovid, R. K. (2020). What drives FinTech adoption? A multi-method evaluation using an adapted technology acceptance model. Management Decision, 58(8), 1675-1697.
39. Smith, R. A., & Tinto, V. (2024). Unraveling student engagement: Exploring its relational and longitudinal character. Journal of College Student Retention: Research, Theory & Practice, 26(2), 528-543.
40. To, A. T., & Trinh, T. H. M. (2021). Understanding behavioural intention to use mobile wallets in vietnam: Extending the tam model with trust and enjoyment. Cogent Business & Management, 8(1), 1891661.
41. Velasco, J.C., C. Manguera, J.J., V. Navalan, E.J., C. Limchiko, J.N., D. Bayot, T.A., & V. Mercado, M.G. (2024). The Digital Dynamics of Political Engagement Among Filipino Youth: Examining Participation in Social Media Platforms. Pertanika Journal of Social Sciences and Humanities, 32(3), 1051-1071.
42. Veluvali, P., & Surisetti, J. (2023).  Perspective chapter:  Alumni engagement in higher education institutions – perspectives from India.  doi: 10.5772/intechopen.111457
43. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
44. White, A., & Caccamo, F. (2023). Let me confess … Can a social media page create a sense of belonging? ASCILITE Publications.
45. Yan, M., Filieri, R., & Gorton, M. (2021). Continuance intention of online technologies: A systematic literature review. International Journal of Information Management, 58, 102315.
46. Yin, H. (2024). From Virality to Engagement: Examining the Transformative Impact of Social Media, Short Video Platforms, and Live Streaming on Information Dissemination and Audience Behaviour in the Digital Age. Advances in Social Behaviour Research, 14, 10-14.