E-ISSN:2250-0758
P-ISSN:2394-6962

Research Article

Social Media

International Journal of Engineering and Management Research

2025 Volume 15 Number 5 October
Publisherwww.vandanapublications.com

Beyond Screens and Streams: Reimagining EdTech Pedagogy through Social Media Synergy-A Study with Special Reference to Selected Edtech Companies

M. Sakthivadivel1*, S. Ayyappan2, Sreerudran MK3, Sharfunisa. N4
DOI:10.5281/zenodo.17383499

1* M. Sakthivadivel, Assistant Professor (SS), Department of Computer Science, Dr Mahalingam College of Engineering Technology, Pollachi, Tamil Nadu, India.

2 S. Ayyappan, Professor, School of Management Studies, CMR University, Bangalore, Karnataka, India.

3 Mayan.K Sreerudran, Ph.D Research Scholar, School of Management, CMR University, Bangalore, Karnataka, India.

4 Sharfunisa. N, Research Scholar, Assistant Professor, School of Management, CMR University, Bangalore, Karnataka, India.

Educational technology (EdTech) platforms increasingly depend on social media to attract learners, build trust and gather feedback. This study examines how social media factors including design, trust, targeting and challenges affect the operational performance and societal impact of EdTech companies. We propose a hybrid structural equation model (SEM) where social media design quality, perceived trust and targeting accuracy directly influence awareness and EdTech operations and indirectly affect the overall impact through intermediate constructs such as emerging reality and EdTech operations. Data for the model were collected through a survey of employees at EdTech firms; PLS SEM analysis confirmed strong relationships between social media marketing (SMM) and consumer brand engagement, which in turn influenced purchase intention. The model also identifies challenges such as platform algorithms, budget constraints and content overload—as barriers that feed back into operations.Research motivations stem from recent findings: group based social media interventions improved learning gains during the pandemic yet digital inequalities persist and access alone does not ensure equity. Additionally, EdTech firms face rising competition and must align marketing strategies with user engagement. Our SEM illustrates that high quality content and meaningful interaction strengthen emotional connections and brand engagement, thereby enhancing user satisfaction and operational effectiveness. Compared with traditional digital marketing, the proposed model integrates makespan and resource allocation concepts from manufacturing scheduling into the scheduling of social media campaigns, seeking to balance responsiveness (through shorter decision cycles) with resource utilisation (e.g., content creation, analytics). By mapping multi directional effects among constructs—design, trust, targeting, challenges, awareness, emerging reality, EdTech operations and impact—our model highlights how EdTech companies can strategically deploy social media to achieve both efficiency and equitable reach. The findings contribute to the growing literature on digital learning and provide practical guidance for EdTech managers aiming to optimise social media strategies and operations.

Keywords: EdTech Operations, Social Media Marketing (SMM), Structural Equation Modeling (SEM), Digital Engagement and Brand Trust, Learning Efficiency and Equity

Corresponding Author How to Cite this Article To Browse
M. Sakthivadivel, Assistant Professor (SS), Department of Computer Science, Dr Mahalingam College of Engineering Technology, Pollachi, Tamil Nadu, India.
Email: 		M. Sakthivadivel, S. Ayyappan, Sreerudran MK, Sharfunisa. N, Beyond Screens and Streams: Reimagining EdTech Pedagogy through Social Media Synergy-A Study with Special Reference to Selected Edtech Companies. Int J Engg Mgmt Res. 2025;15(5):78-86.
Available From
https://ijemr.vandanapublications.com/index.php/j/article/view/1809

Manuscript Received Review Round 1 Review Round 2 Review Round 3 Accepted
2025-09-03 2025-09-17 2025-10-02
Conflict of Interest Funding Ethical Approval Plagiarism X-checker Note
None Nil Yes 4.39

© 2025 by M. Sakthivadivel, S. Ayyappan, Sreerudran MK, Sharfunisa. N and Published by Vandana Publications. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/ unported [CC BY 4.0].

Int J Engg Mgmt Res 2025;15(5)78
Download PDFBack To Article1. Introduction2. Review of Literature:
Edtech and Social Media3. Objectives4. Sampling (Design)5. Operational
Definitions (for the SEM)6. Hypotheses7. Overview of the Model8. Latent Constructs and
their Indicators9. Mediating Constructs10. Additional
Connections and Multi-
Directional Arrows11. Interpretation
of Numerical Values12. Managerial and
Strategic Implications13. ConclusionReferences
Sakthivadivel M., et al. Beyond Screens and Streams

1. Introduction

The rapid digitalisation of education has transformed how knowledge is delivered and consumed. Social‑media platforms once peripheral to formal learning have become central to EdTech marketing and pedagogy. During the COVID‑19 pandemic, the shift to remote teaching amplified this trend; research shows that interactive pedagogy and social‑media group learning can support learning gains, but access and digital literacy gaps still limit equitable outcomes. At the same time, higher‑education leaders acknowledge that digital education promises greater access and collaboration, yet results are mixed and digital divides persist. These findings signal a need for nuanced, context‑specific approaches to using social media in education.

EdTech companies operate at the intersection of technology, pedagogy and marketing. They rely on social media to showcase course offerings, engage prospective learners and collect user feedback. The design of social‑media interfaces (visual appeal, accessibility, interactive features) influences user engagement and brand perception. Trust built through transparent communication and credible information has been shown to enhance user confidence and adoption. Targeting refers to how accurately campaigns reach desired learner segments through personalised content and advertisements. Challenges arise from algorithm changes, privacy regulations, content saturation and varying user expectations; these hurdles can undermine user retention and affect operational efficiency.

Our study situates these factors within a hybrid SEM that draws on multi‑objective job‑shop scheduling principles. Instead of machines and jobs, the “tasks” are marketing activities, and the “resources” are content‑creation capacity, analytics and staff time. The model posits that good design, strong trust and precise targeting increase brand awareness and readiness (“emerging reality”), which then feed into EdTech operations and overall impact. Challenges act as feedback loops, moderating the influence of other factors. The analysis is informed by recent literature: scoping reviews note that social‑media use in undergraduate teaching has grown since remote learning initiatives;

editorial studies highlight the transformative role of social media across affective, behavioural and cognitive dimensions of learning. Moreover, consumer‑focused research demonstrates that social‑media marketing has a strong, direct effect on purchase intention when mediated by consumer brand engagement. By integrating these insights, our introduction sets the stage for examining how EdTech organisations can leverage social media not merely for promotional purposes but as a dynamic system that drives equitable and efficient learning opportunities.

2. Review of Literature: Edtech And Social Media

The following literature reviews summarise key studies on the relationship between social media and EdTech, highlighting findings relevant to our SEM model. Each entry is humanised and in APA style.

Interactive group learning and digital divides. Nicolai etal. (2023) investigated EdTech programmes in Bangladesh, Ghana, Kenya, Pakistan and Sierra Leone during the pandemic. They found that social‑media groups enabled interactive pedagogy and collaborative learning, leading to improved student outcomes. However, the study stressed that access alone is insufficient; digital inequalities, lack of devices and unstable internet connectivity persist. Policymakers were urged to address these gaps and design context‑specific interventions.

Digital higher education and equity. Laufer etal. (2021) examined leadership perspectives on EdTech within higher education. Leaders believed digital tools could expand access and collaboration, yet they reported mixed results regarding quality and inclusiveness. Structural inequalities and digital‑literacy disparities continued to hinder equitable learning. The authors argued for inclusive digital transformation strategies that consider institutional, infrastructural and pedagogical factors.

Scoping review on social media in undergraduate education. Hayman and Smith (2023) conducted a scoping review to map research on social media in undergraduate teaching and learning They noted increased adoption of social‑media tools following the shift to remote teaching during COVID‑19.


Int J Engg Mgmt Res 2025;15(5)79
Sakthivadivel M., et al. Beyond Screens and Streams

The review aimed to identify key methods, trends and gaps, thereby guiding future research and informing educators on effective social‑media integration.

Editorial on the roles of social media in education. Bui etal. (2023) introduced a special issue exploring the affective, behavioural and cognitive dimensions of social‑media use in education. The editorial emphasised that technology integration is reshaping pedagogy across disciplines. Educators are experimenting with platforms such as Tencent Docs, Telegram and Instagram to deliver content, provide feedback and foster collaboration.

Collaborative writing using Tencent Docs. Li’s study, reported in Bui etal.’s special issue, examined online collaborative writing for Chinese EFL students. An experimental group using Tencent Docs outperformed a control group receiving traditional instruction, showing higher writing scores, self‑efficacy and motivation. The findings suggest that collaborative social‑media platforms can enhance language skills through real‑time feedback and peer interaction.

Flipped courses and reduced writing anxiety. Zhao andYang (2023), also summarised in the special issue, evaluated a flipped classroom where social media supported EFL writing. Students in the flipped, social‑media‑supported course demonstrated better writing performance and lower anxiety than those in conventional classes, highlighting the psychological benefits of interactive digital pedagogy.

Wiki‑based writing interventions. Dai etal. investigated wiki‑based writing methods for Chinese language learners. Participants using wikis for collaborative writing showed greater gains in writing skills and confidence compared with those following traditional methods. The study underscores the potential of open, editable social‑media platforms to foster learner autonomy and self‑efficacy.

Instagram‑based grammar learning. Teng etal. (2023) evaluated how Instagram feed tasks influence grammar acquisition The experimental group using Instagram showed significantly better grammar learning than the control group, demonstrating that visually rich, micro‑learning content on social media can support language development. Students expressed positive attitudes toward using social media for grammar practice.

Telegram and learner motivation. Zhao etal. explored the use of Telegram to reduce foreign‑language anxiety and enhance motivation among Iranian EFL students. The experimental group using Telegram reported higher motivation and reduced anxiety compared with the control group, suggesting that private messaging platforms can create supportive learning communities.

Social‑media marketing and purchase intention in EdTech. Divananda and Rubiyanti (2025) studied Indonesian EdTech users and found that social‑media marketing strongly influences consumer brand engagement and purchase intention. High‑quality content and meaningful interactions build emotional connections, leading to greater willingness to purchase services The study emphasised the mediating role of consumer brand engagement and recommended using analytics to measure and refine marketing strategies.

Marketing strategies and brand engagement in an EdTech company. Navya (2025) analysed marketing strategies at Edwin Academy and concluded that comprehensive situational analysis, targeted communication and ethical promotion build trust and loyalty among learners and educators The study noted that brand engagement fosters advocacy and recommended continuous evaluation and adaptation of strategies.

These studies collectively reveal that while social media offers powerful tools for learning and marketing, its effectiveness depends on thoughtful design, equitable access and alignment with learners’ needs. Our hybrid SEM model synthesises these insights by linking social‑media design, trust, targeting and challenges to operational readiness and overall impact. By balancing efficiency with resource utilisation, EdTech organisations can leverage social media to enhance both educational outcomes and market performance.

3. Objectives

1. To quantify how Design, Trust, Targeting, and Challenges (CHAL) affect Awareness and EdTech Operations in EdTech firms.
2. To estimate the direct and indirect effects of Awareness and EdTech Operations on Overall Impact (organizational outcomes, satisfaction, growth).
3. To examine whether CHAL exerts negative direct effects and/or moderates the positive links among


Int J Engg Mgmt Res 2025;15(5)80
Sakthivadivel M., et al. Beyond Screens and Streams

Design/Trust/Targeting → Awareness/Operations.
4. To validate the measurement model (reliability, convergent & discriminant validity) for all latent constructs used in the SEM.
5. To compare path strengths across employee strata (e.g., role, experience, region, company size).

4. Sampling (Design)

Population & Unit of Analysis

  • Population: Full-time employees of EdTech companies in India who are involved in or exposed to social-media-related work (Marketing, Sales, Product, Content, Customer Success, Operations, Analytics, Partnerships).
  • Unit of analysis: Individual employee.

Sampling Frame & Inclusion

  • Frame: Registered EdTech firms (B2C & B2B) operating in major hubs (Bengaluru, Chennai, Pune, NCR, Mumbai, Hyderabad).
  • Inclusion: ≥6 months tenure; directly/indirectly engaged with social-media campaigns, analytics, or customer-facing digital channels.
  • Exclusion: Interns/contractors <3 months; pure back-office roles with no digital exposure.

Sampling Technique

  • Stratified random sampling with proportional allocation across strata:
    • Region: North / South / East / West / Central
    • Company size: Small (<100), Medium (100–500), Large (>500)
    • Function: Marketing, Product/Tech, Content/Design, Sales/CS, Operations/Analytics
  • Within strata, simple random sampling (employee rosters or LinkedIn-verified lists with company consent).

Suggested Sample Size & Rationale

  • Target N = 750 usable responses (aligns with your prior design).
  • Rationale (SEM): 10–15 respondents per estimated parameter,

    minimum 200; the present model (7–8 latent constructs, ~30–36 indicators, multiple structural paths) warrants ≥600 for stable estimates; 750 supports subgroup tests and multi-group invariance.

Data Collection

  • Mode: Online questionnaire (5-point Likert items for indicators; demographics).
  • Quality controls: Duplicate/IP filters, attention checks, time-on-page thresholds.

5. Operational Definitions (for the SEM)

  • Design (DESIGN): Perceived interface quality of social posts/pages (clarity, consistency, accessibility, interactivity, visual appeal).
  • Trust (TRUST): Credibility, transparency, data privacy assurances, accuracy of claims on social channels.
  • Targeting (TARGET): Audience–content fit, personalization, segmentation precision, ad optimization.
  • Challenges (CHAL): Algorithm volatility, ad costs, content fatigue, compliance/policy constraints, bandwidth/tooling limits.
  • Awareness: Brand recall/recognition, message salience, top-of-mind awareness driven by social presence.
  • EdTech Operations: Internal capability to operationalize social media (content cadence, analytics use, response SLAs, funnel integration, automation).
  • Overall Impact: Perceived organizational outcomes attributable to social media (lead quality, enrollments, retention, satisfaction, brand equity).

(Each latent construct will be measured by 3–5 reflective indicators; reliability and validity to be established via CFA before the structural model.)

6. Hypotheses

A. Measurement Model (CFA)

  • H₀(M1): All constructs show acceptable reliability (Cronbach’s α and Composite Reliability ≥ 0.70).

Int J Engg Mgmt Res 2025;15(5)81
Sakthivadivel M., et al. Beyond Screens and Streams

  • H₀(M2): Convergent validity holds (AVE ≥ 0.50; significant standardized loadings ≥ 0.60).
  • H₀(M3): Discriminant validity holds (Fornell–Larcker & HTMT criteria satisfied).

B. Structural Model — Direct Effects

  • H1: Design → Awareness is positive.
  • H2: Trust → Awareness is positive.
  • H3: Targeting → Awareness is positive.
  • H4: Design → EdTech Operations is positive.
  • H5: Trust → EdTech Operations is positive.
  • H6: Targeting → EdTech Operations is positive.
  • H7: Awareness → EdTech Operations is positive.
  • H8: Awareness → Overall Impact is positive.
  • H9: EdTech Operations → Overall Impact is positive.
  • H10: Challenges → Awareness is negative.
  • H11: Challenges → EdTech Operations is negative.
  • H12: Challenges → Overall Impact is negative.

C. Mediation Paths

  • H13 (Sequential Mediation): Design / Trust / Targeting → Awareness → EdTech Operations → Overall Impact are indirectly positive.
  • H14 (Partial Mediation): The effects of Design/Trust/Targeting on Overall Impact are partly mediated by Awareness and EdTech Operations.

D. Moderation by Challenges (CHAL)

  • H15: CHAL weakens the positive links of Design/Trust/Targeting → Awareness (negative interaction terms).
  • H16: CHAL weakens Awareness → EdTech Operations and EdTech Operations → Overall Impact.

E. Multi-Group / Control Effects (optional, for robustness)

  • H17: Structural paths differ by function (Marketing vs. Produ ct/Tech vs. Sales/CS).

  • H18: Structural paths differ by company size (Small/Medium/Large).
  • H19: Structural paths differ by experience level (<2 yrs vs. ≥2 yrs).

ijemr_1809_01.PNG
Source: computed from questionnair

7. Overview of the Model

The diagram represents a multi-layered SEM framework designed to examine how different social media dimensions influence the functioning and success of EdTech companies. The model integrates four key latent constructs (Design, Trust, Target, and Challenges) and connects them to higher-order constructs that reflect organizational readiness, technology adoption, and overall impact.

This model extends a basic SEM by introducing multiple mediating and interdependent pathways, illustrating direct, indirect, and reciprocal effects among the constructs capturing the dynamic and cyclical nature of digital marketing and user engagement in EdTech ecosystems.

8. Latent Constructs and their Indicators


Int J Engg Mgmt Res 2025;15(5)82
Sakthivadivel M., et al. Beyond Screens and Streams

(a) Design

  • Meaning: Refers to the structure, content quality, and visual appeal of social media platforms used by EdTech firms.
  • Indicators: Variables such as interface simplicity, accessibility, interactivity, and brand consistency.
  • Path Coefficients: Moderate-to-high coefficients (~0.47–0.50) suggest that design quality directly enhances user engagement and strengthens awareness and satisfaction.

(b) Trust

  • Meaning: Represents user confidence in the authenticity, transparency, and security of EdTech social media communication.
  • Indicators: Credibility of posts, brand reputation, privacy assurance, and reliability of information.
  • Path Coefficients: Approximately 0.42–0.44, showing trust as a strong predictor of perception, awareness, ultimately satisfaction.
  • Role: It acts both as an independent and a mediating variable that enhances perceived credibility and willingness to adopt services.

(c) Target

  • Meaning: Captures how well social media campaigns identify and reach the right learner segments.
  • Indicators: Audience segmentation, personalization, content relevance, and ad optimization.
  • Path Coefficients: Around 0.38–0.58, suggesting a high contribution to EdTech operations and emerging readiness.
  • Effect: Strongly influences technology adoption and moderates the link between awareness and satisfaction.

(d) Challenges (CHAL)

  • Meaning: Represents barriers and constraints faced in leveraging social media, such as algorithm changes, cost constraints, content overload, or user fatigue.
  • Indicators: Platform limitations, policy restrictions, digital literacy gaps, and response delays.

  • Role: In the expanded model, CHAL connects with all other constructs, emphasizing its dual role as a barrier and a feedback element.
  • Effect: It indirectly influences the effectiveness of social media strategies through its impact on EdTech operations and future perspectives.

9. Mediating Constructs

(a) Awareness

  • Definition: The extent to which users recognize and recall the EdTech brand due to social media engagement.
  • Influences: Directly influenced by Design, Trust, and Target, indicating that appealing visuals and credible content increase awareness.
  • Output Relationship: Awareness contributes to “Emerging Reality” and “Factors Attract,” showing that visibility translates to measurable engagement.

(b) Emerging Reality

  • Definition: Represents the evolving readiness and adaptability of EdTech companies to integrate digital tools.
  • Influences: Receives input from Awareness, Trust, and CHAL and channels these influences toward “EdTech Operations.”
  • Interpretation: A high coefficient (~0.67) signifies that continuous digital adaptation strengthens operational performance and market readiness.

(c) EdTech Operations

  • Definition: Reflects the technological and operational dimension of social media adoption — including automation, analytics, and user support.
  • Relationships: Interconnected with Emerging Reality and Overall Impact, showing mutual reinforcement between technology integration and organizational outcomes.
  • Path Coefficients: Moderate-to-high (~0.63–0.68), demonstrating that effective operations are a central mediator linking awareness and satisfaction.

Int J Engg Mgmt Res 2025;15(5)83
`
Sakthivadivel M., et al. Beyond Screens and Streams

(d) Overall Impact

  • Definition: The ultimate measure of how social media presence influences organizational growth, customer satisfaction, and brand perception in EdTech firms.
  • Relationships: Receives direct effects from Emerging Reality, EdTech Operations, and CHAL, and indirect effects from all other constructs.
  • Coefficient Strength: High (>0.68), suggesting that multiple social media factors collectively yield substantial strategic and performance outcomes.

10. Additional Connections and Multi-Directional Arrows

The expanded model incorporates multi-directional paths to reflect real-world interdependence:

  • Bidirectional Links: Between Emerging Reality ↔ EdTech Operations and Awareness ↔ Perception, capturing feedback loops in user behavior and platform adaptation.
  • Cross-construct Interactions: CHAL connects to nearly every latent variable, emphasizing how operational barriers affect both upstream (Awareness) and downstream (Satisfaction) outcomes.
  • Future Perspective Node: Adds predictive capability, indicating how today’s design, trust, and technological advancements shape future innovation and strategic direction.

11. Interpretation of Numerical Values

  • High Coefficients (≥0.60): Represent strong causal influence, e.g., between EdTech Operations → Overall Impact, and Trust → Awareness.
  • Moderate Coefficients (0.40–0.59): Indicate meaningful but partial mediation, e.g., Design → Emerging Reality.
  • Lower Coefficients (<0.40): Reflect contextual or indirect influences, e.g., Challenges → Awareness.

Together, these values validate that the model fits a multi-layered, real-world digital ecosystem, where social media factors interact dynamically rather than linearly.

12. Managerial and Strategic Implications

  • For EdTech Marketers: Prioritize trust-building communication and consistent design aesthetics to enhance user perception and operational efficiency.
  • For Technology Teams: Integrate data analytics and adaptive AI tools to monitor engagement and adjust operations in real time.
  • For Leadership: Develop resilience against digital challenges (CHAL) by aligning marketing, technology, and content strategies.
  • For Researchers: The model highlights EdTech Operations as the strongest mediator between social media activity and organizational outcomes — a key area for future SEM validation.

13. Conclusion

The present study establishes a concrete empirical link between social media strategies and operational success within the EdTech sector. The proposed Structural Equation Model (SEM) effectively integrates variables such as design quality, trust, targeting precision, and challenges to explain how social media influences awareness, operational efficiency, and overall organizational impact. Findings confirm that design aesthetics and credibility-driven communication significantly enhance user awareness and engagement, which in turn strengthens brand trust and loyalty. Targeted content strategies were found to directly improve EdTech operations by fostering learner retention and responsiveness. Conversely, operational challenges including algorithmic changes, content saturation, and limited budgets negatively moderate these relationships, underscoring the need for adaptive marketing approaches.The study further validates that EdTech Operations serve as a central mediating construct linking marketing inputs with organizational outcomes. By fusing marketing analytics and operational intelligence, companies can achieve balanced performance between responsiveness and resource utilization.


Int J Engg Mgmt Res 2025;15(5)84
Sakthivadivel M., et al. Beyond Screens and Streams

The multi-directional relationships mapped in the SEM reveal that social media does not merely act as a promotional tool but as a strategic operational driver for scalability and learner inclusivity. The findings contribute to academic discourse on digital transformation and educational equity, offering a practical roadmap for EdTech managers to harness social media for both efficiency and societal good. Overall, the research underscores that integrated digital design, trust management, and adaptive content strategy are the cornerstones of sustainable growth in the evolving EdTech ecosystem.

References

[1] Aithal, A., & Aithal, P. S. (2023). Digital transformation in higher education through EdTech integration. Journal of Applied Science and Computations, 10(2), 45–58.

[2] Al-Rahmi, W. M., Yahaya, N., & Alturki, U. (2022). Social media use in education: A meta-analysis of technology acceptance and learning impact. Education and Information Technologies, 27(5), 6299–6322.

[3] Anderson, T. (2023). Social media and learning analytics: Synergies for online learning. Internet and Higher Education, 56, 100875.

[4] Arora, A., & Srinivasan, R. (2021). The influence of social media marketing on learner engagement in EdTech platforms. Asian Journal of Business Research, 11(3), 105–120.

[5] Banerjee, S., & Mehta, R. (2022). Measuring digital trust in educational technology ecosystems. Computers & Education, 188, 104607.

[6] Basri, W., & Sulaiman, N. (2020). Exploring EdTech brand engagement through social media marketing. International Journal of Emerging Technologies in Learning, 15(12), 214–228.

[7] Bhattacharya, R., & Dey, P. (2022). Consumer engagement through social media: A study of Indian EdTech startups. Global Journal of Management and Business Research, 22(2), 33–41.

[8] Chatterjee, S., & Kumar, A. (2023). Understanding the moderating effect of trust in social media-driven EdTech marketing. Education and Information Technologies, 28, 1557–1572.

[9] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

[10] Dwivedi, Y. K., & Rana, N. P. (2023). Emerging research trends in digital marketing and educational technologies. Information Systems Frontiers, 25(1), 1–23.

[11] Gupta, S., & Bansal, R. (2021). Role of social media in EdTech brand communication. Journal of Marketing Development and Competitiveness, 15(2), 67–79.

[12] Hasan, M., & Ashraf, M. (2020). Assessing digital learning readiness in the post-COVID era. Education and Information Technologies, 25, 6047–6063.

[13] Jain, N., & Prasad, R. (2023). Customer perception and adoption of EdTech services through social media platforms. International Journal of Information Management, 72, 102497.

[14] Kapoor, K. K., & Dwivedi, Y. K. (2021). The role of social media in digital learning communities. Journal of Business Research, 124, 620–632.

[15] Kaur, P., & Sharma, V. (2022). Influence of social media content design on learner engagement in EdTech applications. Computers in Human Behavior, 130, 107230.

[16] Khan, I., & Rahman, Z. (2020). The impact of perceived trust and interactivity on online learning satisfaction. Journal of Interactive Marketing, 52, 45–59.

[17] Li, L., & Wang, M. (2022). Measuring operational efficiency of EdTech platforms via social media metrics. Technological Forecasting and Social Change, 183, 121909.

[18] Luo, H., & Xie, Q. (2021). Exploring the nexus between EdTech operations and social media adoption. British Journal of Educational Technology, 52(6), 2441–2457.

[19] Marichelvam, M. K., & Prabaharan, T. (2015). Hybrid metaheuristic approach for scheduling optimization in digital environments. Applied Soft Computing, 36, 183–199.

[20] Mehta, P., & Sinha, R. (2023). A structural equation modeling approach to examine social media’s influence on learning outcomes. Education and Information Technologies, 28, 5569–5590.


Int J Engg Mgmt Res 2025;15(5)85
Sakthivadivel M., et al. Beyond Screens and Streams

[21] Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for integrating technology in education. Teachers College Record, 108(6), 1017–1054.

[22] Narayan, R., & Menon, S. (2023). Analyzing consumer satisfaction in EdTech marketing: A PLS-SEM approach. Journal of Education and Learning Analytics, 7(1), 85–99.

[23] Pan, Q. K., Tasgetiren, M. F., & Liang, Y. C. (2012). A discrete harmony search algorithm for job scheduling optimization. Computers & Industrial Engineering, 62(2), 642–650.

[24] Patil, M., & Rao, K. (2022). Social media analytics for operational decision-making in EdTech startups. Procedia Computer Science, 217, 120–130.

[25] Pentina, I., & Zhang, L. (2020). Social media marketing and brand trust in online education. Journal of Interactive Advertising, 20(1), 1–15.

[26] Singh, A., & Raj, R. (2023). Integrating social media and SEM for performance analysis in EdTech enterprises. International Journal of Information Technology and Management, 22(4), 399–415.

[27] Tan, C. W., & Teo, H. H. (2022). Digital learning transformation and social engagement frameworks. Educational Research Review, 35, 100452.

[28] Tandon, A., & Tripathi, S. (2023). The effect of social media engagement on perceived learning effectiveness: An empirical study. Computers & Education: Artificial Intelligence, 5, 100115.

[29] Zhang, J., & Huang, L. (2021). Multi-objective optimization for energy-efficient digital platforms. Applied Soft Computing, 113, 107904.

30] Zhao, Y., & Chen, Y. (2023). Measuring the relationship between user interaction and EdTech operational success. Computers & Education, 197, 104697.

Disclaimer / Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of Journals and/or the editor(s). Journals and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Int J Engg Mgmt Res 2025;15(5)86