Understanding Financial Inclusion Through Social and Behavioural Lenses

Authors

  •   Taufeeque Ahmad Siddiqui Associate Professor (Corresponding Author), Department of Management Studies, Jamia Millia Islamia, New Delhi - 110 025
  •   Mohd Shahid Ali Assistant Professor, School of Liberal Arts & Humanities, Woxsen University, Hyderabad - 500 033, Telangana
  •   Sunayana Associate Professor, Department of Management Studies, Jamia Millia Islamia, New Delhi - 110 025
  •   Naushad Alam Associate Professor, Department of Finance and Economics, College of Commerce and Business Administration, Dhofar University, Salalah
  •   Prashant Ranjan Research Scholar, Department of Management Studies, Jamia Millia Islamia, New Delhi - 110 025

DOI:

https://doi.org/10.17010/pijom/2025/v18i4/174316

Keywords:

financial inclusion

, structural equation modeling, behavioral biases, social norms, social network

JEL Classification Codes

, D91, G20, G21, O16

Paper Submission Date

, September 15, 2024, Paper sent back for Revision, March 5, 2025, Paper Acceptance Date, March 20, Paper Published Online, April 15, 2025

Abstract

Purpose : The present study focused on assessing the behavioral and societal factors of financial inclusion and aimed to develop a working model from a demand-side perspective.

Methodology : This study utilized the structural equation modeling (SEM) technique to analyze the relationships between different constructs, such as financial literacy, government scheme awareness, behavioral biases, social norms, social trust, subjective norms, social networks, and financial inclusion.

Findings : The study found that three out of six paths for behavioral biases, 21 out of 30 paths for social factors, and 11 out of 12 paths for financial literacy demonstrated significant impacts. This underscored the utmost significance of financial literacy, followed by social factors and behavioral biases. This study is limited to the Nuh (Mewat) district of Haryana, which might have influenced the applicability of the findings to other regions. Future research could be expanded to other geographic areas and incorporate longitudinal data to validate and refine the proposed model.

Practical Implications : Actionable insights are offered by this study for policymakers and financial service providers to design and implement more effective financial inclusion strategies and tailored products. Enhancing financial inclusion could have led to improved economic stability and empowerment of individuals in marginalized communities, fostering overall societal development.

Originality : This research proposed a unique demand-side approach to financial inclusion by combining several societal and behavioral constructs into a comprehensive model, offering a deeper insight into the factors inducing financial inclusion in the special context of backward regions of the country.

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Published

2025-04-01

How to Cite

Siddiqui, T. A., Ali, M. S., Sunayana, Alam, N., & Ranjan, P. (2025). Understanding Financial Inclusion Through Social and Behavioural Lenses. Prabandhan: Indian Journal of Management, 18(4), 52–71. https://doi.org/10.17010/pijom/2025/v18i4/174316

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References

Agnihotri, S., Malhan, S., & Singh, A. B. (2023). The emergence of social media as an antecedent of employability: A PLS-SEM approach. Prabandhan: Indian Journal of Management, 16(5), 57–73. https://doi.org/10.17010/pijom/2023/v16i5/171226

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Annim, S. K., Mariwah, S., & Sebu, J. (2012). Spatial inequality and household poverty in Ghana. Economic Systems, 36(4), 487–505. https://doi.org/10.1016/j.ecosys.2012.05.002

Bashir, T., Javed, A., Ali, U., Meer, U. I., & Naseem, M. M. (2013). Empirical testing of heuristics interrupting the investor's rational decision making. European Scientific Journal, 9(28), 432–444. https://core.ac.uk/download/pdf/236418178.pdf

Bhuvana, M., & Vasantha, S. (2016). Dimensions for measuring financial inclusion in the rural areas of Tamil Nadu. Indian Journal of Science and Technology, 9(32), 1–8. https://doi.org/10.17485/ijst/2016/v9i32/98663

Birkenmaier, J., & Fu, Q. (2020). Financial behavior and financial access: A latent class analysis. Journal of Financial Counseling and Planning, 31(2), 179–192. https://doi.org/10.1891/JFCP-18-00067

Bongomin, G. O., Ntayi, J. M., & Malinga, C. A. (2020). Analyzing the relationship between financial literacy and financial inclusion by microfinance banks in developing countries: Social network theoretical approach. International Journal of Sociology and Social Policy, 40(11/12), 1257–1277. https://doi.org/10.1108/IJSSP-12-2019-0262

Cámara, N., & Tuesta, D. (2014). Measuring financial inclusion: A muldimensional index. SSRN. https://doi.org/10.2139/ssrn.2634616

Chai, S., Chen, Y., Huang, B., & Ye, D. (2019). Social networks and informal financial inclusion in China. Asia Pacific Journal of Management, 36(2), 529–563. https://doi.org/10.1007/s10490-017-9557-5

Chakrabarty, K. C. (2013). Financial inclusion in India: Journey so far and way forward. RBI Monthly Bulletin. https://rbidocs.rbi.org.in/rdocs/Bulletin/PDFs/01SP071013F.pdf

Cheng, P. Y. (2010). Improving financial decision making with unconscious thought: A transcendent model. Journal of Behavioral Finance, 11(2), 92–102. https://doi.org/10.1080/15427560.2010.482877

Chipunza, K. J., & Fanta, A. (2022). Quality financial inclusion and its determinants in South Africa: Evidence from survey data. African Journal of Economic and Management Studies, 13(2), 177–189. https://doi.org/10.1108/AJEMS-06-2021-0290

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587

CRISIL. (2013). CRISIL Inclusix: An index to measure India's progress on financial inclusion. https://www.crisil.com/content/dam/crisil/crisil-foundation/generic-pdf/CRISIL-Inclusix-Volume-I.pdf

CRISIL. (2014). CRISIL Inclusix: An index to measure India's progress on financial inclusion. https://www.crisil.com/content/dam/crisil/crisil-foundation/generic-pdf/CRISIL-Inclusix-Volume-II.pdf

CRISIL. (2015). CRISIL Inclusix: An index to measure India's progress on financial inclusion. https://www.crisil.com/content/dam/crisil/crisil-foundation/generic-pdf/CRISIL-Inclusix-Volume-III.pdf

CRISIL. (2018). CRISIL Inclusix: Financial inclusion surges, driven by Jan-Dhan Yojana. https://www.crisil.com/content/dam/crisil/our-analysis/reports/Research/documents/2018/march/crisil-inclusix-financial-inclusion-surges-driven-by-Jan-Dhan-yojana.pdf

Dinev, T., & Hart, P. (2004). Internet privacy concerns and their antecedents - Measurement validity and a regression model. Behaviour & Information Technology, 23(6), 413–422. https://doi.org/10.1080/01449290410001715723

Dupas, P., Green, S., Keats, A., & Robinson, J. (2014). Challenges in banking the rural poor: Evidence from Kenya's western province. In African successes, Volume III: Modernization and development (pp. 63–101). University of Chicago Press. https://doi.org/10.7208/chicago/9780226315867.001.0001

Dutta, P., Goswami, G., & Barman, H. (2023). Financial inclusion among backward communities: A study of the tea garden workers in Assam, India. Forum for Social Economics, 52(2), 203–218. https://doi.org/10.1080/07360932.2021.1999297

Goel, S., & Sharma, R. (2017). Developing a financial inclusion index for India. Procedia Computer Science, 122, 949–956. https://doi.org/10.1016/j.procs.2017.11.459

Hair Jr., J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069

Hair Jr., J. F., Lukas, B., Roberts, K., & Lee-Lukas, S. (2014). Marketing research (4th ed.). McGraw-Hill Education. https://books.google.co.in/books?id=UL7OngEACAAJ

Hair Jr., J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001

Hair Jr., J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part I - method. European Business Review, 28(1), 63–76. https://doi.org/10.1108/EBR-09-2015-0094

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202

Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130

Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Joseph, D., Girish, S., & Suresh, G. (2023). Fintech and financial capability, what do we know and what we do not know: A scoping review. Indian Journal of Finance, 17(12), 40–55. https://doi.org/10.17010/ijf/2023/v17i12/170910

Kling, G., Pesqué-Cela, V., Tian, L., & Luo, D. (2022). A theory of financial inclusion and income inequality. The European Journal of Finance, 28(1), 137–157. https://doi.org/10.1080/1351847X.2020.1792960

Lea, S. E., Webley, P., & Levine, R. M. (1993). The economic psychology of consumer debt. Journal of Economic Psychology, 14(1), 85–119. https://doi.org/10.1016/0167-4870(93)90041-I

Mindra, R., & Moya, M. (2017). Financial self-efficacy: A mediator in advancing financial inclusion. Equality, Diversity and Inclusion, 36(2), 128–149. https://doi.org/10.1108/EDI-05-2016-0040

Panchasara, M., & Sharma, V. (2019). Exploring the role of behavioral theories in financial inclusion. SSRN. https://doi.org/10.2139/ssrn.3634040

Patacchini, E., & Rainone, E. (2014). The word on banking-Social ties, trust, and the adoption of financial products (EIEF Working Papers Series No. 1404). Einaudi Institute for Economics and Finance. https://ideas.repec.org/p/eie/wpaper/1404.html

Pattarin, F., & Cosma, S. (2012). Psychological determinants of consumer credit: The role of attitudes. Review of Behavioural Finance, 4(2), 113–129. https://doi.org/10.1108/19405971211284899

Saxena, M., Bagga, T., Gupta, S., & Kaushik, N. (2022). Exploring common method variance in analytics research in the Indian context: A comparative study with known techniques. FIIB Business Review, 13(5), 553–569. https://doi.org/10.1177/23197145221099098

Scarampi, A., AlBashar, D., & Burjorjee, D. M. (2020). Gendered social norms in financial inclusion: A diagnostic study from southeastern Turkey. FinDev Gateway. https://www.findevgateway.org/paper/2020/07/gendered-social-norms-financial-inclusion

Siddik, M. N., Sun, G., & Kabiraj, S. (2015). Financial inclusion and its determinants: A study of Bangladesh. Indian Journal of Finance, 9(6), 7–29. https://doi.org/10.17010//2015/v9i6/70988

Siddiqui, T. A., & Siddiqui, K. I. (2020). Telecommunication, socioeconomic, and financial inclusion: An empirical evidence from Bihar. Prabandhan: Indian Journal of Management, 13(10–11), 46–61. https://doi.org/10.17010/pijom/2020/v13i10-11/156008

Subramanian, R., & Arjun, T. P. (2024). Do explicit and implicit parental financial socialization influence students' financial literacy? Evidence from India. Indian Journal of Finance, 18(10), 24–39. https://doi.org/10.17010/ijf/2024/v18i10/174612

Thomas, B., & Subhashree, P. (2020). Behavioural and psychological factors that influence the usage of formal financial services among the low-income households. Research in World Economy, 11(5). https://doi.org/10.5430/rwe.v11n5p326

Ursachi, G., Horodnic, I. A., & Zait, A. (2015). How reliable are measurement scales? External factors with indirect influence on reliability estimators. Procedia Economics and Finance, 20, 679–686. https://doi.org/10.1016/S2212-5671(15)00123-9

Walia, N., & Kiran, R. (2012). Understanding the risk anatomy of experienced mutual fund investors. Journal of Behavioral Finance, 13(2), 119–125. https://doi.org/10.1080/15427560.2012.673517

Yadav, P., & Sharma, A. K. (2018). An investigation into factors affecting access to financial services in farmers' suicide-prone Bundelkhand region of India. Indian Journal of Finance, 12(6), 46–62. https://doi.org/10.17010/ijf/2018/v12i6/128135