An Empirical Study of Trust Formation through Emerging Technologies in Bangalore, India

Digital trust has emerged as a critical determinant of sustainable digital transformation, particularly in technology-intensive urban environments. As digital platforms increasingly mediate economic and social interactions, user confidence in the security, transparency, and ethical functioning of digital systems has become essential. This study investigates the formation of digital trust within Bangalore’s rapidly expanding digital ecosystem by examining the influence of emerging technologies, including artificial intelligence, blockchain, cloud computing, and data-driven systems. Drawing on trust theory and technology acceptance literature, the study employs a quantitative research design using primary data collected from 420 digitally active respondents in Bangalore, India. Structural Equation Modeling (SEM) is applied to analyze relationships between data privacy assurance, cybersecurity strength, AI transparency, blockchain-enabled trust mechanisms, and overall digital trust. The results indicate that data privacy and cybersecurity exert the strongest influence on trust formation, while AI transparency and blockchain adoption play significant supporting roles. The findings highlight that technological sophistication alone is insufficient to build trust; instead, trust emerges from the interaction between technology design, governance mechanisms, and user perceptions. This study contributes to digital trust literature by providing empirical evidence from an emerging economy context and offers practical guidance for organizations and policymakers seeking to foster trustcentric digital ecosystems.

Digital Trust, Emerging Technologies, Artificial

1. AlHogail, A. (2018). Improving IoT technology adoption through trust. Future Generation Computer Systems, 89, 235–246. [Google Scholar] [Crossref]

2. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … Vayena, E. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707. [Google Scholar] [Crossref]

3. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. [Google Scholar] [Crossref]

4. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. [Google Scholar] [Crossref]

5. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage. [Google Scholar] [Crossref]

6. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. [Google Scholar] [Crossref]

7. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press. [Google Scholar] [Crossref]

8. Kshetri, N. (2018). Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80–89. [Google Scholar] [Crossref]

9. Martin, K. (2018). The penalty for privacy violations: How privacy violations impact trust online. Business Ethics Quarterly, 28(1), 43–75. [Google Scholar] [Crossref]

10. Narayanan, A., Bonneau, J., Felten, E., Miller, A., & Goldfeder, S. (2016). Bitcoin and cryptocurrency technologies: A comprehensive introduction. Princeton University Press. [Google Scholar] [Crossref]

11. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill. [Google Scholar] [Crossref]

12. Peters, G. W., & Panayi, E. (2016). Understanding modern banking ledgers through blockchain technologies: Future of transaction processing and smart contracts on the Internet of Money. Journal of Banking Regulation, 17(3), 239–261. [Google Scholar] [Crossref]

13. Shin, D. (2021). User perceptions of explainable artificial intelligence. Telematics and Informatics, 61, 101593. [Google Scholar] [Crossref]

14. Zucker, L. G. (1986). Production of trust: Institutional sources of economic structure. Research in Organizational Behavior, 8, 53–111. [Google Scholar] [Crossref]

• LeafQuest: A Mobile-Based Augmented Reality for Plant Placement, Discovery, and Growth
• Participatory Ergonomic Intervention Approach on Musculoskeletal Disorder (MSD) in Construction Sectors: A Systematic Review
• Integrating GIS into Traffic Incident Management: A Web-Based System
• RideSmart: A Personalized Motorcycle Product Recommendation System Using TF-IDF and Descriptive Analytics for Javidson Motorshop
• Educational Technology Course Design in Pre-Service Teachers Education: A Bibliometric Review of the Research Landscape