Digital Transformation in Business and Commerce

Main Article Content

Salesha B. Belgaum

Abstract

Digital transformation in business and commerce refers to the integration of technologies such as Artificial Intelligence (AI), cloud computing, Big Data, and the Internet of Things (IoT) into business operations. It improves efficiency, enhances customer experience, and supports global business growth. This study examines how data analysis and AI assist in better decision-making, while also exploring challenges such as cybersecurity risks, regulatory compliance, and resistance to change. The research uses methods including literature reviews, case studies, and quantitative analysis, supported by frameworks like the Technology Acceptance Model (TAM), Diffusion of Innovation Theory, and the TOE framework. Findings show that automation, AI tools, and cloud-based systems reduce costs, improve accuracy, and enable real-time decision-making. Businesses increasingly use CRM systems, chatbots, and predictive analytics to deliver personalized customer experiences. The study concludes that digital transformation is essential for business survival and long-term growth. Companies adopting scalable digital tools and data-driven strategies achieve improved productivity, operational efficiency, and customer satisfaction.

Article Details

Section

Research Articles

Author Biography

Salesha B. Belgaum

Associate Professor, Department of Commerce, Govt. First Grade College, Kalaghatgi.

How to Cite

Salesha B. Belgaum. (2026). Digital Transformation in Business and Commerce. ಅಕ್ಷರಸೂರ್ಯ (AKSHARASURYA), 17(01), 08 to 12. https://aksharasurya.com/index.php/latest/article/view/2250

References

Agarwal et al., 2010. The digital transformation of healthcare: current status and the road ahead. Information and Organization, 21 (4) (2010), pp. 796-809, 10.1287/isre.1100.0327

Bartsch et al., 2021. Leadership matters in crisis-induced digital transformation: how to lead service employees effectively during the COVID-19 pandemic. Journal of Service Management, 32 (1) (2021), pp. 71-85, 10.1108/JOSM-05-2020-0160

Cukusic, 2021. Contributing to the current research agenda in digital transformation in the context of smart cities. International Journal of Information Management, 58 (2021), Article 102330, 10.1016/j.ijinfomgt.2021.102330

Gfrerer et al., 2021. Ready or not: managers’ and employees’ different perceptions of digital readiness. California Management Review, 63 (2) (2021), pp. 23-48, 10.1177/0008125620977487

Hansen et al., 2011. Rapid adaptation in digital transformation: A participatory process for engaging is and business leaders. MIS Quarterly Executive, 10 (4) (2011), pp. 175-185

Jammulamadaka, 2021. Enabling processes as routines that facilitate cognitive change. Management Decision, 59 (3) (2021), pp. 653-668, 10.1108/MD-09-2019-1311

Kumar et al., 2021. Applications of text mining in services management: A systematic literature review. International Journal of Information Management Data Insights, 1 (1) (2021), Article 100008, 10.1016/j.jjimei.2021.100008

Manfreda et al., 2021. Autonomous vehicles in the smart city era: An empirical study of adoption factors important for millennials. International Journal of Information Management, 58 (2021), Article 102050, 10.1016/j.ijinfomgt.2019.102050

Richard et al., 2021. A business process and portfolio management approach for Industry 4.0 transformation. Business Process Management Journal, 27 (2) (2021), pp. 505-528, 10.1108/BPMJ-05-2020-0216

Seepma et al., 2021. Designing digital public service supply chains: four country-based cases in criminal justice. Supply Chain Management-An International Journal, 26 (3) (2021), pp. 418-446, 10.1108/SCM-03-2019-0111

Tangi et al., 2021. Digital government transformation: A structural equation modelling analysis of driving and impeding factors. International Journal of Information Management, 60 (2021), Article 102356, 10.1016/j.ijinfomgt.2021.102356

vom Brocke et al., 2021. IT-enabled organizational transformation: a structured literature review. Business Process Management Journal, 27 (1) (2021), pp. 204-229, 10.1108/BPMJ-10-2019-0423

Zekic-Susac et al., 2021. Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities. International Journal of Information Management, 58 (2021), Article 102074, 10.1016/j.ijinfomgt.2020.1