Factors Influencing the Intention to use Cloud Computing: Empirical Evidence from Apparel Companies in the Western Province, Sri Lanka

Abstract

This study explores the factors influencing the intention to adopt cloud computing technologies among apparel garment companies in the Western Province of Sri Lanka. Despite the advantages of cloud computing, its adoption remains low in Sri Lanka compared to developed nations. The research identifies critical elements affecting the use of cloud-based tools in this sector, which is vital for the national economy. Utilizing a survey method, data were collected from 365 employees across major apparel firms, including Brandix Apparel Holdings Ltd and MAS Intimates Pvt Ltd. Key variables examined include Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, analyzed through correlation and multiple regression methods. Findings reveal that while Performance Expectancy positively correlates with the intention to use cloud computing, it lacks significant predictive power. In contrast, Effort Expectancy, Social Influence, and Facilitating Conditions demonstrate strong positive relationships with adoption intention. This research contributes valuable insights for stakeholders in the apparel industry, aiding in the development of effective strategies for cloud technology integration. By addressing barriers to adoption, the study underscores the transformative potential of cloud computing for enhancing operational efficiency in the apparel sector. with the intention to use cloud computing, it lacks significant predictive power. In contrast, Effort Expectancy, Social Influence, and Facilitating Conditions demonstrate strong positive relationships with adoption intention. This research contributes valuable insights for stakeholders in the apparel industry, aiding in the development of effective strategies for cloud technology integration. By addressing barriers to adoption, the study underscores the transformative potential of cloud computing for enhancing operational efficiency in the apparel sector.