Actual usage assessment among cloud storage consumers in the Philippines using a machine learning ensemble approach

Area of Science:

• Information Science
• Computer Science
• Technology Adoption

Background:

• Cloud storage is crucial for data management in both developed and developing nations.
• Developing countries, including the Philippines, are exploring cloud storage, particularly free services.
• Understanding user intention and actual cloud storage use is vital for sustained adoption and development strategies.

Purpose of the Study:

• To analyze the factors influencing the actual use of cloud storage in the Philippines.
• To investigate the antecedents of cloud storage adoption using an integrated technology acceptance model and valence framework.
• To provide insights for developing effective strategies for cloud storage marketing and utility.

Main Methods:

• Employed a machine learning ensemble (MLE) for data analysis.
• Utilized 616 valid responses and processed 33,264 datasets.
• Measured actual cloud storage use based on the integrated extended technology acceptance model and valence framework.

Main Results:

• Machine learning ensemble achieved an average accuracy of 93% and 90%.
• Key factors influencing actual cloud storage use include voluntariness, subjective norm, perceived benefit, perceived usefulness, and perceived ubiquity.
• Both personal and professional usage contribute to adoption, facilitated by general technology readiness.

Conclusions:

• Cloud storage adoption in the Philippines is influenced by user perceptions and social factors.
• Enhancing technological infrastructure is necessary for sustained cloud storage use.
• The integrated framework can be applied to other technology utilities in various countries.