Creating and Verifying Empirical Evidence for Information Technology Acceptance

Aysha Siddiky Pinky^1, , Tughlok Talukder², Saddam Nasir Chowdhury³, Ashrafuzzaman Hera⁴, Dr. Nure Alam Khan⁵, Md Omar Faruque⁶ and Prof. Dr. Mohammed Julfikar Ali⁶

¹MBA in Digital Strategic Marketing Westcliff University, Los Angeles, California. United

²Master of Business Administration in Management Information Systems, International American University, Los Angeles, California. United

³Doctorate of Business Administration (DBA), International American University, Los Angeles, California. United States

⁴Master of Business Administration in Management Information Systems, International American University, Los Angeles, California. United States

⁵Head of Department, Department of HRM, Fareast International University. Dhaka, Bangladesh

⁶Master of Business Administration in Management Information Systems, International American University, Los Angeles, California. United States School of Business & Economics, Presidency University

Cite this paper:
Aysha Siddiky Pinky, Tughlok Talukder, Saddam Nasir Chowdhury, Ashrafuzzaman Hera, Dr. Nure Alam Khan, Md Omar Faruque and Prof. Dr. Mohammed Julfikar Ali. Creating and Verifying Empirical Evidence for Information Technology Acceptance. American Journal of Information Systems. 2024; 9(1):11-18. doi: 10.12691/ajis-9-1-2

Abstract

This paper explores the IT acceptance constructs with an emphasis on extending and testing new IT acceptance constructs that can complement existing models such as the TAM and UTAUT. Historical structures, however, remain inadequate, especially considering the technological innovations and organizational peculiarities of sub-sectors like health, commerce, and civil service. The paper points out those existing models often incorporate end-of-the-pipe technologies that must align with today's IT systems, such as cloud computing, mobile applications, and artificial intelligence. Because IT is gradually expanding its penetration into various sectors of the economy and society, the factors that contribute to acceptance become more specific to the context of the process, which re-examines and improves traditional IT acceptance models. The research proposes advanced constructs to overcome these challenges that consider current technology vulnerabilities. Pioneers have created these constructs to capture the modern technological context and address data privacy, cyber security, and user agency issues. Hence, this research uses empirical evidence to assess the validity of these new constructs with the different industries and technologies. This study uses a deductive research approach to test theories derived from questionnaire data. Based on this, the sample size of the 150 participants is considered sufficient to guarantee the reliability of conclusions and measures' validity. Measurement data is performed using structured questionnaires, emphasizing key factors such as perceived usefulness, perceived ease of use, perceived behavioural control and perceived social pressure. The research works concerning early results show an inefficacy of the traditional models in depicting the present-day advertisement of IT infrastructure. Several newly developed concepts relevant to data privacy and cyber security become crucial antecedents of IT acceptance, contribution, and usage. The validity of these constructs is established by conducting a statistical analysis of the constructed models that can be used to explain behavior in different industry contexts. The study's contributions are twofold: It contributes to theoretical knowledge of IT acceptance by proposing and establishing new variables for consideration, Moreover, it enlightens practitioners of organizations that are seeking to increase the rate of end user adoption of embraced technologies. It focuses on the issues unaddressed by current models and casts light on the modern technological factors influencing IT acceptance in the modern rapidly advancing technological environment.

Keywords:
Information Technology (IT) Acceptance Models Constructs Empirical Validation and User Behavior

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