In today's fast-paced and technology-driven world, the adoption of digital tools and technology has become paramount in every aspect of our lives, especially within the context of the modern workplace. Businesses and organizations are in a constant state of evolution, and so are the tools and technologies they rely on. This relentless digital transformation has ushered in significant changes in how employees interact, collaborate, and perform their daily tasks. Understanding and embracing this digital evolution is no longer a choice; it's necessary for individuals, teams, and organizations striving to remain competitive, efficient, and adaptable.
The workplace is now characterized by rapid changes and demands for employee flexibility and skill acquisition. Information and communication technology (ICT) is ubiquitous in this environment, leading to a high demand for ICT-proficient employees. In this context, understanding the motivations and factors that drive technology adoption becomes crucial, particularly in the field of Information Systems (IS) research.
What are the drivers of digital use and acceptance?
In the journey towards embracing technology in the workplace and beyond, it is essential to understand the key drivers that influence an individual's decision to accept and use technology. While the specific factors may vary depending on the context and the nature of the technology, several common drivers have been identified across various technology acceptance models and theories. These drivers play a pivotal role in shaping technology adoption and include:
Perceived Usefulness: Users are more inclined to embrace technology when they perceive it as a valuable tool that can enhance their efficiency and help them achieve their objectives.
Perceived Ease of Use: The ease individuals can learn and use technology significantly impacts their willingness to adopt it. Users prefer technology that is user-friendly and doesn't require extensive training.
Social Influence: Recommendations and support from colleagues, friends, or family members can sway an individual's decision to accept technology, making social influence a powerful driver.
Facilitating Conditions: Access to resources such as training, support, and necessary hardware or software can facilitate technology adoption. A lack of these resources can pose barriers to acceptance.
It's crucial to understand that facilitating conditions are mainly influenced by external factors, whereas perceived usefulness, ease of use, and social influence are significantly molded by individual differences. This underscores the complex interplay between internal and external elements in the process of technology acceptance. Despite substantial research in the field, individual differences have often been overlooked.
However, a growing body of literature is now illuminating the pivotal role that personal inclinations, attitudes, and personality traits play in shaping decisions related to technology adoption.
Factors such as age, gender, and prior experience have been acknowledged as influencers of acceptance and technology adoption. Moreover, there is an increasing understanding that an individual's unique personality traits significantly mold their approach to digital tools and technology in the workplace.
What Bryq can offer?
Drawing from the extensive body of research and understanding in this domain, Bryq offers a unique solution designed to assess and quantify the intricate concept of Tech Savviness. We recognize that technology usage and adoption is a complex process influenced by a myriad of factors, and one such factor is an individual's personality traits. While it's important to acknowledge that Tech Savviness cannot be entirely quantified through personality traits alone, our approach allows for a more holistic understanding of how personal inclinations and attitudes interact with technology.
If you're curious and want to learn more about Bryq's solution please reach out to our support team to enable it for your account.
Indicative Roles: Software Developer/ Engineer, Data scientist, Cybersecurity analyst, Business analyst, DevOps Engineer
References
Barnett, T., Pearson, A. W., Pearson, R. A., & Kellermanns, F. W. (2015). Five-factor model personality traits as predictors of perceived and actual usage of technology. European Journal of Information Systems, 24(4), 374–390. https://doi.org/10.1057/ejis.2014.10
Devaraj, S., Easley, R. F., & Crant, J. M. (2008). Research Note—How Does Personality Matter? Relating the Five-Factor Model to Technology Acceptance and Use. Information Systems Research, 19(1), 93–105. https://doi.org/10.1287/isre.1070.0153
Lakhal, S., & Khechine, H. (2017). Relating personality (Big Five) to the core constructs of the Unified Theory of Acceptance and Use of Technology. Journal of Computers in Education, 4(3), 251–282. https://doi.org/10.1007/s40692-017-0086-5
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