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Clinical Trial Summary

Technologically advanced tool solutions (i.e., the highest level of automation) may not always be the preferred choice for participants when given an option. Various experiments show that in vehicles, low to moderate levels of automation are often preferred over highly automated vehicles. The association of higher task demands with smarter tools has been reported to influence tool selection along with task demand. However, participants may avoid using a fully automatic tool even if the task is highly laborious. In fact, in a series of experiments, participants more distinctly preferred manually completing routine tasks over more efficient fully automatic task completion. Based on these collective theories, the Smart Tools Proneness Questionnaire (STP-Q), developed by Navarro J. and colleagues, aims to explore and measure interindividual differences influencing an individual's propensity to use smart tools, their initial selection of smart tools, and subsequent usage. In practice, measuring the propensity of any individual to use smart tools will be significantly beneficial for both professionals and non-professionals involved in designing, producing, and implementing such tools. Therefore, the purpose of this study is to translate, validate, and establish the reliability of the Turkish version of the STP-Q.


Clinical Trial Description

Individuals have developed a unique and enduring relationship with the tools they use. This relationship can be described as the creation and use of tools to define the human species. With advancements in techniques and technologies, complex human-tool interactions now occur in many daily situations. The development of these more complex human-tool interactions accompanies the creation of smarter tools. These advanced tools are increasingly inclined towards autonomy and their functionalities now surpass the understanding of most users, and even the initial designers. Smart tools are understood as any machine or device that can complete tasks involving information, mechanics, or electronics in lieu of or in connection with individuals. The smartphone can now be considered a ubiquitous example of such tools, reaching approximately half or more of the global population in the past decade. In high-income countries in North America and Europe, over 80% of the population owns a smartphone. The idea that individual differences play a crucial role in the implementation of any technological innovation has become a recurring theme in various disciplines, including information systems, manufacturing, and marketing. Technologically advanced tool solutions (i.e., the highest level of automation) may not always be the preferred choice for participants when given an option. Various experiments show that in vehicles, low to moderate levels of automation are often preferred over highly automated vehicles. The association of higher task demands with smarter tools has been reported to influence tool selection along with task demand. However, participants may avoid using a fully automatic tool even if the task is highly laborious. In fact, in a series of experiments, participants more distinctly preferred manually completing routine tasks over more efficient fully automatic task completion. Based on these collective theories, the Smart Tools Proneness Questionnaire (STP-Q), developed by Navarro J. and colleagues, aims to explore and measure interindividual differences influencing an individual's propensity to use smart tools, their initial selection of smart tools, and subsequent usage. In practice, measuring the propensity of any individual to use smart tools will be significantly beneficial for both professionals and non-professionals involved in designing, producing, and implementing such tools. Therefore, the purpose of this study is to translate, validate, and establish the reliability of the Turkish version of the STP-Q. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT06148987
Study type Observational
Source Çankiri Karatekin University
Contact
Status Completed
Phase
Start date December 15, 2023
Completion date February 6, 2024

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