Understanding User Perspectives: What is the Meaning of Technology Mastery?

Abstract

Technology plays an increasingly important role in our daily lives, and it is imperative to operationalize its effective use, or mastery, to enhance human-computer interactions. To better understand what it means to individuals, participants were asked about their understanding of technology mastery and their experiences with the technology they have and have not mastered. A thematic analysis revealed that participants define mastery as the knowledgeable, correct, and efficient use of technology. Participants attribute their own mastery to comfort, regular use, and efficiency with the technology. Convenience, success, and productivity emerged as motivators for participants to master technology, while poor functionality, lack of familiarity, and time demands were identified as barriers for participants to master technology. The findings from this study will inform the development of a generalizable scale for evaluating technology mastery and is a pivotal step toward establishing a comprehensive understanding of technology mastery and its societal impact.

Keywords

Technology mastery Human-computer interactions Qualitative Research Thematic analysis Scale development Interview User perceptions Technology adoption User experience

Introduction

Over the past 30 years, information and communication technologies (ICT) have become a regular part of everyday life. This rapid proliferation of technology has revolutionized and fundamentally changed how individuals live, work, and socialize with one another. For example, home computer ownership has increased by over 75% since 1990, teleworking and e-commerce have increased by over 12% since 2015, and as of 2021, 72.3% of the United States population uses social media (Bureau of Labor Statistics, 1999; International Trade Commission, n.d.; US Census Bureau, 2021, 2022).

Considering how ubiquitous technology has become, researchers must continue optimizing technology for human use. Many users recognize the personal and professional benefits of becoming proficient or even mastering technology (Arthanat et al., 2019; Barnard, 1997; Venkatesh et al., 2000; Venkatesh & Morris, 2000). Previous literature has defined technology mastery as how individuals use their knowledge, skills, and abilities (KSAs) to effectively use technology (Dahlman & Westphal, 1981; Dias et al., 2022). Although this construct is defined, there is no consensus on what technology mastery truly means. Could mastery mean the simple operation of a technology to complete a task, or does mastery entail in-depth knowledge about the features or inner mechanism of the technology?

This study is the first of a three-part study that aims to uncover what technology mastery means to various users. Additionally, this research will contribute to a better understanding of how individuals interact with everyday technologies, specifically how user perspectives of technology mastery relate to the acceptance or rejection of the product and or features of the product. This study aims to understand and define technology mastery so that a scale can be developed in subsequent research that will be used to assess how users interact with technology. Ultimately, researchers and product designers might use such a scale to create new training materials and optimize user interfaces by understanding technology mastery in terms of facilitating conditions and barriers.

Method

Participants

The study included 36 participants who were required to be at least 18 years old and fluent in English. 42% of participants identified as male, 46% as female, and 12% as nonbinary/other. Participant ages were captured in ranges, with 75% of participants falling into the 18-24 range and 8% falling into the 65+ range. Roughly 70% of participants were recruited through an undergraduate participant pool of a large university in the southeastern U.S., while the rest were recruited through word of mouth or social media. In addition, 86% of participants work or study in a STEM field, while the rest work in other industries or are retired. Most participants (97%) subjectively indicated they were comfortable or highly comfortable with technology.

Procedure

Participants were interviewed in person or virtually, depending on their availability. They were provided with informed consent information and asked to verbally agree or disagree to participate in the study. Once consent was obtained, participants were asked questions by the interviewer, who took notes on a secure online platform Participants were asked to elaborate when they gave imprecise or irrelevant answers.

Each interview was conducted in a structured format, with each participant responding to a set of 24 questions. The survey questions were divided into three sections. Section 1 focused on defining technology mastery in layman's terms, gathering insight into how participants operationalized the term in their daily lives, and determining how comfortable participants were with technology.

Section 2 asked the participants to think of a technology they felt they had mastered and then prompted them for their reasoning behind this judgment, their motivation for using that specific technology, and how they learned to use it. Participants were asked to provide this information for up to three “mastered” technologies.

Finally, in section 3, participants were prompted to think of a technology they could not master. We asked them to explain why they were unsuccessful with it, to describe any barriers they faced while attempting to learn or interact with their chosen technology, and to discuss any training they had completed with it. Participants completed this section up to three times, considering a different technology each time. We ended the interview with an open-ended question prompting the participants for any final thoughts regarding the study.

Analysis

The structured interview data from this study were thematically analyzed. Themes in a thematic analysis capture “something important about the data in relation to the research question and represents some level of a patterned response” (Braun & Clarke, 2006, p. 82). Each question was examined for this study, and responses were systematically analyzed. An inductive approach was taken, meaning that the researchers attempted to remain unbiased and separated from epistemological commitments to capture more holistic data (Braun & Clarke, 2006). After completing the analysis, the themes were reviewed for clarity and summarized. Finally, we calculated how common each theme was by averaging the times each theme was mentioned across all participant responses.

Results

Interview Section 1

This analysis produced 13 common themes regarding how participants operationalized technology mastery. A full list of all themes associated with this section (and others) is included in Table 1. A large majority of participant responses included some form of knowledge, which ranged from knowing how to use the product to knowing how to troubleshoot it, to having a comprehensive understanding of the technology’s available functionalities. As one participant explained, “mastery includes being able to troubleshoot any issue like fixing remotes or television issues. In a professional environment, being able to easily come up with solutions and solve problems using technology.” The most identified themes were Knowledge (mentioned in 57% of responses), Correct Use (22%), and Efficiency (20%).

Table 1.

Each identified theme and its corresponding occurrence rate across the thematic analysis results.

Theme	Percentage	Theme	Percentage	Theme	Percentage
Adaptability	8.6%^a	Work Success	36.9% ^b	Formal Training	8.7% ^d \| 1.9%^f
Accomplishment	11.4% ^a	Optimize Productivity	20.4% ^b	Needs Formal Learning	9.3%^e
Comfort	14.3% ^a \| 40.4%^c	Improve Daily Life	38.8% ^b	Unfamiliarity	38.9% ^e
Independence	8.6% ^a	Personal Interest	12.6% ^b	Poor Instructions	6.5% ^e
Knowledge	57.1% ^a \| 5.8%^c	Gain New Skill Set	8.7% ^b	Large Learning Curve	20.4% ^e
Efficiency	20% ^a	Safety	4.9% ^b	Poor Design	9.3% ^e
Work Quality	2.9% ^a \| 5.8%^c	Continued Use	31.7%^c	Time Demand	13.9% ^e
Problem-solving	5.7% ^a \| 26.9%^c	Use for Desired Purpose	17.3% ^c	Low Interest	13% ^e
Intuition	14.3% ^a \| 13.5%^c	Social Recognition	2.9% ^c	Low Confidence	4.6% ^e
Functionality	14.3% ^a \| 23.1%^c	No Formal Training	91.4%^d \| 31.8%^f	Price	1.9% ^e
Teaching Ability	14.3% ^a \| 8.65%^c	Peer Education	26% ^d \| 18.7%^f	Low Family Support	.9% ^e
Skill	2.9% ^a	Self-taught	55.8% ^d	Previous Experience	22.4%^f
Correct Use	22.9% ^a	Tutorials/Guides	34.6% ^d \| 19.6%^f	Observation	8.4%^f
Societal Pressure	15.5%^b	Introductory Course	9.6% ^d \| 16.8%^f	Product Manual	3.7%^f

Note. ^a = Defining Mastery; ^b = Motivation to Master; ^c = Why a Master; ^d = Training for Mastered Technology; ^e = Barriers to Mastery; ^f = Training for Unmastered Technology

Interview Section 2

In this section, participants began by identifying technologies they “mastered.” These responses were coded into nine categories of technology. Here, the most common themes were Program/Software (31%), Cell Phone (22%), and Computer (16%).

Seven themes were identified as pertaining to the participants' motivation for mastering technology. Many participants indicated that they had mastered technology due to its ubiquitous role in their daily life. For example, one college student participant highlighted the importance of mastering their phone “because everything lives in there and it is a big part of life, so I need to be able to use it well to communicate and take care of everything in life and college.” The most common theme was Improve Daily Life (38%), followed by Work Success (36%) and Optimize Productivity (20%). These results show that most users are motivated to learn technologies that fit seamlessly into their daily lives.

Ten themes were identified that pertain to why participants felt they had mastered their technology. As expected, many of these themes are similar to, or overlap, those found when we asked participants to define technology mastery. One STEM student, for example, attributed their mastery to “using it every day for long periods of time, which has made me achieve mastery level proficiency. I use a lot of the features it has to offer. If someone asked me a question, I'd be able to teach.” In contrast to the Defining Mastery question, however, the main themes were Comfort (40%), Extended/Continuous Use (31%), and Efficiency (27%).

Six themes were identified that pertain to how the participants became masters of their chosen technology. The majority of participants identified that they had No Formal Training (91%) and were instead

Self-taught (55%). Participants often described self-taught as, “Trial and error, click and touch everything to figure out how features work and master them.” The next most common theme was Tutorials/Guides (34%), which participants noted generally took the form of YouTube videos, books, or online forums, as indicated by one participant’s response, “No courses, but a lot of YouTube videos and Google searches.” These results reflect trends found in other research areas: users tend to prefer social learning or some form of “learning by doing” over user manuals or formal training (Portouli et al., 2008).

Interview Section 3

Regarding unmastered technology, we synthesized the participants’ responses into eight themes. Here, Applications/Software (35%) was the most common theme, followed by Computer/Computer Parts (17%) and Transportation (9%).

The most common themes pertaining to barriers to technology mastery were Unfamiliarity (39%), Large Learning Curve (20%), and Time Demand (14%). One participant expressed difficulty with their unmastered technology, stating, “I’m not good at using it; I don't have much experience with it; Its tutorials are confusing, and multiple versions seem to differ too much.” These results mirror the responses found for Section 2, where successful technology mastery is mainly a product of the user’s knowledge, skills, and abilities (KSAs). When these KSAs are not established, users experience frustration with the technology and consider this to be a barrier to use.

Finally, we had participants discuss any training they may have had with their unmastered technologies. Similar to the mastered technologies, the most common theme was No Formal Training (32%), followed by Self-taught (22%) and Tutorials/Guides (20%). This theme was No Formal Training (32%), followed by Self-taught (22%) and Tutorials/Guides (20%). This further supports the finding that users are not motivated to use formal user guides and information.

Discussion

The results of this work largely indicated that technology users operationalize mastery as being able to understand the features and correctly use the product or interface in their daily life. Furthermore, efficiently using technology was a theme identified throughout the survey. Most participants indicated they did not receive formal training on the technologies they mastered.

This finding highlights the importance of considering subjective expertise and its potential impact on technology adoption and skill acquisition. It also suggests that self-perceived mastery might stem from hands-on experience and informal, self-directed learning.

It should also be considered that participants also stated that they had not received formal training for technologies they had not mastered. This is an important result to consider and is also supported by the technology acceptance literature, where if a technology is difficult to use, it might not be as readily accepted by the user (Davis, 1989). Additional research is needed to explore the implications of this phenomenon and the influence of instruction and training on individual confidence and performance regarding technology mastery.

Limitations

Most participants indicated they were comfortable or highly comfortable with technology in general. In addition, the majority of our sample was young and educated. As a result, this distribution could skew our results, making them less generalizable to the population. Additionally, given the limited literature on conducting rigorous thematic analysis, some believe it is less advantageous than other qualitative analysis methodologies (Braun & Clarke, 2006; Nowell et al., 2017). To mitigate these concerns, the researchers have employed best practices, such as ensuring that the analysis is clearly documented and easy to follow and making the conclusions from the thematic analysis easy to understand (Nowell et al., 2017).

Future Work

As previously mentioned, this was the first part of a three-part study to understand and quantify technology mastery. The results of this study will directly inform the development of a generalizable scale that can be used to quantify technology mastery. The first step in developing the scale is to use the themes identified in this work to facilitate the item-generation process. It is essential to ensure that each item contains understandable wording, an appropriate question form that participants can respond to the items (Boateng et al., 2018). Upon the creation of items, content validity will be assessed. Next, the researchers will complete an Item Reduction Analysis and Factor Analysis. This will be done to ensure that unrelated items are removed from the developing measure and that the appropriate number of domains are used (Raykov & Marcoulides, 2010; Thurstone, 1931). Lastly, we will conduct reliability, criterion, and construct validity tests. These tests are important for establishing whether the scale can be generalized to multiple products and users to create a scale that is robust in a variety of circumstances yet also valid; that is, it measures the constructs that it was designed to measure (Boateng et al., 2018).

Understanding the scale development process can allow researchers to create new measures that contribute to improving the human experience with technology. By focusing on the end user, researchers and developers can collaborate to develop products that are intuitive, accessible, and satisfying for multiple stakeholders.

Footnotes

ORCID iD

Emily Gleaton

Emily Parcell

Marianna Madera

References

Arthanat

Wilcox

Macuch

(2019). Profiles and Predictors of Smart Home Technology Adoption by Older Adults. OTJR: Occupation, Participation and Health, 39(4), 247–256. https://doi.org/10.1177/1539449218813906

Barnard

(1997). A critical review of the belief that technology is a neutral object and nurses are its master. Journal of Advanced Nursing, 26(1), 126–131. https://doi.org/10.1046/j.1365-2648.1997.1997026126.x

Boateng

G. O.

Neilands

T. B.

Frongillo

E. A.

Melgar-Quiñonez

H. R.

Young

S. L.

(2018). Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer. Frontiers in Public Health, 6. https://www.frontiersin.org/articles/10.3389/fpubh.2018.00149

Braun

Clarke

(2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Bureau of Labor Statistics. (1999). Computer Ownership Up Sharply in the 1990s. https://ecommons.cornell.edu/handle/1813/78926

Dahlman

C. J.

Westphal

L. E.

(1981). The Meaning of Technological Mastery in Relation to Transfer of Technology. The ANNALS of the American Academy of Political and Social Science, 458(1), 12–26. https://doi.org/10.1177/000271628145800102

Davis

(1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Dias

Lopes

Peixoto

(2022). Mastering new technologies: Does it relate to teleworkers’ (in)voluntariness and well-being? Journal of Knowledge Management, 26(10), 2618–2633. https://doi.org/10.1108/JKM-01-2021-0003

International Trade Commission. (n.d.). Impact of COVID Pandemic on eCommerce. Retrieved February 21, 2023, from https://www.trade.gov/impact-covid-pandemic-ecommerce

10.

Nowell

L. S.

Norris

J. M.

White

D. E.

Moules

N. J.

(2017). Thematic Analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods, 16(1), 1609406917733847. https://doi.org/10.1177/1609406917733847

11.

Portouli

Bekiaris

Boets

Henriksson

(2008). Comparative Evaluation of Training Methods in Improving and Drivers' Understanding About the Functionalities and Potential Limitations of ADAS. Proceedings of European Conference on Human Centered Design for Intelligent Transport Systems, 217–226.

12.

Raykov

Marcoulides

G. A.

(2010). Introduction to Psychometric Theory. Routledge. https://doi.org/10.4324/9780203841624

13.

Thurstone

L. L.

(1931). Multiple factor analysis. Psychological Review, 38, 406–427. https://doi.org/10.1037/h0069792

14.

US Census Bureau. (2021). Computer and Internet Use in the United States: 2018. Census.Gov. https://www.census.gov/newsroom/press-releases/2021/computer-internet-use.html

15.

US Census Bureau. (2022). The Number of People Primarily Working From Home Tripled Between 2019 and 2021. Census.Gov. https://www.census.gov/newsroom/press-releases/2022/people-working-from-home.html

16.

Venkatesh

Morris

M. G.

(2000). Why Don’t Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior. MIS Quarterly, 24(1), 115–139. https://doi.org/10.2307/3250981

17.

Venkatesh

Morris

M. G.

Ackerman

P. L.

(2000). A Longitudinal Field Investigation of Gender Differences in Individual Technology Adoption Decision-Making Processes. Organizational Behavior and Human Decision Processes, 83(1), 33–60. https://doi.org/10.1006/obhd.2000.2896