Factors affecting digital technology access in vocational education

If policies are not thoroughly designed, technology integration may fail. As a result, users’ perceptions of technology, especially access to digital technology, are critical for technology

integration in education. This study aimed to develop and validate a scale to model factors affecting digital technology access for instructional use in Indonesian vocational schools. The

study also reports the structural model of the path analysis and tests of differences based on geographical areas. A scale adapted from prior studies was established, validated, and examined

for its validity and reliability. A total of 1355 responses were measurable; partial least squares structural equation modeling (PLS-SEM) and t-test procedures were applied for the data

analysis. The findings informed that the scale was valid and reliable. For the structural model, the strongest relationship emerged between motivational access and skills access, while the

lowest existed between material access and skills access. However, motivational access has an insignificant effect on instructional use. The t-test results show that geographical areas were

significantly different regarding all involved variables.

Digital technology has been very important in today’s modern civilization as a source of communication, entertainment, information, and education1. Nevertheless, due to the digital divide,

not everyone has equal access to technology. The digital divide measures the gap between people who might have access to technology and those who do not, which amplifies related disparities

such as financial, informational, social, and educational divides. In the past, the issue focused solely on physical access to digital technology, such as computers and the Internet2,3.

However, physical access may not be the only factor; other characteristics, such as motivational and skills access, should also be addressed2. Differences in personal and social status

result in an unequal distribution of resources in the community, unbalanced access to digital technologies, and social participation. As a result, all societies must investigate the

mitigation of the digital divide.

Access to technology was already addressed in academic contexts. This has, nevertheless, been dealt within a limited manner4; for instance, it mainly concentrated on students’ physical

access, whereas it solely focused on verifying and applying an instrument for measuring technology access in learning5. Limited studies on technological access among teachers have been

published, especially in specific contexts and settings8,67. For instance, Moldovan et al. (2022) informed perspectives from 10 mathematic teachers on the digital divide during Covid-19

teaching, elaborating on the importance of an in-depth understanding of technology-associated systemic inequalities in marginalized urban communities and strategies to integrate technology

in urban areas7.

In the vocational school teachers’ context, which is the focus of the current study, more limited studies were conducted8. Vocational education is a type of education that prepares students

to be employed or self-employed with requisite skills, preparing individuals to work as a technician or to take up employment in a skilled craft. In Indonesia, vocational education has nine

areas of expertise, from technology and engineering to creative industries9. Teachers have significant roles in shaping how technology is integrated during teaching6,10. Therefore, the

current study contributes to filling the gap by aiming to report the scale validity for a model that involves factors affecting digital technology access in a vocational context of a

developing country setting, Indonesia. The model was evaluated through PLS-SEM procedures to test the structural hypotheses. Besides, a test of differences was also addressed based on the

participants’ geographical areas for all involved variables.

Van Dijk’s theory has established the rectification of access to technology conceptions2,3. The theory promoted the rule of technological access called successive technology dimensional

norms by breaking the thought into four parts of access (motivational, material, skills, and usage)2,3. Technical access challenges shifted from motivational and material access (1st two

phases) to skills and usage (2nd two phases)2. The digital divide might occur at any time or even at all stages. The process of using digital technology has indeed been characterized as

access to digital technology11. At first, the approach focused on attitude and motivation before moving on to material or physical access. The theory progressed from material access to

skills and utilization12,13.

Vocational school teachers’ comprehension of how knowledge can be developed and how technology-related competencies can be improved through various tools14. After school closure due to the

Covid-19 pandemic, the use of digital technology in schools is significantly implemented and becomes a trend in education, including vocational education15. The demands for teachers to use

digital technology during teaching are certainly expanding; thus, they need to improve their knowledge and competencies in digital technology use for instruction. Using digital technology in

teaching can improve skills for vocational school teachers that can make their students more competent and skillful as future generations for better workforces14,15,16. However, barriers to

digital technology use during teaching can hinder the teachers from using technology during teaching14,15,16,17. In the context of vocational education, some reports informed barriers to

digital technologies used faced by teachers, namely teachers’ lack of confidence, competence, and access to digital technology resources17,18. Other studies revealed that lack of supporting

infrastructures, ineffective professional development, and lack of supporting technical support as barriers to digital technology use in vocational schools14,16.

When creating instruments, researchers include a sufficient set of appropriate indicators. The idea is to capture the most important feature of the structures. This study aimed to develop

and validate a scale to model factors affecting digital technology access for instructional use in Indonesian vocational schools. Prior studies referred to the instrument development and

validation of technology integration, resulting in some academic models. Technological pedagogical and content knowledge, or TPACK19, technology acceptance model, or TAM20, and theory of

planned behavior, or TPB21 are examples of the models. These models have been adapted and tested in different contexts and settings22,23. Similar to the prior studies4,5,6, which explored

the instrumentation processes for the digital divide, the current study also addresses a similar topic with a different context and setting, vocational school teachers in Indonesia.

Studies on correlations regarding the digital divide framework have been conducted6,12,24,25,26. For example, Wei et al. (2011) presented the intercorrelation of adapted van Dijk’s

three-level digital access model12. They created a model with three hierarchical tiers of factors: (1) digital access divides, (2) digital capacity divides, and (3) digital outcome divides.

The findings revealed a link between the variables. For instance, individuals with no computers at home were shown to possess modest self-efficacy despite having access to enabling

technology resources in the classroom. They also informed unsatisfying learning results among the students12. The origin of implementing and quantifying the digital divide is another example

demonstrating the existence of connections among technology integration availability6,24,25,26. Barzilai-Nahon et al. (2006) discovered a correlation between many aspects of the digital

divide, such as respondents’ demographics, accessibility, utilization, facility, context, and assistance24. Accessibility or material access not only has a direct impact on the digital

divide but also has an indirect impact on use access. The connection approaches were used to determine causal intercorrelation among digital divide issues2.

In this study, we proposed a model comprising six hypotheses of the structural model and four hypotheses of differences. Figure 1 exhibits the proposed model of the study. The proposed model

and scale refer to the context and setting of the digital divide perceived by vocational school teachers in Indonesia, adding to the geographical differences.

Motivational access in this study is defined as vocational school teachers’ readiness to incorporate digital technologies during their teaching. Technology integration in teaching needs

teachers’ readiness27. Based on van Dijk’s theory, two types of motivation access were proposed: external and internal motivation. Commitment to incorporating digital technology into

educational activities to achieve specified learning objectives is external motivation. Meanwhile, internal motivation is a dedication to teaching with technology motivated by personal

preferences and necessaries28,29,30. Regarding the motivational access, three hypotheses (H1, H2, and H3) were proposed.

H1. Motivational access significantly predicts material access.

H2. Motivational access significantly affects skills access.

H3. Motivational access is a significant predictor of instructional use.

Categorical inequalities in society lead to an unequal allocation of resources, leading to unequal access to digital technology, known as material access2,3. Social and technological

settings have an impact on the appropriation process. Personal and positional differences among users create the social context. Variations in technology access caused by resources

perpetuate inequalities of involvement, resulting in increased inequalities between people, positions, and resources. Economic resources, specifically the income required to buy and maintain

digital technology, are likely to significantly impact material access. In comparison to people with low income, people with high income have more desktops, laptops, and consoles.

Therefore, this study proposed that material access has a major impact on skill access and instructional usage. The categorical inequalities may affect the users’ skills in educational

activities, especially during teaching. In a recent report30, material access was significant in predicting skills access and use. Two hypotheses were proposed for the role of material

access on skill access and instructional use.

H5. Material access positively influences instructional use.

Teachers’ capacity to use, connect, control, and grasp digital technology is skills access30. Three skills are included in the determined phases of digital technology access: strategic,

informational, and operational skills. In this study, vocational school teachers’ strategic skills are their abilities to use digital technologies. The capacity to manage digital technology,

such as smartphones, laptops, and the Internet, is classified as operational skills2,6. Informational skills refer to vocational school teachers’ capacity to find, choose, and interpret

information using digital technologies, particularly the Internet and data-sharing technologies5,6,30,31. One hypothesis was established to report the effect of skills access on

instructional use perceived by Indonesian vocational school teachers;

H6. Skills access has a positive effect on instructional use.

Within the context of this study, the phrase “instructional use” aimed to denote (van Dijk, 2005) usage of access to digital technology. It is the result of integrating the outcomes of

motivation, material, and skills access3. Within the context of this research, the phrase itself can be explored in terms of how vocational school teachers use digital technology in their

classrooms5,6.

The expansion of digital technology performs an increasingly vital role in economic, social, geopolitical, and social settings. Even though digital technology has reached nearly every part

of the planet, a digital divide stems from geographical differences between urban and rural areas32,33. Oyelaran-Oyeyinka and Lal (2005) state that poor online activity is frequently caused

by a lack of infrastructure and low ownership of computers and other technological devices33,34. According to Lesame and Robinson et al. (2015), education, income, and economic development

inequalities between urban and rural areas are among the variables that impede technology integration33,35. Besides the structural model, the current study also elaborated on the differences

between the suburban and urban locations regarding all proposed variables. In this study, urban areas relate to places with a large population. The term “urban” refers to the main

metropolis and adjacent towns. On the other hand, suburban areas refer to residential regions (also known as suburbs). A suburb refers to the residential areas surrounding a larger city.

They can be a part of a larger metropolis or a collection of residential communities spread out across a large area. Four hypotheses were proposed to meet the purpose of the study.

H7. A significant difference emerges for motivational access based on geographical area.

H8. A significant difference exists in material access based on geographical area.

H9. A difference in skills access is reported based on geographical area.

H10. There is a significant difference in instructional use based on geographical area.

The data used in this study was gathered using a survey. We developed the survey instrument by analyzing prior studies36. Afterward, the instrument was content-validated before being

disseminated for a pilot study37,38. PLS-SEM was used to evaluate the model. We assessed the study model for causality using a predictive approach since the data distribution constraint did

not hamper the process. In addition, t-tests were used to determine the difference among all involved constructs based on geographical areas, big and small cities.

Researchers can use literature review to help them investigate a theoretical framework, choose relevant methodologies, and provide tools. We developed measures from prior studies2,6,39,

resulting 30 items with four variables (motivational access, skills access, material access, and instructional access). Content validity was conducted through discussions with five experts

in educational technology and policy. The procedure was carried out in the form of interactive dialogue. Some items were amended; two were removed because they did not suit the Indonesian

context. This process creates a significant contribution to social, cultural, and setting suitability40.

We emailed 15 experts to assess the scale (28 items) for their relevance and clarity; ten agreed to participate using the content validity index (CVI)41. However, three experts refused to

participate; two others had no responses. Item level (I-CVI) and scale level (S-CVI) were evaluated. The item was calculated by dividing the expert numbers giving a score of three or four

(positive). I-CVI scores should not be less than 0.780 for the ten experts. S-CVI was calculated when the sum of the I-CVI by the item’s total number to determine the scale level was

divided. Excellent content validity is represented by S-CVI/UA 0.800 and S-CVI/Ave 0.90. Using Microsoft Excel, we calculated the CVI scoring requirements. Two items were dropped due to the

low value of I-CVI. The results were satisfactory after the elimination process, and the instrumentation scale’s validity (n-26) was verified in the initial stage of the instrumentation.

The instrument was administered to 77 respondents for a pilot study after the I-CVI and S-CVI computations. The pilot study is essential to test a technique’s reliability in a small cohort

before applying it to a larger-scale data collection42,43,44. A pilot study is required to investigate a novel intervention. The pilot study within the current context was evaluated through

a reliability test45. We used the Statistical Package for the Social Sciences (SPSS) 25 to perform the reliability test. The findings were adequate to back up the scale’s reliability; no

variable had a Cronbach’s alpha of