2  Self-efficacy divide in the context of Digital Competences

2.1 Digital Divide framework

One of the main theme on sociology have been the social stratification, sparking numerous debates and proposals aimed at adressing the complex task of hierarchically ordering individuals in society and accounting for the inequalities they experience.

Following the digital revolution, individuals faced a variety of new capabilities and tools essential for social life, which at the same time reflected older social inequalities and express other news by the level of competence on technologies (not only access). This shift forced academia and public policy to conceptualize ICT related gaps no longer as merely access disparities, but as a competence issue, and also as a enhancer of preexisting social inequalities among population.

The concept of the digital divide, as outlined by Lythreatis et al. (2022), refers to the various gaps in access, usage, and outcomes associated with Information and Communication Technologies (ICT). This concept, coined by scholars during the 1990s, has gained increasing prominence due to the rise of the digital economy and has been embraced by numerous organizations and public policy institutions. For instance, the United Nations places significant emphasis on ICT as a means to achieve its Sustainable Development Goals (SDGs) and combat social inequalities. The significance of the concept lies not only in its impact on individuals’ relationships with the digital world but also in its implications for social mobility, given the pervasive integration of ICT into both the labor market and educational systems.

Scholars have proposed three dimensions of the digital divide, each characterized by specific features and contexts. The first to emerge is the most restrictive, focusing solely on disparities in ICT access within the population. This was conceptualized simply as a dichotomy between those with effective access to technology and those without.

At the dawn of the new millennium, it was recognized the need to broaden the concept, as access disparities alone failed to capture the growing inequalities tied to technology and digitalization. The second level of the digital divide encompasses differences in access to relevant content, the quality of connectivity, digital knowledge and skills, and attitudes, behaviors, and emotions toward technology. In general, there is a tendency to combine this second dimension with the concept of digital competence.

A third level of the digital divide emerged as a critique of the assumption that equal access and use of ICT would necessarily yield equal outcomes. This dimension emphasizes the need to examine the benefits or consequences derived from ICT usage.

Recent literature has proposed additional dimensions of the digital divide. Bartikowski et al. (2018, as cited in Lythreatis et al., 2022) argued that the type of internet use is a critical component of the divide. Cinamon (2020, as cited in Lythreatis et al., 2022) highlighted data inequalities as a significant but underexplored aspect. Gran et al. (2021, as cited in Lythreatis et al., 2022) introduced a fourth dimension of the divide, which examines the positive and negative effects of algorithms on individuals. Awareness of and access to information about algorithms is emerging as a crucial societal issue, particularly as algorithms play an increasing role in public participation, posing challenges to democracy.

2.2 The Digital Competence Agenda

Respect the second dimension of digital divide, studies on technologies and inequalities has open up a large research agenda around the competence on digital enviroments.

At the end of the 20th century, the massification of ICT, as well as the increased use of digital environments in labor and academic environments, turned competence with the internet and ICT relevant for lifelong learning. Then, International organizations and public policy scholars started to discuss how to approach levels of mastery of digital technologies in individuals worldwide.

Digital Literacy was proposed as a first agenda to understand individuals’ capabilities with emergent technologies and their applications, principally on educational achievement (Spante et al., 2018). Initially, the studies dealing with digital literacy focused on technical knowledge of technologies, such as software specifications and operating systems. As soon as the Internet began to become an everyday space in social life, studies on digital literacy began to problematize issues specific to the relationship that occurs between individuals in digital environments, i.e., communication and navigation skills, increasing the dimensions to the fully informational part (Falloon, 2020).

As the discussion developed, it began to be realized that a question of knowledge or intellect did not mainly determine the development of technological skills, but rather, that it was a multidimensional and heterogeneous problem that brought together issues ranging from the proper use of digital applications to the formation of a ‘mindset’ or attitudinal dispositions towards technologies that would be beneficial for learning how to relate with them. Self-efficacy would be a domain of this attitudinal aspect of technology adoption and learning. As an alternative to the digital literacy agenda, digital competences began to be discussed (Ulfert-Blank & Schmidt, 2022).

Digital Competence (DigComp) is defined as “the confident, critical and responsible use of, and engagement with, digital technologies for learning, at work, and for participation in society”. It encompasses a combination of knowledge, that is, understanding how digital systems may be used, how they function, and how to judge their capabilities or restrictions. Also include skills: “to use, access, filter, evaluate, create, program, and share digital content”, as well as to “protect, information, content, and digital identities”, and attitudes, including the reflective and critical handling of these systems.

2.2.1 Operationalizing Digital Competence

A lot of scales operationalize DigComp as a unidimensional concept with different significations. As a standardized alternative, The European Digital Competence Framework for Citizens proposes five domains:

Competence.area	Competence
1. Information and data literacy	1.1 Browsing, searching and filtering data, information and digital content
1. Information and data literacy	1.2 Evaluating data, information and digital content
1. Information and data literacy	1.3 Managing data, information and digital content
2. Communication and collaboration	2.1 Interacting through digital technologies
2. Communication and collaboration	2.2 Sharing through digital technologies
2. Communication and collaboration	2.3 Engaging in citizenship through digital technologies
2. Communication and collaboration	2.4 Collaborating through digital technologies
2. Communication and collaboration	2.5 Netiquette
2. Communication and collaboration	2.6 Managing digital identity
3. Digital content creation	3.1 Developing digital content
3. Digital content creation	3.2 Integrating and re-elaborating digital content
3. Digital content creation	3.3 Copyright and licenses
3. Digital content creation	3.4 Programming
4. Safety	4.1 Protecting devices
4. Safety	4.2 Protecting personal data and privacy
4. Safety	4.3 Protecting health and well-being
4. Safety	4.4 Protecting the environment
5. Problem-solving	5.1 Solving technical problems
5. Problem-solving	5.2 Identifying needs and technological responses
5. Problem-solving	5.3 Creatively using digital technologies
5. Problem-solving	5.4 Identifying digital competence gaps

The current operationalization includes Safety and Problem-solving, which are not regarded in the majority of measures of digital competence. The last one, when is studied, mainly addresses solving technical problems. In contrast, DigComp highlights the skill of utilizing digital systems for solving various problems, not being limited to technical error. In this way, problem-solving also includes the aspect of being aware of one’s own competences and detecting competency gaps. Furthermore, competently dealing with risks and safety digital concerns offers an overview of Digital Self-efficacy, which is an important element in explaining the formation of the five digital competence domains. In this way, DigComp emphasizes this variable, which is minimized in the case of digital literacy definitions (Ulfert-Blank & Schmidt, 2022).

2.2.2 Digital Competence and Self-efficacy relationship

The concept of self-efficacy has become central to studies in social psychology, especially in the case of thematic disciplines such as health, psychotherapy, education or citizenship. In recent years, a whole thematic research agenda has opened up around self-efficacy role on the adoption and competences on digital technologies.

Society has changed at an accelerated pace in different aspects up to the present day. One of the most rapidly changing areas today is the development and innovation of digital technologies. Year by year the devices, patterns and uses of these technologies are modified, which requires users an unfinished learning process. Given these conditions, self-efficacy has become central than ever to strengthen the ability to adapt to new digital environments (Bandura, 1995), and then, have a central position on the development and updating of digital competences in individuals. Additionaly, digital self-efficacy is an important predictor of learning outcomes with technologies, under/overestimation of competences, knowledge creation and acceptance of technological change (adoption of new ICT’s)(Ulfert-Blank & Schmidt, 2022).

2.3 The evolution of Digital Self-efficacy

Like the DigComp concept, several different approaches have conceptualized self-efficacy in digital environments over the years. The first antecedents of self-efficacy applied to digital issues resorted to ‘Computer self-efficacy’. Compeau & Higgins (1995) proposed this early instrument focused on general computer domains and specific software application tasks. Defined as an individual’s perceptions of his or her ability to use computers in the accomplishment of a task (ie., using a software package for data analysis, writing a mailmerge letter using a word processor), rather than reflecting simple component skills (ie., formatting diskettes, booting up a computer, using a specific software feature such as “bolding text” or “changing margin”). The computer self-efficacy construct was criticized and overcome for neglecting the changing dynamics of digital systems, which extender the digital enviroment over computers. The items of these scales tend to become outdated rapidly (Weigel & Hazen, 2014).

While the increasing importance of interconnection with technologies, the focus was on general one’s judgment of confidence regarding different tasks related to internet use. Internet self-efficacy focuses on what a person believes he or she can accomplish online now or in the future. It does not refer to a person’s skill at performing specific Internet-related tasks, such as writing HTML, using a browser, or transferring files, for example. Instead, it assesses a person’s judgment of their ability to apply Internet skills in a more encompassing mode, such as finding information or troubleshooting search problems. Internet self-efficacy may be distinguished from computer self-efficacy as the belief that one can successfully perform a distinct set of behaviors required to establish, maintain, and utilize effectively the Internet over and above basic personal computer skills (Eastin & LaRose, 2000a) ¹.

Although this new construct partially addressed the obsolescence of technologies, the set of digital activities was reduced to a particular domain, as is the case with the Internet. An ICT Self-efficacy scale was proposed to comprise Computer and internet tasks on the same construct. ICT Self-efficacy construct considers digital information processing or communication (Aesaert & van Braak, 2014; Hatlevik et al., 2018) and more advanced skills, such as programming (Rohatgi et al., 2016). Although to its new measures, ICT Self-efficacy usually presents unidimensional concepts or focuses on specific application domains (using ICT for work, school, or leisure) rather than competencies applicably for general life domains (Ulfert-Blank & Schmidt, 2022).

The current measures presented have common limitations in various ways. First, they often do not consider more recent frameworks of digital competences, such as the DigComp, regarding their level of generality, the competences included, and their multidimensionality. The DigComp describes digital competences in terms of general actions (i.e., tasks, functions), such as protecting devices or managing data, that can be applied to a heterogeneous group of individuals and are independent of specific digital systems. Most DSE scales are still system (e.g., specific computer software) or technology-specific (e.g., data storage such as floppy disc) and may thus become outdated. Second, critical competence areas, such as safety and problem-solving are often disregarded. Most of the scales focus on the informational, communicative, and creative aspects of the technologies without exhaustively capturing their dimensions of mastery. Third, the term DSE has been used interchangeably for measuring general competence beliefs (i.e., including items assessing self-concept, another competence belief) or actual proficiency. As a result, this has led to inconsistencies in the representation of the DSE construct in the literature. This is in spite of self-efficacy literature offering clear definitions of how measures should be constructed and its well-defined differentiation from related constructs, such as self-concept (Ulfert-Blank & Schmidt, 2022)

2.3.1 Standarized Digital Self-efficacy Scale based on DigComp

Ulfert-Blank & Schmidt (2022) suggests that to reach a high-level of research on Digital Self-efficacy, scales have to (1) be theoretically-grounded multi-dimensional measures of DSE, encompassing diverse digital competence areas, (2) cover different functions and tasks of digital systems, (3) be independent of a specific digital system (e.g., Word), (4) be also labor or economical, not only educational-oriented.

Then, they propose a scale, which is an actual referent on the Digital Self-efficacy Research Agenda. They follow the approach suggested by Bandura (2006) to measure self-efficacy. The scale is formulated generically and adapted to each specific dimension. According to the document, the questions use expressions such as “I am confident that I can…” or “I believe I am able to…”.

These questions are customized to reflect each specific competency or task within the digital self-efficacy dimensions defined in the DigComp framework.

Items	Dimension/Subscale
search for specific information in digital environments.	Information and data literacy (iSE)
distinguish between correct and incorrect digital information.	Information and data literacy (iSE)
store and organize digital content so that I can easily find it again.	Information and data literacy (iSE)
interact with others in digital environments.	Communication and collaboration (cSE)
share information and data with others digitally.	Communication and collaboration (cSE)
participate in public discussions and activities in digital environments.	Communication and collaboration (cSE)
Defend myself against injustice in digital environments.	Communication and collaboration (cSE)
Defend myself and others against injustice in digital environments.	Communication and collaboration (cSE)
Push back against injustice in digital environments.	Communication and collaboration (cSE)
use digital systems to collaborate with others.	Communication and collaboration (cSE)
use the proper etiquette to communicate in digital environments.	Communication and collaboration (cSE)
manage and delete my digital footprint.	Communication and collaboration (cSE)
present myself the way I want in digital environments.	Communication and collaboration (cSE)
create digital content.	Digital content creation (dSE)
change digital content in a way that new content is created.	Digital content creation (dSE)
identify legal aspects in digital environments, such as terms of use and licenses.	Digital content creation (dSE)
write a simple command in a programming language.	Digital content creation (dSE)
protect my digital devices from unwanted access.	Safety (sSe)
protect my personal data in digital environments.	Safety (sSe)
recognize health risks associated with using digital environments.	Safety (sSe)
use digital environments to promote my health.	Safety (sSe)
recognize the impact of digital environments on nature and the climate.	Safety (sSe)
identify technical problems when using digital environments.	Problem-solving (pSE)
find and apply various solutions to technical problems that arise.	Problem-solving (pSE)
find the right digital system to meet non-technical challenges.	Problem-solving (pSE)
develop novel digital solutions.	Problem-solving (pSE)
identify and improve the digital skills I lack.	Problem-solving (pSE)

Responses are obtained on a 6-point Likert scale ranging from “strongly disagree” to “strongly agree”.

2.4 Exploring the Digital Self-efficacy divide

Digital Self-efficacy inequalities has been studied through various approaches and a wide range of variables as a dimension of the digital divide framework, leading to its characterization as a multidimensional and dynamic phenomenon. Key determinants of the digital self-efficacy divide include age, gender, socioeconomic status, attitudes, beliefs, emotions, ICT experience and training, rights, infrastructure, and large-scale events such as COVID-19.

2.4.1 Access and DSE divide

Access is positioned as a crucial variable for Digital Self-Efficacy (DSE), as it represents the first element of the digital divide problematized in academic studies. Recent literature highlights a positive relationship between greater access to technology and higher levels of DSE. Stone (2020) identifies a significant relationship between increased access to ICT and greater self-efficacy in the domains of basic computing, internet use, applications, and content creation. Previous studies have also documented this relationship [Tondeur, Sinnaee, Van Houtte, & Van Braak, 2011, as cited in Hatlevik et al. (2018); Aesaert & Van Braak, 2014; Tsai & Tsai, 2010; Zhong, 2011, as cited in Senkbeil (2023)].

To better understand the relationship between technological access and self-efficacy, different categories have been established, such as ICT availability at school, at home, for leisure, or academic purposes. A study by Ball et al. (2020) explores how these dimensions relate to Digital Self-Efficacy (DSE), distinguishing between Technology Self-Efficacy (TSE) and Application Self-Efficacy (ASE). The findings indicate a strong connection between computer use at home and TSE, whereas no such link was observed with ASE.

2.4.2 Socio-economic status and DSE divide

The relationship between DSE and socioeconomic variables has also garnered academic interest, focusing on the connection between technological self-efficacies and factors such as cultural capital, parental education, and income. Hatlevik et al. (2018) found that ICT self-efficacy significantly increases with higher socioeconomic status in 11 countries. Similarly, Chen & Hu (2020), using PISA 2015 data, identified a significant relationship between socioeconomic level and ICT self-efficacy, although the effect was not very strong (varying across countries between 0.0241 and 0.1678). Additionally, Chikezie (2024) reported that socioeconomic status is a significant predictor of technological self-efficacy. In contrast, Stone (2020) did not find a significant relationship between parental income (as reported by university students) and technology-related self-efficacy. Lastly, Bonanati & Buhl (2022) demonstrated a relationship between parental social capital and technological self-efficacy.

In summary, although there is a relationship between socioeconomic status and self-efficacy, this effect ranges from low to moderate and is not consistent across all countries. For this reason, it seems important to delve deeper into this relationship, considering various factors that determine household socioeconomic status. This is particularly relevant given that socioeconomic status consistently has the most significant effect on CIL across 14 countries (Hatlevik et al. (2018)).

2.4.3 Gender and DSE divide

Gender has been a significant variable in studies on Digital Self-Efficacy (DSE). It is considered important due to the historical gender digital divide across all three levels and because DSE serves as an essential predictor of Computer and Information Literacy (CIL) (Hatlevik et al. (2018)). Moreover, DSE partially mediates gender differences in CIL and Computational Thinking (Campos & Scherer (2024)). Historically, a gender gap favoring males has been observed, with men displaying higher DSE and more positive attitudes toward technology (Whitley, 1997, as cited in Cai et al., 2017). However, more recent studies highlight inconsistencies in the direction of this relationship (Cai et al. (2017)). For instance, one study finds that men demonstrate higher technological self-efficacy [Yau and Cheng, 2012, as cited in Cai et al. (2017); Fraillon, 2014], another reports no significant differences (Compton, Burkett, and Burkett, 2003, as cited in Cai et al., 2017), and some even indicate a reversed relationship (Compton et al., 2003, as cited in Cai et al., 2017; Ray et al., 1999, as cited in Cai et al., 2017). As a result, numerous studies continue to scrutinize the relationship between various types of technological self-efficacy (ICT self-efficacy, internet self-efficacy, DSE, etc.) and gender.

A meta-analysis by Cai et al. (2017) highlights that men exhibit better attitudes toward technology, including self-efficacy. Notably, the technological self-efficacy gap between men and women is the most reduced across years among those examined in the study. Additionally, Scherer & Siddiq (2015), using teacher questionnaire data from ICILS 2013, identify a relationship between being male and having higher computational self-efficacy. Contrary to these findings, a study by Hatlevik et al. (2018) using ICILS 2013 data reveals that women exhibit higher ICT self-efficacy, with effects ranging from 0.04 to 0.15 across nine sampled countries. Consistent with Hatlevik’s findings, Chen and Hu (2020), using PISA 2015 data, report a negative effect of being male on ICT self-efficacy, varying between -0.4369 and -0.1236 depending on the country.

Given the inconsistent results from studies on the relationship between gender and DSE, the construct has been segmented to better understand this complex relationship. As previously mentioned, two dimensions of DSE have been proposed: a basic dimension addressing tasks such as internet searches, communication, and social media use, and a specialized dimension involving more complex skills like programming or server management. Gebhardt et al. (2019) and Castillo et al. (2025) (a study conducted solely in Chile) argue, using ICILS 2018 data, that women have higher basic DSE, while men score higher on average in specialized DSE.

Departing from studies that rely on Large Assessment Study data (which, according to this review, are predominant), Stone (2020) developed a questionnaire comprising various batteries to measure different dimensions of technology-related self-efficacy. Administered to university applicants in the United States (n=260), the study found that women scored higher in the social media dimension (5.0 vs. 4.0, p<0.01), while men scored higher in the basic computing dimension (3.79 vs. 3.48, p<0.05). 

2.4.4 Attitudes, use and behaviors with technology and DSE divide

The second dimension of the digital divide encompasses attitudes, skills, behaviors, and technology-related uses, which recent literature highlights as significant predictors of DSE. Multiple studies have explored the relationship between specific types of ICT use, attitudes toward ICT, ICT-related learning environments, and technological self-efficacy. For instance, a study by Chen & Hu (2020) confirms that interest is positively and significantly related to DSE. Additionally, it finds that ICT use for leisure and socialization are important mediators of this effect, whereas ICT use at school or for school-related tasks is not. This finding is supported by numerous studies; for example, Ball et al. (2020) demonstrates that using ICT to talk with friends or play video games significantly affects technological self-efficacy. Similarly, Hori & Fujii (2021) show that ICT use in schools does not impact self-efficacy, whereas ICT use at home for learning and leisure significantly does.

The above suggests that the development of high technological self-efficacy in an individual occurs primarily in the home environment, particularly influenced by the context of learning, use, and types of technology usage. Bonanati & Buhl (2022) explore the relationship between characteristics of the home learning environment and ICT self-efficacy. The authors reveal that children whose parents engage in more online activities with them tend to have higher self-efficacy and identify a relationship between parental attitudes and their children’s self-efficacy. In line with this, Hammer et al. (2021) demonstrate that parental values and beliefs are related to children’s digital self-efficacy; however, this relationship is mediated only by the age at which children are given smartphones. Relatedly, Lai et al. (2022) argue that greater smartphone use is positively associated with DSE, though problematic uses and cases where the relationship is reversed also exist.

Hatlevik et al. (2018) finds that autonomous learning is positively related to ICT self-efficacy, making it the most consistently observed relationship across countries. Eastin & LaRose (2000b) discover that outcome expectations are also linked to DSE, as is internet use experience, which is identified as the best predictor in their study.

2.4.5 School and country level and DSE divide

Studies addressing digital self-efficacy (DSE) typically involve different nested levels, as they are mostly conducted using data from International Large-Scale Assessments (ILSA). However, the school level is often not a significant variable. For example, Chen & Hu (2020) conducted a multilevel model on PISA 2015 including schools across 30 countries, finding that the intraclass correlation (ICC) was less than 0.05 in all countries, with the sole exception of Mexico. Similarly, Juhaňák et al. (2019) found that the ICC was below 0.05 in all countries in their sample (using PISA 2015 data from 21 countries), concluding that only 0.09% of perceived ICT competence could be explained by school characteristics. These results are replicated in ICILS 2013, where Hatlevik et al. (2018) found that the variation explained by schools was less than 6% in all countries, with Turkey being the only exception. An ICC below 0.05 is commonly conceptualized as small in the context of educational studies. A previous study corroborates these findings, dating back to PISA 2003 (ICC = 0.044) and PISA 2006 (ICC = 0.05, barely reaching the threshold) (Zhong, 2011).

Possible explanations for this emerge from the literature review, suggesting that ICT use in schools, as opposed to ICT use for leisure or socialization, is not a relevant variable for explaining DSE (Ball et al., 2020; Chen & Hu, 2020; Hori & Fujii, 2021). Additionally, one article finds that the use of ICT at home for school-related tasks is a less significant mediator than other ICT-related variables (Juhaňák et al., 2019). A potential hypothesis (arising purely from the author’s intuition) is that digital self-efficacy is reinforced by motivation, which, in the context of technology, might develop more robustly in leisure activities or communication-based activities, such as social media or video games. School activities, and increased ICT adoption in these, might discourage students from developing their digital skills as they associate them with an unavoidable responsibility.

The country level is sometimes included in the analysis for reasons similar to those at the school level. Whether as an additional hierarchical level in the model or through models applied individually to each country, several studies have identified heterogeneity in self-efficacy across countries, as well as in the effect sizes of the independent variables employed. For instance, Zhong (2011) found a higher correlation between classes at the country level than at the school level, with values of 0.077 in PISA 2003 and 0.079 in PISA 2006. However, the inclusion of the country level is usually aimed only at visualizing the heterogeneity or homogeneity of an effect, without specifically focusing on the relationship between country-level dependent variables and self-efficacy or even their effects. This is the case with studies such as those by Hatlevik et al. (2018) and Chen & Hu (2020), for example.

However, future research needs to identify explanations for these differences, which intuitively could stem from various reasons. For instance, the degree of connectivity and technological infrastructure achieved in a country might be a significant factor influencing technology-related self-efficacy, as the population would have different levels of exposure to and familiarity with ICT. Thus, exploring various explanations for this heterogeneity is essential, as it is crucial to address digital inequalities between countries with differing levels of technological development.

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1. A particularly useful contribution to our work made by this construct is that it was one of the first to differentiate between basic and advanced tasks, as did the paper by Hsu & Chiu (2004), which divided a general ISE (GISE) and Web-specific self-efficacy (WSE). The first was oriented to general tasks on internet, while the second on an specific web-site domain.