research papers in education impact factor

• Best Journals - Social Sciences and Humanities

Research Papers in Education

Research Impact Score* 2.1

Ranking & Metrics Impact Score is a novel metric devised to rank conferences based on the number of contributing the best scientists in addition to the h-index estimated from the scientific papers published by the best scientists. See more details on our methodology page .

Journal information.

Aims & Scope of the Journal

Research Papers in Education publishes academic articles exploring new vital contributions in the areas of Educational Psychology and Pedagogy and Education. Research Papers in Education features unique scholarly papers which undergo peer review by experts in the field. The journal encourages submissions from the research community where emphasis will be placed on the novelty and the practical impact of the reported findings. Research Papers in Education is indexed at Research.com, Web of Science and Scopus. There are many top scientists who published in this journal including Robert Thornberg, Robert Thornberg, Louise Archer, Louise Archer and Stephen Gorard For extra details on the guidelines and submission prerequisites for authors, you are advised to consult the journal website for Research Papers in Education at https://www.tandfonline.com/toc/rred20/current .

Best Scientists who published in this Journal

Robert Thornberg

Linköping University

Publications: 6

Louise Archer

University College London

Publications: 4

Stephen Gorard

Durham University

Publications: 3

Gianluca Gini

University of Padua

Publications: 2

Hilde Van Keer

Ghent University

Leen Haerens

Publications: 1

Paul A. Kirschner

Open University in the Netherlands

David Watkins

University of Melbourne

Dennis M. McInerney

Education University of Hong Kong

Faye Mishna

University of Toronto

Marja-Kristiina Lerkkanen

University of Jyväskylä

Anna-Maija Poikkeus

Leslie J. Francis

University of Warwick

Donna M. Bryant

University of North Carolina at Chapel Hill

Eirini Flouri

Geoff Lindsay

Related social sciences and humanities rankings & guides.

Best Online Graduate Certificate in Psychology Programs for 2024

Master’s in Social Work (MSW) Programs in Florida (FL) in 2024 – Online & On-Campus

Best Georgia Psychology Degrees Online in 2024

Social Work Degree Guide: 2024 Costs, Requirements & Job Opportunities

Maryland Psychology Licensure Requirements – How to Become a Psychologist in Maryland in 2024

Best Accredited Schools for Social Work That Offer Multiple Degrees for 2024

What is the difference between an MSW and an LCSW in 2024?

What Can You Do with a Criminology Degree: 2024 Costs & Job Opportunities

Connecticut Psychology Licensure Requirements – How to Become a Psychologist in Connecticut in 2024

Indiana Psychology Licensure Requirements – How to Become a Psychologist in Indiana in 2024

Best Psychology Schools in Tennessee – 2024 Accredited Colleges & Programs

Best Master’s in Social Work (MSW) Degree Programs in 2024

New Jersey Psychology Licensure Requirements – How to Become a Psychologist in New Jersey in 2024

Introduction to Rational Choice Theory in Social Work With Examples in 2024

PsyD vs PhD in Psychology: Comparison of Requirements and Salary in 2024

Michigan Psychology Licensure Requirements – How to Become a Psychologist in MI in 2024

Best Psychology Schools in Connecticut- 2024 Accredited Colleges & Programs

How to Become a Licensed Counselor (LPC) in Minnesota in 2024

How to Become a Psychologist: 2024 Guide to Educational Requirements and Job Outlook

Wyoming Psychology Licensure Requirements – How to Become a Psychologist in Wyoming in 2024

How to Become a Licensed Counselor (LPC) in Alaska in 2024

What is a Master of Social Work (MSW) Degree in 2024?

Best Master’s in Genetic Counseling Programs in 2024

How to Become a Licensed Counselor (LPC) in Wisconsin in 2024

Most Affordable Online Colleges for Counseling Degrees in 2024

Introduction to Social Exchange Theory in Social Work With Examples in 2024

How Much Does a Social Worker Make? 2024 Salary Guide

*The metrics for this journal are compiled based on the data for scientists listed under Social Sciences and Humanities

Recently Published Articles

2024 Most Affordable Online Finance Degree Programs Ranking in Florida

2024 Fastest Online Healthcare Degree Programs Ranking in Oklahoma

How to Become a Mental Health Counselor in Idaho in 2024

2024 Fastest Online Master's in Healthcare Degree Programs Ranking in Mount Pleasant, MI

2024 Most Valuable Online Healthcare Degree Programs Ranking in Brookings, SD

2024 Most Affordable Online Bachelor's in Healthcare Degree Programs Ranking in Orange Beach, AL

2024 most affordable online engineering degree programs ranking in hoboken, nj.

2024 Fastest Online Master's in Healthcare Degree Programs Ranking in New Orleans, LA

2024 Best Online Master's in Fine Arts Degree Programs Ranking in Missouri

2024 Fastest Online Master's in Healthcare Degree Programs Ranking in Joliet, IL

2024 fastest online human resources degree programs ranking in fayetteville, ar.

2024 Most Valuable Online Master's in Healthcare Degree Programs Ranking in Tucson, AZ

2024 Most Affordable Online Healthcare Degree Programs Ranking in Gainesville, GA

2024 Most Affordable Online Healthcare Degree Programs Ranking in San Francisco, CA

2024 fastest online master's in engineering degree programs ranking in florida.

2024 Fastest Online Bachelor's in Healthcare Degree Programs Ranking in Idaho

2024 Most Valuable Online Healthcare Degree Programs Ranking in Saint Charles, MO

2024 Most Valuable Online Finance Degree Programs Ranking in Florida

How to Become a Mental Health Counselor in Alaska in 2024

2024 Best Online Master's in Healthcare Degree Programs Ranking in New York, NY

2024 Best Online Bachelor's in Healthcare Degree Programs Ranking in Minnesota

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

The PMC website is updating on October 15, 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

Stella timotheou.

1 CYENS Center of Excellence & Cyprus University of Technology (Cyprus Interaction Lab), Cyprus, CYENS Center of Excellence & Cyprus University of Technology, Nicosia-Limassol, Cyprus

Ourania Miliou

Yiannis dimitriadis.

2 Universidad de Valladolid (UVA), Spain, Valladolid, Spain

Sara Villagrá Sobrino

Nikoleta giannoutsou, romina cachia.

3 JRC - Joint Research Centre of the European Commission, Seville, Spain

Alejandra Martínez Monés

Andri ioannou, associated data.

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the understanding, adaptation, and design of the education systems in accordance with current technological trends. These issues were emphasized during the recent COVID-19 pandemic that accelerated the use of digital technologies in education, generating questions regarding digitalization in schools. Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses. Such results have engendered the need for schools to learn and build upon the experience to enhance their digital capacity and preparedness, increase their digitalization levels, and achieve a successful digital transformation. Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem, there is a need to show how these impacts are interconnected and identify the factors that can encourage an effective and efficient change in the school environments. For this purpose, we conducted a non-systematic literature review. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors that affect the schools’ digital capacity and digital transformation. The findings suggest that ICT integration in schools impacts more than just students’ performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process. The study results shed light on how ICTs can positively contribute to the digital transformation of schools and which factors should be considered for schools to achieve effective and efficient change.

Introduction

Digital technologies have brought changes to the nature and scope of education. Versatile and disruptive technological innovations, such as smart devices, the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR) and virtual reality (VR), blockchain, and software applications have opened up new opportunities for advancing teaching and learning (Gaol & Prasolova-Førland, 2021 ; OECD, 2021 ). Hence, in recent years, education systems worldwide have increased their investment in the integration of information and communication technology (ICT) (Fernández-Gutiérrez et al., 2020 ; Lawrence & Tar, 2018 ) and prioritized their educational agendas to adapt strategies or policies around ICT integration (European Commission, 2019 ). The latter brought about issues regarding the quality of teaching and learning with ICTs (Bates, 2015 ), especially concerning the understanding, adaptation, and design of education systems in accordance with current technological trends (Balyer & Öz, 2018 ). Studies have shown that despite the investment made in the integration of technology in schools, the results have not been promising, and the intended outcomes have not yet been achieved (Delgado et al., 2015 ; Lawrence & Tar, 2018 ). These issues were exacerbated during the COVID-19 pandemic, which forced teaching across education levels to move online (Daniel, 2020 ). Online teaching accelerated the use of digital technologies generating questions regarding the process, the nature, the extent, and the effectiveness of digitalization in schools (Cachia et al., 2021 ; König et al., 2020 ). Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses (Blaskó et al., 2021 ; Di Pietro et al, 2020 ). Such results have engendered the need for schools to learn and build upon the experience in order to enhance their digital capacity (European Commission, 2020 ) and increase their digitalization levels (Costa et al., 2021 ). Digitalization offers possibilities for fundamental improvement in schools (OECD, 2021 ; Rott & Marouane, 2018 ) and touches many aspects of a school’s development (Delcker & Ifenthaler, 2021 ) . However, it is a complex process that requires large-scale transformative changes beyond the technical aspects of technology and infrastructure (Pettersson, 2021 ). Namely, digitalization refers to “ a series of deep and coordinated culture, workforce, and technology shifts and operating models ” (Brooks & McCormack, 2020 , p. 3) that brings cultural, organizational, and operational change through the integration of digital technologies (JISC, 2020 ). A successful digital transformation requires that schools increase their digital capacity levels, establishing the necessary “ culture, policies, infrastructure as well as digital competence of students and staff to support the effective integration of technology in teaching and learning practices ” (Costa et al, 2021 , p.163).

Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem (Eng, 2005 ), there is a need to show how the different elements of the impact are interconnected and to identify the factors that can encourage an effective and efficient change in the school environment. To address the issues outlined above, we formulated the following research questions:

a) What is the impact of digital technologies on education?

b) Which factors might affect a school’s digital capacity and transformation?

In the present investigation, we conducted a non-systematic literature review of publications pertaining to the impact of digital technologies on education and the factors that affect a school’s digital capacity and transformation. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors which affect the schools’ digital capacity and digital transformation.

Methodology

The non-systematic literature review presented herein covers the main theories and research published over the past 17 years on the topic. It is based on meta-analyses and review papers found in scholarly, peer-reviewed content databases and other key studies and reports related to the concepts studied (e.g., digitalization, digital capacity) from professional and international bodies (e.g., the OECD). We searched the Scopus database, which indexes various online journals in the education sector with an international scope, to collect peer-reviewed academic papers. Furthermore, we used an all-inclusive Google Scholar search to include relevant key terms or to include studies found in the reference list of the peer-reviewed papers, and other key studies and reports related to the concepts studied by professional and international bodies. Lastly, we gathered sources from the Publications Office of the European Union ( https://op.europa.eu/en/home ); namely, documents that refer to policies related to digital transformation in education.

Regarding search terms, we first searched resources on the impact of digital technologies on education by performing the following search queries: “impact” OR “effects” AND “digital technologies” AND “education”, “impact” OR “effects” AND “ICT” AND “education”. We further refined our results by adding the terms “meta-analysis” and “review” or by adjusting the search options based on the features of each database to avoid collecting individual studies that would provide limited contributions to a particular domain. We relied on meta-analyses and review studies as these consider the findings of multiple studies to offer a more comprehensive view of the research in a given area (Schuele & Justice, 2006 ). Specifically, meta-analysis studies provided quantitative evidence based on statistically verifiable results regarding the impact of educational interventions that integrate digital technologies in school classrooms (Higgins et al., 2012 ; Tolani-Brown et al., 2011 ).

However, quantitative data does not offer explanations for the challenges or difficulties experienced during ICT integration in learning and teaching (Tolani-Brown et al., 2011 ). To fill this gap, we analyzed literature reviews and gathered in-depth qualitative evidence of the benefits and implications of technology integration in schools. In the analysis presented herein, we also included policy documents and reports from professional and international bodies and governmental reports, which offered useful explanations of the key concepts of this study and provided recent evidence on digital capacity and transformation in education along with policy recommendations. The inclusion and exclusion criteria that were considered in this study are presented in Table ​ Table1 1 .

Inclusion and exclusion criteria for the selection of resources on the impact of digital technologies on education

Inclusion criteria	Exclusion criteria
• Published in 2005 or later • Review and meta-analysis studies • Formal education K-12 • Peer-reviewed articles • Articles in English • Reports from professional/international bodies • Governmental reports • Book chapters	• Ph.D. dissertations and theses • Conference poster papers • Conference papers without proceedings • Resources on higher education • Resources on pre-school education • Individual studies

To ensure a reliable extraction of information from each study and assist the research synthesis we selected the study characteristics of interest (impact) and constructed coding forms. First, an overview of the synthesis was provided by the principal investigator who described the processes of coding, data entry, and data management. The coders followed the same set of instructions but worked independently. To ensure a common understanding of the process between coders, a sample of ten studies was tested. The results were compared, and the discrepancies were identified and resolved. Additionally, to ensure an efficient coding process, all coders participated in group meetings to discuss additions, deletions, and modifications (Stock, 1994 ). Due to the methodological diversity of the studied documents we began to synthesize the literature review findings based on similar study designs. Specifically, most of the meta-analysis studies were grouped in one category due to the quantitative nature of the measured impact. These studies tended to refer to student achievement (Hattie et al., 2014 ). Then, we organized the themes of the qualitative studies in several impact categories. Lastly, we synthesized both review and meta-analysis data across the categories. In order to establish a collective understanding of the concept of impact, we referred to a previous impact study by Balanskat ( 2009 ) which investigated the impact of technology in primary schools. In this context, the impact had a more specific ICT-related meaning and was described as “ a significant influence or effect of ICT on the measured or perceived quality of (parts of) education ” (Balanskat, 2009 , p. 9). In the study presented herein, the main impacts are in relation to learning and learners, teaching, and teachers, as well as other key stakeholders who are directly or indirectly connected to the school unit.

The study’s results identified multiple dimensions of the impact of digital technologies on students’ knowledge, skills, and attitudes; on equality, inclusion, and social integration; on teachers’ professional and teaching practices; and on other school-related aspects and stakeholders. The data analysis indicated various factors that might affect the schools’ digital capacity and transformation, such as digital competencies, the teachers’ personal characteristics and professional development, as well as the school’s leadership and management, administration, infrastructure, etc. The impacts and factors found in the literature review are presented below.

Impacts of digital technologies on students’ knowledge, skills, attitudes, and emotions

The impact of ICT use on students’ knowledge, skills, and attitudes has been investigated early in the literature. Eng ( 2005 ) found a small positive effect between ICT use and students' learning. Specifically, the author reported that access to computer-assisted instruction (CAI) programs in simulation or tutorial modes—used to supplement rather than substitute instruction – could enhance student learning. The author reported studies showing that teachers acknowledged the benefits of ICT on pupils with special educational needs; however, the impact of ICT on students' attainment was unclear. Balanskat et al. ( 2006 ) found a statistically significant positive association between ICT use and higher student achievement in primary and secondary education. The authors also reported improvements in the performance of low-achieving pupils. The use of ICT resulted in further positive gains for students, namely increased attention, engagement, motivation, communication and process skills, teamwork, and gains related to their behaviour towards learning. Evidence from qualitative studies showed that teachers, students, and parents recognized the positive impact of ICT on students' learning regardless of their competence level (strong/weak students). Punie et al. ( 2006 ) documented studies that showed positive results of ICT-based learning for supporting low-achieving pupils and young people with complex lives outside the education system. Liao et al. ( 2007 ) reported moderate positive effects of computer application instruction (CAI, computer simulations, and web-based learning) over traditional instruction on primary school student's achievement. Similarly, Tamim et al. ( 2011 ) reported small to moderate positive effects between the use of computer technology (CAI, ICT, simulations, computer-based instruction, digital and hypermedia) and student achievement in formal face-to-face classrooms compared to classrooms that did not use technology. Jewitt et al., ( 2011 ) found that the use of learning platforms (LPs) (virtual learning environments, management information systems, communication technologies, and information- and resource-sharing technologies) in schools allowed primary and secondary students to access a wider variety of quality learning resources, engage in independent and personalized learning, and conduct self- and peer-review; LPs also provide opportunities for teacher assessment and feedback. Similar findings were reported by Fu ( 2013 ), who documented a list of benefits and opportunities of ICT use. According to the author, the use of ICTs helps students access digital information and course content effectively and efficiently, supports student-centered and self-directed learning, as well as the development of a creative learning environment where more opportunities for critical thinking skills are offered, and promotes collaborative learning in a distance-learning environment. Higgins et al. ( 2012 ) found consistent but small positive associations between the use of technology and learning outcomes of school-age learners (5–18-year-olds) in studies linking the provision and use of technology with attainment. Additionally, Chauhan ( 2017 ) reported a medium positive effect of technology on the learning effectiveness of primary school students compared to students who followed traditional learning instruction.

The rise of mobile technologies and hardware devices instigated investigations into their impact on teaching and learning. Sung et al. ( 2016 ) reported a moderate effect on students' performance from the use of mobile devices in the classroom compared to the use of desktop computers or the non-use of mobile devices. Schmid et al. ( 2014 ) reported medium–low to low positive effects of technology integration (e.g., CAI, ICTs) in the classroom on students' achievement and attitude compared to not using technology or using technology to varying degrees. Tamim et al. ( 2015 ) found a low statistically significant effect of the use of tablets and other smart devices in educational contexts on students' achievement outcomes. The authors suggested that tablets offered additional advantages to students; namely, they reported improvements in students’ notetaking, organizational and communication skills, and creativity. Zheng et al. ( 2016 ) reported a small positive effect of one-to-one laptop programs on students’ academic achievement across subject areas. Additional reported benefits included student-centered, individualized, and project-based learning enhanced learner engagement and enthusiasm. Additionally, the authors found that students using one-to-one laptop programs tended to use technology more frequently than in non-laptop classrooms, and as a result, they developed a range of skills (e.g., information skills, media skills, technology skills, organizational skills). Haßler et al. ( 2016 ) found that most interventions that included the use of tablets across the curriculum reported positive learning outcomes. However, from 23 studies, five reported no differences, and two reported a negative effect on students' learning outcomes. Similar results were indicated by Kalati and Kim ( 2022 ) who investigated the effect of touchscreen technologies on young students’ learning. Specifically, from 53 studies, 34 advocated positive effects of touchscreen devices on children’s learning, 17 obtained mixed findings and two studies reported negative effects.

More recently, approaches that refer to the impact of gamification with the use of digital technologies on teaching and learning were also explored. A review by Pan et al. ( 2022 ) that examined the role of learning games in fostering mathematics education in K-12 settings, reported that gameplay improved students’ performance. Integration of digital games in teaching was also found as a promising pedagogical practice in STEM education that could lead to increased learning gains (Martinez et al., 2022 ; Wang et al., 2022 ). However, although Talan et al. ( 2020 ) reported a medium effect of the use of educational games (both digital and non-digital) on academic achievement, the effect of non-digital games was higher.

Over the last two years, the effects of more advanced technologies on teaching and learning were also investigated. Garzón and Acevedo ( 2019 ) found that AR applications had a medium effect on students' learning outcomes compared to traditional lectures. Similarly, Garzón et al. ( 2020 ) showed that AR had a medium impact on students' learning gains. VR applications integrated into various subjects were also found to have a moderate effect on students’ learning compared to control conditions (traditional classes, e.g., lectures, textbooks, and multimedia use, e.g., images, videos, animation, CAI) (Chen et al., 2022b ). Villena-Taranilla et al. ( 2022 ) noted the moderate effect of VR technologies on students’ learning when these were applied in STEM disciplines. In the same meta-analysis, Villena-Taranilla et al. ( 2022 ) highlighted the role of immersive VR, since its effect on students’ learning was greater (at a high level) across educational levels (K-6) compared to semi-immersive and non-immersive integrations. In another meta-analysis study, the effect size of the immersive VR was small and significantly differentiated across educational levels (Coban et al., 2022 ). The impact of AI on education was investigated by Su and Yang ( 2022 ) and Su et al. ( 2022 ), who showed that this technology significantly improved students’ understanding of AI computer science and machine learning concepts.

It is worth noting that the vast majority of studies referred to learning gains in specific subjects. Specifically, several studies examined the impact of digital technologies on students’ literacy skills and reported positive effects on language learning (Balanskat et al., 2006 ; Grgurović et al., 2013 ; Friedel et al., 2013 ; Zheng et al., 2016 ; Chen et al., 2022b ; Savva et al., 2022 ). Also, several studies documented positive effects on specific language learning areas, namely foreign language learning (Kao, 2014 ), writing (Higgins et al., 2012 ; Wen & Walters, 2022 ; Zheng et al., 2016 ), as well as reading and comprehension (Cheung & Slavin, 2011 ; Liao et al., 2007 ; Schwabe et al., 2022 ). ICTs were also found to have a positive impact on students' performance in STEM (science, technology, engineering, and mathematics) disciplines (Arztmann et al., 2022 ; Bado, 2022 ; Villena-Taranilla et al., 2022 ; Wang et al., 2022 ). Specifically, a number of studies reported positive impacts on students’ achievement in mathematics (Balanskat et al., 2006 ; Hillmayr et al., 2020 ; Li & Ma, 2010 ; Pan et al., 2022 ; Ran et al., 2022 ; Verschaffel et al., 2019 ; Zheng et al., 2016 ). Furthermore, studies documented positive effects of ICTs on science learning (Balanskat et al., 2006 ; Liao et al., 2007 ; Zheng et al., 2016 ; Hillmayr et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ; Lei et al., 2022a ). Çelik ( 2022 ) also noted that computer simulations can help students understand learning concepts related to science. Furthermore, some studies documented that the use of ICTs had a positive impact on students’ achievement in other subjects, such as geography, history, music, and arts (Chauhan, 2017 ; Condie & Munro, 2007 ), and design and technology (Balanskat et al., 2006 ).

More specific positive learning gains were reported in a number of skills, e.g., problem-solving skills and pattern exploration skills (Higgins et al., 2012 ), metacognitive learning outcomes (Verschaffel et al., 2019 ), literacy skills, computational thinking skills, emotion control skills, and collaborative inquiry skills (Lu et al., 2022 ; Su & Yang, 2022 ; Su et al., 2022 ). Additionally, several investigations have reported benefits from the use of ICT on students’ creativity (Fielding & Murcia, 2022 ; Liu et al., 2022 ; Quah & Ng, 2022 ). Lastly, digital technologies were also found to be beneficial for enhancing students’ lifelong learning skills (Haleem et al., 2022 ).

Apart from gaining knowledge and skills, studies also reported improvement in motivation and interest in mathematics (Higgins et. al., 2019 ; Fadda et al., 2022 ) and increased positive achievement emotions towards several subjects during interventions using educational games (Lei et al., 2022a ). Chen et al. ( 2022a ) also reported a small but positive effect of digital health approaches in bullying and cyberbullying interventions with K-12 students, demonstrating that technology-based approaches can help reduce bullying and related consequences by providing emotional support, empowerment, and change of attitude. In their meta-review study, Su et al. ( 2022 ) also documented that AI technologies effectively strengthened students’ attitudes towards learning. In another meta-analysis, Arztmann et al. ( 2022 ) reported positive effects of digital games on motivation and behaviour towards STEM subjects.

Impacts of digital technologies on equality, inclusion and social integration

Although most of the reviewed studies focused on the impact of ICTs on students’ knowledge, skills, and attitudes, reports were also made on other aspects in the school context, such as equality, inclusion, and social integration. Condie and Munro ( 2007 ) documented research interventions investigating how ICT can support pupils with additional or special educational needs. While those interventions were relatively small scale and mostly based on qualitative data, their findings indicated that the use of ICTs enabled the development of communication, participation, and self-esteem. A recent meta-analysis (Baragash et al., 2022 ) with 119 participants with different disabilities, reported a significant overall effect size of AR on their functional skills acquisition. Koh’s meta-analysis ( 2022 ) also revealed that students with intellectual and developmental disabilities improved their competence and performance when they used digital games in the lessons.

Istenic Starcic and Bagon ( 2014 ) found that the role of ICT in inclusion and the design of pedagogical and technological interventions was not sufficiently explored in educational interventions with people with special needs; however, some benefits of ICT use were found in students’ social integration. The issue of gender and technology use was mentioned in a small number of studies. Zheng et al. ( 2016 ) reported a statistically significant positive interaction between one-to-one laptop programs and gender. Specifically, the results showed that girls and boys alike benefitted from the laptop program, but the effect on girls’ achievement was smaller than that on boys’. Along the same lines, Arztmann et al. ( 2022 ) reported no difference in the impact of game-based learning between boys and girls, arguing that boys and girls equally benefited from game-based interventions in STEM domains. However, results from a systematic review by Cussó-Calabuig et al. ( 2018 ) found limited and low-quality evidence on the effects of intensive use of computers on gender differences in computer anxiety, self-efficacy, and self-confidence. Based on their view, intensive use of computers can reduce gender differences in some areas and not in others, depending on contextual and implementation factors.

Impacts of digital technologies on teachers’ professional and teaching practices

Various research studies have explored the impact of ICT on teachers’ instructional practices and student assessment. Friedel et al. ( 2013 ) found that the use of mobile devices by students enabled teachers to successfully deliver content (e.g., mobile serious games), provide scaffolding, and facilitate synchronous collaborative learning. The integration of digital games in teaching and learning activities also gave teachers the opportunity to study and apply various pedagogical practices (Bado, 2022 ). Specifically, Bado ( 2022 ) found that teachers who implemented instructional activities in three stages (pre-game, game, and post-game) maximized students’ learning outcomes and engagement. For instance, during the pre-game stage, teachers focused on lectures and gameplay training, at the game stage teachers provided scaffolding on content, addressed technical issues, and managed the classroom activities. During the post-game stage, teachers organized activities for debriefing to ensure that the gameplay had indeed enhanced students’ learning outcomes.

Furthermore, ICT can increase efficiency in lesson planning and preparation by offering possibilities for a more collaborative approach among teachers. The sharing of curriculum plans and the analysis of students’ data led to clearer target settings and improvements in reporting to parents (Balanskat et al., 2006 ).

Additionally, the use and application of digital technologies in teaching and learning were found to enhance teachers’ digital competence. Balanskat et al. ( 2006 ) documented studies that revealed that the use of digital technologies in education had a positive effect on teachers’ basic ICT skills. The greatest impact was found on teachers with enough experience in integrating ICTs in their teaching and/or who had recently participated in development courses for the pedagogical use of technologies in teaching. Punie et al. ( 2006 ) reported that the provision of fully equipped multimedia portable computers and the development of online teacher communities had positive impacts on teachers’ confidence and competence in the use of ICTs.

Moreover, online assessment via ICTs benefits instruction. In particular, online assessments support the digitalization of students’ work and related logistics, allow teachers to gather immediate feedback and readjust to new objectives, and support the improvement of the technical quality of tests by providing more accurate results. Additionally, the capabilities of ICTs (e.g., interactive media, simulations) create new potential methods of testing specific skills, such as problem-solving and problem-processing skills, meta-cognitive skills, creativity and communication skills, and the ability to work productively in groups (Punie et al., 2006 ).

Impacts of digital technologies on other school-related aspects and stakeholders

There is evidence that the effective use of ICTs and the data transmission offered by broadband connections help improve administration (Balanskat et al., 2006 ). Specifically, ICTs have been found to provide better management systems to schools that have data gathering procedures in place. Condie and Munro ( 2007 ) reported impacts from the use of ICTs in schools in the following areas: attendance monitoring, assessment records, reporting to parents, financial management, creation of repositories for learning resources, and sharing of information amongst staff. Such data can be used strategically for self-evaluation and monitoring purposes which in turn can result in school improvements. Additionally, they reported that online access to other people with similar roles helped to reduce headteachers’ isolation by offering them opportunities to share insights into the use of ICT in learning and teaching and how it could be used to support school improvement. Furthermore, ICTs provided more efficient and successful examination management procedures, namely less time-consuming reporting processes compared to paper-based examinations and smooth communications between schools and examination authorities through electronic data exchange (Punie et al., 2006 ).

Zheng et al. ( 2016 ) reported that the use of ICTs improved home-school relationships. Additionally, Escueta et al. ( 2017 ) reported several ICT programs that had improved the flow of information from the school to parents. Particularly, they documented that the use of ICTs (learning management systems, emails, dedicated websites, mobile phones) allowed for personalized and customized information exchange between schools and parents, such as attendance records, upcoming class assignments, school events, and students’ grades, which generated positive results on students’ learning outcomes and attainment. Such information exchange between schools and families prompted parents to encourage their children to put more effort into their schoolwork.

The above findings suggest that the impact of ICT integration in schools goes beyond students’ performance in school subjects. Specifically, it affects a number of school-related aspects, such as equality and social integration, professional and teaching practices, and diverse stakeholders. In Table ​ Table2, 2 , we summarize the different impacts of digital technologies on school stakeholders based on the literature review, while in Table ​ Table3 3 we organized the tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript.

The impact of digital technologies on schools’ stakeholders based on the literature review

Impacts	References
Students
Knowledge, skills, attitudes, and emotions
• Learning gains from the use of ICTs across the curriculum	Eng, ; Balanskat et al., ; Liao et al., ; Tamim et al., ; Higgins et al., ; Chauhan, ; Sung et al., ; Schmid et al., ; Tamim et al., ; Zheng et al., ; Haßler et al., ; Kalati & Kim, ; Martinez et al., ; Talan et al., ; Panet al., ; Garzón & Acevedo, ; Garzón et al., ; Villena-Taranilla, et al., ; Coban et al.,
• Positive learning gains from the use of ICTs in specific school subjects (e.g., mathematics, literacy, language, science)	Arztmann et al., ; Villena-Taranilla, et al., ; Chen et al., ; Balanskat et al., ; Grgurović, et al., ; Friedel et al., ; Zheng et al., ; Savva et al., ; Kao, ; Higgins et al., ; Wen & Walters, ; Liao et al., ; Cheung & Slavin, ; Schwabe et al., ; Li & Ma, ; Verschaffel et al., ; Ran et al., ; Liao et al., ; Hillmayr et al., ; Kalemkuş & Kalemkuş, ; Lei et al., ; Condie & Munro, ; Chauhan, ; Bado, ; Wang et al., ; Pan et al.,
• Positive learning gains for special needs students and low-achieving students	Eng, ; Balanskat et al., ; Punie et al., ; Koh,
• Oportunities to develop a range of skills (e.g., subject-related skills, communication skills, negotiation skills, emotion control skills, organizational skills, critical thinking skills, creativity, metacognitive skills, life, and career skills)	Balanskat et al., ; Fu, ; Tamim et al., ; Zheng et al., ; Higgins et al., ; Verschaffel et al., ; Su & Yang, ; Su et al., ; Lu et al., ; Liu et al., ; Quah & Ng, ; Fielding & Murcia, ; Tang et al., ; Haleem et al.,
• Oportunities to develop digital skills (e.g., information skills, media skills, ICT skills)	Zheng et al., ; Su & Yang, ; Lu et al., ; Su et al.,
• Positive attitudes and behaviours towards ICTs, positive emotions (e.g., increased interest, motivation, attention, engagement, confidence, reduced anxiety, positive achievement emotions, reduction in bullying and cyberbullying)	Balanskat et al., ; Schmid et al., ; Zheng et al., ; Fadda et al., ; Higgins et al., ; Chen et al., ; Lei et al., ; Arztmann et al., ; Su et al.,
Learning experience
• Enhance access to resources	Jewitt et al., ; Fu,
• Opportunities to experience various learning practices (e.g., active learning, learner-centred learning, independent and personalized learning, collaborative learning, self-directed learning, self- and peer-review)	Jewitt et al., ; Fu,
• Improved access to teacher assessment and feedback	Jewitt et al.,
Equality, inclusion, and social integration
• Improved communication, functional skills, participation, self-esteem, and engagement of special needs students	Condie & Munro, ; Baragash et al., ; Koh,
• Enhanced social interaction for students in general and for students with learning difficulties	Istenic Starcic & Bagon,
• Benefits for both girls and boys	Zheng et al., ; Arztmann et al.,
Teachers
Professional practice
• Development of digital competence	Balanskat et al.,
• Positive attitudes and behaviours towards ICTs (e.g., increased confidence)	Punie et al., ,
• Formalized collaborative planning between teachers	Balanskat et al.,
• Improved reporting to parents	Balanskat et al.,
Teaching practice
• Efficiency in lesson planning and preparation	Balanskat et al.,
• Facilitate assessment through the provision of immediate feedback	Punie et al.,
• Improvements in the technical quality of tests	Punie et al.,
• New methods of testing specific skills (e.g., problem-solving skills, meta-cognitive skills)	Punie et al.,
• Successful content delivery and lessons	Friedel et al.,
• Application of different instructional practices (e.g., scaffolding, synchronous collaborative learning, online learning, blended learning, hybrid learning)	Friedel et al., ; Bado, ; Kazu & Yalçin, ; Ulum,
Administrators
Data-based decision-making
• Improved data-gathering processes	Balanskat et al.,
• Support monitoring and evaluation processes (e.g., attendance monitoring, financial management, assessment records)	Condie & Munro,
Organizational processes
• Access to learning resources via the creation of repositories	Condie & Munro,
• Information sharing between school staff	Condie & Munro,
• Smooth communications with external authorities (e.g., examination results)	Punie et al.,
• Efficient and successful examination management procedures	Punie et al.,
Home-school communication
• Support reporting to parents	Condie & Munro,
• Improved flow of communication between the school and parents (e.g., customized and personalized communications)	Escueta et al.,
School leaders
Professional practice
• Reduced headteacher isolation	Condie & Munro,
• Improved access to insights about practices for school improvement	Condie & Munro,
Parents
Home-school relationships
• Improved home-school relationships	Zheng et al.,
• Increased parental involvement in children’s school life	Escueta et al.,

Tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript

Technologies/tools/practices/policies	References
ICT general – various types of technologies	Eng, (review) Moran et al., (meta-analysis) Balanskat et al., (report) Punie et al., (review) Fu, (review) Higgins et al., (report) Chauhan, (meta-analysis) Schmid et al., (meta-analysis) Grgurović et al., (meta-analysis) Higgins et al., (meta-analysis) Wen & Walters, (meta-analysis) Cheung & Slavin, (meta-analysis) Li & Ma, (meta-analysis) Hillmayr et al., (meta-analysis) Verschaffel et al., (systematic review) Ran et al., (meta-analysis) Fielding & Murcia, (systematic review) Tang et al., (review) Haleem et al., (review) Condie & Munro, (review) Underwood, (review) Istenic Starcic & Bagon, (review) Cussó-Calabuig et al., (systematic review) Escueta et al. ( ) (review) Archer et al., (meta-analysis) Lee et al., (meta-analysis) Delgado et al., (review) Di Pietro et al., (report)
Practices/policies on schools’ digital transformation	Bingimlas, (review) Hardman, (review) Hattie, (synthesis of multiple meta-analysis) Trucano, (book-Knowledge maps) Ređep, (policy study) Conrads et al, (report) European Commission, (EU report) Elkordy & Lovinelli, (book chapter) Eurydice, (EU report) Vuorikari et al., (JRC paper) Sellar, (review) European Commission, (EU report) OECD, (international paper)
Computer-assisted instruction, computer simulations, activeboards, and web-based learning	Liao et al., (meta-analysis) Tamim et al., (meta-analysis) Çelik, (review) Moran et al., (meta-analysis) Eng, (review)
Learning platforms (LPs) (virtual learning environments, management information systems, communication technologies and information and resource sharing technologies)	Jewitt et al., (report)
Mobile devices—touch screens (smart devices, tablets, laptops)	Sung et al., (meta-analysis and research synthesis) Tamim et al., (meta-analysis) Tamim et al., (systematic review and meta-analysis) Zheng et al., (meta-analysis and research synthesis) Haßler et al., (review) Kalati & Kim, (systematic review) Friedel et al., (meta-analysis and review) Chen et al., (meta-analysis) Schwabe et al., (meta-analysis) Punie et al., (review)
Digital games (various types e.g., adventure, serious; various domains e.g., history, science)	Wang et al., (meta-analysis) Arztmann et al., (meta-analysis) Martinez et al., (systematic review) Talan et al., (meta-analysis) Pan et al., (systematic review) Chen et al., (meta-analysis) Kao, (meta-analysis) Fadda et al., (meta-analysis) Lu et al., (meta-analysis) Lei et al., (meta-analysis) Koh, (meta-analysis) Bado, (review)
Augmented reality (AR)	Garzón & Acevedo, (meta-analysis) Garzón et al., (meta-analysis and research synthesis) Kalemkuş & Kalemkuş, (meta-analysis) Baragash et al., (meta-analysis)
Virtual reality (VR) Immersive virtual reality (IVR)	Villena-Taranilla et al., (meta-analysis) Chen et al., (meta-analysis) Coban et al., (meta-analysis)
Artificial intelligence (AI) and robotics	Su & Yang, (review) Su et al., (meta review)
Online learning/elearning	Ulum, (meta-analysis) Cheok & Wong, (review)
Blended learning	Grgurović et al., (meta-analysis)
Synchronous parallel participation	Friedel et al., (meta-analysis and review)
Electronic books/digital storytelling	Savva et al., (meta-analysis) Quah & Ng, (systematic review)
Multimedia technology	Liu et al., (meta-analysis)
Hybrid learning	Kazu & Yalçin, (meta-analysis)

Additionally, based on the results of the literature review, there are many types of digital technologies with different affordances (see, for example, studies on VR vs Immersive VR), which evolve over time (e.g. starting from CAIs in 2005 to Augmented and Virtual reality 2020). Furthermore, these technologies are linked to different pedagogies and policy initiatives, which are critical factors in the study of impact. Table ​ Table3 3 summarizes the different tools and practices that have been used to examine the impact of digital technologies on education since 2005 based on the review results.

Factors that affect the integration of digital technologies

Although the analysis of the literature review demonstrated different impacts of the use of digital technology on education, several authors highlighted the importance of various factors, besides the technology itself, that affect this impact. For example, Liao et al. ( 2007 ) suggested that future studies should carefully investigate which factors contribute to positive outcomes by clarifying the exact relationship between computer applications and learning. Additionally, Haßler et al., ( 2016 ) suggested that the neutral findings regarding the impact of tablets on students learning outcomes in some of the studies included in their review should encourage educators, school leaders, and school officials to further investigate the potential of such devices in teaching and learning. Several other researchers suggested that a number of variables play a significant role in the impact of ICTs on students’ learning that could be attributed to the school context, teaching practices and professional development, the curriculum, and learners’ characteristics (Underwood, 2009 ; Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Tang et al., 2022 ).

Digital competencies

One of the most common challenges reported in studies that utilized digital tools in the classroom was the lack of students’ skills on how to use them. Fu ( 2013 ) found that students’ lack of technical skills is a barrier to the effective use of ICT in the classroom. Tamim et al. ( 2015 ) reported that students faced challenges when using tablets and smart mobile devices, associated with the technical issues or expertise needed for their use and the distracting nature of the devices and highlighted the need for teachers’ professional development. Higgins et al. ( 2012 ) reported that skills training about the use of digital technologies is essential for learners to fully exploit the benefits of instruction.

Delgado et al. ( 2015 ), meanwhile, reported studies that showed a strong positive association between teachers’ computer skills and students’ use of computers. Teachers’ lack of ICT skills and familiarization with technologies can become a constraint to the effective use of technology in the classroom (Balanskat et al., 2006 ; Delgado et al., 2015 ).

It is worth noting that the way teachers are introduced to ICTs affects the impact of digital technologies on education. Previous studies have shown that teachers may avoid using digital technologies due to limited digital skills (Balanskat, 2006 ), or they prefer applying “safe” technologies, namely technologies that their own teachers used and with which they are familiar (Condie & Munro, 2007 ). In this regard, the provision of digital skills training and exposure to new digital tools might encourage teachers to apply various technologies in their lessons (Condie & Munro, 2007 ). Apart from digital competence, technical support in the school setting has also been shown to affect teachers’ use of technology in their classrooms (Delgado et al., 2015 ). Ferrari et al. ( 2011 ) found that while teachers’ use of ICT is high, 75% stated that they needed more institutional support and a shift in the mindset of educational actors to achieve more innovative teaching practices. The provision of support can reduce time and effort as well as cognitive constraints, which could cause limited ICT integration in the school lessons by teachers (Escueta et al., 2017 ).

Teachers’ personal characteristics, training approaches, and professional development

Teachers’ personal characteristics and professional development affect the impact of digital technologies on education. Specifically, Cheok and Wong ( 2015 ) found that teachers’ personal characteristics (e.g., anxiety, self-efficacy) are associated with their satisfaction and engagement with technology. Bingimlas ( 2009 ) reported that lack of confidence, resistance to change, and negative attitudes in using new technologies in teaching are significant determinants of teachers’ levels of engagement in ICT. The same author reported that the provision of technical support, motivation support (e.g., awards, sufficient time for planning), and training on how technologies can benefit teaching and learning can eliminate the above barriers to ICT integration. Archer et al. ( 2014 ) found that comfort levels in using technology are an important predictor of technology integration and argued that it is essential to provide teachers with appropriate training and ongoing support until they are comfortable with using ICTs in the classroom. Hillmayr et al. ( 2020 ) documented that training teachers on ICT had an important effecton students’ learning.

According to Balanskat et al. ( 2006 ), the impact of ICTs on students’ learning is highly dependent on the teachers’ capacity to efficiently exploit their application for pedagogical purposes. Results obtained from the Teaching and Learning International Survey (TALIS) (OECD, 2021 ) revealed that although schools are open to innovative practices and have the capacity to adopt them, only 39% of teachers in the European Union reported that they are well or very well prepared to use digital technologies for teaching. Li and Ma ( 2010 ) and Hardman ( 2019 ) showed that the positive effect of technology on students’ achievement depends on the pedagogical practices used by teachers. Schmid et al. ( 2014 ) reported that learning was best supported when students were engaged in active, meaningful activities with the use of technological tools that provided cognitive support. Tamim et al. ( 2015 ) compared two different pedagogical uses of tablets and found a significant moderate effect when the devices were used in a student-centered context and approach rather than within teacher-led environments. Similarly, Garzón and Acevedo ( 2019 ) and Garzón et al. ( 2020 ) reported that the positive results from the integration of AR applications could be attributed to the existence of different variables which could influence AR interventions (e.g., pedagogical approach, learning environment, and duration of the intervention). Additionally, Garzón et al. ( 2020 ) suggested that the pedagogical resources that teachers used to complement their lectures and the pedagogical approaches they applied were crucial to the effective integration of AR on students’ learning gains. Garzón and Acevedo ( 2019 ) also emphasized that the success of a technology-enhanced intervention is based on both the technology per se and its characteristics and on the pedagogical strategies teachers choose to implement. For instance, their results indicated that the collaborative learning approach had the highest impact on students’ learning gains among other approaches (e.g., inquiry-based learning, situated learning, or project-based learning). Ran et al. ( 2022 ) also found that the use of technology to design collaborative and communicative environments showed the largest moderator effects among the other approaches.

Hattie ( 2008 ) reported that the effective use of computers is associated with training teachers in using computers as a teaching and learning tool. Zheng et al. ( 2016 ) noted that in addition to the strategies teachers adopt in teaching, ongoing professional development is also vital in ensuring the success of technology implementation programs. Sung et al. ( 2016 ) found that research on the use of mobile devices to support learning tends to report that the insufficient preparation of teachers is a major obstacle in implementing effective mobile learning programs in schools. Friedel et al. ( 2013 ) found that providing training and support to teachers increased the positive impact of the interventions on students’ learning gains. Trucano ( 2005 ) argued that positive impacts occur when digital technologies are used to enhance teachers’ existing pedagogical philosophies. Higgins et al. ( 2012 ) found that the types of technologies used and how they are used could also affect students’ learning. The authors suggested that training and professional development of teachers that focuses on the effective pedagogical use of technology to support teaching and learning is an important component of successful instructional approaches (Higgins et al., 2012 ). Archer et al. ( 2014 ) found that studies that reported ICT interventions during which teachers received training and support had moderate positive effects on students’ learning outcomes, which were significantly higher than studies where little or no detail about training and support was mentioned. Fu ( 2013 ) reported that the lack of teachers’ knowledge and skills on the technical and instructional aspects of ICT use in the classroom, in-service training, pedagogy support, technical and financial support, as well as the lack of teachers’ motivation and encouragement to integrate ICT on their teaching were significant barriers to the integration of ICT in education.

School leadership and management

Management and leadership are important cornerstones in the digital transformation process (Pihir et al., 2018 ). Zheng et al. ( 2016 ) documented leadership among the factors positively affecting the successful implementation of technology integration in schools. Strong leadership, strategic planning, and systematic integration of digital technologies are prerequisites for the digital transformation of education systems (Ređep, 2021 ). Management and leadership play a significant role in formulating policies that are translated into practice and ensure that developments in ICT become embedded into the life of the school and in the experiences of staff and pupils (Condie & Munro, 2007 ). Policy support and leadership must include the provision of an overall vision for the use of digital technologies in education, guidance for students and parents, logistical support, as well as teacher training (Conrads et al., 2017 ). Unless there is a commitment throughout the school, with accountability for progress at key points, it is unlikely for ICT integration to be sustained or become part of the culture (Condie & Munro, 2007 ). To achieve this, principals need to adopt and promote a whole-institution strategy and build a strong mutual support system that enables the school’s technological maturity (European Commission, 2019 ). In this context, school culture plays an essential role in shaping the mindsets and beliefs of school actors towards successful technology integration. Condie and Munro ( 2007 ) emphasized the importance of the principal’s enthusiasm and work as a source of inspiration for the school staff and the students to cultivate a culture of innovation and establish sustainable digital change. Specifically, school leaders need to create conditions in which the school staff is empowered to experiment and take risks with technology (Elkordy & Lovinelli, 2020 ).

In order for leaders to achieve the above, it is important to develop capacities for learning and leading, advocating professional learning, and creating support systems and structures (European Commission, 2019 ). Digital technology integration in education systems can be challenging and leadership needs guidance to achieve it. Such guidance can be introduced through the adoption of new methods and techniques in strategic planning for the integration of digital technologies (Ređep, 2021 ). Even though the role of leaders is vital, the relevant training offered to them has so far been inadequate. Specifically, only a third of the education systems in Europe have put in place national strategies that explicitly refer to the training of school principals (European Commission, 2019 , p. 16).

Connectivity, infrastructure, and government and other support

The effective integration of digital technologies across levels of education presupposes the development of infrastructure, the provision of digital content, and the selection of proper resources (Voogt et al., 2013 ). Particularly, a high-quality broadband connection in the school increases the quality and quantity of educational activities. There is evidence that ICT increases and formalizes cooperative planning between teachers and cooperation with managers, which in turn has a positive impact on teaching practices (Balanskat et al., 2006 ). Additionally, ICT resources, including software and hardware, increase the likelihood of teachers integrating technology into the curriculum to enhance their teaching practices (Delgado et al., 2015 ). For example, Zheng et al. ( 2016 ) found that the use of one-on-one laptop programs resulted in positive changes in teaching and learning, which would not have been accomplished without the infrastructure and technical support provided to teachers. Delgado et al. ( 2015 ) reported that limited access to technology (insufficient computers, peripherals, and software) and lack of technical support are important barriers to ICT integration. Access to infrastructure refers not only to the availability of technology in a school but also to the provision of a proper amount and the right types of technology in locations where teachers and students can use them. Effective technical support is a central element of the whole-school strategy for ICT (Underwood, 2009 ). Bingimlas ( 2009 ) reported that lack of technical support in the classroom and whole-school resources (e.g., failing to connect to the Internet, printers not printing, malfunctioning computers, and working on old computers) are significant barriers that discourage the use of ICT by teachers. Moreover, poor quality and inadequate hardware maintenance, and unsuitable educational software may discourage teachers from using ICTs (Balanskat et al., 2006 ; Bingimlas, 2009 ).

Government support can also impact the integration of ICTs in teaching. Specifically, Balanskat et al. ( 2006 ) reported that government interventions and training programs increased teachers’ enthusiasm and positive attitudes towards ICT and led to the routine use of embedded ICT.

Lastly, another important factor affecting digital transformation is the development and quality assurance of digital learning resources. Such resources can be support textbooks and related materials or resources that focus on specific subjects or parts of the curriculum. Policies on the provision of digital learning resources are essential for schools and can be achieved through various actions. For example, some countries are financing web portals that become repositories, enabling teachers to share resources or create their own. Additionally, they may offer e-learning opportunities or other services linked to digital education. In other cases, specific agencies of projects have also been set up to develop digital resources (Eurydice, 2019 ).

Administration and digital data management

The digital transformation of schools involves organizational improvements at the level of internal workflows, communication between the different stakeholders, and potential for collaboration. Vuorikari et al. ( 2020 ) presented evidence that digital technologies supported the automation of administrative practices in schools and reduced the administration’s workload. There is evidence that digital data affects the production of knowledge about schools and has the power to transform how schooling takes place. Specifically, Sellar ( 2015 ) reported that data infrastructure in education is developing due to the demand for “ information about student outcomes, teacher quality, school performance, and adult skills, associated with policy efforts to increase human capital and productivity practices ” (p. 771). In this regard, practices, such as datafication which refers to the “ translation of information about all kinds of things and processes into quantified formats” have become essential for decision-making based on accountability reports about the school’s quality. The data could be turned into deep insights about education or training incorporating ICTs. For example, measuring students’ online engagement with the learning material and drawing meaningful conclusions can allow teachers to improve their educational interventions (Vuorikari et al., 2020 ).

Students’ socioeconomic background and family support

Research show that the active engagement of parents in the school and their support for the school’s work can make a difference to their children’s attitudes towards learning and, as a result, their achievement (Hattie, 2008 ). In recent years, digital technologies have been used for more effective communication between school and family (Escueta et al., 2017 ). The European Commission ( 2020 ) presented data from a Eurostat survey regarding the use of computers by students during the pandemic. The data showed that younger pupils needed additional support and guidance from parents and the challenges were greater for families in which parents had lower levels of education and little to no digital skills.

In this regard, the socio-economic background of the learners and their socio-cultural environment also affect educational achievements (Punie et al., 2006 ). Trucano documented that the use of computers at home positively influenced students’ confidence and resulted in more frequent use at school, compared to students who had no home access (Trucano, 2005 ). In this sense, the socio-economic background affects the access to computers at home (OECD, 2015 ) which in turn influences the experience of ICT, an important factor for school achievement (Punie et al., 2006 ; Underwood, 2009 ). Furthermore, parents from different socio-economic backgrounds may have different abilities and availability to support their children in their learning process (Di Pietro et al., 2020 ).

Schools’ socioeconomic context and emergency situations

The socio-economic context of the school is closely related to a school’s digital transformation. For example, schools in disadvantaged, rural, or deprived areas are likely to lack the digital capacity and infrastructure required to adapt to the use of digital technologies during emergency periods, such as the COVID-19 pandemic (Di Pietro et al., 2020 ). Data collected from school principals confirmed that in several countries, there is a rural/urban divide in connectivity (OECD, 2015 ).

Emergency periods also affect the digitalization of schools. The COVID-19 pandemic led to the closure of schools and forced them to seek appropriate and connective ways to keep working on the curriculum (Di Pietro et al., 2020 ). The sudden large-scale shift to distance and online teaching and learning also presented challenges around quality and equity in education, such as the risk of increased inequalities in learning, digital, and social, as well as teachers facing difficulties coping with this demanding situation (European Commission, 2020 ).

Looking at the findings of the above studies, we can conclude that the impact of digital technologies on education is influenced by various actors and touches many aspects of the school ecosystem. Figure  1 summarizes the factors affecting the digital technologies’ impact on school stakeholders based on the findings from the literature review.

Factors that affect the impact of ICTs on education

The findings revealed that the use of digital technologies in education affects a variety of actors within a school’s ecosystem. First, we observed that as technologies evolve, so does the interest of the research community to apply them to school settings. Figure  2 summarizes the trends identified in current research around the impact of digital technologies on schools’ digital capacity and transformation as found in the present study. Starting as early as 2005, when computers, simulations, and interactive boards were the most commonly applied tools in school interventions (e.g., Eng, 2005 ; Liao et al., 2007 ; Moran et al., 2008 ; Tamim et al., 2011 ), moving towards the use of learning platforms (Jewitt et al., 2011 ), then to the use of mobile devices and digital games (e.g., Tamim et al., 2015 ; Sung et al., 2016 ; Talan et al., 2020 ), as well as e-books (e.g., Savva et al., 2022 ), to the more recent advanced technologies, such as AR and VR applications (e.g., Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ), or robotics and AI (e.g., Su & Yang, 2022 ; Su et al., 2022 ). As this evolution shows, digital technologies are a concept in flux with different affordances and characteristics. Additionally, from an instructional perspective, there has been a growing interest in different modes and models of content delivery such as online, blended, and hybrid modes (e.g., Cheok & Wong, 2015 ; Kazu & Yalçin, 2022 ; Ulum, 2022 ). This is an indication that the value of technologies to support teaching and learning as well as other school-related practices is increasingly recognized by the research and school community. The impact results from the literature review indicate that ICT integration on students’ learning outcomes has effects that are small (Coban et al., 2022 ; Eng, 2005 ; Higgins et al., 2012 ; Schmid et al., 2014 ; Tamim et al., 2015 ; Zheng et al., 2016 ) to moderate (Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Liao et al., 2007 ; Sung et al., 2016 ; Talan et al., 2020 ; Wen & Walters, 2022 ). That said, a number of recent studies have reported high effect sizes (e.g., Kazu & Yalçin, 2022 ).

Current work and trends in the study of the impact of digital technologies on schools’ digital capacity

Based on these findings, several authors have suggested that the impact of technology on education depends on several variables and not on the technology per se (Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Lei et al., 2022a ). While the impact of ICTs on student achievement has been thoroughly investigated by researchers, other aspects related to school life that are also affected by ICTs, such as equality, inclusion, and social integration have received less attention. Further analysis of the literature review has revealed a greater investment in ICT interventions to support learning and teaching in the core subjects of literacy and STEM disciplines, especially mathematics, and science. These were the most common subjects studied in the reviewed papers often drawing on national testing results, while studies that investigated other subject areas, such as social studies, were limited (Chauhan, 2017 ; Condie & Munro, 2007 ). As such, research is still lacking impact studies that focus on the effects of ICTs on a range of curriculum subjects.

The qualitative research provided additional information about the impact of digital technologies on education, documenting positive effects and giving more details about implications, recommendations, and future research directions. Specifically, the findings regarding the role of ICTs in supporting learning highlight the importance of teachers’ instructional practice and the learning context in the use of technologies and consequently their impact on instruction (Çelik, 2022 ; Schmid et al., 2014 ; Tamim et al., 2015 ). The review also provided useful insights regarding the various factors that affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the transformation process. Specifically, these factors include a) digital competencies; b) teachers’ personal characteristics and professional development; c) school leadership and management; d) connectivity, infrastructure, and government support; e) administration and data management practices; f) students’ socio-economic background and family support and g) the socioeconomic context of the school and emergency situations. It is worth noting that we observed factors that affect the integration of ICTs in education but may also be affected by it. For example, the frequent use of ICTs and the use of laptops by students for instructional purposes positively affect the development of digital competencies (Zheng et al., 2016 ) and at the same time, the digital competencies affect the use of ICTs (Fu, 2013 ; Higgins et al., 2012 ). As a result, the impact of digital technologies should be explored more as an enabler of desirable and new practices and not merely as a catalyst that improves the output of the education process i.e. namely student attainment.

Conclusions

Digital technologies offer immense potential for fundamental improvement in schools. However, investment in ICT infrastructure and professional development to improve school education are yet to provide fruitful results. Digital transformation is a complex process that requires large-scale transformative changes that presuppose digital capacity and preparedness. To achieve such changes, all actors within the school’s ecosystem need to share a common vision regarding the integration of ICTs in education and work towards achieving this goal. Our literature review, which synthesized quantitative and qualitative data from a list of meta-analyses and review studies, provided useful insights into the impact of ICTs on different school stakeholders and showed that the impact of digital technologies touches upon many different aspects of school life, which are often overlooked when the focus is on student achievement as the final output of education. Furthermore, the concept of digital technologies is a concept in flux as technologies are not only different among them calling for different uses in the educational practice but they also change through time. Additionally, we opened a forum for discussion regarding the factors that affect a school’s digital capacity and transformation. We hope that our study will inform policy, practice, and research and result in a paradigm shift towards more holistic approaches in impact and assessment studies.

Study limitations and future directions

We presented a review of the study of digital technologies' impact on education and factors influencing schools’ digital capacity and transformation. The study results were based on a non-systematic literature review grounded on the acquisition of documentation in specific databases. Future studies should investigate more databases to corroborate and enhance our results. Moreover, search queries could be enhanced with key terms that could provide additional insights about the integration of ICTs in education, such as “policies and strategies for ICT integration in education”. Also, the study drew information from meta-analyses and literature reviews to acquire evidence about the effects of ICT integration in schools. Such evidence was mostly based on the general conclusions of the studies. It is worth mentioning that, we located individual studies which showed different, such as negative or neutral results. Thus, further insights are needed about the impact of ICTs on education and the factors influencing the impact. Furthermore, the nature of the studies included in meta-analyses and reviews is different as they are based on different research methodologies and data gathering processes. For instance, in a meta-analysis, the impact among the studies investigated is measured in a particular way, depending on policy or research targets (e.g., results from national examinations, pre-/post-tests). Meanwhile, in literature reviews, qualitative studies offer additional insights and detail based on self-reports and research opinions on several different aspects and stakeholders who could affect and be affected by ICT integration. As a result, it was challenging to draw causal relationships between so many interrelating variables.

Despite the challenges mentioned above, this study envisaged examining school units as ecosystems that consist of several actors by bringing together several variables from different research epistemologies to provide an understanding of the integration of ICTs. However, the use of other tools and methodologies and models for evaluation of the impact of digital technologies on education could give more detailed data and more accurate results. For instance, self-reflection tools, like SELFIE—developed on the DigCompOrg framework- (Kampylis et al., 2015 ; Bocconi & Lightfoot, 2021 ) can help capture a school’s digital capacity and better assess the impact of ICTs on education. Furthermore, the development of a theory of change could be a good approach for documenting the impact of digital technologies on education. Specifically, theories of change are models used for the evaluation of interventions and their impact; they are developed to describe how interventions will work and give the desired outcomes (Mayne, 2015 ). Theory of change as a methodological approach has also been used by researchers to develop models for evaluation in the field of education (e.g., Aromatario et al., 2019 ; Chapman & Sammons, 2013 ; De Silva et al., 2014 ).

We also propose that future studies aim at similar investigations by applying more holistic approaches for impact assessment that can provide in-depth data about the impact of digital technologies on education. For instance, future studies could focus on different research questions about the technologies that are used during the interventions or the way the implementation takes place (e.g., What methodologies are used for documenting impact? How are experimental studies implemented? How can teachers be taken into account and trained on the technology and its functions? What are the elements of an appropriate and successful implementation? How is the whole intervention designed? On which learning theories is the technology implementation based?).

Future research could also focus on assessing the impact of digital technologies on various other subjects since there is a scarcity of research related to particular subjects, such as geography, history, arts, music, and design and technology. More research should also be done about the impact of ICTs on skills, emotions, and attitudes, and on equality, inclusion, social interaction, and special needs education. There is also a need for more research about the impact of ICTs on administration, management, digitalization, and home-school relationships. Additionally, although new forms of teaching and learning with the use of ICTs (e.g., blended, hybrid, and online learning) have initiated several investigations in mainstream classrooms, only a few studies have measured their impact on students’ learning. Additionally, our review did not document any study about the impact of flipped classrooms on K-12 education. Regarding teaching and learning approaches, it is worth noting that studies referred to STEM or STEAM did not investigate the impact of STEM/STEAM as an interdisciplinary approach to learning but only investigated the impact of ICTs on learning in each domain as a separate subject (science, technology, engineering, arts, mathematics). Hence, we propose future research to also investigate the impact of the STEM/STEAM approach on education. The impact of emerging technologies on education, such as AR, VR, robotics, and AI has also been investigated recently, but more work needs to be done.

Finally, we propose that future studies could focus on the way in which specific factors, e.g., infrastructure and government support, school leadership and management, students’ and teachers’ digital competencies, approaches teachers utilize in the teaching and learning (e.g., blended, online and hybrid learning, flipped classrooms, STEM/STEAM approach, project-based learning, inquiry-based learning), affect the impact of digital technologies on education. We hope that future studies will give detailed insights into the concept of schools’ digital transformation through further investigation of impacts and factors which influence digital capacity and transformation based on the results and the recommendations of the present study.

Acknowledgements

This project has received funding under Grant Agreement No Ref Ares (2021) 339036 7483039 as well as funding from the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy. The UVa co-authors would like also to acknowledge funding from the European Regional Development Fund and the National Research Agency of the Spanish Ministry of Science and Innovation, under project grant PID2020-112584RB-C32.

Data availability statement

Declarations.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Archer K, Savage R, Sanghera-Sidhu S, Wood E, Gottardo A, Chen V. Examining the effectiveness of technology use in classrooms: A tertiary meta-analysis. Computers & Education. 2014; 78 :140–149. doi: 10.1016/j.compedu.2014.06.001. [ CrossRef ] [ Google Scholar ]
• Aromatario O, Van Hoye A, Vuillemin A, Foucaut AM, Pommier J, Cambon L. Using theory of change to develop an intervention theory for designing and evaluating behavior change SDApps for healthy eating and physical exercise: The OCAPREV theory. BMC Public Health. 2019; 19 (1):1–12. doi: 10.1186/s12889-019-7828-4. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Arztmann, M., Hornstra, L., Jeuring, J., & Kester, L. (2022). Effects of games in STEM education: A meta-analysis on the moderating role of student background characteristics. Studies in Science Education , 1-37. 10.1080/03057267.2022.2057732
• Bado N. Game-based learning pedagogy: A review of the literature. Interactive Learning Environments. 2022; 30 (5):936–948. doi: 10.1080/10494820.2019.1683587. [ CrossRef ] [ Google Scholar ]
• Balanskat, A. (2009). Study of the impact of technology in primary schools – Synthesis Report. Empirica and European Schoolnet. Retrieved 30 June 2022 from: https://erte.dge.mec.pt/sites/default/files/Recursos/Estudos/synthesis_report_steps_en.pdf
• Balanskat, A. (2006). The ICT Impact Report: A review of studies of ICT impact on schools in Europe, European Schoolnet. Retrieved 30 June 2022 from:  https://en.unesco.org/icted/content/ict-impact-report-review-studies-ict-impact-schools-europe
• Balanskat, A., Blamire, R., & Kefala, S. (2006). The ICT impact report.  European Schoolnet . Retrieved from: http://colccti.colfinder.org/sites/default/files/ict_impact_report_0.pdf
• Balyer, A., & Öz, Ö. (2018). Academicians’ views on digital transformation in education. International Online Journal of Education and Teaching (IOJET), 5 (4), 809–830. Retrieved 30 June 2022 from  http://iojet.org/index.php/IOJET/article/view/441/295
• Baragash RS, Al-Samarraie H, Moody L, Zaqout F. Augmented reality and functional skills acquisition among individuals with special needs: A meta-analysis of group design studies. Journal of Special Education Technology. 2022; 37 (1):74–81. doi: 10.1177/0162643420910413. [ CrossRef ] [ Google Scholar ]
• Bates, A. W. (2015). Teaching in a digital age: Guidelines for designing teaching and learning . Open Educational Resources Collection . 6. Retrieved 30 June 2022 from: https://irl.umsl.edu/oer/6
• Bingimlas KA. Barriers to the successful integration of ICT in teaching and learning environments: A review of the literature. Eurasia Journal of Mathematics, Science and Technology Education. 2009; 5 (3):235–245. doi: 10.12973/ejmste/75275. [ CrossRef ] [ Google Scholar ]
• Blaskó Z, Costa PD, Schnepf SV. Learning losses and educational inequalities in Europe: Mapping the potential consequences of the COVID-19 crisis. Journal of European Social Policy. 2022; 32 (4):361–375. doi: 10.1177/09589287221091687. [ CrossRef ] [ Google Scholar ]
• Bocconi S, Lightfoot M. Scaling up and integrating the selfie tool for schools' digital capacity in education and training systems: Methodology and lessons learnt. European Training Foundation. 2021 doi: 10.2816/907029,JRC123936. [ CrossRef ] [ Google Scholar ]
• Brooks, D. C., & McCormack, M. (2020). Driving Digital Transformation in Higher Education . Retrieved 30 June 2022 from: https://library.educause.edu/-/media/files/library/2020/6/dx2020.pdf?la=en&hash=28FB8C377B59AFB1855C225BBA8E3CFBB0A271DA
• Cachia, R., Chaudron, S., Di Gioia, R., Velicu, A., & Vuorikari, R. (2021). Emergency remote schooling during COVID-19, a closer look at European families. Retrieved 30 June 2022 from  https://publications.jrc.ec.europa.eu/repository/handle/JRC125787
• Çelik B. The effects of computer simulations on students’ science process skills: Literature review. Canadian Journal of Educational and Social Studies. 2022; 2 (1):16–28. doi: 10.53103/cjess.v2i1.17. [ CrossRef ] [ Google Scholar ]
• Chapman, C., & Sammons, P. (2013). School Self-Evaluation for School Improvement: What Works and Why? . CfBT Education Trust. 60 Queens Road, Reading, RG1 4BS, England.
• Chauhan S. A meta-analysis of the impact of technology on learning effectiveness of elementary students. Computers & Education. 2017; 105 :14–30. doi: 10.1016/j.compedu.2016.11.005. [ CrossRef ] [ Google Scholar ]
• Chen, Q., Chan, K. L., Guo, S., Chen, M., Lo, C. K. M., & Ip, P. (2022a). Effectiveness of digital health interventions in reducing bullying and cyberbullying: a meta-analysis. Trauma, Violence, & Abuse , 15248380221082090. 10.1177/15248380221082090 [ PubMed ]
• Chen B, Wang Y, Wang L. The effects of virtual reality-assisted language learning: A meta-analysis. Sustainability. 2022; 14 (6):3147. doi: 10.3390/su14063147. [ CrossRef ] [ Google Scholar ]
• Cheok ML, Wong SL. Predictors of e-learning satisfaction in teaching and learning for school teachers: A literature review. International Journal of Instruction. 2015; 8 (1):75–90. doi: 10.12973/iji.2015.816a. [ CrossRef ] [ Google Scholar ]
• Cheung, A. C., & Slavin, R. E. (2011). The Effectiveness of Education Technology for Enhancing Reading Achievement: A Meta-Analysis. Center for Research and reform in Education .
• Coban, M., Bolat, Y. I., & Goksu, I. (2022). The potential of immersive virtual reality to enhance learning: A meta-analysis. Educational Research Review , 100452. 10.1016/j.edurev.2022.100452
• Condie, R., & Munro, R. K. (2007). The impact of ICT in schools-a landscape review. Retrieved 30 June 2022 from: https://oei.org.ar/ibertic/evaluacion/sites/default/files/biblioteca/33_impact_ict_in_schools.pdf
• Conrads, J., Rasmussen, M., Winters, N., Geniet, A., Langer, L., (2017). Digital Education Policies in Europe and Beyond: Key Design Principles for More Effective Policies. Redecker, C., P. Kampylis, M. Bacigalupo, Y. Punie (ed.), EUR 29000 EN, Publications Office of the European Union, Luxembourg, 10.2760/462941
• Costa P, Castaño-Muñoz J, Kampylis P. Capturing schools’ digital capacity: Psychometric analyses of the SELFIE self-reflection tool. Computers & Education. 2021; 162 :104080. doi: 10.1016/j.compedu.2020.104080. [ CrossRef ] [ Google Scholar ]
• Cussó-Calabuig R, Farran XC, Bosch-Capblanch X. Effects of intensive use of computers in secondary school on gender differences in attitudes towards ICT: A systematic review. Education and Information Technologies. 2018; 23 (5):2111–2139. doi: 10.1007/s10639-018-9706-6. [ CrossRef ] [ Google Scholar ]
• Daniel SJ. Education and the COVID-19 pandemic. Prospects. 2020; 49 (1):91–96. doi: 10.1007/s11125-020-09464-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Delcker J, Ifenthaler D. Teachers’ perspective on school development at German vocational schools during the Covid-19 pandemic. Technology, Pedagogy and Education. 2021; 30 (1):125–139. doi: 10.1080/1475939X.2020.1857826. [ CrossRef ] [ Google Scholar ]
• Delgado, A., Wardlow, L., O’Malley, K., & McKnight, K. (2015). Educational technology: A review of the integration, resources, and effectiveness of technology in K-12 classrooms. Journal of Information Technology Education Research , 14, 397. Retrieved 30 June 2022 from  http://www.jite.org/documents/Vol14/JITEv14ResearchP397-416Delgado1829.pdf
• De Silva MJ, Breuer E, Lee L, Asher L, Chowdhary N, Lund C, Patel V. Theory of change: A theory-driven approach to enhance the Medical Research Council's framework for complex interventions. Trials. 2014; 15 (1):1–13. doi: 10.1186/1745-6215-15-267. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Di Pietro G, Biagi F, Costa P, Karpiński Z, Mazza J. The likely impact of COVID-19 on education: Reflections based on the existing literature and recent international datasets. Publications Office of the European Union; 2020. [ Google Scholar ]
• Elkordy A, Lovinelli J. Competencies, Culture, and Change: A Model for Digital Transformation in K12 Educational Contexts. In: Ifenthaler D, Hofhues S, Egloffstein M, Helbig C, editors. Digital Transformation of Learning Organizations. Springer; 2020. pp. 203–219. [ Google Scholar ]
• Eng TS. The impact of ICT on learning: A review of research. International Education Journal. 2005; 6 (5):635–650. [ Google Scholar ]
• European Commission. (2020). Digital Education Action Plan 2021 – 2027. Resetting education and training for the digital age. Retrieved 30 June 2022 from  https://ec.europa.eu/education/sites/default/files/document-library-docs/deap-communication-sept2020_en.pdf
• European Commission. (2019). 2 nd survey of schools: ICT in education. Objective 1: Benchmark progress in ICT in schools . Retrieved 30 June 2022 from: https://data.europa.eu/euodp/data/storage/f/2019-03-19T084831/FinalreportObjective1-BenchmarkprogressinICTinschools.pdf
• Eurydice. (2019). Digital Education at School in Europe , Luxembourg: Publications Office of the European Union. Retrieved 30 June 2022 from: https://eacea.ec.europa.eu/national-policies/eurydice/content/digital-education-school-europe_en
• Escueta, M., Quan, V., Nickow, A. J., & Oreopoulos, P. (2017). Education technology: An evidence-based review. Retrieved 30 June 2022 from  https://ssrn.com/abstract=3031695
• Fadda D, Pellegrini M, Vivanet G, Zandonella Callegher C. Effects of digital games on student motivation in mathematics: A meta-analysis in K-12. Journal of Computer Assisted Learning. 2022; 38 (1):304–325. doi: 10.1111/jcal.12618. [ CrossRef ] [ Google Scholar ]
• Fernández-Gutiérrez M, Gimenez G, Calero J. Is the use of ICT in education leading to higher student outcomes? Analysis from the Spanish Autonomous Communities. Computers & Education. 2020; 157 :103969. doi: 10.1016/j.compedu.2020.103969. [ CrossRef ] [ Google Scholar ]
• Ferrari, A., Cachia, R., & Punie, Y. (2011). Educational change through technology: A challenge for obligatory schooling in Europe. Lecture Notes in Computer Science , 6964 , 97–110. Retrieved 30 June 2022  https://link.springer.com/content/pdf/10.1007/978-3-642-23985-4.pdf
• Fielding, K., & Murcia, K. (2022). Research linking digital technologies to young children’s creativity: An interpretive framework and systematic review. Issues in Educational Research , 32 (1), 105–125. Retrieved 30 June 2022 from  http://www.iier.org.au/iier32/fielding-abs.html
• Friedel, H., Bos, B., Lee, K., & Smith, S. (2013). The impact of mobile handheld digital devices on student learning: A literature review with meta-analysis. In Society for Information Technology & Teacher Education International Conference (pp. 3708–3717). Association for the Advancement of Computing in Education (AACE).
• Fu JS. ICT in education: A critical literature review and its implications. International Journal of Education and Development Using Information and Communication Technology (IJEDICT) 2013; 9 (1):112–125. [ Google Scholar ]
• Gaol FL, Prasolova-Førland E. Special section editorial: The frontiers of augmented and mixed reality in all levels of education. Education and Information Technologies. 2022; 27 (1):611–623. doi: 10.1007/s10639-021-10746-2. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Garzón J, Acevedo J. Meta-analysis of the impact of Augmented Reality on students’ learning gains. Educational Research Review. 2019; 27 :244–260. doi: 10.1016/j.edurev.2019.04.001. [ CrossRef ] [ Google Scholar ]
• Garzón, J., Baldiris, S., Gutiérrez, J., & Pavón, J. (2020). How do pedagogical approaches affect the impact of augmented reality on education? A meta-analysis and research synthesis. Educational Research Review , 100334. 10.1016/j.edurev.2020.100334
• Grgurović M, Chapelle CA, Shelley MC. A meta-analysis of effectiveness studies on computer technology-supported language learning. ReCALL. 2013; 25 (2):165–198. doi: 10.1017/S0958344013000013. [ CrossRef ] [ Google Scholar ]
• Haßler B, Major L, Hennessy S. Tablet use in schools: A critical review of the evidence for learning outcomes. Journal of Computer Assisted Learning. 2016; 32 (2):139–156. doi: 10.1111/jcal.12123. [ CrossRef ] [ Google Scholar ]
• Haleem A, Javaid M, Qadri MA, Suman R. Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers. 2022; 3 :275–285. doi: 10.1016/j.susoc.2022.05.004. [ CrossRef ] [ Google Scholar ]
• Hardman J. Towards a pedagogical model of teaching with ICTs for mathematics attainment in primary school: A review of studies 2008–2018. Heliyon. 2019; 5 (5):e01726. doi: 10.1016/j.heliyon.2019.e01726. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hattie J, Rogers HJ, Swaminathan H. The role of meta-analysis in educational research. In: Reid AD, Hart P, Peters MA, editors. A companion to research in education. Springer; 2014. pp. 197–207. [ Google Scholar ]
• Hattie J. Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge. 2008 doi: 10.4324/9780203887332. [ CrossRef ] [ Google Scholar ]
• Higgins S, Xiao Z, Katsipataki M. The impact of digital technology on learning: A summary for the education endowment foundation. Education Endowment Foundation and Durham University; 2012. [ Google Scholar ]
• Higgins, K., Huscroft-D’Angelo, J., & Crawford, L. (2019). Effects of technology in mathematics on achievement, motivation, and attitude: A meta-analysis. Journal of Educational Computing Research , 57(2), 283-319.
• Hillmayr D, Ziernwald L, Reinhold F, Hofer SI, Reiss KM. The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis. Computers & Education. 2020; 153 (1038):97. doi: 10.1016/j.compedu.2020.103897. [ CrossRef ] [ Google Scholar ]
• Istenic Starcic A, Bagon S. ICT-supported learning for inclusion of people with special needs: Review of seven educational technology journals, 1970–2011. British Journal of Educational Technology. 2014; 45 (2):202–230. doi: 10.1111/bjet.12086. [ CrossRef ] [ Google Scholar ]
• Jewitt C, Clark W, Hadjithoma-Garstka C. The use of learning platforms to organise learning in English primary and secondary schools. Learning, Media and Technology. 2011; 36 (4):335–348. doi: 10.1080/17439884.2011.621955. [ CrossRef ] [ Google Scholar ]
• JISC. (2020). What is digital transformation?.  Retrieved 30 June 2022 from: https://www.jisc.ac.uk/guides/digital-strategy-framework-for-university-leaders/what-is-digital-transformation
• Kalati, A. T., & Kim, M. S. (2022). What is the effect of touchscreen technology on young children’s learning?: A systematic review. Education and Information Technologies , 1-19. 10.1007/s10639-021-10816-5
• Kalemkuş, J., & Kalemkuş, F. (2022). Effect of the use of augmented reality applications on academic achievement of student in science education: Meta-analysis review. Interactive Learning Environments , 1-18. 10.1080/10494820.2022.2027458
• Kao C-W. The effects of digital game-based learning task in English as a foreign language contexts: A meta-analysis. Education Journal. 2014; 42 (2):113–141. [ Google Scholar ]
• Kampylis P, Punie Y, Devine J. Promoting effective digital-age learning - a European framework for digitally competent educational organisations. JRC Technical Reports. 2015 doi: 10.2791/54070. [ CrossRef ] [ Google Scholar ]
• Kazu IY, Yalçin CK. Investigation of the effectiveness of hybrid learning on academic achievement: A meta-analysis study. International Journal of Progressive Education. 2022; 18 (1):249–265. doi: 10.29329/ijpe.2022.426.14. [ CrossRef ] [ Google Scholar ]
• Koh C. A qualitative meta-analysis on the use of serious games to support learners with intellectual and developmental disabilities: What we know, what we need to know and what we can do. International Journal of Disability, Development and Education. 2022; 69 (3):919–950. doi: 10.1080/1034912X.2020.1746245. [ CrossRef ] [ Google Scholar ]
• König J, Jäger-Biela DJ, Glutsch N. Adapting to online teaching during COVID-19 school closure: Teacher education and teacher competence effects among early career teachers in Germany. European Journal of Teacher Education. 2020; 43 (4):608–622. doi: 10.1080/02619768.2020.1809650. [ CrossRef ] [ Google Scholar ]
• Lawrence JE, Tar UA. Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educational Media International. 2018; 55 (1):79–105. doi: 10.1080/09523987.2018.1439712. [ CrossRef ] [ Google Scholar ]
• Lee, S., Kuo, L. J., Xu, Z., & Hu, X. (2020). The effects of technology-integrated classroom instruction on K-12 English language learners’ literacy development: A meta-analysis. Computer Assisted Language Learning , 1-32. 10.1080/09588221.2020.1774612
• Lei, H., Chiu, M. M., Wang, D., Wang, C., & Xie, T. (2022a). Effects of game-based learning on students’ achievement in science: a meta-analysis. Journal of Educational Computing Research . 10.1177/07356331211064543
• Lei H, Wang C, Chiu MM, Chen S. Do educational games affect students' achievement emotions? Evidence from a meta-analysis. Journal of Computer Assisted Learning. 2022; 38 (4):946–959. doi: 10.1111/jcal.12664. [ CrossRef ] [ Google Scholar ]
• Liao YKC, Chang HW, Chen YW. Effects of computer application on elementary school student's achievement: A meta-analysis of students in Taiwan. Computers in the Schools. 2007; 24 (3–4):43–64. doi: 10.1300/J025v24n03_04. [ CrossRef ] [ Google Scholar ]
• Li Q, Ma X. A meta-analysis of the effects of computer technology on school students’ mathematics learning. Educational Psychology Review. 2010; 22 (3):215–243. doi: 10.1007/s10648-010-9125-8. [ CrossRef ] [ Google Scholar ]
• Liu, M., Pang, W., Guo, J., & Zhang, Y. (2022). A meta-analysis of the effect of multimedia technology on creative performance. Education and Information Technologies , 1-28. 10.1007/s10639-022-10981-1
• Lu Z, Chiu MM, Cui Y, Mao W, Lei H. Effects of game-based learning on students’ computational thinking: A meta-analysis. Journal of Educational Computing Research. 2022 doi: 10.1177/07356331221100740. [ CrossRef ] [ Google Scholar ]
• Martinez L, Gimenes M, Lambert E. Entertainment video games for academic learning: A systematic review. Journal of Educational Computing Research. 2022 doi: 10.1177/07356331211053848. [ CrossRef ] [ Google Scholar ]
• Mayne J. Useful theory of change models. Canadian Journal of Program Evaluation. 2015; 30 (2):119–142. doi: 10.3138/cjpe.230. [ CrossRef ] [ Google Scholar ]
• Moran J, Ferdig RE, Pearson PD, Wardrop J, Blomeyer RL., Jr Technology and reading performance in the middle-school grades: A meta-analysis with recommendations for policy and practice. Journal of Literacy Research. 2008; 40 (1):6–58. doi: 10.1080/10862960802070483. [ CrossRef ] [ Google Scholar ]
• OECD. (2015). Students, Computers and Learning: Making the Connection . PISA, OECD Publishing, Paris. Retrieved from: 10.1787/9789264239555-en
• OECD. (2021). OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. Retrieved from: https://www.oecd-ilibrary.org/education/oecd-digital-education-outlook-2021_589b283f-en
• Pan Y, Ke F, Xu X. A systematic review of the role of learning games in fostering mathematics education in K-12 settings. Educational Research Review. 2022; 36 :100448. doi: 10.1016/j.edurev.2022.100448. [ CrossRef ] [ Google Scholar ]
• Pettersson F. Understanding digitalization and educational change in school by means of activity theory and the levels of learning concept. Education and Information Technologies. 2021; 26 (1):187–204. doi: 10.1007/s10639-020-10239-8. [ CrossRef ] [ Google Scholar ]
• Pihir, I., Tomičić-Pupek, K., & Furjan, M. T. (2018). Digital transformation insights and trends. In Central European Conference on Information and Intelligent Systems (pp. 141–149). Faculty of Organization and Informatics Varazdin. Retrieved 30 June 2022 from https://www.proquest.com/conference-papers-proceedings/digital-transformation-insights-trends/docview/2125639934/se-2
• Punie, Y., Zinnbauer, D., & Cabrera, M. (2006). A review of the impact of ICT on learning. Working Paper prepared for DG EAC. Retrieved 30 June 2022 from: http://www.eurosfaire.prd.fr/7pc/doc/1224678677_jrc47246n.pdf
• Quah CY, Ng KH. A systematic literature review on digital storytelling authoring tool in education: January 2010 to January 2020. International Journal of Human-Computer Interaction. 2022; 38 (9):851–867. doi: 10.1080/10447318.2021.1972608. [ CrossRef ] [ Google Scholar ]
• Ran H, Kim NJ, Secada WG. A meta-analysis on the effects of technology's functions and roles on students' mathematics achievement in K-12 classrooms. Journal of computer assisted learning. 2022; 38 (1):258–284. doi: 10.1111/jcal.12611. [ CrossRef ] [ Google Scholar ]
• Ređep, N. B. (2021). Comparative overview of the digital preparedness of education systems in selected CEE countries. Center for Policy Studies. CEU Democracy Institute .
• Rott, B., & Marouane, C. (2018). Digitalization in schools–organization, collaboration and communication. In Digital Marketplaces Unleashed (pp. 113–124). Springer, Berlin, Heidelberg.
• Savva M, Higgins S, Beckmann N. Meta-analysis examining the effects of electronic storybooks on language and literacy outcomes for children in grades Pre-K to grade 2. Journal of Computer Assisted Learning. 2022; 38 (2):526–564. doi: 10.1111/jcal.12623. [ CrossRef ] [ Google Scholar ]
• Schmid RF, Bernard RM, Borokhovski E, Tamim RM, Abrami PC, Surkes MA, Wade CA, Woods J. The effects of technology use in postsecondary education: A meta-analysis of classroom applications. Computers & Education. 2014; 72 :271–291. doi: 10.1016/j.compedu.2013.11.002. [ CrossRef ] [ Google Scholar ]
• Schuele CM, Justice LM. The importance of effect sizes in the interpretation of research: Primer on research: Part 3. The ASHA Leader. 2006; 11 (10):14–27. doi: 10.1044/leader.FTR4.11102006.14. [ CrossRef ] [ Google Scholar ]
• Schwabe, A., Lind, F., Kosch, L., & Boomgaarden, H. G. (2022). No negative effects of reading on screen on comprehension of narrative texts compared to print: A meta-analysis. Media Psychology , 1-18. 10.1080/15213269.2022.2070216
• Sellar S. Data infrastructure: a review of expanding accountability systems and large-scale assessments in education. Discourse: Studies in the Cultural Politics of Education. 2015; 36 (5):765–777. doi: 10.1080/01596306.2014.931117. [ CrossRef ] [ Google Scholar ]
• Stock WA. Systematic coding for research synthesis. In: Cooper H, Hedges LV, editors. The handbook of research synthesis, 236. Russel Sage; 1994. pp. 125–138. [ Google Scholar ]
• Su, J., Zhong, Y., & Ng, D. T. K. (2022). A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence , 100065. 10.1016/j.caeai.2022.100065
• Su J, Yang W. Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence. 2022; 3 :100049. doi: 10.1016/j.caeai.2022.100049. [ CrossRef ] [ Google Scholar ]
• Sung YT, Chang KE, Liu TC. The effects of integrating mobile devices with teaching and learning on students' learning performance: A meta-analysis and research synthesis. Computers & Education. 2016; 94 :252–275. doi: 10.1016/j.compedu.2015.11.008. [ CrossRef ] [ Google Scholar ]
• Talan T, Doğan Y, Batdı V. Efficiency of digital and non-digital educational games: A comparative meta-analysis and a meta-thematic analysis. Journal of Research on Technology in Education. 2020; 52 (4):474–514. doi: 10.1080/15391523.2020.1743798. [ CrossRef ] [ Google Scholar ]
• Tamim, R. M., Bernard, R. M., Borokhovski, E., Abrami, P. C., & Schmid, R. F. (2011). What forty years of research says about the impact of technology on learning: A second-order meta-analysis and validation study. Review of Educational research, 81 (1), 4–28. Retrieved 30 June 2022 from 10.3102/0034654310393361
• Tamim, R. M., Borokhovski, E., Pickup, D., Bernard, R. M., & El Saadi, L. (2015). Tablets for teaching and learning: A systematic review and meta-analysis. Commonwealth of Learning. Retrieved from: http://oasis.col.org/bitstream/handle/11599/1012/2015_Tamim-et-al_Tablets-for-Teaching-and-Learning.pdf
• Tang C, Mao S, Xing Z, Naumann S. Improving student creativity through digital technology products: A literature review. Thinking Skills and Creativity. 2022; 44 :101032. doi: 10.1016/j.tsc.2022.101032. [ CrossRef ] [ Google Scholar ]
• Tolani-Brown, N., McCormac, M., & Zimmermann, R. (2011). An analysis of the research and impact of ICT in education in developing country contexts. In ICTs and sustainable solutions for the digital divide: Theory and perspectives (pp. 218–242). IGI Global.
• Trucano, M. (2005). Knowledge Maps: ICTs in Education. Washington, DC: info Dev / World Bank. Retrieved 30 June 2022 from  https://files.eric.ed.gov/fulltext/ED496513.pdf
• Ulum H. The effects of online education on academic success: A meta-analysis study. Education and Information Technologies. 2022; 27 (1):429–450. doi: 10.1007/s10639-021-10740-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Underwood, J. D. (2009). The impact of digital technology: A review of the evidence of the impact of digital technologies on formal education. Retrieved 30 June 2022 from: http://dera.ioe.ac.uk/id/eprint/10491
• Verschaffel, L., Depaepe, F., & Mevarech, Z. (2019). Learning Mathematics in metacognitively oriented ICT-Based learning environments: A systematic review of the literature. Education Research International , 2019 . 10.1155/2019/3402035
• Villena-Taranilla R, Tirado-Olivares S, Cózar-Gutiérrez R, González-Calero JA. Effects of virtual reality on learning outcomes in K-6 education: A meta-analysis. Educational Research Review. 2022; 35 :100434. doi: 10.1016/j.edurev.2022.100434. [ CrossRef ] [ Google Scholar ]
• Voogt J, Knezek G, Cox M, Knezek D, ten Brummelhuis A. Under which conditions does ICT have a positive effect on teaching and learning? A call to action. Journal of Computer Assisted Learning. 2013; 29 (1):4–14. doi: 10.1111/j.1365-2729.2011.00453.x. [ CrossRef ] [ Google Scholar ]
• Vuorikari, R., Punie, Y., & Cabrera, M. (2020). Emerging technologies and the teaching profession: Ethical and pedagogical considerations based on near-future scenarios  (No. JRC120183). Joint Research Centre. Retrieved 30 June 2022 from: https://publications.jrc.ec.europa.eu/repository/handle/JRC120183
• Wang LH, Chen B, Hwang GJ, Guan JQ, Wang YQ. Effects of digital game-based STEM education on students’ learning achievement: A meta-analysis. International Journal of STEM Education. 2022; 9 (1):1–13. doi: 10.1186/s40594-022-00344-0. [ CrossRef ] [ Google Scholar ]
• Wen X, Walters SM. The impact of technology on students’ writing performances in elementary classrooms: A meta-analysis. Computers and Education Open. 2022; 3 :100082. doi: 10.1016/j.caeo.2022.100082. [ CrossRef ] [ Google Scholar ]
• Zheng B, Warschauer M, Lin CH, Chang C. Learning in one-to-one laptop environments: A meta-analysis and research synthesis. Review of Educational Research. 2016; 86 (4):1052–1084. doi: 10.3102/0034654316628645. [ CrossRef ] [ Google Scholar ]

The factors influencing the continuance intention of MOOCs: The perspective of socio-technical approach

• Published: 20 September 2024

Cite this article

• Yalin Li   ORCID: orcid.org/0000-0001-7163-6741 1  

The enduring challenges faced by MOOCs have consistently revolved around the low completion and high dropout rates. To explore the factors that affect the continuance intention of MOOCs, this study constructed a new theoretical model to analyze how the social and technical factors influenced the intention to continue using MOOCs through attachment (emotional attachment and functional attachment) and learning stickiness. To test the hypotheses, 334 valid samples were collected through a questionnaire from individuals in China who claimed that they had the experience using MOOCs and analyzed using PLS-SEM method. With the help of Smart PLS 3.0, the proposed model explained 64.10% variance of the continuous intention of MOOCs. The results found that the continuance intention of MOOCs was influenced by learning stickiness, which affected by students’ emotional and functional attachments to MOOCs. And the social factors (identification and social presence) and technical factors (interactivity and personalization) positively and significantly influence emotional attachment and functional attachment, respectively. The paper contributes to explain the factors affecting the intention to continue using MOOCs from the socio-technical perspective, which enriches the theoretical basis for the evaluation of the continuous use of MOOCs, and provides practical references for practitioners to better design MOOCs to enhance the student’s intention to continue using them.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Explore related subjects

• Artificial Intelligence
• Digital Education and Educational Technology

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abuhassna, H., Busalim, A., Yahaya, N., Zakaria, M. A. Z., & Latif, A. B. A. (2023). Study from home! The antecedents and consequences of collaborative learning on Malaysian university students. Journal of Information Technology Education: Research , 22 , 71–95. https://doi.org/10.28945/5074

Article   Google Scholar  

Al-Mekhlafi, A. B. A., Othman, I., Kineber, A. F., Mousa, A. A., & Zamil, A. M. (2022). Modeling the impact of massive open online courses (MOOC) implementation factors on continuance intention of students: PLS-SEM approach. Sustainability , 14 (9), 5342. https://doi.org/10.3390/su14095342

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin , 103 (3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411

Animesh, A., Pinsonneault, A., Yang, S. B., & Oh, W. (2011). An odyssey into virtual worlds: Exploring the impacts of technological and spatial environments on intention to purchase virtual products. MIS Quarterly , 35 (3), 789–810. https://doi.org/10.2307/23042809

Aron, A., McLaughlin-Volpe, T., Mashek, D., Lewandowski, G., Wright, S. C., & Aron, E. N. (2004). Including others in the self. European Review of Social Psychology , 15 (1), 101–132. https://doi.org/10.1080/10463280440000008

Ashforth, B. E., Harrison, S. H., & Corley, K. G. (2008). Identification in organizations: An examination of four fundamental questions. Journal of Management , 34 (3), 325–374. https://doi.org/10.1177/0149206308316059

Badali, M., Hatami, J., Banihashem, S. K., Rahimi, E., Noroozi, O., & Eslami, Z. (2022). The role of motivation in MOOCs’ retention rates: A systematic literature review. Research and Practice in Technology Enhanced Learning , 17 (1), 1–20. https://doi.org/10.1186/s41039-022-00181-3

Bahtar, A. Z., Muthusamy, G., Yazid, Z. A., & Daud, S. (2022). The e-servqual effect on mobile stickiness intention of e-commerce marketplace. In Proceedings (vol. 82, no. 1, p. 34). MDPI.  https://doi.org/10.3390/proceedings2022082034

Baylari, A., & Montazer, G. A. (2009). Design a personalized e-learning system based on item response theory and artificial neural network approach. Expert Systems with Applications , 36 (4), 8013–8021. https://doi.org/10.1016/j.eswa.2008.10.080

Bhattacharya, C. B., & Sen, S. (2003). Consumer–company identification: A framework for understanding consumers’ relationships with companies. Journal of Marketing , 67 (2), 76–88. https://doi.org/10.1509/jmkg.67.2.76.18609

Bostrom, R. P., & Heinen, J. S. (1977). MIS problems and failures: A socio-technical perspective. Part I: The causes. MIS Quarterly , 17–32. https://doi.org/10.2307/248710

Bowlby, J. (1973). Attachment and loss, Vol. II: Separation Vol. 2 . Basic Books.

Bowlby, J. (1977). The making and breaking of affectional bonds: II. Some principles of psychotherapy: The Fiftieth Maudsley lecture (expanded version). The British Journal of Psychiatry , 130 (5), 421–431. https://doi.org/10.1192/bjp.130.5.421

Brown, W. C., William, F. S., Guy, H. P., Kelly, A. B., McGuire, T. C., Norimine, J., & Job, E. L. (2005). Identification of novel antigenic proteins in a complex Anaplasma Marginale outer membrane immunogen by mass spectrometry and genomic mapping. Infection and Immunity , 73 (12), 8109–8118. https://doi.org/10.1128/iai.73.12.8109-8118.2005

Burks, D. (2016). Learner’s perception of social presence in the online learning environment . Northcentral University.

Google Scholar  

Cao, X., Gong, M., Yu, L., & Dai, B. (2020). Exploring the mechanism of social media addiction: An empirical study from WeChat users. Internet Research , 30 (4), 1305–1328. https://doi.org/10.1108/INTR-08-2019-0347

Carlson, B. D., Donavan, D. T., Deitz, G. D., Bauer, B. C., & Lala, V. (2020). A customer-focused approach to improve celebrity endorser effectiveness. Journal of Business Research , 109 , 221–235. https://doi.org/10.1016/j.jbusres.2019.11.048

Chen, Y. C. (2014). An empirical examination of factors affecting college students’ proactive stickiness with a web-based English learning environment. Computers in Human Behavior , 31 , 159–171. https://doi.org/10.1016/j.chb.2013.10.040

Chen, C. (2022). Factors affecting online Chinese as a foreign language learning stickiness: A study on international students in China. Frontiers in Psychology , 12 , 803669. https://doi.org/10.3389/fpsyg.2021.803669

Chen, M. H., & Tsai, K. M. (2020). An empirical study of brand fan page engagement behaviors. Sustainability , 12 (1), 434. https://doi.org/10.3390/su12010434

Chen, N., & Yang, Y. (2023). The role of influencers in live streaming e-commerce: Influencer trust, attachment, and consumer purchase intention. Journal of Theoretical and Applied Electronic Commerce Research , 18 (3), 1601–1618. https://doi.org/10.3390/jtaer18030081

Chen, H., De, P., Hu, Y., & Hwang, B. H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. Review of Financial Studies , 27 (5), 1367–1403. https://doi.org/10.1093/rfs/hhu001

Chen, M. H., Tsai, K. M., & Ke, Y. A. (2019). Enhancing consumers’ stickiness to online brand communities as an innovative relationship marketing strategy. International Journal on Semantic Web and Information Systems (IJSWIS) , 15 (3), 16–34. https://doi.org/10.4018/IJSWIS.2019070102

Chen, Y., Wang, X., Le, B., & Wang, L. (2024). Why people use augmented reality in heritage museums: A socio-technical perspective. Heritage Science , 12 (1), 108. https://doi.org/10.1186/s40494-024-01217-1

Cheng, Y. M. (2014). Roles of interactivity and usage experience in e-learning acceptance: A longitudinal study. International Journal of Web Information Systems , 10 (1), 2–23. https://doi.org/10.1108/IJWIS-05-2013-0015

Article   MathSciNet   Google Scholar  

Cheng, Y. M. (2020). Students’ satisfaction and continuance intention of the cloud-based e-learning system: Roles of interactivity and course quality factors. Education + Training , 62 (9), 1037–1059. https://doi.org/10.1108/ET-10-2019-0245

Cheng, Y. M. (2023a). Which quality determinants cause MOOCs continuance intention? A hybrid extending the expectation-confirmation model with learning engagement and information systems success. Library Hi Tech , 41 (6), 1748–1780. https://doi.org/10.1108/LHT-11-2021-0391

Cheng, Y. M. (2023b). How gamification and social interaction stimulate MOOCs continuance intention via cognitive presence, teaching presence and social presence? Library Hi Tech , 41 (6), 1781–1801. https://doi.org/10.1108/LHT-03-2022-0160

Cheng, Y. M. (2024). What makes learners enhance learning outcomes in MOOCs? Exploring the roles of gamification and personalization. Interactive Technology and Smart Education , 21 (2), 308–330. https://doi.org/10.1108/ITSE-05-2023-0097

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research , 295 (2), 295–336.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.

Dai, H. M., Teo, T., & Rappa, N. A. (2020a). Understanding continuance intention among MOOC participants: The role of habit and MOOC performance. Computers in Human Behavior , 112 , 106455. https://doi.org/10.1016/j.chb.2020.106455

Dai, H. M., Teo, T., Rappa, N. A., & Huang, F. (2020b). Explaining Chinese university students’ continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective. Computers & Education , 150 (June), 103850. https://doi.org/10.1016/j.compedu.2020.103850

Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation and satisfaction on continuance intention in using massive open online course (MOOC). Knowledge Management & E-Learning , 11 (2), 201–214. https://doi.org/10.34105/j.kmel.2019.11.010

Dastane, O., & Haba, H. F. (2023). What drives mobile MOOC’s continuous intention? A theory of perceived value perspective. The International Journal of Information and Learning Technology , 40 (2), 148–163. https://doi.org/10.1108/IJILT-04-2022-0087

Durcikova, A., Lee, A. S., & Brown, S. A. (2018). Making rigorous research relevant: Innovating statistical action research. MIS Quarterly , 42 (1), 241–263. https://doi.org/10.25300/MISQ/2018/14146

Ebadi, S., & Amini, A. (2024). Examining the roles of social presence and human-likeness on Iranian EFL learners’ motivation using artificial intelligence technology: A case of CSIEC chatbot. Interactive Learning Environments , 32 (2), 655–673. https://doi.org/10.1080/10494820.2022.2096638

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equations models with unobservable variables and measurement error. Journal of Marketing Research , 18 , 39–50. https://doi.org/10.1177/002224378101800104

Gan, C., & Li, H. (2018). Understanding the effects of gratifications on the continuance intention to use WeChat in China: A perspective on uses and gratifications. Computers in Human Behavior , 78 , 306–315. https://doi.org/10.1016/j.chb.2017.10.003

Gao, X., Yee, C. L., & Choo, W. C. (2022). How attachment and community identification affect user stickiness in social commerce: A consumer engagement experience perspective. Sustainability 14 13633 . https://doi.org/10.3390/su142013633

Garcia-Loro, F., Martin, S., Ruipérez-Valiente, J. A., Sancristobal, E., & Castro, M. (2020). Reviewing and analyzing peer review Inter-rater Reliability in a MOOC platform. Computers & Education , 154 , 103894. https://doi.org/10.1016/j.compedu.2020.103894

Gefen, D., Straub, D. W., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems , 4 (7), 1–70. https://doi.org/10.17705/1CAIS.00407

Ghali, Z., Rather, R. A., & Khan, I. (2024). Investigating metaverse marketing-enabled consumers’ social presence, attachment, engagement and (re) visit intentions. Journal of Retailing and Consumer Services . https://doi.org/10.1016/j.jretconser.2023.103671

Ghorbanzadeh, D., & Rahehagh, A. (2021). Emotional brand attachment and brand love: The emotional bridges in the process of transition from satisfaction to loyalty. Rajagiri Management Journal , 15 (1), 16–38. https://doi.org/10.1108/RAMJ-05-2020-0024

Guo, Z., Van Toorn, X. L., Lai, C.,Y., & Seo, C. (2016). Promoting online learners’ continuance intention: An integrated flow framework. Information & Management , 53 (2), 279–295. https://doi.org/10.1016/j.im.2015.10.010

Guo, Q., Zeng, Q., & Zhang, L. (2023). What social factors influence learners’ continuous intention in online learning? A social presence perspective. Information Technology & People , 36 (3), 1076–1094. https://doi.org/10.1108/ITP-02-2021-0151

Gupta, K. P., & Maurya, H. (2022). Adoption, completion and continuance of MOOCs: A longitudinal study of students’ behavioural intentions. Behaviour & Information Technology , 41 (3), 611–628. https://doi.org/10.1080/0144929X.2020.1829054

Hair, J. F. (2014). A primer on partial least squares structural equations modeling (PLS-SEM) . SAGE Publications.

Hair, J. F., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems , 117 (3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review , 31 (1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Henkel, A. P., Čaić, M., Blaurock, M., & Okan, M. (2020). Robotic transformative service research: Deploying social robots for consumer well-being during Covid-19 and beyond. Journal of Service Management, 31 (6), 1131–1148. https://doi.org/10.1108/JOSM-05-2020-0145

Ho, S. Y. (2012). The effects of location personalization on individuals’ intention to use mobile services. Decision Support Systems , 53 (4), 802–812doi. https://doi.org/10.1016/j.dss.2012.05.012

Horváth, C., & Birgelen, M. V. (2015). The role of brands in the behavior and purchase decisions of compulsive versus noncompulsive buyers. European Journal of Marketing , 49 (1/2), 2–21. https://doi.org/10.1108/EJM-10-2012-0627

Hsiao, C. H., & Tang, K. Y. (2021). Who captures whom–Pokémon or tourists? A perspective of the stimulus-organism-response model. International Journal of Information Management , 61 , 102312. https://doi.org/10.1016/j.ijinfomgt.2021.102312

Hsu, C. L., & Liao, Y. C. (2014). Exploring the linkages between perceived information accessibility and microblog stickiness: The moderating role of a sense of community. Information & Management , 51 , 833–844. https://doi.org/10.1016/j.im.2014.08.005

Hu, M., Zhang, M., & Wang, Y. (2017). Why do audiences choose to keep watching on live video streaming platforms? An explanation of dual identification framework. Computers in Human Behavior , 75 , 594–606. https://doi.org/10.1016/j.chb.2017.06.006

Hu, L., Min, Q., Han, S., & Liu, Z. (2020). Understanding followers stickiness to digital influencers: The effect of psychological responses. International Journal of Information Management , 54 , 102169. https://doi.org/10.1016/j.ijinfomgt.2020.102169

Janelli, M., & Lipnevich, A. (2021). Effects of pre-tests and feedback on performance outcomes and persistence in massive open online courses. Computers and Education , 161 , 104076. https://doi.org/10.1016/j.compedu.2020.104076

Japutra, A., Ekinci, Y., & Simkin, L. (2018). Positive and negative behaviours resulting from brand attachment. European Journal of Marketing , 52 (5/6), 1185–1202. https://doi.org/10.1108/EJM-10-2016-0566

Jiao, S., Wang, X., Ma, C., & Deng, Y. (2024). How does sports e-commerce influence consumer behavior through short video live broadcast platforms? Attachment theory perspective. Asia Pacific Journal of Marketing and Logistics , ahead-of-print. https://doi.org/10.1108/APJML-08-2023-0777

Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among students’ self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education , 122 , 260–272. https://doi.org/10.1016/j.compedu.2018.01.003

Kang, W., Shao, B., Du, S., Chen, H., & Zhang, Y. (2024). How to improve voice assistant evaluations: Understanding the role of attachment with a socio-technical systems perspective. Technological Forecasting and Social Change , 200 , 123171. https://doi.org/10.1016/j.techfore.2023.123171

Kashian, N. (2022). Can mediated communication moderate the relationship between being in isolation and quarantine for COVID-19 and loneliness? Cyberpsychology: Journal of Psychosocial Research on Cyberspace , 16 (4). https://doi.org/10.5817/CP2022-4-6

Kim, J. (2011). Developing an instrument to measure social presence in distance higher education. British Journal of Educational Technology , 42 (5), 763–777. https://doi.org/10.1111/j.1467-8535.2010.01107.x

Kim, R., & Song, H. D. (2022). Examining the influence of teaching presence and task-technology fit on continuance intention to use MOOCs. The Asia-Pacific Education Researcher , 31 (4), 395–408. https://doi.org/10.1007/s40299-021-00581-x

Kim, S. J., Wang, R. J. H., & Malthouse, E. C. (2015). The effects of adopting and using a brand’s mobile application on customers’ subsequent purchase behavior. Journal of Interactive Marketing , 31 , 28–41. https://doi.org/10.1016/j.intmar.2015.05.004

Kim, S., Baek, T. H., Kim, Y. K., & Yoo, K. (2016). Factors affecting stickiness and word of mouth in mobile applications. Journal of Research in Interactive Marketing , 10 (3), 177–192. https://doi.org/10.1108/JRIM-06-2015-0046

Kim, J., Kim, J., & Yang, H. (2019). Loneliness and the use of social media to follow celebrities: A moderating role of social presence. The Social Science Journal , 56 (1), 21–29. https://doi.org/10.1016/j.soscij.2018.12.007

Kiselev, B., & Yakutenko, V. (2020). An overview of massive open online course platforms:Personalization and semantic web technologies and standards. Procedia Computer Science , 169 , 373–379. https://doi.org/10.1016/j.procs.2020.02.232

Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration , 11 (4), 1–10. https://doi.org/10.4018/ijec.2015100101

Kovanovic, V., Joksimovic, S., Poquet, O., Hennis, T., Cukic, I., de Vries, P., & Gasevic, D. (2018). Exploring communities of inquiry in massive open online courses. Computers & Education , 119 (April), 44–58. https://doi.org/10.1016/j.compedu.2017.11.010

Kreijns, K., Kirschner, P. A., Jochems, W., & Van Buuren, H. (2007). Measuring perceived sociability of computer-supported collaborative learning environments. Computers & Education , 49 (2), 176–192. https://doi.org/10.1016/j.compedu.2005.05.004

Krotov, V. (2015). Critical success factors in m-learning: A socio-technical perspective. Communications of the Association for Information Systems , 36 (1), 6. https://doi.org/10.17705/1CAIS.03606

Ladhari, R., Massa, E., & Skandrani, H. (2020). YouTube vloggers’ popularity and influence: The roles of homophily, emotional attachment, and expertise. Journal of Retailing and Consumer Services , 54 , 102027. https://doi.org/10.1016/j.jretconser.2019.102027

Lee, K. M., Jeong, E. J., Park, N., & Ryu, S. (2011). Effects of interactivity in educational games: A mediating role of social presence on learning outcomes. International Journal of Human-Computer Interaction , 27 (7), 620–633. https://doi.org/10.1080/10447318.2011.555302

Lerís, D., Sein-Echaluce, M. L., Hernandez, M., & Bueno, C. (2017). Validation of indicators for implementing an adaptive platform for MOOCs. Computers in Human Behavior , 72 , 783–795. https://doi.org/10.1016/j.chb.2016.07.054

Li, Y., & Peng, Y. (2021). What drives gift-giving intention in live streaming? The perspectives of emotional attachment and flow experience. International Journal of Human-Computer Interaction , 37 (14), 1317–1329. https://doi.org/10.1080/10447318.2021.1885224

Li, Y., & Zhao, M. (2024). The study on the influence factors of intention to continue using MOOCs: Integrating UTAUT model and social presence. Interactive Learning Environments , 1–19. https://doi.org/10.1080/10494820.2024.2318562

Li, A., Islam, A. A., & Gu, X. (2021a). Factors engaging college students in online learning: An investigation of learning stickiness. SAGE Open , 11 (4), 21582440211059181. https://doi.org/10.1177/21582440211059181

Li, W., Mao, Y., & Zhou, L. (2021b). The impact of interactivity on user satisfaction in digital social reading: Social presence as a mediator. International Journal of Human–Computer Interaction , 37 (17), 1636–1647. https://doi.org/10.1080/10447318.2021.1898850

Li, Y., Li, X., & Cai, J. (2021c). How attachment affects user stickiness on live streaming platforms: A socio-technical approach perspective. Journal of Retailing and Consumer Services , 60 , 102478. https://doi.org/10.1016/j.jretconser.2021.102478

Lien, C. H., Cao, Y., & Zhou, X. (2017). Service quality, satisfaction, stickiness, and usage intentions: An exploratory evaluation in the context of WeChat services. Computers in Human Behavior , 68 , 403–410. https://doi.org/10.1016/j.chb.2016.11.061

Lin, J. C. C. (2007). Online stickiness: Its antecedents and effect on purchasing intention. Behaviour & Information Technology , 26 , 507–516. https://doi.org/10.1080/01449290600740843

Lin, Y., Yao, D., & Chen, X. (2021). Happiness begets money: Emotion and engagement in live streaming. Journal of Marketing Research , 58 (3), 417–438. https://doi.org/10.1177/00222437211002477

Liu, Y. P. (2003). Developing a scale to measure the interactivity of websites. Journal of Advertising Research , 43 (2), 207–216. https://doi.org/10.1017/S0021849903030204

Liu, N., & Pu, Q. (2020). Factors influencing learners’ continuance intention toward one-to-one online learning. Interactive Learning Environments , 1–22. https://doi.org/10.1080/10494820.2020.1857785

Liu, Q., Ding, Y., Qian, P., Li, R., & Zhou, J. (2022). Analysis of the influencing factors of online classes satisfaction based on text mining take MOOC platform art education online classes as an example. Journal of Education Humanities and Social Sciences , 2 , 249–256. https://doi.org/10.54097/ehss.v2i.817

Loh, H. S., Martins van Jaarsveld, G., Mesutoglu, C., & Baars, M. (2024). Supporting social interactions to improve MOOC participants’ learning outcomes: A literature review. Frontiers in Education , 9 , 1345205. https://doi.org/10.3389/feduc.2024.1345205

Lu, Y., Xiang, C., Wang, B., & Wang, X. (2011). What affects information systems development team performance? An exploratory study from the perspective of combined socio-technical theory and coordination theory. Computers in Human Behavior , 27 (2), 811–822. https://doi.org/10.1016/j.chb.2010.11.006

Lu, B., Yan, L., & Chen, Z. (2022). Perceived values, platform attachment and repurchase intention in on-demand service platforms: A cognition-affection-conation perspective. Journal of Retailing and Consumer Services , 67 , 103024. https://doi.org/10.1016/j.jretconser.2022.103024

Ma, Y. (2023). Effects of interactivity affordance on user stickiness in livestream shopping: Identification and gratification as mediators. Heliyon , 9 (1), e12917. https://doi.org/10.1016/j.heliyon.2023.e12917

Meng, Z., & Lin, M. (2023). The driving factors analysis of live streamers’ characteristics and perceived value for consumer repurchase intention on live streaming platforms. Journal of Organizational and End User Computing (JOEUC) , 35 (1), 1–24. https://doi.org/10.4018/JOEUC.323187

Miao, D., Wu, Z., & Yan, H. (2022). Factors affecting online learning stickiness from the perspective of comprehensive learning theory. Frontiers of Education in China , 17 (1), 1–22.

Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior , 45(April) , 359–374. https://doi.org/10.1016/j.chb.2014.07.044

Motaghian, H., Hassanzadeh, A., & Moghadam, D. K. (2013). Factors affecting university instructors’ adoption of web-based learning system: Case study of Iran. Computers & Education , 61 (February), 158–167. https://doi.org/10.1016/j.compedu.2012.09.016

Ng, T. W. (2015). The incremental validity of organizational commitment, organizational trust, and organizational identification. Journal of Vocational Behavior , 88 , 154–163. https://doi.org/10.1016/j.jvb.2015.03.003

Nong, Y. X., & Buavaraporna, N.,Punnakitikashemb.P (2022). Exploring the factors influencing users’ satisfaction and continuance intention of MOOCs in China. Kasetsart Journal of Social Sciences , 43 (2), 403–408. https://doi.org/10.34044/j.kjss.2022.43.2.18

Pang, H., Ruan, Y., & Zhang, K. (2024). Deciphering technological contributions of visibility and interactivity to website atmospheric and customer stickiness in AI-driven websites: The pivotal function of online flow state. Journal of Retailing and Consumer Services , 78 , 103795. https://doi.org/10.1016/j.jretconser.2024.103795

Papadakis, S. (2023). MOOCs 2012–2022: An overview. Advances in Mobile Learning Educational Research , 3 (1), 682–693. https://doi.org/10.25082/AMLER.2023.01.017

Park, T. J., Whang, J., Watts, S., & Han, D. G. (2022). Key success factors in the continuous use of MOOC education in South Korea. International Journal of Innovation and Learning , 31 (2), 137–165. https://doi.org/10.1504/IJIL.2022.120665

Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education , 47 (2), 222–244. https://doi.org/10.1016/j.compedu.2004.10.007

Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management , 12 (4), 531–544. https://doi.org/10.1177/014920638601200

Podsakoff, P., MacKenzie, S., Lee, J., & Podsakoff, N. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology , 88 (5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

Poquet, O., Kovanović, V., de Vries, P., Hennis, T., Joksimović, S., Gašević, D., & Dawson, S. (2018). Social presence in massive open online courses. The International Review of Research in Open and Distributed Learning , 19 (3), 43–68. https://doi.org/10.19173/irrodl.v19i3.3370

Rajam, V., Banerjee, S., & Alok, S. (2024). Can MOOC be a medium of lifelong learning? Examining the role of perceived reputation and self-efficacy on continuous use intention of MOOC. Journal of e-Learning and Knowledge Society , 20 (1), 1–14. https://doi.org/10.20368/1971-8829/1135788

Ray, J. A. (2009). An investigation of online course management systems in higher education: Platform selection, faculty training, and instructional quality. International Journal of Information & Communication Technology Education , 5 (2), 117–132. https://doi.org/10.4018/jicte.2009040105

Rekha, I. S., Shetty, J., & Basri, S. (2023). Students’ continuance intention to use MOOCs: Empirical evidence from India. Education & Information Technologies , 28 , 4265–4286. https://doi.org/10.1007/s10639-022-11308-w

Ren, Y., Harper, F. M., Drenner, S., Terveen, L., Kiesler, S., Riedl, J., & Kraut, R. E. (2012). Building member attachment in online communities: Applying theories of group identity and interpersonal bonds. MIS Quarterly , 36 (3), 841–864. https://doi.org/10.2307/41703483

Richardson, J. C., Maeda, Y., Lv, J., & Caskurlu, S. (2017). Social presence in relation to students’ satisfaction and learning in the online environment: A meta-analysis. Computers in Human Behavior , 71 (June), 402–417. https://doi.org/10.1016/j.chb.2017.02.001

Robinson, A., & Cook, D. (2018). Stickiness: Gauging students’ attention to online learning activities. Information and Learning Science , 119 (7/8), 460–468. https://doi.org/10.1108/ILS-03-2018-0014

Rohan, R., Pal, D., Funilkul, S., Chutimaskul, W., & Eamsinvattana, W. (2021). How gamification leads to continued usage of MOOCs? A theoretical perspective. Ieee Access : Practical Innovations, Open Solutions , 9 , 108144–108161. https://doi.org/10.1109/ACCESS.2021.3102293

Sánchez-Fernández, R., & Jiménez-Castillo, D. (2021). How social media influencers affect behavioural intentions towards recommended brands: The role of emotional attachment and information value. Journal of Marketing Management , 37 (11–12), 1123–1147. https://doi.org/10.1080/0267257X.2020.1866648

Setterstorm, A. J., Pearson, J. M., & Orwig, R. A. (2013). Web-enabled wireless technology: An exploratory study of adoption and continued use intentions. Behaviour & Information Technology , 32 , 1139–1154. https://doi.org/10.1080/0144929x.2012.708785

Shanshan, S., & Wenfei, L. (2024). Continuance intention to use MOOCs: The effects of psychological stimuli and emotions. The Asia-Pacific Education Researcher , 33(1), 27-45. https://doi.org/10.1007/s40299-022-00705-x

Shao, Z., & Chen, K. (2021). Understanding individuals’ engagement and continuance intention of MOOCs: The effect of interactivity and the role of gender. Internet Research , 31 (4), 1262–1289. https://doi.org/10.1108/INTR-10-2019-0416

Shao, Z., Liang, X., & Yang, M. (2017). Impact of interactivity on individuals’ continuance participation in MOOCs platform. Proceedings of Pacific Asia Conference on Information Systems , Langkawi, Malaysia, paper 267.

Shi, G., Li, J., & Yang, J. (2023). A study on the influencing factors of university students’ online persistent learning supported by intelligent technology in the post-pandemic era: An empirical study with PLS-SEM. Interactive Learning Environments , 1–23. https://doi.org/10.1080/10494820.2023.2205901

Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications . Wiley.

Singh, R. (2022). Hey Alexa–order groceries for me-the effect of consumer–VAI emotional attachment on satisfaction and repurchase intention. European Journal of Marketing , 56 (6), 1684–1720. https://doi.org/10.1108/EJM-12-2019-0942

Song, J. H., & Zinkhan, G. M. (2008). Determinants of perceived web site interactivity. Journal of Marketing , 72 (2), 99–113. https://doi.org/10.1509/jmkg.72.2.99

Sony, M., & Naik, S. (2020). Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model. Technology in Society , 61 (1), 101248. https://doi.org/10.1016/j.techsoc.2020.101248

Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society , 36 (2), 111–147. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x

Su, L., & Li, Y. (2019). Understanding consumers’ purchase intention for online paid knowledge: A customer value perspective. Sustainability , 11 (19), 5420. https://doi.org/10.3390/su11195420

Suriazdin, S., Hidayanto, A., Maulida, M., Kurtinus, A., Arrumaisha, H., Aisyah, N., & Pradana, R. (2022). The technology attractiveness and its impact on MOOC continuance intention. International Journal of Emerging Technologies in Learning (IJET) , 17 (4), 239–250. https://doi.org/10.3991/ijet.v17i04.28853

Sutcliffe, A., & Hart, J. (2017). Analyzing the role of interactivity in user experience. International Journal of Human-Computer Interaction , 33 (3), 229–240. https://doi.org/10.1080/10447318.2016.1239797

Tan, B. F., & Chou, P. J. (2008). The relationship between mobile service quality, perceived technology compatibility, and users’ perceived playfulness in the context of mobile information and entertainment services. International Journal of Human-Computer Interaction , 24 (7), 649–671. https://doi.org/10.1080/10447310802335581

Thomson, M., MacInnis, D. J., & Whan Park, C. (2005). The ties that bind: Measuring the strength of consumers’ emotional attachments to brands. Journal of Consumer Psychology , 15 (1), 77–91. https://doi.org/10.1207/s15327663jcp1501_10

Trist, E. L., Higgin, G. W., Murray, H., & Pollock, A. B. (1963). Organizational choice: Capabilities of groups at the coal face under changing technologies . Tavistock.

Tsao, W. U. (2014). Enhancing competitive advantages: The contribution of mediator and moderator on stickiness in the LINE. Journal of Retailing and Consumer Services, 21 (6), 933e941. https://doi.org/10.1016/j.jretconser.2014.08.011

Tuškej, U., Golob, U., & Podnar, K. (2013). The role of consumer–brand identification in building brand relationships. Journal of Business Research , 66 (1), 53–59. https://doi.org/10.1016/j.jbusres.2011.07.022

Upadhyaya, K. T., & Mallik, D. (2013). E-learning as a socio-technical system: An insight into factors influencing its effectiveness. Business Perspectives and Research , 2 (1), 1–12. https://doi.org/10.1177/227853372013

Wan, J., Lu, Y., Wang, B., & Zhao, L. (2017). How attachment influences users’ willingness to donate to content creators in social media: A socio-technical systems perspective. Information & Management , 54 (7), 837–850. https://doi.org/10.1016/j.im.2016.12.007

Wang, J. L., Jackson, L. A., Wang, H. Z., & Gaskin, J. (2015). Predicting social networking site (SNS) use: Personality, attitudes, motivation and internet self-efficacy. Personality and Individual Differences , 80 , 119–124. https://doi.org/10.1016/j.paid.2015.02.016

Wang, P., Huang, Q., & Zhang, Y. (2022). How do social network ties influence purchases in social commerce communities? A lens of attachment theory. Internet Research , 33 (4), 1495–1518. https://doi.org/10.1108/INTR-10-2021-0753

Warrington, P., & Shim, S. (2000). An empirical investigation of the relationship between product involvement and brand commitment. Psychology & Marketing , 17 , 761–782. https://doi.org/10.1002/1520-6793(200009)17:9<761:AID-MAR2>3.0.CO;2-9

Williams, F., Rice, R. E., & Rogers, E. M. (1988). Research methods and the new media . the Free.

Windasari, N. A., & Lin, F. R. (2021). Why do people continue using fitness wearables? The effect of interactivity and gamification. Sage Open , 11 (4), 21582440211056606. https://doi.org/10.1177/21582440211056

Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior , 67 , 221–232. https://doi.org/10.1016/j.chb.2016.10.028

Wut, T., & Xu, J. (2021). Person-to-person interactions in online classroom settings under the impact of COVID-19: A social presence theory perspective. Asia Pacific Education Review , 22 , 371–383. https://doi.org/10.1007/s12564-021-09673-1

Xiong, Q., Li, W. Y., Chen, X. Y., & Li, Z. W. (2022). Research on the relationship among interaction, experience value and user stickiness of online teaching platform: A moderated mediation effect model. Management Review , 34 (6), 153–161. https://doi.org/10.14120/j.cnki.cn11-5057/f.2022.06.013

Xu, Y. (2023). An exploration of the role played by attachment factors in the formation of social media addiction from a cognition-affect-conation perspective. Acta Psychologica , 236 , 103904. https://doi.org/10.1016/j.actpsy.2023.103904

Xue, J., & Deng, Z. (2024). How patients with chronic disease create value in online health communities? A mixed methods study from social technical perspective. Information Technology and Management , 1–19. https://doi.org/10.1007/s10799-024-00424-3

Yang, R., Wibowo, S., Mubarak, S., & Rahamathulla, M. (2023). Managing students’ attitude, learning engagement, and stickiness towards e-learning post-COVID-19 in Australian universities: A perceived qualities perspective. Journal of Marketing for Higher Education , 1–32. https://doi.org/10.1080/08841241.2023.2204466

Yin, M., Tayyab, S. M. U., Xu, X. Y., Jia, S. W., & Wu, C. L. (2021). The investigation of mobile health stickiness: The role of social support in a. Sustainable Health Approach Sustainability, 13 , 1693. https://doi.org/10.3390/su13041693

Yu, Z., Xu, W., & Sukjairungwattana, P. (2024). A meta-analysis of eight factors influencing MOOC-based learning outcomes across the world. Interactive Learning Environments , 32 (2), 707–726. https://doi.org/10.1080/10494820.2022.2096641

Yun, S., & Lee, J. (2015). Advancing societal readiness toward renewable energy system adoption with a socio-technical perspective. Technological Forecasting and Social Change , 95 , 170–181. https://doi.org/10.1016/j.techfore.2015.01.016

Zankadi, H., Idrissi, A., Daoudi, N., & Hilal, I. (2023). Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques. Education and Information Technologies , 28 (5), 5567–5584. https://doi.org/10.1007/s10639-022-11373-1

Zhang, M., Guo, L., Hu, M., & Liu, W. (2017). Influence of customer engagement with company social networks on stickiness. International Journal of Information Management , 37 (3), 229–240. https://doi.org/10.1016/j.ijinfomgt.2016.04.010

Zhang, X., Wu, Y., & Liu, S. (2019). Exploring short-form video application addiction: Socio-technical and attachment perspectives. Telematics and Informatics , 42 , 101243. https://doi.org/10.1016/j.tele.2019.101243

Zhang, Y., Sun, Y., & Wang, N. (2024). Understanding the determinants of gifting and social sharing in live streaming: A dual-attachment perspective. Industrial Management & Data Systems , 124 (2), 564–582. https://doi.org/10.1108/IMDS-06-2023-0380

Zhao, Y. M., Wang, A., & Sun, Y. Q. (2020). Technological environment, virtual experience, and MOOC continuance: A stimulus–organism–response perspective. Computers & Education , 144 , 103721. https://doi.org/10.1016/j.compedu.2019.103721

Zhu, X., & Wang, Y. (2018). An empirical study on the users’ continuance intention about MOOC: Based on attachment theory. In 2018 International Symposium on Educational Technology (ISET) (pp. 209–213). https://doi.org/10.1109/ISET.2018.00054

Download references

Acknowledgements

This work was supported by the China Association of Higher Education [grant number 23XXK0405], Scientific Research Foundation for the Introduction of Talents, Suqian University, Jiangsu Innovative and Entrepreneurial Talent Project [grant number JSSCBS20221478], the Ministry of Education of Humanities and Social Science Project [grant number 17YJA630047], and Hubei Province Philosophy and Social Science Research Major Project [grant number 16ZD031].

Author information

Authors and affiliations.

School of Economics and Management, Suqian University, No. 399, Huanghe South Road, Jiangsu Province, 223800, Suqian, P.R. China

You can also search for this author in PubMed   Google Scholar

Contributions

This article was completed by the sole author, who was responsible for all aspects of the research design, data collection and analysis, and paper writing.

Corresponding author

Correspondence to Yalin Li .

Ethics declarations

Ethics approval and consent to participate.

The author’s affiliation Ethics Committee approved this study. The participants are aware of the purpose of this study and agree to participate in conducting the study.

Consent for publication

Written informed consent for publication was obtained from all participants.

Competing interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Li, Y. The factors influencing the continuance intention of MOOCs: The perspective of socio-technical approach. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-13042-x

Download citation

Received : 23 June 2024

Accepted : 03 September 2024

Published : 20 September 2024

DOI : https://doi.org/10.1007/s10639-024-13042-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Attachment theory
• Learning stickiness
• Socio-technical system theory
• Find a journal
• Publish with us
• Track your research

• All subject areas
• Agricultural and Biological Sciences
• Arts and Humanities
• Biochemistry, Genetics and Molecular Biology
• Business, Management and Accounting
• Chemical Engineering
• Computer Science
• Decision Sciences
• Earth and Planetary Sciences
• Economics, Econometrics and Finance
• Engineering
• Environmental Science
• Health Professions
• Immunology and Microbiology
• Materials Science
• Mathematics
• Multidisciplinary
• Neuroscience
• Pharmacology, Toxicology and Pharmaceutics
• Physics and Astronomy
• Social Sciences
• All subject categories
• Acoustics and Ultrasonics
• Advanced and Specialized Nursing
• Aerospace Engineering
• Agricultural and Biological Sciences (miscellaneous)
• Agronomy and Crop Science
• Algebra and Number Theory
• Analytical Chemistry
• Anesthesiology and Pain Medicine
• Animal Science and Zoology
• Anthropology
• Applied Mathematics
• Applied Microbiology and Biotechnology
• Applied Psychology
• Aquatic Science
• Archeology (arts and humanities)
• Architecture
• Artificial Intelligence
• Arts and Humanities (miscellaneous)
• Assessment and Diagnosis
• Astronomy and Astrophysics
• Atmospheric Science
• Atomic and Molecular Physics, and Optics
• Automotive Engineering
• Behavioral Neuroscience
• Biochemistry
• Biochemistry, Genetics and Molecular Biology (miscellaneous)
• Biochemistry (medical)
• Bioengineering
• Biological Psychiatry
• Biomaterials
• Biomedical Engineering
• Biotechnology
• Building and Construction
• Business and International Management
• Business, Management and Accounting (miscellaneous)
• Cancer Research
• Cardiology and Cardiovascular Medicine
• Care Planning
• Cell Biology
• Cellular and Molecular Neuroscience
• Ceramics and Composites
• Chemical Engineering (miscellaneous)
• Chemical Health and Safety
• Chemistry (miscellaneous)
• Chiropractics
• Civil and Structural Engineering
• Clinical Biochemistry
• Clinical Psychology
• Cognitive Neuroscience
• Colloid and Surface Chemistry
• Communication
• Community and Home Care
• Complementary and Alternative Medicine
• Complementary and Manual Therapy
• Computational Mathematics
• Computational Mechanics
• Computational Theory and Mathematics
• Computer Graphics and Computer-Aided Design
• Computer Networks and Communications
• Computer Science Applications
• Computer Science (miscellaneous)
• Computer Vision and Pattern Recognition
• Computers in Earth Sciences
• Condensed Matter Physics
• Conservation
• Control and Optimization
• Control and Systems Engineering
• Critical Care and Intensive Care Medicine
• Critical Care Nursing
• Cultural Studies
• Decision Sciences (miscellaneous)
• Dental Assisting
• Dental Hygiene
• Dentistry (miscellaneous)
• Dermatology
• Development
• Developmental and Educational Psychology
• Developmental Biology
• Developmental Neuroscience
• Discrete Mathematics and Combinatorics
• Drug Discovery
• Drug Guides
• Earth and Planetary Sciences (miscellaneous)
• Earth-Surface Processes
• Ecological Modeling
• Ecology, Evolution, Behavior and Systematics
• Economic Geology
• Economics and Econometrics
• Economics, Econometrics and Finance (miscellaneous)
• Electrical and Electronic Engineering
• Electrochemistry
• Electronic, Optical and Magnetic Materials
• Emergency Medical Services
• Emergency Medicine
• Emergency Nursing
• Endocrine and Autonomic Systems
• Endocrinology
• Endocrinology, Diabetes and Metabolism
• Energy Engineering and Power Technology
• Energy (miscellaneous)
• Engineering (miscellaneous)
• Environmental Chemistry
• Environmental Engineering
• Environmental Science (miscellaneous)
• Epidemiology
• Experimental and Cognitive Psychology
• Family Practice
• Filtration and Separation
• Fluid Flow and Transfer Processes
• Food Animals
• Food Science
• Fuel Technology
• Fundamentals and Skills
• Gastroenterology
• Gender Studies
• Genetics (clinical)
• Geochemistry and Petrology
• Geography, Planning and Development
• Geometry and Topology
• Geotechnical Engineering and Engineering Geology
• Geriatrics and Gerontology
• Gerontology
• Global and Planetary Change
• Hardware and Architecture
• Health Informatics
• Health Information Management
• Health Policy
• Health Professions (miscellaneous)
• Health (social science)
• Health, Toxicology and Mutagenesis
• History and Philosophy of Science
• Horticulture
• Human Factors and Ergonomics
• Human-Computer Interaction
• Immunology and Allergy
• Immunology and Microbiology (miscellaneous)
• Industrial and Manufacturing Engineering
• Industrial Relations
• Infectious Diseases
• Information Systems
• Information Systems and Management
• Inorganic Chemistry
• Insect Science
• Instrumentation
• Internal Medicine
• Issues, Ethics and Legal Aspects
• Leadership and Management
• Library and Information Sciences
• Life-span and Life-course Studies
• Linguistics and Language
• Literature and Literary Theory
• LPN and LVN
• Management Information Systems
• Management, Monitoring, Policy and Law
• Management of Technology and Innovation
• Management Science and Operations Research
• Materials Chemistry
• Materials Science (miscellaneous)
• Maternity and Midwifery
• Mathematical Physics
• Mathematics (miscellaneous)
• Mechanical Engineering
• Mechanics of Materials
• Media Technology
• Medical and Surgical Nursing
• Medical Assisting and Transcription
• Medical Laboratory Technology
• Medical Terminology
• Medicine (miscellaneous)
• Metals and Alloys
• Microbiology
• Microbiology (medical)
• Modeling and Simulation
• Molecular Biology
• Molecular Medicine
• Nanoscience and Nanotechnology
• Nature and Landscape Conservation
• Neurology (clinical)
• Neuropsychology and Physiological Psychology
• Neuroscience (miscellaneous)
• Nuclear and High Energy Physics
• Nuclear Energy and Engineering
• Numerical Analysis
• Nurse Assisting
• Nursing (miscellaneous)
• Nutrition and Dietetics
• Obstetrics and Gynecology
• Occupational Therapy
• Ocean Engineering
• Oceanography
• Oncology (nursing)
• Ophthalmology
• Oral Surgery
• Organic Chemistry
• Organizational Behavior and Human Resource Management
• Orthodontics
• Orthopedics and Sports Medicine
• Otorhinolaryngology
• Paleontology
• Parasitology
• Pathology and Forensic Medicine
• Pathophysiology
• Pediatrics, Perinatology and Child Health
• Periodontics
• Pharmaceutical Science
• Pharmacology
• Pharmacology (medical)
• Pharmacology (nursing)
• Pharmacology, Toxicology and Pharmaceutics (miscellaneous)
• Physical and Theoretical Chemistry
• Physical Therapy, Sports Therapy and Rehabilitation
• Physics and Astronomy (miscellaneous)
• Physiology (medical)
• Plant Science
• Political Science and International Relations
• Polymers and Plastics
• Process Chemistry and Technology
• Psychiatry and Mental Health
• Psychology (miscellaneous)
• Public Administration
• Public Health, Environmental and Occupational Health
• Pulmonary and Respiratory Medicine
• Radiological and Ultrasound Technology
• Radiology, Nuclear Medicine and Imaging
• Rehabilitation
• Religious Studies
• Renewable Energy, Sustainability and the Environment
• Reproductive Medicine
• Research and Theory
• Respiratory Care
• Review and Exam Preparation
• Reviews and References (medical)
• Rheumatology
• Safety Research
• Safety, Risk, Reliability and Quality
• Sensory Systems
• Signal Processing
• Small Animals
• Social Psychology
• Social Sciences (miscellaneous)
• Social Work
• Sociology and Political Science
• Soil Science
• Space and Planetary Science
• Spectroscopy
• Speech and Hearing
• Sports Science
• Statistical and Nonlinear Physics
• Statistics and Probability
• Statistics, Probability and Uncertainty
• Strategy and Management
• Stratigraphy
• Structural Biology
• Surfaces and Interfaces
• Surfaces, Coatings and Films
• Theoretical Computer Science
• Tourism, Leisure and Hospitality Management
• Transplantation
• Transportation
• Urban Studies
• Veterinary (miscellaneous)
• Visual Arts and Performing Arts
• Waste Management and Disposal
• Water Science and Technology
• All regions / countries
• Asiatic Region
• Eastern Europe
• Latin America
• Middle East
• Northern America
• Pacific Region
• Western Europe
• ARAB COUNTRIES
• IBEROAMERICA
• NORDIC COUNTRIES
• Afghanistan
• Bosnia and Herzegovina
• Brunei Darussalam
• Czech Republic
• Dominican Republic
• Netherlands
• New Caledonia
• New Zealand
• Papua New Guinea
• Philippines
• Puerto Rico
• Russian Federation
• Saudi Arabia
• South Africa
• South Korea
• Switzerland
• Syrian Arab Republic
• Trinidad and Tobago
• United Arab Emirates
• United Kingdom
• United States
• Vatican City State
• Book Series
• Conferences and Proceedings
• Trade Journals

• Citable Docs. (3years)
• Total Cites (3years)

-->

	Title	Type
1		journal	106.094 Q1	211	49	124	4844	35427	89	381.89	98.86	43.95
2		journal	37.044 Q1	39	3	13	897	955	13	100.11	299.00	27.78
3		journal	35.910 Q1	508	123	336	11462	13599	153	34.50	93.19	29.41
4		journal	30.448 Q1	306	47	136	3645	2240	136	11.14	77.55	26.67
5		journal	26.837 Q1	505	105	304	10805	10951	163	31.23	102.90	44.33
6		journal	24.342 Q1	892	439	1496	32820	53447	1207	31.30	74.76	40.19
7		journal	22.399 Q1	391	239	731	8584	13091	153	19.72	35.92	34.15
8		journal	22.344 Q1	359	95	353	6242	4811	351	10.33	65.71	23.89
9		journal	21.836 Q1	184	117	335	8842	13775	196	31.17	75.57	26.86
10		journal	21.048 Q1	217	127	400	9888	10807	183	28.36	77.86	38.85
11		journal	20.544 Q1	1184	1388	4522	14603	107246	1824	21.69	10.52	38.26
12		journal	19.139 Q1	352	83	227	5043	1938	221	7.00	60.76	16.91
13		journal	19.045 Q1	630	595	1363	16478	36243	729	27.23	27.69	43.99
14		journal	18.663 Q1	71	0	19	0	963	19	0.00	0.00	0.00
15		journal	18.587 Q1	23	11	16	0	802	16	47.57	0.00	81.69
16		journal	18.530 Q1	215	83	261	4493	2531	258	7.04	54.13	17.80
17		journal	18.509 Q1	1391	3770	8037	74917	160102	3840	19.40	19.87	38.12
18		journal	18.117 Q1	511	485	1066	13393	17008	461	13.24	27.61	35.19
19		journal	17.828 Q1	833	271	851	115878	50519	819	49.76	427.59	30.50
20		journal	17.701 Q1	223	75	273	3371	1946	268	6.24	44.95	13.84
21		journal	17.654 Q1	234	108	410	6448	4495	409	8.04	59.70	16.43
22		journal	17.507 Q1	398	178	590	11546	12604	360	19.83	64.87	41.91
23		journal	17.497 Q1	229	229	609	6629	16808	379	26.18	28.95	29.53
24		journal	17.300 Q1	639	336	654	13672	13100	504	19.88	40.69	37.01
25		journal	16.061 Q1	388	36	103	14097	4303	99	42.66	391.58	14.94
26		journal	16.009 Q1	467	169	540	11148	13815	304	23.17	65.96	36.44
27		journal	15.966 Q1	264	102	252	19168	11266	244	38.64	187.92	24.30
28		journal	15.827 Q1	140	106	297	4359	4041	62	12.99	41.12	41.35
29		journal	15.620 Q1	328	23	84	4178	2696	83	27.02	181.65	40.68
30		journal	14.943 Q1	115	16	42	403	896	41	24.10	25.19	77.78
31		journal	14.796 Q1	388	400	978	11477	15900	588	17.52	28.69	33.83
32		journal	14.780 Q1	123	0	13	0	374	11	12.56	0.00	0.00
33		journal	14.707 Q1	32	46	35	4815	2160	34	61.71	104.67	36.44
34		journal	14.618 Q1	160	70	247	587	5353	230	21.11	8.39	58.79
35		journal	14.605 Q1	109	23	72	5797	1938	70	14.90	252.04	45.57
36		journal	14.577 Q1	419	262	637	10044	17562	466	27.42	38.34	28.93
37		journal	14.293 Q1	421	123	346	10202	6211	207	17.40	82.94	32.86
38		journal	14.231 Q1	558	306	834	9499	20730	593	24.08	31.04	24.85
39		journal	14.175 Q1	210	28	92	3163	1260	86	10.59	112.96	42.59
40		journal	13.942 Q1	294	144	670	5180	12698	362	18.81	35.97	39.02
41		book series	13.670 Q1	210	14	42	3772	1271	39	23.96	269.43	26.09
42		journal	13.655 Q1	311	89	563	4857	6315	559	11.14	54.57	23.11
43		journal	13.609 Q1	165	93	250	5332	1699	250	6.02	57.33	15.88
44		journal	13.578 Q1	455	233	688	15608	13409	550	16.89	66.99	40.35
45		journal	13.315 Q1	136	180	471	6682	12109	368	24.27	37.12	26.28
46		journal	13.080 Q1	260	243	827	1865	14374	679	16.58	7.67	62.53
47		journal	12.511 Q1	635	252	983	61439	44032	979	38.71	243.81	32.40
48		journal	12.324 Q1	81	55	137	2838	862	137	6.20	51.60	17.36
49		journal	12.294 Q1	46	62	154	8174	4174	86	27.10	131.84	31.14
50		journal	12.288 Q1	44	60	79	4858	3323	78	42.06	80.97	33.06

Follow us on @ScimagoJR Scimago Lab , Copyright 2007-2024. Data Source: Scopus®

Cookie settings

Cookie Policy

Legal Notice

Privacy Policy

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 17 September 2024

Exploring the impact of perceived early marriage on women’s education and employment in Bangladesh through a mixed-methods study

• Md. Nuruzzaman Khan 1 , 2 ,
• Shimlin Jahan Khanam 1 ,
• Md. Mostaured Ali Khan 3 ,
• Md Arif Billah 4 &
• Shahinoor Akter 5  

Scientific Reports volume  14 , Article number:  21683 ( 2024 ) Cite this article

1 Altmetric

Metrics details

• Epidemiology
• Medical research

Child marriage negatively affects women’s socio-economic empowerment, particularly in education and employment. This study aimed to explore women’ perspectives on the timing of their marriages, considering their educational and employment status at the time. It also sought to identify factors influencing early married women’s perception of their marriages as timely. We analyzed both quantitative and qualitative data. The quantitative data included a sample of 5,596 women aged 15–24 from the 2017/18 Bangladesh Demographic and Health Survey. Additionally, we collected qualitative data through six in-depth interviews, two focus group discussions, and 13 key informant interviews. We used a multilevel mixed-effects Poisson regression model to examine the relationship between women’s formal employment, education, and child marriage. Thematic analysis was employed for the qualitative data. Around 62% of the total women analysed reported their married occurred early with the mean age at marriage was 15.2 years. Approximately 55% of the total early married women believed their marriages occurred at the right time, especially among those who were employed at the time of their marriage. Among this subset, we also noticed a higher likelihood of discontinuing work and education following marriage. Qualitative findings revealed reasons behind this perception, such as escaping poverty, safety concerns, limited job prospects, and the impact of non-marital relationship and societal norms. While many early-married women perceived their marriage as timely, particularly those initially employed, this decision often coincides with a subsequent withdrawal from work and education. This underscores the pressing need for policies and programs aimed at educating women about the legal age for marriage and the negative consequences associated with early marriage while also equipping them with knowledge and resources for informed decision-making.

Similar content being viewed by others

A study of factors affecting women’s lived experiences in STEM

Single motherhood in Ghana: analysis of trends and predictors using demographic and health survey data

Exploring how unemployment and grandparental support influence reproductive decisions in sub-Saharan African countries: Nigeria in focus

Introduction.

Child marriage is a pervasive issue in low- and middle-income countries (LMICs), especially in South Asia and sub-Saharan Africa 1 . The 2022 UNICEF report on child marriage revealed that approximately 12 million girls in LMICs are married before the age of 18 each year, which translates to one in every five girls in those settings 2 . In Bangladesh, the situation is even more alarming, with recent estimates indicating that about 59% of women aged 20–24 years were married before turning 18 3 , and 22% were married before the age of 15 4 . The rate is further higher in rural areas, particularly those with widespread poverty, low education enrolment and significant concerns about family’s reputation 4 , 5 , 6 .

Child marriage can have devastating consequences for girls and their families, as it often leads to a cycle of poverty and disempowerment 7 . Girls who are forced into early marriage are often forced to drop out of school, which reduces and restricts their opportunities for education and limits their economic prospects 8 . Additionally, child marriage has been associated with higher rates of domestic violence and divorce, which can have significant negative impacts on girls’ physical and mental health, hampering the development and wellbeing of girls 9 , 10 . Moreover, child marriage has serious implications for maternal health. Child brides are more likely to experience complications during pregnancy and childbirth, such as obstetric fistula and maternal mortality 11 , 12 . Children born to child brides are also at a higher risk of mortality and malnutrition 13 . This leads to an intergenerational effect because malnourished children are often more likely to drop out of school and subsequently become child brides themselves 14 . These adverse consequences associated with child marriage, coupled with the high number of girls affected, highlight a significant burden in LMICs 1 . Therefore, it poses challenges to achieving Sustainable Development Goals (SDGs) related to health and well-being (SDG 3), gender equality (SDG 5), education (SDG 4), and poverty reduction (SDG 1) 15 .

Socio-demographic factors associated with child marriage have been extensively studied in LMICs, including Bangladesh 16 , 17 , 18 , 19 . Although such observational studies played a crucial role in developing relevant policies and programs to reduce the occurrence of child marriage, they fall short comprehensively addressing the issue. Girls’ views over their marriage along with marriage age can carry a significant weight and override all socio-demographic factors by mediating their roles 20 . It is commonly assumed that every child marriage occurs on parental wishes and is not desired by the girls. However, this is not always the case in reality. The underlying reasons for girls choosing to marry at an early age instead of continuing education and work has remained understudied 21 . Therefore, it is crucial to explore the percentage of girls who perceive their marital age as appropriate or not, as this can inform the development of more effective policies and programs. However, undertaking this requires comprehensive research, integrating determinant factors of early marriage and perceptions of those who marry early. Unfortunately, such information is largely absent in existing literature due to the nature of available data in LMICs, including Bangladesh, where DHS surveys serve as primary data sources 16 , 17 , 18 , 19 , 22 . The survey provides important information on prevalence of early marriage and its socio-demographic predictors, however, lacking content related to girls’ views over early marriage as it typically requires qualitative study. This indicates a need for mixed-methods studies, which are mostly lacking in LMICs, with none conducted in Bangladesh 23 , 24 , 25 , 26 . To address these limitations, we aimed to investigate women’s perceptions of getting married at an earlier age, taking into account their education and employment status at the time of marriage. We also sought to identify the factors that influenced earlier-aged women to perceive their marriages as occurring at the right age.

Study design

The study applied a sequential explanatory mixed-methods design where analysis of secondary quantitative data were followed by the collection and analysis of qualitative data 27 . The qualitative findings, therefore, aimed to explain and interpret the findings of the quantitative study.

Quantitative study

Data source and sampling.

This study analysed data of most recent 2017/18 Bangladesh Demography and Health Survey (BDHS). The survey employed a two-stage stratified random sampling method to select the respondents. In the first stage, 672 Primary Sampling Units (PSUs) were selected from a list of 293,579 PSUs generated during the 2011 National Population Census of Bangladesh, excluding three PSUs due to extreme floods. In the second stage, 30 households were randomly selected from each of the PSUs, using probability proportional to PSU size. This generated a list of 20,160 households, and 19,457 of these households were interviewed. There were 20,376 respondents eligible in the selected households with the eligibility criteria: (i) being a married woman of reproductive age and (ii) spent the previous night of the survey day in the selected households. Of them data were collected from 20,127 women. Details of the BDHS survey procedure were published elsewhere 3 . A sub-sample, 5,596 women aged 15–24, of them was analysed in this study selected based on the following two inclusion criteria: (i) aged 15–24 years (to ensure inclusion of only recently married women following the recommendation of global literature 2 , 6 , 7 , 9 , 24 , 28 ) and (ii) married at the time of survey.

Outcome variable

The focus of our quantitative study was child marriage, which we defined as a binary outcome variable (yes or no). To collect this data, the BDHS asked women to report the age at which they first began living with their spouse or first spouse in case of more than one marriage. We categorized responses as either child marriage (1, if the marriage occurred before the woman turned 18) or normal-aged marriage (0, if the marriage occurred at age 18 or later) according to the universal recommendation which is also followed in Bangladesh 2 .

Exposure variables

Working status and educational status of respondents at the time of their marriage as well as following marriage were our primary exposure variables. The BDHS collected this data by asking whether the respondents were working or studying in school just before they got married. If the response was affirmative, the respondents were then asked two follow-up questions to determine their work or education continuity and the number of years of continuity. These follow-up questions were: (i) Did you continue working/studying after marriage? and (ii) If yes, for how long? Based on the responses, we created four variables: (i) study before marriage (yes, no), (ii) work before marriage (yes, no), (iii) after marriage study (no, continue less than a year, continue less than five years), and (iv) after marriage work (no, continue less than a year, less than five years). We also considered additional exposure variables by reviewing the available literature for Bangladesh and other LMICs 16 , 17 , 18 , 19 . These variables included respondents’ age, education level, partner’s education level, partner’s occupation, wealth quintile, place of residence, and region of residence.

Statistical analysis

Descriptive statistics, including frequency and percentage, were used to describe the characteristics of the respondents. Multilevel mixed-effect Poisson regression model was utilized to explore the association between early marriage and the working and studying status of the respondents, as well as the continuity of their working and studying status following child marriage. The reason for using a multilevel Poisson regression model was higher prevalence of child marriage (> 10%) and the clustering structure of the BDHS data. Previous studies have found that simple logistic regression analysis produces less precise findings when the prevalence of the outcome variable is high and the data come from a clustered structure 29 . Both adjusted and unadjusted models were run, where one particular exposure variable was considered with the child marriage variable in the unadjusted model, and other factors were adjusted in the adjusted model. Multicollinearity was checked before running each model. Results were reported as unadjusted or adjusted Prevalence Ratios (PRs) and corresponding 95% confidence intervals. Stata version 18.0 was used for data analysis.

Qualitative exploration

In our quantitative study, we identified a higher prevalence of child marriage among working women. To explore why working women get married at an early age, we conducted a qualitative study during January 2023 and January 2024 as this information was not available in the BDHS survey data. The Gazipur district of Dhaka division was purposively selected as the study area. This area has a higher concentration of ready-made garments and small-scale industries where the majority of workers are women and married 30 . Two focus group discussions (FGDs) involving 16 participants (8 in each FGD), six in-depth interviews (IDIs), and 13 key informant interviews (KIIs) were conducted using two pre-developed interview topic guides. The topic guides covered several areas, including marriage experience, perceptions at the time of marriage, education and employment after marriage, respondents’ perspectives on marriage over time, and community and religious norms related to early marriage. The length of these interviews ranged from 75 to 90 min.

The participants included in the FGD and IDI were selected purposively selected based on the following criteria: (i) currently aged between 15 and 24 years, (ii) married before their 18th birthday, (iii) involved in either work or education just before marriage, and (iv) currently either continuing education or work or have left them to become housewives. These criteria aimed to ensure that the qualitative study participants were similar to those in the quantitative study. To recruit the participants, data collectors first approached them by sharing the details of the study plan and collected data on their preferred date. To protect privacy of the participants and ensure limited interruption during interview process, the participant and interviewer choose a private location. Prior to the qualitative data collection, participants were again briefed about its objectives and assured of the confidentiality of their responses. Informed consent was obtained from participants above 18 years old, while for participants under 18, informed consent was obtained from their legal guardians (father or husband).

Additionally, 13 key informant interviews (KIIs, male = 9; female = 4) were conducted with managers of ready-made garment factories ( n  = 2), small-scale industries ( n  = 1), local leaders (members of the Pourosova, n  = 3 [male = 2, female = 1]), the Upazila Nirbahi officer ( n  = 1, female), and parents of girls who married at an early age ( n  = 6 [male = 4, female = 2]). Their perspectives on early marriage were sought due to their significant involvement in the issue, including shaping cultural norms and exerting social pressures that perpetuate early marriage practices.

Participation was completely voluntary, and no participants were provided with any gifts or incentives to participate in this study. Experienced social researchers were involved in the qualitative data collection. The FGDs and IDIs were conducted by a female interviewer (second author). The KIIs were conducted by two male interviewers (first and third author). All conversations during FDGs, IDIs and KIIs were audio recorded with consent. The recorded audio files were subsequently reviewed and translated into English by the respective research team members. Relevant sections on the reasons behind early marriage and its impact on work and educational pursuits were extracted and discussed among the team to ensure accurate interpretation and presentation of the data. Qualitative data were thematically analysed 31 , using Nvivo version 12.10 32 . Key themes identified in the analyses were synthesized and presented as study findings. Ethical approval for the qualitative study was obtained from the Institutional Review Board of the University of Rajshahi.

Background characteristics of the respondents

Table  1 presents the background characteristics of the respondents included in the quantitative exploration. The mean age at marriage was 15.2 years (SD, ± 1.41) and the mean years of education were 6.9 years (SD, ± 2.95). About 32.8% of the respondents reported being engaged in a formal job. Of the respondents analyzed, 61.7% reported their marriage occurred before reaching their age 18 years. Over half of the (55.1%) total 61.7% who reported being married before 18 years reported that their marriage occurred at the right time, while 44.9% of them felt that they should have delayed their marriage.

Respondents’ perception about their marriage timing

Table  2 illustrates the distribution of respondents’ perception about their marriage timing as per their socio-demographic characteristics. Among the respondents who believed that their marriage occurred earlier, and they should delay, 49.8% were aged between 15 and 19 years, and 41.2% were aged between 20 and 24 years. Earlier-aged married women who believe their marriage occurred earlier and should be delayed, 36.6% were illiterate. This number was half to 63.4% of illiterate women who thought their marriage occurred at right age.

How women’s education and employment status before marriage influences their perception of marriage age

The unadjusted associations suggest that earlier-married women who thought their marriage occurred at the right time were more likely to have worked before marriage (PR = 1.88, 95% CI = 1.34–2.64) and less likely to have studied before marriage (PR = 0.55, 95% CI = 0.47–0.66) compared to women who thought they should delay their marriage (Table  3 ). After adjusting for confounding variables, the associations remain significant, but the effect sizes are attenuated. Earlier aged women who thought their marriage occurred at the right time were still more likely to have worked before marriage (aPR = 1.47, 95% CI = 1.01–2.18), but the association was no longer significant. However, adjusted likelihood of early marriage was found lower among studying girls (aPR = 0.43, 95% CI = 0.35–0.53).

Impact of early marriage on the continuation of education and employment

Table  4 presents the results of the analysis of the association between the women’s perception of their marriage timing and their continuation of education and work after marriage. In the unadjusted analysis, earlier aged women who think that their marriage occurred at right time were more likely to report continuation of their education up to five years (PR = 1.73, 95% CI 1.31–2.28) as compared to the women who did not continue their education. However, this association was not significant for those who continued their education for less than one year. In the adjusted analysis, the association between continuing education and women’s perception of their marriage timing was attenuated, with women who continued their education for less than five years having a non-significant lower likelihood of perceiving their marriage as occurring at the right time (aPR = 0.92, 95% CI 0.65–1.28). The association between continuing work and women’s perception of their marriage timing remained non-significant in the adjusted analysis.

Through qualitative investigation, we conducted an extensive exploration into the reasons why working women who married at an earlier age believed that their marriage took place at the right time, and also examined why studying women who married at an earlier age perceived their marriage as occurring earlier, as revealed in our quantitative analysis. The characteristics of the participants participated in qualitative interviews are presented in supplementary Tables 1 , 2 and 3 . Our comprehensive findings have uncovered a multitude of factors that can be categorized into distinct thematic patterns (supplementary Table 4). These include: (i) getting married is a way to recover from poverty, (ii) marriage was perceived as a means to ensure the safety and security of young unmarried girls, (iii) less hope for job prospects, and (iv) intimate relationships and social norms.

Getting married way to recover from poverty

Participants reported that a common influencing factor behind early marriage among young girls was their perception that it would help them escape poverty.

“I had dreams of studying and becoming a government service holder, but poverty choked those dreams before they could bloom. Marriage was the only path open to me, even if it means leaving those dreams behind.” (IDI participant 3, age 20). “My parents couldn’t afford to keep me in school anymore, and marriage seemed like the only way to have a roof over my head and food on the table.” (IDI participant 2, age 19).

Most female participants reported that they entered the labour market before reaching their 18th birthday. Since their families were from lower socio-economic backgrounds, they started looking for a job to support their families. They often engaged in low-wage occupations, such as house cleaning or garment factories.

“I worked in house cleaning before my marriage and received a very low wage, which was not enough for my living. As a result, I could not send money to my parents, even though they expected me to do so.” (FGD 1 participant, age 19).

According to them, like many young girls, they also moved to the city from rural areas leaving their families behind in search of jobs and started living in rental accommodations. The income derived from these jobs proved insufficient to meet their daily expenses, including paying for food, rent and utilities while providing financial assistance to their families. This issue was also highlighted in the KIIs. One ready-made garment manager reported that young female ready-made garment factory workers’ wages are not enough to support themselves and their parents. Therefore, they usually decide or agree to get married at an early age to overcome their financial struggles.

“Girls who started working here usually work on a daily basis and earn only 150–200 BDT (1.5-2 USD) per day, which is not enough to maintain their daily expenses. What’s even worse is that many of these girls migrated from rural areas to work here, meaning they have to pay for their rent and other associated costs. It’s no surprise that many of them end up choosing to get married, as it seems like the only way out of this financial struggle”. (Ready-garment manager, male, age 45).

Marriage was perceived as a means to ensure the safety and security of young unmarried girls

In the context of working girls who opt for early marriage, safety and security emerge as crucial factors. Participants reported that young girls working in different industries (such as ready-made garments factories) usually work alongside male workers. Due to the demands of their jobs, they usually spend prolonged hours working together at workplace. The nature of their joint work and spending prolonged hours at workplace often create tensions and a sense of insecurity among themselves and within their families. They fear being exposed to physical and sexual abuse and/or violence at workplace, thereby reinforcing the inclination towards early marriage.

“My parents were concerned about the potential risks of sexual and physical abuse when working outside the home, especially during evening hours and interacting with male colleagues. I would hold the same belief and take similar precautions if I had a young daughter engaged in employment, as there are multiple reasons to support this perspective”. (FGD 1 participant, age 18). “Living alone as a young woman in this city felt dangerous. Marriage, even if it’s not ideal, meant having someone to protect me and a family to belong to.” (IDI participant 2, age 20).

This fear also reflected in the KIIs with fathers. Families of young girls working in ready-made garment factories tend to marry off their young girls to protect their daughters or girls from any future risks of abuse.

“Soon after starting working at age 15, I arranged her marriage. Although it was not my intention, we did not feel secure leaving my young daughter outside the house, as she could be at risk of rape or harassment from strangers. My neighbours also suggested that I do so”. (Father of a married and working young girl, age 55).

One key informant, a supervisor of a ready-made garments industry, also confirmed that young girls working in the industries often become victims of physical and sexual harassments. He also added that the prevalence of sexual violence and harassment had been underreported like in other sectors in Bangladesh and remained a concerning reality. However, the families of the young girls are aware of these risks, even if based on limited evidence, which significantly influenced marry off their girls at a young age against the law.

“A significant number of girls who start working at a young age face violence from their male counterparts, including sexual violence. We are aware of this, and our organization has very strict laws against it. However, these incidents often go unreported, similar to other sectors in Bangladesh”. (A supervisor of a garment factory, male, age 38).

Even parents of young school or college-going girls also voiced their concern regarding their daughters’ safety while traveling to school or college. Sometimes local boys or men harassed these young girls on the way to home or college, and parents of these young girls usually perceived that marrying the young girls off was the only solution.

“I wanted my daughter to continue her education. However, a mischievous boy started following her to school. I contacted his parents and asked them to discipline their son and prevent him from following my daughter, but it did not work. Eventually, I had to marry off my daughter when she was only 16 years old”. (Father of earlier aged girls who marry off while studying, age 38).

However, during the FGDs, female participants expressed their viewpoints that the claims regarding safety concerns and incidents of violence were not always accurate. They argued that this phenomenon might stem from parents’ genuine apprehensions regarding the safety and their desire to uphold societal prestige, their parents mostly forced them to get married at an early age against their will. This highlights the need to delve deeper into the complexities surrounding early marriage and the factors that shape this decision.

“I started working as a house cleaner when I was 15 and got married at age 16. I wasn’t intending to get married at that time, but my parents pressured me to do so. They had heard that young girls working as house cleaners often face sexual and physical violence from homeowners. Though my homeowners treated me like their daughter. Therefore, they were strict in their decision to marry me off”. (FGD 2 participant, age 20).

Less hope for job prospects

Several female participants highlighted the interconnectedness between school dropout, limited employment opportunities for people with lower educational attainment, early entry into workforce, and early marriage. According to them, education could not guarantee job prospects in Bangladesh. Securing a job could be more challenging for individuals with low academic achievements. Therefore, they perceived that entering the job market early would be more worthwhile for them and their families than continuing their education, as it required financial resources.

“Studying seemed pointless when I knew there were barely any jobs at the end. Marriage offered a chance at some stability, even if it wasn’t the kind I hoped for.” (IDI participant 5, age 21).

One FGD participant reported that she perceived that she did not have the necessary qualities to compete in the increasingly competitive job market. She felt early employment would give her practical work experience, opening opportunities for better job prospects. Hence, she decided to dropout from school. Other FGD participants agreed with her, indicating they shared similar perceptions.

“Obtaining a job after completing education is only possible for highly meritorious students. I did not fall into that category. Hence, I decided to start working with the hope that by the time I finished my education, I would have accumulated several years of work experience, which would undoubtedly enhance my chances of securing a comparatively better job”. (FGD 2 participant, age 19).

Some guardians of young school or college-going girls also perceived that education would not guarantee any employment for their daughters and married them off at an earlier age.

“Why would I continue my daughter’s education? What hope was there? Even many educated and meritorious students are now unemployed. Therefore, I married off my daughter when she was only 15, and she is now leading her own life. I have no concern now”. (Father of earlier aged girls who marry off while studying, age 40).

Intimate relationships and social norms

Several female participants revealed that working girls were found to be more susceptible to developing relationships with their colleagues. These close bonds often evolve into sexual relationships, contributing to early marriage among working girls.

“Shortly after I began working, I entered into a relationship with one of my colleagues, which later turned into a sexual relationship. We then decided to marry, although our parents were unhappy with our decision. However, we did it without their approval because we knew what we did was not right according to our religion”. (FGD 1 participant, age 18).

The issue of having a relationship at the workplace without parents’ permission was identified as a growing concern for early marriage, even in the absence of an intimate relationship.

“Parents of the earlier-aged working girls are mostly uneducated and strongly influenced by the social norms and misconceptions. They believe female and male could not be co-worker. Therefore, they prefer to marry off their daughters earlier”. (Upazila Nirbahi officer, age 38).

Importantly, this perception was found to be common among parents of both working and studying girls. However, it does not pose a significant concern for the parents of studying girls. This is because their awareness of the negative effects of child marriage motivates them to prioritize the education of their daughters and prioritize their daughters’ safety above all else.

“I had concerns about the possibility of my school-going daughters engaging in intimate relationships, which have become more common due to modernization. However, my intention was not to abruptly end her education and arrange her marriage solely based on this risk. I believed she was young and had a promising future ahead. Eventually, I did make the decision to marry her, but it was primarily motivated by the opportunity to find a comparatively better groom within my family lineage”. (Father of earlier aged girls who marry off while studying, age 38).

Social norms were highlighted as significant reasons for early marriage among the women who married at a younger age.

“Marriage is a way to uphold our traditions, to show respect to our families and ancestors. Even if I had doubts, I knew I had to follow the path laid out for me.” (IDI participant 4, age 22).

One participant mentioned that she felt she did not belong in her society and remained a minor in society’s eyes when she found many of peers were getting married at an earlier age than her.

“Everyone around me was getting married young, building families. It felt like I was the only one left behind, stuck in a childhood that was no longer fitting. Marriage was a way to belong, to be seen as a responsible adult.” (IDI participant 5, age 21).

Another participant explicitly mentioned why marriage was important for them to become important in the society.

“Marriage is a social currency here. It defines your status, your worth. Choosing a career over marriage felt like choosing shame over acceptance, a path less traveled and less understood.” (IDI participant 3, age 19).

The primary objective of this study was to explore the perspectives of girls regarding the timing of their own marriages, taking into consideration their educational and employment backgrounds at the time of marriage. Furthermore, we aimed to investigate the factors that influenced earlier-aged women to perceive their marriages as occurring at the appropriate time. Our findings indicate that among the total population of earlier-aged women, 55% believe that their marriages took place at the right time, with a higher percentage observed among women who were employed at the time of marriage. Among those who held this perception, there was a notable trend of discontinuing work and education after getting married. Through qualitative analysis, we gained insights into the underlying reasons why these women considered their marriages to be timely, including the desire to escape poverty, concerns regarding safety and security, and the influence of intimate relationships and societal norms.

The study findings convey three significant messages concerning early marriage in the country. Firstly, a substantial portion of early marriages are a result of girls’ choice. Secondly, the engagement of girls in formal employment contributes to an increase in early marriage rates, unless measures are implemented to ensure economic security and safety. Lastly, early-married girls who believe that their marriages occurred at the right time are more likely to discontinue their education and withdraw from the workforce.

The perception of working girls who marry early that their marriage occurred at the right time can be understood from two distinct directions. Firstly, these girls may lack awareness regarding the appropriate age for marriage and the potential negative consequences associated with marrying at a young age. Secondly, their working environment and the challenges they face may have influenced their decision to marry early, despite being aware of the adverse outcomes of early marriage. This may include parental pressure for marriage once girls start working, especially if they work alongside male colleagues or develop intimate relationships with them, which can conflict with societal norms 33 . Traditional patriarchal values in Bangladesh further reinforce these pressures 33 . Regardless of the direction, these perceptions indicate a failure of policies and programs, which can result in long-term burdens for the country.

If the first direction holds true, it suggests that a portion of women have not received the message regarding the correct age for marriage at early ages and consequences of early marriage. Factors such as early dropout from education to enter the workforce and limited exposure to mass media due to work obligations may contribute to this lower level of awareness 28 , 29 , 34 . These directions are influenced by various socio-demographic and socio-cultural factors. While leaving the parental home to enter the workforce can indicate a degree of freedom for girls, this is not always the case in many LMICs, including Nepal and India 8 , 18 . In these contexts, girls who start working early, often without continuing their education, typically come from low-income families where they are expected to support their families. Early marriage remains a long-standing norm in these communities, perpetuated by the fact that their mothers and grandmothers also married at a young age. Furthermore, these girls often move from their parental homes to their workplace, which is often seen as opposite of social norms in Bangladesh as well as other LMICs 35 .

On the other hand, if the second directions is true, it indicates a failure of long-standing governmental priorities to ensure continuing girls’ education and prevent child labour, as well as a failure to ensure the safety of working girls 18 . Though Bangladesh has made remarkable progress in reducing violence against women, incidents still occur frequently with one in three women faces it 36 . Importantly, any such incidents are usually spread widely with additional rumours, causing concern for the girls’ security and motivating their parents to marry them off early. However, the above explanations may not be true for the girls who continue education. They are usually better aware off about the right age of marriage and risk of early marriage as well as usually stay at home with less security issue. They are also from a comparatively better family status.

In this study, we have also found that there is a lower likelihood of continuing work after marriage. This change is mainly due to the presence of family pressure or intention to have a child, as well as the desire to give more time to the family 37 , 38 . Moreover, we found that while women are employed, their wages in the factories are very low. This financial constraint limits their opportunities, often pushing them towards early marriage or leading them to leave work altogether to assume traditional roles as wives, mothers, and homemakers 39 . However, this practice can have several adverse consequences on women’s economic flexibility, empowerment and decision making abilities in the family 40 , 41 . For instance, stopping work can lead to face earlier aged pregnancy, which is associated with various adverse maternal and child health outcomes, including lower utilization of maternal healthcare services, pregnancy complications, and maternal and child mortality 40 , 42 . Furthermore, this trend indicates a significant dropout rate of girls from continuing their education and working status, which negatively affects women’s empowerment and decision-making abilities 40 , 41 . These consequences can lead to higher household poverty, greater sensitivity to economic shocks, and less income diversification 41 . These factors, in turn, can have significant intergenerational impacts, resulting in poorer health among children and lower investment in education and other forms of human capital accumulation 43 , 44 . All of these factors increase the likelihood of early marriage in subsequent generations 43 .

Regardless of the explanation provided, these findings indicate challenges for the country to achieve relevant SDG’s targets related to the improvement of sexual and reproductive health rights as well as equity. This highlights the need for policies and programs to educate and raise awareness among studying and working girls about the correct age of marriage and the adverse effects of early marriage. Increasing the wages of working girls at the initial level is also important. Nevertheless, the existing initiatives remain ineffective unless appropriate engagement of multiple stakeholders including girl’s parents and local leaders for implementation of target-oriented policies and programs to reduce early marriage. Reducing gender-based violence and improving their safety in the workplace are also crucial to reducing early marriage.

This study exhibits several notable strengths as well as a few limitations. It stands as the first investigation in Bangladesh and other LMICs that delves into women’s perceptions of their marriage age, accounting for their educational and employment statuses, and utilizing nationally representative quantitative survey data. Additionally, the qualitative survey findings offer insight into the motivating factors behind the marriage decisions of working and educated girls. The qualitative interviews were conducted by the authors of this study, who have extensive experience in academia and public health research. They hold postgraduate degrees in population science, public health, and anthropology, and possess substantial expertise in conducting research in LMICs, including Bangladesh. All authors agreed on and approved the interpretation presented in the manuscript. The study employed appropriate statistical modelling techniques to analyze the data, incorporating a diverse range of confounding variables. As a result, the reported findings possess sufficient robustness to inform national-level policies and programs. However, one key limitation of this study is that the quantitative data analyzed in this study were derived from a cross-sectional survey, which restricts the ability to establish causal relationships. While the study explored cultural factors associated with early marriage through qualitative analysis, these factors were not adjusted for in the quantitative analysis due to their unavailability within the survey. We were also unable to account for other important factors, such as spousal age differences and the extent of early marriage within the women’s families, due to a lack of data, despite their relevance to the occurrence of early marriage. Furthermore, the women’s age of marriage was self-reported, introducing the potential for recall bias. Nevertheless, any such bias is expected to be random in nature and should not significantly skew the reported results in any particular direction. Conducting qualitative study was another strength of this study, where representation of women (in IDIs and FGDs) and men (in the KIIs) allowed us to capture diverse perspectives on this complex issue. We conducted the qualitative study in a purposively selected district and utilized data to explain and interpret findings from the nationally representative quantitative data. However, this comparison may introduce errors due to different social norms and cultural issues regarding early marriage in various parts of the country. Moreover, the perception of early marriage among this group of women might differ slightly from that of women in other regions and rural areas due to factors such as their relocation, economic stability, community engagement, and relatively higher decision-making autonomy. This indicates a need for qualitative interviews to be conducted in different regions of the country. However, we were unable to do so due to a lack of funding. It is worth noting that although participants included in our qualitative survey were from only one district, a significant portion of them reported their origins as being from different parts of the country, including rural areas, rather than their present location. They had moved to this district for employment, given that ready-made garment and small-scale industries are predominantly located in this area. Moreover, we conducted qualitative interviews in 2023–2024, while the quantitative data we analysed was collected in 2017–2018. Comparing data from different time points may introduce some distortion in the reported associations and conclusions. However, we could not address this issue further as the quantitative survey data we analysed is the most recent available in Bangladesh.

Our findings revealed that approximately 55% of women who married at an early age believed that their marriage took place at the right time. Among early-married women, those who were employed at the time of their marriage were more likely to perceive their marriage as timely, whereas those who were pursuing studies at the time of their marriage were more inclined to view their marriage as occurring too early and should have been delayed. Multiple factors emerged as influential in shaping the perception of earlier-aged married women regarding the timing of their marriage, including the desire to escape poverty, concerns related to safety and security, and the influence of intimate relationships and societal norms. These findings highlight that a significant proportion of early-married women believe their marriage occurred at the right time, indicating a gap in policies and programs designed to raise awareness about the risks of early marriage and early childbearing. It is crucial for policies and programs to prioritize comprehensive education for all girls and those around them, including parents, to ensure they are informed about the appropriate age for marriage and the potential consequences of early marriage. Additionally, those involved in decision-making and upholding social norms around early marriage should receive extensive counselling on its adverse effects. This focus should particularly target working girls, who may be more vulnerable to early marriage.

Data availability

“The data that support the findings of this study are available from The DHS Program, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the corresponding author upon reasonable request and with permission of The DHS Program. To proceed, researchers are required to submit a research proposal via the website (https://dhsprogram.com/data/available-datasets.cfm). Subsequently, the designated individual will review the proposal and approve access for data download. We are unable to share the qualitative interview data due to restrictions imposed by the ethical review committee”.

Abbreviations

Low- and middle-income countries

Demographic Health Survey

Bangladesh Demographic Health Survey

adjusted odds ratio

Confidence interval

Sustainable development goal

National Institute of Population Research and Training

Primary sampling unit

Subramanee, S. D. et al. Child marriage in South Asia: a systematic review. Int. J. Environ. Res. Public Health 19 (22), 15138 (2022).

Article   PubMed   PubMed Central   Google Scholar  

UNICEF. UN General Assembly Resolutions on Child Marriage . https://www.unicef.org/protection/child-marriage (2022).

National Institute of Population Research and Training (NIPORT). Bangladesh Demographic and Health Survey 2017–2018 (2020).

Bangladesh Bureau of Statistics (BBS) & UNICEF Bangladesh Progotir Pathey, Bangladesh Multiple Indicator Cluster Survey 2019, Survey Findings Report (2019).

Marphatia, A. A., Ambale, G. S. & Reid, A. M. Women’s marriage age matters for public health: a review of the broader health and social implications in South Asia. Front. Public Health 5 , 269 (2017).

UNICEF. Ending Child Marriage: A Profile of Progress in Ethiopia (UNICEF, 2018).

Scolaro, E. et al. Child marriage legislation in the Asia-Pacific region. Rev. Faith Int. Affairs 13 (3), 23–31 (2015).

Article   Google Scholar  

Sekine, K. & Hodgkin, M. E. Effect of child marriage on girls’ school dropout in Nepal: analysis of data from the multiple Indicator Cluster Survey 2014. PLoS ONE 12 (7), e0180176 (2017).

Islam, M. M. Child marriage, marital disruption, and marriage thereafter: evidence from a national survey. BMC Women’s Health 485 , 22485 (2022).

Google Scholar  

Naved, R. T. et al. Impact of Tipping Point Initiative, a Social Norms Intervention, in Addressing Child Marriage and Other Adolescent Health and Behavioral Outcomes in a Northern District of Bangladesh (2022).

Dadras, O., Khampaya, T. & Nakayama, T. Child marriage, reproductive outcomes, and service utilization among young Afghan women: findings from a nationally Representative Survey in Afghanistan. Stud. Fam. Plann. 53 (3), 417–431 (2022).

Article   PubMed   Google Scholar  

Fan, S. & Koski, A. The health consequences of child marriage: a systematic review of the evidence. BMC Public Health 22 (1), 1–17 (2022).

Hossain, M. M. et al. Child marriage and its association with morbidity and mortality of under-5 years old children in Bangladesh. PLoS ONE 17 (2), e0262927 (2022).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Khanam, S. J. & Khan, M. N. Effects of parental migration on early childhood development of left-behind children in Bangladesh: evidence from a nationally representative survey. PLoS ONE 18 (11), e0287828 (2023).

Julitta Onabanjo, M. M. M. F. Urgent Action Needed to Meet SDG to End Child Marriage by 2030 (UNICEF, 2022).

Kamal, S. M. et al. Child marriage in Bangladesh: trends and determinants. J. Biosoc. Sci. 47 (1), 120–139 (2015).

Article   MathSciNet   PubMed   Google Scholar  

MacQuarrie, K., Juan, C. & Fish, T. D. Trends, Inequalities, and Contextual Determinants of Child Marriage in Asia (ICF, 2019).

Billah, M. A. et al. Spatial Pattern and Influential Factors for Early Marriage: Evidence from Bangladesh Demographic Health Survey Data 2017–2018 (2023).

Modak, P. Determinants of girl-child marriage in high prevalence states in India. J. Int. Women’s Stud. 20 (7), 374–394 (2019).

Boertien, D. & Härkönen, J. Why does women’s education stabilize marriages? The role of marital attraction and barriers to divorce. Demogr. Res. 38 , 1241–1276 (2018).

Mourtada, R., Schlecht, J. & DeJong, J. A qualitative study exploring child marriage practices among Syrian conflict-affected populations in Lebanon. Confl. Health 11 (1), 53–65 (2017).

Biswas, R. K., Khan, J. R. & Kabir, E. Trend of child marriage in Bangladesh: a reflection on significant socioeconomic factors. Child Youth Serv. Rev. 104 , 104382 (2019).

Saleh, A. M. et al. Exploring Iraqi people’s perception about early marriage: a qualitative study. BMC Women’s Health 22 (1), 393 (2022).

UNICEF. Child marriage knowledge, attitudes, and perceptions among affected communities in Albania. UNICEF 1 , 1–101 (2018).

Seta, R. Child marriage and its impact on health: a study of perceptions and attitudes in Nepal. J. Glob. Health Rep. 7 , e2023073 (2023).

Mardi, A. et al. Perceptions of teenage women about marriage in adolescence in an Iranian setting: a qualitative study. Electron. Phys. 10 (2), 6292 (2018).

Maxwell, J. A. Qualitative Research Design: An Interactive Approach (Sage, 2012).

UNICEF. Child Marriage, Aolescent Pregnancy and School Dropout in South Asia . https://www.unicef.org/rosa/reports/child-marriage-adolescent-pregnancy-and-school-dropout-south-asia (2022).

Barros, A. J. & Hirakata, V. N. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med. Res. Methodol. 3 (1), 1–13 (2003).

Swazan, I. S. & Das, D. Bangladesh’s emergence as a ready-made garment export leader: an examination of the competitive advantages of the garment industry. Int. J. Glob. Bus. Competitive. 17 (2), 162–174 (2022).

Clarke, V. & Braun, V. Thematic analysis. J. Posit. Psychol. 12 (3), 297–298 (2017).

Dhakal, K. NVivo J. Med. Libr. Assoc. 110 (2), 270 (2022).

PubMed   Google Scholar  

Ferdous, D. S., Saha, P. & Yeasmin, F. Preventing Child, Early and Forced Marriage in Bangladesh: Understanding Socio-economic Drivers and Legislative Gaps (2019).

Bajracharya, A., Psaki, S. & Sadiq, M. Child Marriage, Adolescent Pregnancy and School Dropout in South Asia (Report by the Population Council for the United Nations Children’s Fund Regional Office for South Asia, 2019).

Jayachandran, S. Social norms as a barrier to women’s employment in developing countries. IMF Econ. Rev. 69 (3), 576–595 (2021).

Article   MathSciNet   Google Scholar  

Shahen, M. A. Gender-based violence in Bangladesh: a critical analysis. Int. J. Qual. Res. 1 (2), 127–139 (2021).

Bhowmik, J., Biswas, R. K. & Hossain, S. Child marriage and adolescent motherhood: a nationwide vulnerability for women in Bangladesh. Int. J. Environ. Res. Public Health 18 (8), 4030 (2021).

Daily & Star From Early Marriage to Risky Pregnancy: We Must Break the Cycle (Dhaka, 2022).

Nagata, H. Female workers’ skills, wages, and householding in Bangladesh’s readymade garment industry: the case of a Japanese multinational company. J. Interdiscp. Econ. 32 (1), 47–74 (2020).

MathSciNet   Google Scholar  

Ahinkorah, B. O. et al. Girl child marriage and its association with maternal healthcare services utilization in sub-saharan Africa. BMC Health Serv. Res. 22 (1), 777 (2022).

Abera, M. et al. Early marriage and women’s empowerment: the case of child-brides in Amhara National Regional State, Ethiopia. BMC Int. Health Hum. Rights 20 (1), 1–16 (2020).

Li, C., Cheng, W. & Shi, H. Early Marriage and Maternal Health care Utilisation: Evidence from sub-Saharan Africa 101054 (Economics & Human Biology, 2021).

Sekhri, S. & Debnath, S. Intergenerational consequences of early age marriages of girls: Effect on children’s human capital. J. Dev. Stud. 50 (12), 1670–1686 (2014).

Otoo-Oyortey, N. & Pobi, S. Early marriage and poverty: exploring links and key policy issues. Gend. Dev. 11 (2), 42–51 (2003).

Download references

Acknowledgements

We are thankful to MEASURE DHS for the data support and also grateful to icddr, b where the data for this study was analysed. We are also acknowledged the Governments of Bangladesh, Canada, Sweden and the UK for providing core/unrestricted support for to run icddr, b. The authors also acknowledge the support of Health System and Population Studies Division of icddr, b and Department of Population Science of Jatiya Kabi Kazi Nazrul Islam University, where this study was designed and conducted.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and affiliations.

Department of Population Science, Jatiya Kabi Kazi Nazrul Islam University, Namapara, Mymensingh, 2220, Bangladesh

Md. Nuruzzaman Khan & Shimlin Jahan Khanam

Nossal Institute for Global Health, Melbourne School of Population and Global health, The University of Melbourne, Melbourne, 3010, Australia

Md. Nuruzzaman Khan

Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), 68 Shaheed Tajuddin Ahmed Sarani, Mohakhali, Dhaka, 1212, Bangladesh

Md. Mostaured Ali Khan

Health System and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), 68 Shaheed Tajuddin Ahmed Sarani, Mohakhali, Dhaka, 1212, Bangladesh

Md Arif Billah

La Trobe Rural Health School, John Richards Centre for Rural Ageing Research, La Trobe University, Melbourne, VIC, 3689, Australia

Shahinoor Akter

You can also search for this author in PubMed   Google Scholar

Contributions

“Khan MN designed the study. Khan MN, Khanam SJ and Khan MMA collected qualitative data. Khan MN and Billah MA analysed quantitative data while all authors analysed qualitative data. Khan MN, Khan MMA, Billah MA and Khanam SJ wrote the first draft of this manuscript. Akter S critically reviewed and edited the previous versions of this manuscript. All authors approved this final version of the manuscript”.

Corresponding author

Correspondence to Md. Nuruzzaman Khan .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

The quantitative data analysed in this study was extracted from the survey which was approved by the institutional review board of ICF macro (Inner City Fund) and the National Research Ethics Committee of the Bangladesh Medical Research Council. Informed consent was obtained from all participants. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. No separate ethical approval was required to conduct this study. We obtained permission to access this survey and conduct this research. All methods were performed in accordance with the relevant guidelines and regulations. Ethical approval for conducting the qualitative survey was obtained from the Institutional Review Board of the University of Rajshahi (123/430/IAMEBBC/IBSc), ensuring compliance with ethical guidelines and protocols. Informed consent was obtained from all participants who were 18 years of age or older. For participants who were under 18 years of age, their legal guardian, such as their husband or father, provided informed consent on their behalf. This process ensured that all participants had a clear understanding of the survey’s purpose, procedures, and potential risks, and voluntarily agreed to participate. So that this applies to illiterate respondents as well.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Khan, M.N., Khanam, S.J., Khan, M.M.A. et al. Exploring the impact of perceived early marriage on women’s education and employment in Bangladesh through a mixed-methods study. Sci Rep 14 , 21683 (2024). https://doi.org/10.1038/s41598-024-73137-w

Download citation

Received : 15 February 2024

Accepted : 13 September 2024

Published : 17 September 2024

DOI : https://doi.org/10.1038/s41598-024-73137-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Child marriage
• Working status
• Educational status

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.