critique paper about technology in education

Education Technology: An Evidence-Based Review

In recent years, there has been widespread excitement around the potential for technology to transform learning. As investments in education technology continue to grow, students, parents, and teachers face a seemingly endless array of education technologies from which to choose—from digital personalized learning platforms to educational games to online courses. Amidst the excitement, it is important to step back and understand how technology can help—or in some cases hinder—how students learn. This review paper synthesizes and discusses experimental evidence on the effectiveness of technology-based approaches in education and outlines areas for future inquiry. In particular, we examine RCTs across the following categories of education technology: (1) access to technology, (2) computer-assisted learning, (3) technology-enabled behavioral interventions in education, and (4) online learning. While this review focuses on literature from developed countries, it also draws upon extensive research from developing countries. We hope this literature review will advance the knowledge base of how technology can be used to support education, outline key areas for new experimental research, and help drive improvements to the policies, programs, and structures that contribute to successful teaching and learning.

We are extremely grateful to Caitlin Anzelone, Rekha Balu, Peter Bergman, Brad Bernatek, Ben Castleman, Luke Crowley, Angela Duckworth, Jonathan Guryan, Alex Haslam, Andrew Ho, Ben Jones, Matthew Kraft, Kory Kroft, David Laibson, Susanna Loeb, Andrew Magliozzi, Ignacio Martinez, Susan Mayer, Steve Mintz, Piotr Mitros, Lindsay Page, Amanda Pallais, John Pane, Justin Reich, Jonah Rockoff, Sylvi Rzepka, Kirby Smith, and Oscar Sweeten-Lopez for providing helpful and detailed comments as we put together this review. We also thank Rachel Glennerster for detailed support throughout the project, Jessica Mardo and Sophie Shank for edits, and to the Spencer Foundation for financial support. Any errors or omissions are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

Download Citation Data

Mentioned in the News

More from nber.

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .

Advertisement

Educational Technology Adoption: A systematic review

• Published: 05 April 2022
• Volume 27 , pages 9725–9744, ( 2022 )

Cite this article

• Andrina Granić   ORCID: orcid.org/0000-0002-4266-3406 1  

21k Accesses

57 Citations

1 Altmetric

Explore all metrics

A Publisher Correction to this article was published on 19 April 2022

This article has been updated

During the past decades a respectable number and variety of theoretical perspectives and practical approaches have been advanced for studying determinants for prediction and explanation of user’s behavior towards acceptance and adoption of educational technology. Aiming to identify the most prominent factors affecting and reliably predicting successful educational technology adoption, this systematic review offers succinct account of technology adoption and acceptance theories and models related to and widely applied in educational research. Recognised journals of the Web of Science (WoS) database were searched with no time frame limit, and a total of 47 studies published between 2003 and 2021 were critically analysed. The key research findings revealed that in educational context a vast majority of selected studies explore the validity of Technology Acceptance Model (TAM) and its many different extensions (N=37), along with TAM’s integrations with other contributing theories and models (N=5). It was exposed that among numerous predictors, thematically grouped into user aspects, task & technology aspects, and social aspects, self-efficacy, subjective norm, (perceived) enjoyment, facilitating conditions, (computer) anxiety, system accessibility, and (technological) complexity were the most frequent predictive factors (i.e. antecedents) affecting educational technology adoption. Considering types of technologies, e-learning was found to be the most common validated mode of delivery, followed by m-learning, Learning Management Systems (LMSs), and social media services. The results also revealed that the majority of analysed studies were conducted in higher education environments. New directions of research along with potential challenges in educational technology acceptance, adoption, and actual use are discussed as well.

Similar content being viewed by others

Technology Acceptance and Adoption in Education

Utilitarian and experiential aspects in acceptance models for learning technology

Explore related subjects.

• Artificial Intelligence
• Digital Education and Educational Technology

Avoid common mistakes on your manuscript.

1 Introduction

Over the last half-century, a vast number of adoption theories and technology acceptance models, along with a plethora of their extensions and modifications has been advanced. Aiming to explore their applicability, as well as to enhance their predictive validity, proposed theories and models have been extensively used in assessment of various Information and Communication Technology (ICT) products and services. Commonly, technology adoption is a term that refers to the acceptance, integration, and embracement of any types of new technology. Technology acceptance, as the first step of technology adoption, is an attitude towards technology, and it is influenced by various factors. According to the Innovation Diffusion Theory (IDT) (Rogers, 1962 , 1995 ), adoption is a decision to make full use of technology innovation as the best course of action available. The key to adoption is that the adopter (individual or organization) must perceive the idea, behavior, or product as new or innovative. As for technology adoption research at the individual level, numerous theories and models have been used to predict and explain human behavior towards technology acceptance, adoption and usage.

Education presents an area of great interest in incorporating new technologies, thus technology acceptance and adoption theories and models are often used to inform research in educational context. Such setting is characterised by a great variety of potential users of various types of technology embraced in the process of learning, teaching, and assessment. Some of the most influential theoretical approaches involve (listed in chronological order with relevant illustrative example research):

Technology Acceptance Model (TAM) (Davis, 1986 , 1989 ), the widely used reliable model, to explore new facilitating technologies in educational context, ranging from social media platforms (Yu, 2020 ) to the technology aimed at helping the learning process through teaching assistant robots (Park and Kwon, 2016 ), simulators (Lemay, Morin, Bazelais & Doleck, 2018 ), and virtual reality (Jang, Ko, Shin & Han, 2021 );

Decomposed Theory of Planned Behavior (DTPB) (Taylor & Todd, 1995 ) to understand university students’ adoption of WhatsApp in learning (Nyasulu & Chawinga, 2019 ), to explore factors that influence teachers’ intentions to integrate digital literacy (Sadaf & Gezer, 2020 ), as well as to examine factors that impact the acceptance and usage of e-assessment by academics (Alruwais, Wills & Wald, 2017 );

Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, Morris, Davis & Davis, 2003 ) to study core factors affecting the university students’ attitude towards adoption of online classes during COVID-19 (Tiwari, 2020 ), to explore the factors that influence preservice teachers’ acceptance of ICT integration in the classroom (Birch & Irvine, 2009 ), and students’ usage of e-learning systems in developing countries (Abbad, 2021 );

Extended UTAUT (UTAUT2) (Venkatesh, Thong & Xu, 2012 ) to evaluate acceptance of blended learning in executive education (Dakduk, Santalla-Banderali & van der Woude, 2018 ), and to examine preservice teachers’ acceptance of learning management software (Raman & Don, 2013 ).

Several reviews and meta-analysis that summarize empirical research have been focused on specific topics in the field of education, for example: (i) particular technology adoption model , like the meta-analysis dealing with TAM in prediction of teachers’ adoption of technology (Scherer, Siddiq & Tondeur, 2019 ), and the quantitative meta-analysis to identify the most commonly used external factors of TAM in the context of e-learning adoption (Abdullah & Ward, 2016 ); (ii) specific type of users , like reviews conducted to understand factors influencing academics’ adoption of learning technologies (Liu, Geertshuis & Grainger, 2020 ), to explore factors that affect teachers’ acceptance and use of ICT in the classroom (Gamage & Tanwar, 2018 ), as well as to study factors affecting students’ adoption and continuation of technology use in online learning (Panigrahi, Srivastava & Sharma, 2018 ); (iii) particular technology and mode of delivery , like reviews carried on to explore factors affecting blended learning adoption and implementation in higher education (Anthony, et al. 2020 ), to study technical factors affecting users’ intentions to use mobile phones as learning tools (Alghazi, Wong, Kamsin, Yadegaridehkordi & Shuib, 2020 ), as well as to examine the most prominent external factors affecting learning management systems (LMSs) adoption in higher educational institutions (Al-Nuaimi & Al-Emran, 2021 ). Besides, some theoretical work aimed to identify determinants of learning technology acceptance, but it was more focused on original constructs of reviewed technology adoption theories, like in the study conducted by Kaushik and Verma ( 2020 ).

However, to the best of authors’ knowledge, currently there is hardly a holistic view of factors that affect and reliably predict successful acceptance and adoption of technology engaged in educational process. Understanding these aspects can be beneficial and can help in an improvement of both, research and educational practices. Hence, this concept-centric review aims at addressing this concern with the following two main research questions (RQs):

RQ1. Which technology acceptance and adoption theories and models are widely applied in educational research?

RQ2. Which are the most prominent predictive factors (i.e. antecedents) affecting educational technology adoption?

2 Research Approach

The research scope of this systematic review is narrowed and piloted towards understanding the most recognized and applied theoretical models, as well as the most influential predictive factors affecting various technologies used to support the process of knowledge transfer and acquisition. Due to massive work worldwide, this study is used to offer succinct account of predominant predictors in educational technology adoption, and certainly cannot be all-encompassing. With the aim to filter and narrow the search, but at the same time to cover representative literature from recognised journals, the Web of Science (WoS) Current Contents Connect (CCC) database was searched. The search was not limited to a precise timespan. To denote different technology acceptance models and theories, the search was conducted using relevant terms connected with Boolean operators “OR” and “AND”, specifically (“theor*” OR “model”) AND (“technolog*”) AND (“adoption” OR “acceptance”) . To locate education related studies, (“education*” OR “learn*”) search terms were joined with the aforementioned ones by means of the operator “AND”. Truncation was used to cover all variations of some keywords, for example, the search term “ technolog* ” was used to search for literature that included the word “technology” as well as “technologies”.

It was searched for studies that have specified search terms in publication title (the filter “TITLE” was selected). For the purpose of this review, specified inclusion criteria enabled selection of studies that report on technology acceptance and adoption theories and models in which some type of ICT products and services to support the process of learning and teaching was used (in this context indicating all classes of technologies, interactive systems, environments, tools, applications, services, platforms, and devices). To be included, studies had to report on empirically evaluated research model and related research hypothesis. Besides, studies must be published as peer-reviewed journal articles written in English language. On the subject of exclusion criteria , studies that do not clearly and credibly describe model/theory constructs or variables, and the relationships among them, were not considered as valid to be selected and included in the analysis. In addition, theoretical studies published as peer-reviewed journal articles, specifically reviews and meta-analysis, were excluded as well.

The literature search was conducted in August 2021. No time frame period was specified; 1998-2021 is the full range of the CCC database search engine. In this inquiry, 71 publications that included specified search terms in the publication title were identified. Considering only peer-reviewed journal articles written in English, the number of 67 journal and review articles was reached. Title, abstract and full text of the filtered literature were screened to ensure publication suitability and relevance. Accordingly, the qualified publications were retained and eleven unrelated ones were excluded, thus narrowing the number and leaving for further detailed analysis 56 publications. Out of 56 identified journal articles, 47 publications were found to be compliant with the purpose of this study, while 9 publications offered theoretical work which summarized empirical research focused on specific topics in educational technology acceptance and adoption.

In view of the identified theoretical work, the majority of studies offered meta-analysis and reviews of Technology Acceptance Model (TAM) based studies in education (N=6), specifically (Dimitrijević & Devedžić, 2021 ; Granić & Marangunić, 2019 ; Kemp, Palmer & Strelan, 2019 ; Scherer et al., 2019 ; Al-Emran, Mezhuyev & Kamaludin, 2018 ; Abdullah & Ward,2016), while just few publications addressed other acceptance models and theories, in particular Unified Theory of Acceptance and Use of Technology (UTAUT) (Bervell & Umar, 2017 ), Senior Technology Exploration, Learning and Acceptance (STELA) model (Tsai, Rikard, Cotton & Shillair, 2019 ), along with Straub’s ( 2009 ) study in a context of informal learning which examined adoption processes through the lenses of Innovation Diffusion Theory (IDT), Concerns-Based Adoption Model (CBAM), TAM and UTAUT.

3 Results and Discussion

The analysis of 47 publications found to be compliant with the purpose of this study is presented and discussed in the following.

3.1 Publication History and Distribution by Countries

Considering the history of publishing, Fig.  1 shows the trend of publication frequency which started in 2003, and can be followed until 2021. The majority of studies has been published in the last decade thus reflecting an increased attention given to the researched domain. It can be noticed that there are only three identified studies in 2021, but this is connected with the fact that the search was undertaken in August 2021, and several potentially relevant articles/studies are not published yet.

Publication history

The interest of researchers worldwide in educational technology acceptance and adoption is evident (see Fig.  2 ). Most of the identified studies were conducted in Taiwan (N=7), followed by relevant research carried out in South Korea and USA (N=4), Spain (N=3), Canada, China, Hong Kong, Malaysia, Pakistan, Singapore and Turkey (N=2). In the rest of illustrated countries only single studies were piloted (alphabetical order): Azerbaijan, Cyprus, France, Hungary, Lebanon, Libya, Netherlands, Nigeria, Oman, Philippines, South Africa, UK, United Arab Emirates, as well as Qatar & USA.

Distribution of selected articles by countries

3.2 Type of Technologies and Modes of Delivery

This research revealed a diversity of ICT products and services employed in educational context, here referring to all classes of technologies, interactive systems, environments, tools, applications, services, platforms, and devices used in the selected research. Considering types of technologies and modes of delivery used to support the process of learning and teaching, it is noticeable that almost half of the analysed studies (N=20) validated e-learning technologies, in selected research referred to as e-learning systems (Hanif, Jamal & Imran 2018 ), e-learning platforms (Song & Kong, 2017 ), e-learning environments (Esteban-Millat, Martinez-Lopez, Pujol-Jover, Gazquez-Abad & Alegret 2018 ), e-learning tools (Tarhini, Hone, Liu & Tarhini 2016 ), web-based learning systems (Calisir, Gumussoy, Bayraktaroglu & Karaali 2014 ), Internet-based learning systems (Saade & Bahli, 2005 ), or just e-learning (Abdou & Jasimuddin, 2020 ). Many studies dealt with mobile learning (N=6) in which context mobile computing devices (Lai, 2020 ), mobile technology and apps (Briz-Ponce & Garcia-Penalvo, 2015 ), tablet personal computers (Moran, Hawkes & El Gayar, 2010 ), or just m-learning (Iqbal & Bhatti, 2015 ) was validated. Learning Management Systems (LMSs) in general, along with specific LMSs in particular, like Blackboard (Yi & Hwang, 2003 ), Moodle (Nagy, 2018 ), and Moodle gamification training platform (Vanduhe, Nat & Hasan, 2020 ), were also frequently researched (N=6).

Besides, some studies (N=5) counted on social media services/platforms at large (Al-Rahmi, Shamsuddin, Alturki, Aldraiweesh, Yusof, Al-Rahmi & Aljeraiwi, 2021), as well as on WeChat (Yu, 2020 ) and YouTube (Lee & Lehto, 2013 ) in particular. Since educational possibilities of virtual reality (VR) and augmented reality (AR) are getting more attention, few studies (N=3) were focused on VR technology (Lin and Yeh, 2019), VR and AR technology (Jang, Ko, Shin & Han, 2021 ), while one earlier study concerned virtual world Second Life (Chow, Herold, Choo & Chan, 2012 ). Use of computer technology in general was examined in a couple of studies (N=2) (e.g. Teo, 2010 ), while a number of single studies considered also assistive technology (Nam, Bahn & Lee, 2013 ), collaborative technology, specifically Google applications for collaborative learning (Cheung & Vogel, 2013 ), simulation-based learning environment (Lemay, Morin, Bazelais & Doleck, 2018 ), university communication model (UCOM) which works similar to Massive Open Online Course (MOOC) (Tawafak, Romli & Arshah, 2018 ), as well as Open Educational Resources (OER) (Kelly, 2014 ). Figure  3 provides insight into a variety of validated technologies and modes of delivery.

Validated technologies and modes of delivery

3.3 Type of Participants & Sample Size

Another aspect refers to different types of involved participants/users. In a great majority of analysed research (N=29) university students were the most commonly chosen sample group, since most data from web-based questionnaires and/or mailed surveys was collected from the universities (e.g. Salloum, Alhamad, Al-Emran, Monem & Shaalan, 2019 ; Park, 2009 ). Several studies involved employees (N=7) from a variety of organizations/companies, specifically faculty & educational stakeholders (Aburagaga, Agoyi & Elgedawy, 2020 ), bank officials (Abdou & Jasimuddin, 2020 ), business workforce (Lee, Hsieh & Hsu, 2011 ), blue-collar workers (Calisir et al., 2014 ), health nurses (Chen, Yang, Tang, Huang & Yu, 2008 ), along with employees from four international agencies of the United Nations (Roca, Chiu & Martinez, 2006 ), as well as from four industries, specifically manufacturing, information technology, marketing and government agencies (Lee, Hsieh & Chen, 2013 ). Quite a few studies engaged teachers (N=5), to be specific pre-service (Teo, 2010 ) and in-service teachers (Jang et al., 2021 ), special education teachers (Nam et al., 2013 ), as well as K-12 educators (Kelly, 2014 ). A small number of research also involved other participants, in particular university instructors (N=2) (Vanduhe et al., 2020 ), older adults (Lai, 2020 ), and senior high school students (Prasetyo, Ong, Concepcion, Navata, Robles, Tomagos, Young, Diaz, Nadlifatin & Redi, 2021). Finally, in one study information about the type of participants who took part in the conducted research was not provided (see Fig.  4 ).

It can be seen that sample size varied from the smallest sample of 72 students (Lin & Yeh, 2019) to the largest one of 2574 students involved in the study conducted by Esteban-Millat et al. ( 2018 ). However, the domination of smaller sample sizes up to 400 participants (N=30) compared to the number of larger sample sizes is notable.

Type of involved participants

3.4 Employed Technology Acceptance and Adoption Models

The conducted review clearly indicated that the vast majority of identified research used TAM model (N=42), in particular the core TAM (N=1), the extended TAM (N=36), along with some studies which integrated TAM with other individual models/theories aiming to advance TAM’s explanatory power (N=5), in particular with:

Innovation Diffusion Theory (IDT) proposed by Rogers ( 1962 , 1995 ) as the most popular model in investigating innovation acceptance and adoption (N=2), specifically (Lee et al., 2011 ; Al-Rahmi, Yahaya, Aldraiweesh, Alamri, Aljarboa, Alturki & Aljeraiwi, 2019),

Information Systems Success Model (ISSM) introduced by DeLone and McLean ( 1992 ) as a robust theoretical basis for the study of technology post-adoption (N=2), specifically (Prasetyo et al., 2021 ; Al-Rahmi et al., 2021 ),

combination of ISSM and Expectation-Confirmation Theory (ECT), a post-adoption theory offered by Oliver ( 1980 ), in work conducted by Roca, Chiu, and Martinez ( 2006 ).

Besides TAM-based research, a few studies explored also the core (N=2) and the extended (N=2) UTAUT model, along with a single research which employed extended UTAUT2 model (refer to Fig.  5 ).

Used technology acceptance and adoption models

3.5 Factors Affecting Educational Technology Adoption

This study revealed that, aiming to increase the predictive validity of TAM and UTAUT, in most selected studies (N=44) the models have been extended with different predictive (antecedent) factors . In view of UTAUT model on the one hand, those factors are related to the behavioral intention (BI) variable/construct. On the other hand, when considering TAM, the majority of identified factors represent antecedents of the two core variables of TAM, perceived ease of use (PEU) and perceived usefulness (PU), while a minor number predicts behavioral intention (BI). Among selected research, only three studies have used original models without any modifications and enhancements, in particular the core TAM (Chipps, Kerr, Brysiewicz & Walters, 2015 ) and the core UTAUT (Lai, 2020 ; Yakubu & Dasuki, 2019 ).

In addition, besides a variety of introduced predictors for the core TAM constructs, as well as TAM’s and UTAUT’s behavioral intention variable, the results exposed also a number of incorporated supplementary factors which aimed to moderate relationships among TAM’s constructs. Consequently, categorization of identified factors from models’ modifications and enhancements included in this review is conducted, and three pools of factors affecting educational technology adoption are documented:

antecedents of perceived ease of use (PEU) and perceived usefulness (PU),

behavioral intention (BI) antecedents, and.

moderating factors.

To shed-light-on, numerous identified predictive factors are thematically grouped into: (i) user aspects (individual attributes, and pleasure & usefulness), (ii) task & technology aspects , and (iii) social aspects . The categorised antecedents of TAM variables (PEU and PU), as well as TAM’s and UTAUT’s BI antecedents, along with related illustrative example research are presented in Tables  1 and 2 , respectively.

Antecedents of Perceived Ease of Use and Perceived Usefulness. By analysing the selected publications, self-efficacy was found as the most widely introduced predictive factor of TAM (N=16). In various empirical studies conducted in educational context it was revealed that self-efficacy, i.e. an individual judgement of one’s capability to use computer (e.g. Salloum et al., 2019 ; Teo, 2009 ), Internet (e.g. Nagy, 2018 ), m-learning (e.g. Park, Nam & Cha, 2012 ), e-learning (e.g. Chen et al., 2008 ) or specific application (e.g. Yi & Hwang, 2003 ), had a significant impact on the perceived usefulness and the perceived ease of use. Another widely researched predictive factors were subjective norm (N=9), defined as the degree to which an individual believes that people who are important to him/her think he/she should or should not perform the behavior in question, as well as perceived enjoyment (N=8) considered as the extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated. It has been revealed that the subjective norm (Song & Kong, 2017 ), and enjoyment (Salloum et al., 2019 ), positively and significantly influence students’ perceived usefulness of e-learning, as well as perceived ease of use of e-learning systems (Hanif et al., 2018 ; Chang, Hajiyev & Su, 2017 )

The results indicated that system quality (e.g. Salloum et al., 2019 ) and system accessibility (e.g. Park et al., 2012 ; Hanif et al., 2018 ), along with technological complexity (e.g. Teo, 2009 ) have a significant influence on perceived ease of use. Besides, facilitating conditions , which originally provide resource factors (such as time and money needed) and technology factors regarding compatibility issues that may constrain usage, were indicated to be an essential factor that affect e-learning system (e.g. Song & Kong, 2017 ) or computer technology (e.g. Teo, 2009 ) acceptance. Finally, while the perceived playfulness, which operationalizes the question of how intrinsic motives affect the individual’s acceptance of technology, had a direct impact on the variables perceived usefulness and perceived ease of use (e.g. Padilla-Melendez, del Aguila-Obra & Garrido-Moreno, 2013 ), anxiety as a personal trait explained as evoking anxious or emotional reactions when it comes to performing a behavior, negatively affects the two core TAM variables (e.g. Chang et al., 2017 ; Calisir et al., 2014 )

Behavioral Intention Antecedents. Both self-efficacy and subjective norm were among frequently employed factors affecting attitude towards technology and behavioral intention. The results indicated that self-efficacy was found to have a direct effect and a positive influence on behavioral intention to use e-learning (e.g. Tarhini, Hone & Liu, 2014 ; Yi & Hwang, 2003 ), m-learning (e.g. Moran et al., 2010 ; Park et al., 2012 ), as well as collaborative technology (e.g. Cheung & Vogel, 2013 ), and computers (e.g. Nam et al., 2013 ; Teo, 2009 ). Subjective norm , as another important construct in providing an understanding of the determinants of usage in educational context, is shown to have strong influence on the behavioral intention to use e-learning systems/platforms (e.g. Song & Kong, 2017 ). It has been revealed that subjective norm represented by peers significantly moderate the relationship between attitude and intention toward the technology (Cheung & Vogel, 2013 )

Furthermore, perceived playfulness is found to be one of the key drivers for the adoption and use of blended learning system depending of user’s gender (Padilla-Melendez et al., 2013 ). Also, direct and indirect effect of perceived playfulness on the intention to use a computer-assisted training program has been confirmed (Lin & Yeh, 2019). Finally, the research has exposed that system accessibility was one of the dominant exogenous constructs affecting behavioral intention to use mobile learning (e.g. Park et al., 2012 )

Moderating Factors. Although the majority of selected research has been focused on finding PEU, PU and BI antecedents, there is also a growing need for understanding incorporated supplementary factors aiming to moderate the relationships among TAM variables, on the one hand, as well as those which have an impact on the model itself, on the other. In the investigation of the moderating effect of gender and age on e-learning acceptance Tarhini and colleagues ( 2014 ) have found that age moderates the effect of perceived ease of use, perceived usefulness and self-efficacy on behavioral intention, and that gender moderates the effect of perceived ease of use and social norms on behavioral intention. Yet, unexpectedly, no significant moderating effect of age on the relationship between social norms and behavioral intention was found; results also revealed no moderating of gender on perceived usefulness or self-efficacy and behavioral intention. Padilla-Melendez et al. ( 2013 ) argued that there exist gender differences in attitude and intentions to use. The main contribution of their study is provided evidence that there exist gender differences in the effect of playfulness in the student attitude toward technology and the intention to use it. In females, playfulness influences attitude toward using the system. In males, playfulness influences attitude moderated by perceived usefulness

When examining the moderating effect of individual-level cultural values on users’ acceptance of e-learning in developing countries, Tarhini et al. ( 2016 ) demonstrated that the relationship between social norms and behavioral intention was particularly sensitive to differences in individual cultural values, with significant moderating effects observed for all studied cultural dimensions, in particular masculinity/femininity , individualism/collectivism , power distance and uncertainty avoidance . As a final point, in an empirical study of the use of the General Extended Technology Acceptance Model for E-learning (GETAMEL) to determine the factors that affect students’ intention to use an e-learning system, Chang and colleagues ( 2017 ) found that technological innovation significantly moderates the relationship between subjective norm and perceived usefulness, as well as perceived usefulness and behavioral intention to use e-learning.

3.6 Integration with Other Models & Theories

Although TAM proved to be a powerful model applicable to various technologies and contexts at the individual level, research also revealed its successful integration with other contributing theories and models within a range of different application fields (Al-Emran & Granić, 2021 ). To evaluate students’ adoption of smartwatches for educational purposes, TAM has been successfully combined with Goodhue and Thompson’s ( 1995 ) Task-Technology Fit (TTF) (Al-Emran, 2021 ), and Rogers ( 1975 ) Protection Motivation Theory (PMT) (Al-Emran, Granić, Al-Sharafi, Nisreen & Sarrab, 2021 ). In addition, the Innovation Diffusion Theory (IDT) has been combined with TAM in an empirical investigation on university students’ intention to use e-learning systems (Al-Rahmi et al., 2019 ), to investigate factors affecting business employees’ behavioral intentions to use the e-learning system (Lee et al., 2011 ), as well as to explore diffusion and adoption of an open source learning platform (Huang, Wang, Yang & Shiau, 2020 ). The Information Systems Success Model (ISSM), as one of the post-adoption theories, has been integrated with TAM to help in determining factors which affected acceptance of e-learning platforms during the COVID-19 pandemic (Prasetyo et al., 2021 ), and in exploring students’ behavioral intention to use social media, specifically the perception of their academic performance and satisfaction (Al-Rahmi et al., 2021 ). Lastly, to understand e-learning continuance intention, TAM has been integrated with ISSM and Oliver’s ( 1980 ) Expectation-Confirmation Theory (ECT) (Roca et al., 2006 ).

3.7 Limitations of the Conducted Review

In the conducted review, specific criteria were used to search the WoS CCC database for relevant studies to be included and analysed. The applied research approach allowed to capture and cover only a representative selection of studies published in numerous recognized journals, and undoubtedly cannot be all-inclusive. Specification of other search criteria along with a selection of other databases might bring more and/or slightly different selection of relevant journal articles and proceeding papers. Hence, this review should be regarded as an attempt to explore relevant challenges and emerged topics in educational technology adoption field during the past. Finally, it should be noted that this study does not describe or pass any judgement on research methods and approaches employed in the analysed literature since this is out of the scope of this review.

4 Conclusion and Future Research

Over the past decades a variety of theoretical perspectives have been advanced to provide an understanding of the determinants of acceptance, adoption and usage of various technologies used to support the process of knowledge transfer and acquisition. However, it has been shown that over the years TAM has emerged as a leading scientific paradigm for studying the determinants affecting human behavior and usage of various technologies through beliefs about two factors: the perceived ease of use and the perceived usefulness (Al-Emran & Granić, 2021 ; Marangunić & Granić, 2015 ). Moreover, the conducted review once again exposed TAM predomination in educational context as well; refer also to (Granić & Marangunić, 2019 ). This study confirmed that TAM is the most widely used powerful and valid model for prediction and explanation of user’s behavior towards acceptance and adoption of various technologies used to support the process of learning and teaching.

Continuous development of new technologies, along with a growing number and diversity of users in educational context, opens new directions of research that could raise understanding of the technology acceptance, adoption, and actual use. Thus, despite the fact that extensive work has already been conducted, there is still a huge potential for further advancements, exploration and practice in this field of research. In light of current research findings, future work could follow new research directions:

to explore predictive validity of technology acceptance models and theories when applied to various supporting ICT technologies employed in a number of emerging teaching strategies , like flipped learning, gamification-based learning, and visual scaffolding, favourable communication support , like chats, discussion forums, and discussion boards, as well as relevant facilitative tools , like blogs and wikis used in educational context;

to further empirically validate predictive factors (antecedents) influencing the acceptance and adoption of technology in education which have not been so widely explored, for example perceived playfulness which has been associated with a high level of perceived usefulness (Lin & Yeh, 2019), social media usage which has indicated a positive and constructive influence on satisfaction and academic performance (Al-Rahmi et al., 2021 ), as well as psychological influence factors such as conformity behavior and self-esteem due to their positive and direct effect on perceived ease of use, perceived usefulness, perceived enjoyment and continuance intention (Yu, 2020 );

to explore some possibly significant predictive factors that still have not been adequately examined, but could be important in understanding educational technology adoption as for example, the factor dealing with task & technology aspects, that can be described as cost-effective/pennyworth , here referring to employment of efficient solutions in educational context with relatively limited budget (e.g. simulation, VR, AR, visual scaffolding/visualization);

to advance the explanatory power of individual technology acceptance and adoption models by reviewing and integrating them with already established theories and models from other fields, like social psychology – Bagozzi and Warshaw’s ( 1990 ) Theory of Trying (TofT), cognitive psychology – Bhattacherjee’s ( 2001 ) Expectation-Confirmation Model (ECM), along with information technology – Goodhue and Thompson’s (1995) Task-Technology Fit (TTF).

Change history

19 april 2022.

A Correction to this paper has been published: https://doi.org/10.1007/s10639-022-11053-0

Abbad, M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies . doi: https://doi.org/10.1007/s10639-021-10573-5

Article   Google Scholar  

Abdou, D., & Jasimuddin, S. M. (2020). The use of the UTAUT model in the adoption of e-learning technologies: An empirical study in France based banks. Journal of Global Information Management , 28(4), 38–51. doi: https://doi.org/10.4018/JGIM.2020100103

Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior , 56, 238–256. doi: https://doi.org/10.1016/j.chb.2015.11.036

Aburagaga, I., Agoyi, M., & Elgedawy, I. (2020). Assessing faculty’s use of social network tools in Libyan higher education via a Technology Acceptance Model. IEEE ACCESS , 8, 116415–116430. doi: https://doi.org/10.1109/ACCESS.2020.3004200

Al-Emran, M. (2021). Evaluating the use of smartwatches for learning purposes through the integration of the Technology Acceptance Model and Task-Technology Fit. International Journal of Human–Computer Interaction . https://doi.org/10.1080/10447318.2021.1921481

Al-Emran, M., & Granić, A. (2021). Is it still valid or outdated? A bibliometric analysis of the Technology Acceptance Model and its applications from 2010 to 2020. In Al-Emran, N., & Shaalan, K. (Eds.) Recent Advances in Technology Acceptance Models and Theories. Studies in Systems, Decision and Control , Springer Nature Switzerland AG. 1-12. doi: https://doi.org/10.1007/978-3-030-64987-6_1

Al-Emran, M., Granić, A., Al-Sharafi, M., Nisreen, A., & Sarrab, M. (2021). Examining the roles of students’ beliefs and security concerns for using smartwatches in higher education. Journal of Enterprise Information Management , 34(4), 1229–1251. doi: https://doi.org/10.1108/JEIM-02-2020-0052

Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology Acceptance Model in m-learning context: A systematic review. Computers & Education , 125, 389–412. doi: https://doi.org/10.1016/j.compedu.2018.06.008

Alghazi, S. S., Wong, S. Y., Kamsin, A., Yadegaridehkordi, E., & Shuib, L. (2020). Towards sustainable mobile learning: A brief review of the factors influencing acceptance of the use of mobile phones as learning tools. Sustainability , 12(24), https://doi.org/10.3390/su122410527

Al-Nuaimi, M. N., & Al-Emran, M. (2021). Learning management systems and technology acceptance models: A systematic review. Education and Information Technologies , 26, 5499–5533. doi: https://doi.org/10.1007/s10639-021-10513-3

Al-Rahmi, A. M., Shamsuddin, A., Alturki, U., Aldraiweesh, A., Yusof, F. M., Al-Rahmi, W. M., & Aljeraiwi, A. A. (2021). The influence of Information System Success and Technology Acceptance Model on social media factors in education. Sustainability , 13(14), 7770–7770. doi: https://doi.org/10.3390/su13147770

Al-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019). Integrating Technology Acceptance Model with Innovation Diffusion Theory: An empirical investigation on students’ intention to use e-learning systems. IEEE ACCESS , 7, 26797–26809. doi: https://doi.org/10.1109/ACCESS.2019.2899368

Alruwais, N. M., Wills, G., & Wald, M. (2017). Validating Factors that impact the acceptance and usage of e-assessment by academics in Saudi universities. International Journal of Information and Education Technology , 7(10), 716–721. doi: https://doi.org/10.18178/ijiet.2017.7.10.960

Anthony, B., Kamaludin, A., Romli, A., et al. (2020). blended learning adoption and implementation in higher education: a theoretical and systematic review. Technology, Knowledge and Learning . doi: https://doi.org/10.1007/s10758-020-09477-z

Bagozzi, R. P., & Warshaw, P. R. (1990). Trying to consume. Journal of Consumer Research , 17(2), 127–140. doi: https://doi.org/10.1086/208543

Bervell, B., & Umar, I. N. (2017). Validation of the UTAUT model: Re-considering non-linear relationships of exogeneous variables in higher education technology acceptance research. EURASIA Journal of Mathematics Science and Technology Education , 13(10), 6471–6490

Bhattacherjee, A. (2001). Understanding information systems continuance: An Expectation-Confirmation Model. MIS Quarterly , 25(3), 351–370. doi: https://doi.org/10.2307/3250921

Birch, A., & Irvine, V. (2009). Preservice teachers’ acceptance of ICT integration in the classroom: Applying the UTAUT model. Educational Media International , 46(4), 295–315. doi: https://doi.org/10.1080/09523980903387506

Briz-Ponce, L., & Garcia-Penalvo, F. J. (2015). An empirical assessment of a Technology Acceptance Model for apps in medical education. Journal of Medical Systems , 39(11), 176–176. doi: https://doi.org/10.1007/s10916-015-0352-x

Calisir, F., Gumussoy, C. A., Bayraktaroglu, A. E., & Karaali, D. (2014). Predicting the intention to use a web-based learning system: perceived content quality, anxiety, perceived system quality, image, and the Technology Acceptance Model. Human Factors and Ergonomics in Manufacturing & Service Industries , 24(5), 515–531. doi: https://doi.org/10.1002/hfm.20548

Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The General Extended Technology Acceptance Model for E-Learning approach. Computers & Education , 111, 128–143. doi: https://doi.org/10.1016/j.compedu.2017.04.010

Chen, I. J., Yang, K. F., Tang, F. I., Huang, C. H., & Yu, S. (2008). Applying the technology acceptance model to explore public health nurses’ intentions towards web-based learning: A cross-sectional questionnaire survey. International Journal of Nursing Studies , 45(6), 869–878. doi: https://doi.org/10.1016/j.ijnurstu.2006.11.011

Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education , 63, 160–175. doi: https://doi.org/10.1016/j.compedu.2012.12.003

Chipps, J., Kerr, J., Brysiewicz, P., & Walters, F. (2015). Survey of university students’ perceptions of learning management systems in a low-resource setting using a Technology Acceptance Model. Computers Informatics Nursing , 33(2), 71–77. doi: https://doi.org/10.1097/CIN.0000000000000123

Chow, M., Herold, D. K., Choo, T. M., & Chan, K. (2012). Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Computers & Education , 59(4), 1136–1144. doi: https://doi.org/10.1016/j.compedu.2012.05.011

Dakduk, S., Santalla-Banderali, Z., & van der Woude, D. (2018). Acceptance of blended learning in executive education. Online Teaching . SAGE Open, 1-16. doi: https://doi.org/10.1177/2158244018800647

Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral dissertation. MIT Sloan School of Management, Cambridge, MA. Retrieved from https://dspace.mit.edu/handle/1721.1/15192

Davis, F. D. (1989). perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly , 13(3), 319–340. doi: https://doi.org/10.2307/249008

DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information System Research , 3(1), 60–95. doi: https://doi.org/10.1287/isre.3.1.60

Dimitrijević, S., & Devedžić, V. (2021). Utilitarian and experiential aspects in acceptance models for learning technology. Educational Technology Research and Development , 69(2), 627–654. doi: https://doi.org/10.1007/s11423-021-09970-x

El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development , 65(3), 743–763. doi: https://doi.org/10.1007/s11423-016-9508-8

Esteban-Millat, I., Martinez-Lopez, F. J., Pujol-Jover, M., Gazquez-Abad, J. C., & Alegret, A. (2018). An extension of the technology acceptance model for online learning environments. Interactive Learning Environments , 26(7), 895–910. doi: https://doi.org/10.1080/10494820.2017.1421560

Gamage, S. N., & Tanwar, T. (2018). Factors affecting teachers’ use of ICTs in the classroom: A systematic review of the literature. Information Technologies & International Development, 14, 105-115. Retrieved from https://itidjournal.org/index.php/itid/article/download/1487/1487-4980-1-PB.pdf

Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly , 19(2), 213–236. doi: https://doi.org/10.2307/249689

Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology , 50(5), 2572–2593. doi: https://doi.org/10.1111/bjet.12864

Hanif, A., Jamal, F. Q., & Imran, M. (2018). Extending the Technology Acceptance Model for use of e-learning systems by digital learners. IEEE ACCESS , 6, 73395–73404

Huang, C. Y., Wang, H. Y., Yang, C. L., & Shiau, S. (2020). A derivation of factors influencing the diffusion and adoption of an open source learning platform. Sustainability , 12(18), doi: https://doi.org/10.3390/su12187532

Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning - A perspective of the extended Technology Acceptance Model. Electronic Library , 25(5), 585–598. doi: https://doi.org/10.1108/02640470710829569

Iqbal, S., & Bhatti, Z. A. (2015). An investigation of university student readiness towards m-learning using Technology Acceptance Model. International Review of Research in Open and Distributed Learning , 16(4), 83–103. doi: https://doi.org/10.19173/irrodl.v16i4.2351

Article   MathSciNet   Google Scholar  

Jang, J., Ko, Y., Shin, W. S., & Han, I. (2021). Augmented reality and virtual reality for learning: An examination using an extended Technology Acceptance Model. IEEE ACCESS , 9, 6798–6809. doi: https://doi.org/10.1109/ACCESS.2020.3048708

Kaushik, M. K., & Verma, D. (2020). Determinants of digital learning acceptance behavior: A systematic review of applied theories and implications for higher education. Journal of Applied Research in Higher Education , 12(4), 659–672. doi: https://doi.org/10.1108/JARHE-06-2018-0105

Kelly, H. (2014). Path analysis of educator perceptions of open educational resources using the Technology Acceptance Model. International Review of Research in Open and Distance Learning , 15(2), 26–42. doi: https://doi.org/10.19173/irrodl.v15i2.1715

Kemp, A., Palmer, E., & Strelan, P. (2019). A taxonomy of factors affecting attitudes towards educational technologies for use with technology acceptance models. British Journal of Educational Technology , 50(5), 2394–2413. doi: https://doi.org/10.1111/bjet.12833

Lai, H. J. (2020). Investigating older adults’ decisions to use mobile devices for learning, based on the unified theory of acceptance and use of technology. Interactive Learning Environments , 28(7), 890–901. doi: https://doi.org/10.1080/10494820.2018.1546748

Lee, D. Y., & Lehto, M. R. (2013). User acceptance of You Tube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education , 61, 193–208. doi: https://doi.org/10.1016/j.compedu.2012.10.001

Lee, Y. H., Hsieh, Y. C., & Chen, Y. H. (2013). An investigation of employees’ use of e-learning systems: applying the technology acceptance model. Behaviour & Information Technology , 32(2), 173–189. doi: https://doi.org/10.1080/0144929X.2011.577190

Lee, Y. H., Hsieh, Y. C., & Hsu, C. N. (2011). Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting employees’ intentions to use e-learning systems. Educational Technology & Society, 14 (4), 124-137. Retrieved from https://www.ds.unipi.gr/et&s/journals/14_4/12.pdf

Lemay, D. J., Morin, M. M., Bazelais, P., & Doleck, T. (2018). Modeling students’ perceptions of simulation-based learning using the Technology Acceptance Model. Clinical Simulation in Nursing , 20, 28–37. doi: https://doi.org/10.1016/j.ecns.2018.04.004

Lin, P.H. &; Yeh, S.C. (2019). How motion-control influences a VR-supported technology for mental rotation learning: from the perspectives of playfulness, gender difference and Technology Acceptance Model. International Journal of Human-Computer Interaction, 35 (18), 1736-1746. doi:10.1080/10447318.2019.1571784

Liu, Q., Geertshuis, S., & Grainger, R. (2020). Understanding academics’ adoption of learning technologies: A systematic review. Computers & Education , 151, 103857. doi: https://doi.org/10.1016/j.compedu.2020.103857

Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society , 14(1), 81–95. https://doi.org/10.1007/s10209-014-0348-1

Moran, M., Hawkes, M., & El Gayar, O. (2010). Tablet personal computer integration in higher education: Applying the Unified Theory of Acceptance and Use Technology model to understand supporting factors. Journal of Educational Computing Research , 42(1), 79–101. doi: https://doi.org/10.2190/EC.42.1.d

Nagy, J. T. (2018). Evaluation of Online Video Usage and Learning Satisfaction: An Extension of the Technology Acceptance Model. International Review of Research in Open and Distributed Learning , 19(1), 160–185. doi: https://doi.org/10.19173/irrodl.v19i1.2886

Nam, C. S., Bahn, S., & Lee, R. (2013). Acceptance of assistive technology by special education teachers: A structural equation model approach. International Journal of Human-Computer Interaction , 29(5), 365–377. doi: https://doi.org/10.1080/10447318.2012.711990

Nyasulu, C., & Chawinga, W. D. (2019). Using the decomposed theory of planned behaviour to understand university students’ adoption of WhatsApp in learning. E-Learning and Digital Media , 16(5), 413–429. doi: https://doi.org/10.1177/2042753019835906

Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research , 17(4), 460–469. doi: https://doi.org/10.2307/3150499

Padilla-Melendez, A., del Aguila-Obra, A. R., & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education , 63, 306–317. doi: https://doi.org/10.1016/j.compedu.2012.12.014

Panigrahi, R., Srivastava, P. R., & Sharma, D. (2018). Online learning: Adoption, continuance, and learning outcome - A review of literature. International Journal of Information Management , 43, 1–14. doi: https://doi.org/10.1016/j.ijinfomgt.2018.05.005

Park, E., & Kwon, S. J. (2016). The adoption of teaching assistant robots: a technology acceptance model approach. Program-Electronic Library and Information Systems , 50(4), 354–366. doi: https://doi.org/10.1108/PROG-02-2016-0017

Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students’ Behavioral Intention to Use e-Learning. Educational Technology & Society, 12 (3), 150-162. Retrieved from: https://www.jstor.org/stable/jeductechsoci.12.3.150

Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology , 43(4), 592–605. doi: https://doi.org/10.1111/j.1467-8535.2011.01229.x

Prasetyo, Y. T., Ong, A. K. S., Concepcion, G. K. F., Navata, F. M. B., Robles, R. A. V., Tomagos, I. J. T. … Redi, A. A. N. P. (2021). Determining factors affecting acceptance of e-learning platforms during the COVID-19 pandemic: Integrating extended Technology Acceptance Model and DeLone & McLean IS Success Model. Sustainability , 13(15), 8365–8365. doi: https://doi.org/10.3390/su13158365

Raman, A., & Don, Y. (2013). preservice teachers’ acceptance of learning management software: an application of the UTAUT2 Model. International Education Studies , 6(7), doi: https://doi.org/10.5539/ies.v6n7p157

Roca, J. C., Chiu, C. M., & Martinez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies , 64(8), 683–696. doi: https://doi.org/10.1016/j.ijhcs.2006.01.003

Rogers, E. (1962). Diffusion of Innovations . New York, NY: The Free Press

Google Scholar  

Rogers, E. (1995). Diffusion of Innovations (4th ed.). New York, NY: The Free Press

Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. Journal of Psychology , 91(1), 93–114. doi: https://doi.org/10.1080/00223980.1975.9915803

Saade, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model. Information & Management , 42(2), 317–327. doi: https://doi.org/10.1016/j.im.2003.12.013

Sadaf, A., & Gezer, T. (2020). Exploring factors that influence teachers’ intentions to integrate digital literacy using the decomposed theory of planned behaviour. Journal of Digital Learning in Teacher Education , 36(3), 1–22. doi: https://doi.org/10.1080/21532974.2020.1719244

Salloum, S. A., Alhamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive Technology Acceptance Model. IEEE ACCESS , 7, 128445–128462. doi: https://doi.org/10.1109/ACCESS.2019.2939467

Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128 , 13-35. doi:0.1016/j.compedu.2018.09.009

Schoonenboom, J. (2014). Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Computers & Education , 71, 247–256. doi: https://doi.org/10.1016/j.compedu.2013.09.016

Shyu, S. H. P., & Huang, J. H. (2011). Elucidating usage of e-government learning: A perspective of the extended Technology Acceptance Model. Government Information Quarterly , 28(4), 491–502. doi: https://doi.org/10.1016/j.giq.2011.04.002

Song, Y. J., & Kong, S. C. (2017). Investigating students’ acceptance of a statistics learning platform using Technology Acceptance Model. Journal of Educational Computing Research , 55(6), 865–897. doi: https://doi.org/10.1177/0735633116688320

Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research , 79(2), 625–649. doi: https://doi.org/10.3102/0034654308325896

Tarhini, A., Hone, K., & Liu, X. H. (2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended Technology Acceptance Model. Journal of Educational Computing Research , 51(2), 163–184. doi: https://doi.org/10.2190/EC.51.2.b

Tarhini, A., Hone, K., Liu, X. H., & Tarhini, T. (2016). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: A structural equation modeling of an extended technology acceptance model. Interactive Learning Environments , 25(3), 306–328. doi: https://doi.org/10.1080/10494820.2015.1122635

Tawafak, R. M., Romli, A. B. T., & Arshah, R. B. (2018). Continued intention to use UCOM: Four factors for integrating with a Technology Acceptance Model to moderate the satisfaction of learning. IEEE ACCESS , 6, 66481–66498. doi: https://doi.org/10.1109/ACCESS.2018.2877760

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research , 6 (2), 144-176. Retrieved from http://www.jstor.org/stable/23011007

Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education , 52(2), 302–312. doi: https://doi.org/10.1016/j.compedu.2008.08.006

Teo, T. (2010). A path analysis of pre-service teachers’ attitudes to computer use: applying and extending the technology acceptance model in an educational context. Interactive Learning Environments , 18(1), 65–79. doi: https://doi.org/10.1080/10494820802231327

Tiwari, P. (2020). Measuring the impact of students” attitude towards adoption of online classes during COVID 19: Integrating UTAUT Model with perceived cost. Test Engineering and Management , 83(1), 8374–8382. doi: https://doi.org/10.1007/s10639-021-10573-5

Tsai, H. Y. S., Rikard, R. V., Cotten, S. R., & Shillair, R. (2019). Senior technology exploration, learning, and acceptance (STELA) model: from exploration to use - a longitudinal randomized controlled trial. Educational Gerontology , 45(12), 728–743. doi: https://doi.org/10.1080/03601277.2019.1690802

Vanduhe, V. Z., Nat, M., & Hasan, H. F. (2020). continuance intentions to use gamification for training in higher education: Integrating the Technology Acceptance Model (TAM), social motivation, and Task Technology Fit (TTF). IEEE ACCESS , 8, 21473–21484. doi: https://doi.org/10.1109/ACCESS.2020.2966179

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly , 36, 157–178. doi: https://doi.org/10.2307/41410412

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly , 27(3), 425–478. doi: https://doi.org/10.2307/30036540

Yakubu, M. N., & Dasuki, S. I. (2019). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria A structural equation modelling approach. Information Development , 35(3), 492–502. doi: https://doi.org/10.1177/0266666918765907

Yi, M. Y., & Hwang, Y. J. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the Technology Acceptance Model. International Journal of Human-Computer Studies , 59(4), 431–449. doi: https://doi.org/10.1016/S1071-5819(03)00114-9

Yu, Z. G. (2020). Extending the learning Technology Acceptance Model of WeChat by adding new psychological constructs. Journal of Educational Computing Research, 58(6), 1121-1143. doi.10.1177/0735633120923772

Download references

Author information

Authors and affiliations.

Faculty of Science, Department of Computer Science, University of Split, Rudera Boskovica 33, 21000, Split, Croatia

Andrina Granić

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Andrina Granić .

Ethics declarations

Statements on open data, conflict of interest and ethics.

The data of the systematic review consist of articles published in journals and conferences. Many of these are freely available online, others can be accessed for a fee or through subscription.

The authors declare no conflicts of interest.

No ethics review was required to undertake this literature review.

Additional information

Publisher’s note.

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

The original online version of this article was revised: Figures 2, 3 and 4 were incorrectly captured in the html version.

Rights and permissions

Reprints and permissions

About this article

Granić, A. Educational Technology Adoption: A systematic review. Educ Inf Technol 27 , 9725–9744 (2022). https://doi.org/10.1007/s10639-022-10951-7

Download citation

Received : 28 December 2021

Accepted : 09 February 2022

Published : 05 April 2022

Issue Date : August 2022

DOI : https://doi.org/10.1007/s10639-022-10951-7

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

• Technology acceptance
• Technology adoption
• Predictive factors
• Systematic review
• Find a journal
• Publish with us
• Track your research

• Review article
• Open access
• Published: 14 December 2022

The use of technology in higher education teaching by academics during the COVID-19 emergency remote teaching period: a systematic review

• McQueen Sum   ORCID: orcid.org/0000-0002-7763-1105 1 &
• Alis Oancea 1  

International Journal of Educational Technology in Higher Education volume  19 , Article number:  59 ( 2022 ) Cite this article

12k Accesses

21 Citations

6 Altmetric

Metrics details

This paper presents a systematic review of scholarly efforts that uniquely emerged at the onset of the COVID-19 pandemic and focused primarily on higher education teachers’ perspectives on technology use and on associated changes in the relationship between teachers and students amidst the transition to emergency remote teaching worldwide. Our narrative synthesis of 32 studies, the majority of which come from lower-and middle-income countries/regions, suggests that numerous factors interact to shape academics’ technology use in emergency remote teaching across higher education contexts. We report strong findings of teachers’ resilience and resourcefulness in their self-exploration of various technologies and teaching strategies in response to the continued severity of the pandemic. Ultimately, this review suggests directions for further research on engaging educational leaders and faculty in reimagining teaching as not only a core academic function of higher education, but also, and importantly, a humanising experience shaped by an ethics of care.

Review of literature and research questions

Since the continued devastating spread of COVID-19 across continents from early 2020, the coronavirus pandemic has led to massive numbers of hospitalisations and deaths around the world, abruptly upending public health and many other domains of life. As the disaster has unfolded, a multitude of sweeping challenges have continued to reshape the global higher education (‘HE’) landscape. With HE institutions (‘HEIs’) worldwide closing their campuses in Spring 2020, teachers were forced to make a hasty transition from typically in-person teaching configured in physically proximate space to alternative teaching approaches in response to the COVID-19 emergency (Crawford et al., 2020 ).

The term ‘emergency remote teaching’ (‘ERT’) is used by Hodges et al. ( 2020 ) and subsequent literature to denote the rapid and putatively ephemeral shift to remote teaching to continue teaching and learning during emergencies. Although ‘ERT’ and ‘online teaching’ may be two domains with considerable overlaps, ‘online teaching’ is importantly distinguished from ‘ERT’ as it includes teaching and learning arising from a prolonged collective effort in curriculum planning and instructional design from a wide range of stakeholders pre-launching (Hodges et al., 2020 ).

Despite the growing literature on ERT, few efforts had been made to review this body of research systematically at the time of conducting this review (see Table 1 for a few exceptions). Since there have been abundant discussions on the perspectives of students at the HE level during COVID-19 [see, for example, Chakraborty et al. ( 2021 ) on Indian students’ opinions on various aspects of ERT; Mok et al. ( 2021 ) on Hong Kong students’ evaluation of their learning experiences during ERT; Resch et al. ( 2022 ) on social and academic integration of Austrian students; and Salas-Pilco et al. ( 2022 ) for a systematic review focusing on student engagement in Latin American HE], our review focuses systematically on synthesising the body of worldwide literature on teachers’ perspectives on technology use during the period of ERT. Moreover, much attention has been devoted to medical education (Rajab et al., 2020 ; see also Table 1 ) and STEM education since the coronavirus outbreak (Amunga, 2021 ; Bond et al., 2021 ; Gaur et al., 2020 ; Singh-Pillay & Naidoo, 2020 ). Our review focuses on the less explored perspectives of humanities, arts, and social sciences (HASS) teachers—whose perceived difficulties of using digital technologies in teaching were reportedly distinct from those of their counterparts in other disciplines, both before (Mercader & Gairín, 2020 ) and during the COVID-19 outbreak (Wu et al., 2020 ).

Prior to COVID-19, a respectable amount of scholarly work was devoted to the development and adaptation of theoretical models to identify, explain, and even predict factors that influenced technology use in educational contexts (Granić & Marangunić, 2019 ). But Lee and Jung ( 2021 ) argue that ‘in higher education contexts, crisis-driven changes may happen differently from pre-planned, voluntary change, and that factors influencing crisis-driven changes are different from those influencing voluntary changes; as reported in previous studies based on technology acceptance theories and models’ (p. 16). Given the novelty of COVID-19, few studies have been conducted to explicate the factors shaping HE teachers’ decisions about, and experiences of, technology use in the unique context of the global pandemic [see Mittal et al. ( 2021 ) for an exception that studies faculty members in Northern India and Lee and Jung ( 2021 ) for another study on South Korean university educators]. Therefore, the first question that this review aims to answer is: How have different potential factors, as identified by teachers in the included studies, shaped teachers’ technology use across various higher education contexts during the COVID-19 emergency remote teaching period?

Existing scholarly efforts that aim to provide an overview of the literature focus predominantly on a bifurcated discussion of the opportunities and challenges, or advantages and disadvantages pertinent to using technologies in teaching during the COVID-19 crisis (Adedoyin & Soykan, 2020 ; Dhawan, 2020 ; Pokhrel & Chhetri, 2021 ; Stewart, 2021 ). We therefore frame the second research question in a way that circumvents a binary pros-and-cons discussion of the implications of technology use in times of the COVID pandemic, as already well-documented in the literature. Hence, our second question is: What are the implications of technology use in COVID-19 emergency remote teaching from the perspectives of higher education teachers?

The broader term ‘technology’ (in the singular form) used in the review questions includes the socio-cultural contexts of the educational settings in which technology use is situated. The discussion of ‘context’ is of particular importance (Selwyn, 2022 ). Although pre-COVID studies (such as Broadbent & Poon, 2015 ; Liu et al., 2020 ) offered valuable insights into technology use in HE teaching, the pandemic brought about starkly and often perilously different contexts for research as well as for teaching and learning (Stewart, 2021 ; Williamson et al., 2020 ).

We use the term ‘technologies’ in its plural form throughout this review, in a narrower sense, meaning specifically the wide range of digital tools and systems and other technical resources that are used for pedagogical purposes. These can include but are not limited to electronic hardware devices, software systems, online services, and social media. We note, however, that the meanings attached to the term ‘technologies’ may be substantively different across contexts. Some of the studies included in this review, as we will show below, extend it to other-than-digital forms of technologies, leading to results beyond our initial scope of research. As a result, the use of (digital) technologies is understood in this review as an often necessary but not sufficient condition for ERT—a novel concept to many teachers who had been using various ‘technologies’ in other ways in facilitating their teaching for years before the COVID-19 outbreak.

Methodology

Characterised by the principles of replicability and transparency, a systematic review aims to ‘review ... existing research using explicit, accountable rigorous research methods’ (Gough et al., 2017 , p. 4). This methodology is used because it helps elucidate the current understanding and available evidence of the above research questions, clarify any replication of existing research findings, and inform future research and policy directions in HE teaching in a systematic and trustworthy manner. Below is a detailed, transparent report of the processes involved in conducting this systematic review.

Inclusion/exclusion criteria

Our review is restricted to peer-reviewed journal articles that report original empirical studies written in English and/or simplified Chinese. Papers written in these two languages account for a high volume of worldwide literature published at the onset of the COVID-19 outbreak. Also, Chinese studies are particularly valuable for this review, for mainland China was the first region affected by COVID-19 and its HE system was amongst the first to respond to the challenges ensuing from the spread of coronavirus.

Since the review seeks to capture a ‘snapshot’ of perspectives on technology use by teachers during the immediate COVID-19 outbreak, only articles published in 2020 (including those published online ahead of print that year) were eligible for review. Included publications may cover any country/region worldwide but should systematically gather data from teachers other than the authors themselves and focus primarily on the perspectives of HASS teachers on matters pertaining to technology use in ERT in HE settings. Opinion pieces, editorials, reflection articles on one’s own practice, conference papers, and books are not within the purview of this review (see Appendix 1  for detailed inclusion/exclusion criteria).

Search strategy

Prior to conducting the database search, we piloted and modified the search strings several times. Our final search strategy is a combination of Boolean operators and variations of four key terms: ‘higher education’, ‘technology’, ‘teaching’, and ‘COVID-19’ (see Appendix 2  for detailed search terms).

Screening and selection

On 13 January 2021, a targeted search returned 4204 records indexed in fourteen databases including Scopus, Web of Science, and three Chinese databases (see Appendix 3  for PRISMA flow diagram and the complete list of databases). From these, we extracted 20 different papers at random to screen by title and abstract independently by applying the inclusion/exclusion criteria, and with the intention to repeat the process until unanimous agreement was reached. Having achieved full inter-reviewer agreement in our first attempt and after a further calibration session, we then proceeded to de-duplication and title-and-abstract screening, after which only 129 papers remained for full-text retrieval and further screening. Meanwhile, 16 relevant publications from various other sources were also identified and passed the initial screening. We then examined the full text of the resulting total of 145 articles and excluded any that did not fulfil the inclusion criteria, leading to a set of 40 studies to be considered for review.

Quality and relevance assessment and content extraction

To assess the 40 papers’ quality and relevance to this review, we adapted the assessment rubric from Oancea et al. ( 2021 ) (see Appendix 4 ). In parallel with the quality assessment, we developed a grid for content extraction by piloting on three papers, after which multiple revisions of the extraction grid were made. Then both authors used the updated extraction grid (see Appendix 5 ) and extracted content from two full papers independently to check for inter-reviewer agreement. In subsequent communications, discrepancies of our extraction were reconciled and the final quality thresholds for inclusion were agreed upon. As of May 2021, after excluding 8 papers of low quality, the final corpus for review comprised 32 articles.

Analysis and synthesis

We developed an initial coding scheme with broad theme boundaries based on the research questions, and resolved any conflicting views. We coded line-by-line the extracted data both deductively and inductively: we first applied the pre-configured coding scheme to the full set of data, and then updated and re-applied the coding scheme to include further themes identified through inductive coding. For example, we realised that the category of ‘ethical use of technology’ spanned the themes of ‘pedagogical implications’ and ‘work-related implications’. As a result we categorised it under a separate theme titled ‘cross-cutting implications’. After multiple rounds of scheme refinement and iterative coding which started in June 2021, the process of synthesis concluded in late December 2021.

The research synthesis is presented narratively; note that we integrated quantitative findings (for example, from surveys) descriptively into the narrative analysis, as in most cases the samples were not representative, the analysis was largely descriptive and findings from qualitative answers to open questions were presented in detail.

Limitations

Our review did not include insights from reflection pieces (such as Czerniewicz et al., 2020 ; Jandrić et al., 2020 ; Joseph & Trinick, 2021 ) and reports not published in peer-reviewed journals (such as Ferdig et al., 2020 ); these exclusions are not a judgment on either the quality or the level of insight of such pieces, nor on the modes of research and scholarship that they embody. This decision, as well as the focus on studies published in English and Chinese, limit the extent to which this review covers the experiences of ERT technology use by teacher populations across the world.

Due to our international remit, another limitation is the integration of findings grounded in different local contexts and HE environments. We overcome this partially by extracting from each paper the context in which teachers’ technology use is situated and taking such information into account when narratively integrating data across studies and presenting our review findings (see Appendix 5 ). However, the inconsistent terminology used to allude to the notions of ‘technology’ and ‘emergency remote teaching’ in the reviewed articles poses a major challenge to our cross-context comparison [see discussion on the jingle-jangle fallacy in Sum and Oancea ( 2021 )]. Another review conducted by Bond et al. ( 2021 ) also found at least ten different terms used for ‘online teaching’ (including ‘emergency remote teaching’) in their selected papers.

Although uniformly agreed-upon definitions of these terms are absent (Singh & Thurman, 2019 ), the nuances of concepts underlying them have not been given due consideration in the majority of the studies reviewed (see “ Description of included articles ” section). Further terminological complexity arises from the imperfect overlap between Chinese and English vocabularies. Whilst we tried to overcome this by extracting information on each study’s conceptualisation of ‘technology’ and ‘ERT’ (see Appendix 5 ) and accompanying translations with original Chinese terms (for example, the phrase ‘线上教学’ in Chinese can be sometimes translated into ‘online teaching and learning’), we acknowledge that terminological and translation gaps remain in our cross-context synthesis of the selected literature.

Description of included articles

Included in our final synthesis are 32 empirical research studies covering 71 countries and reporting perspectives from 4725 HE teachers altogether. Of these, the largest proportion focuses on the HE context in Asia (n = 15), followed by Europe (n = 7) and Africa (n = 6) (see Table 2 ). Given our inclusion of articles indexed in Chinese databases, Mainland China alone is the focal context of n = 5 studies. A wide range of subject areas in HASS disciplines are covered (see Table 3 ). Studies using qualitative data are most common (n = 14) (see Table 4 ), and a sample size of fewer than 50 teachers is often reported (n = 21) (see Table 5 ). Appendix 6 presents a summary of the characteristics of included studies.

Exactly half of the studies (n = 16) have a local remit (see Table 6 ), amongst which many recruited fellow academics from the authors’ institutions (n = 14). As noted by several researchers in their papers, the public health emergency and its concomitant restrictions had in various ways altered the methods for research and data collection, including shifting to a local focus whilst access to other settings was limited.

Authors of three quarters of the reviewed studies (n = 24) obtained data from participants remotely, either by phone or online. Much empirical data were collected in a space that was relatively new and unfamiliar to the researcher and the researched during a time when both individuals were coping with not only the expected expeditious embrace of various technologies for ERT but also, amongst other things, the physical and psychological burden posed by the coronavirus pandemic. Hence, this review integrates, in a systematic and holistic fashion, data from the discrete, often inevitably limited, yet valiant research initiatives undertaken in different countries during the periods of drastic increases in infections and deaths at the incipient phase of the COVID-19 outbreak.

In terms of substantive focus, whilst most of the included studies describe ‘what’ and/or ‘how’ technologies were being used by teachers during ERT (n = 14) and offer a dichotomous pros-or-cons narrative of technology use for ERT (n = 21), often vis-à-vis in-person teaching prior to COVID-19, some (n = 7) also examine the wider implications for teachers and HE at large.

Due partly to the novelty of COVID-19 and the haste with which research was conducted, the conceptualisation of technology and its relation with remote teaching in times of COVID-19 is either weak or largely absent in the majority of the reviewed studies. Technologically deterministic views seem prevalent in the literature reviewed. Many studies place ‘technology’ as the centre of inquiry and underscore the palpable ‘impact’ that various technical objects impose on teaching. For example, the attribution of recent pedagogical innovations and educational developments to technological advancements features prominently in the introductory paragraphs of numerous papers. Some assert that the emergence of social networking sites has begun to direct all walks of life including the ways in which teaching has been carried out since before the pandemic. Additionally, the discussion of ‘technology-enabled’ and ‘technology-enhanced’ teaching used in some articles implies that ‘technology’ plays an almost indispensable role in teaching and that teaching would be seriously disrupted without it. In contrast, there was little awareness in many of these papers of the extent to which technologies may carry political or commercial agendas or may be underpinned by complex ideologies and social structures (Selwyn et al., 2020 ). This echoes the conclusions of pre-COVID research by An and Oliver ( 2021 ) and Costa et al. ( 2019 ) that theoretical understanding of ‘technology’ in educational research is under-developed.

A brief narrative of ERT experiences from teachers’ perspectives

An eclectic range of technological artefacts and their uses during ERT across HE settings is reported in the studies. Cases of initial technology use range widely from straightforward approaches such as uploading teaching materials online to (mis)uses such as creating excessive recorded lectures and assignments. What is common, however, across reports in most studies is the acutely negative sentiments of intimidation, angst, confusion, and even despair of ERT amongst teachers at the outset of the transitioning period. It gave teachers great shock and pain to make a forced, often slapdash migration to ERT—a terrain that many of them were unfamiliar with and uncertain of—whilst juggling with their home and other work responsibilities during the distressing period. In addition to the psychological burden, teachers were worried about the well-being of their students, particularly those from underprivileged backgrounds and in vulnerable environments. Across HE settings worldwide, teachers had on average less than a week’s preparation time, leaving them feeling woefully unprepared. Hence, it is unsurprising that the majority of teachers in the studies reviewed found the immediate phase of migration to ERT burdensome and emotionally exhausting. Yet, some sought a silver lining and considered ERT as a creative challenge and an opportunity for a long-needed meaningful reflection and overhaul of HE teaching practices.

We mapped each included article’s findings about teachers’ overall attitudes towards ERT using the World Bank’s classification of country development (2020) (see Table 7 ). For studies not examining teachers’ attitudes directly, we inferred negative attitudes from teachers’ reports of dissatisfaction and frustrations over the challenges in ERT, and any indication of concern and anxiety; positive attitudes were inferred from teachers’ expressions of satisfaction and awareness of benefits brought by ERT, and any indication of optimism and hope.

Reports by teachers from higher-income countries/regions were more positive whilst those from lower-and middle-income countries/regions tended to be more negative, though with a few exceptions (for example, teachers in mainland China had relatively positive emotional responses and teachers of hearing-impaired students in high-income Saudi Arabia reported overwhelmingly negative emotional responses during the ERT period). In propitious circumstances, teachers’ emotional responses could change substantially over time from apprehension, frustration, and pessimism to relief, affirmation, and an eventual sense of achievement. Sometimes, as teachers gradually became conversant with various technological artefacts and encountered a suitable way of teaching, either serendipitously or after multiple experimentation, they eventually saw ERT as a humbling and rewarding experience. Some teachers evaluated the pedagogical revisions they made during ERT positively and even expressed the intention to keep part of their teaching online or expected to continue to use the technologies employed for ERT in the future.

Factors shaping technology use by teachers in ERT across HE contexts

The 32 papers reviewed include results on qualitative and quantitative factors identified by teacher participants that potentially shape teachers’ technology use in ERT. Note that these are not always empirically validated, nor explicitly identified as ‘factors’ in the included articles (particularly in qualitative accounts they may be described as reasons, drivers, challenges, barriers, and conditions). Thus, we adopted an open and inclusive definition of factors based on the implied or explicit direction of influence on ERT, and we grouped them thematically. Summary accounts of these thematic groupings based on the data presented in the review corpus are discussed below in descending order of the respective strength of evidence in the reviewed studies (see full references in Table 8 ).

Social-technological factors

Whilst Tartavulea et al. ( 2020 ) note that the transition to ERT can be facilitated by having online platforms and facilities, they also found that access to electronic devices and internet connection can be a luxury. Frequently reported technical concerns by teachers include the unreliability of network conditions, lack of devices and equipment, and limitations of digital infrastructure. These issues are not only powerful barriers to technology use in emergency teaching but they also disproportionately affect teachers and students in lower-income countries/regions. Note, however, that even in the context of an affluent country like the United States, teachers and students may report inequitable access to the necessities of ERT from home (Cutri et al., 2020 ; Sales et al., 2020 ).

Beneath the surface of these technical difficulties are the imbalanced allocation of resources and entrenched socio-economic problems which most commonly beset lower-and middle-income countries and regions (Tanga et al., 2020 ). Whilst the issues teachers face are highly contextualised, a considerable number of students come from underprivileged backgrounds. Even before the pandemic hit, these students had been confronting different challenges such as, particularly in lower-income countries, frequent commute of several miles from rural areas to the city for internet connection. Even if internet access were provided at home, these students would still need to overcome problems of intermittent or no power supply in their localities. In addition, during lockdowns they may shoulder more home-care responsibilities, sometimes in overcrowded or even abusive home environments.

Some teachers were also amongst vulnerable groups and had limited access to the internet at home, for example due to the sharing of cellular data with household members, and therefore exposed themselves to greater health risks by visiting commercial establishments such as cafés with free internet provision in order to teach remotely. Compounding this predicament is that HE teachers reported that they often had little information about students’ backgrounds, which hindered their efforts to address students’ educational and psychological needs and any equity issues pertinent to their studies (Cutri et al., 2020 ). These technical complications are situated in specific social contexts and have been a major hindrance to technology use in ERT.

Institutional factors

In most of the studies reviewed, the migration to ERT was described as mandatory, and teachers’ use of certain applications was often resultant from policies imposed by their institutions—whose regulations on teaching could be heavily influenced by government decisions, for example in universities in Mainland China (Tang et al., 2020 ). To ensure continuity and safety of teaching and learning in times of upheaval and uncertainty, some HEIs exercised greater control over the ways in which technologies were used in teaching, such as mandating the use of certain Learning Management Systems (LMS) in teaching (Khoza & Mpungose, 2020 ) or prohibiting asynchronous methods of teaching (Cutri et al., 2020 ). Whilst some teachers felt that their creative freedoms to use different technologies in their teaching were constrained by institutional policies , others sought detailed guidance and perceived the lack of clear institutional protocols as a significant barrier to technology use in this emergency (Sobaih et al., 2020 ).

Aside from policy, different forms of institutional support (such as the provision of digital infrastructure and training for both teachers and students) could also be of value to teachers in ERT, although the level of support felt by teachers could vary by discipline (Watermeyer et al., 2021 ). However, the value of technical assistance might be undermined when technology specialists were just as confused as teachers about teaching remotely in emergency times (Gyampoh et al., 2020 ; Tanga et al., 2020 ). Another gap in institutional support pointed out by some studies is the lack of recognising teachers’ hardship and efforts in teaching in the form of pecuniary (such as support for procurement of equipment) and non-pecuniary rewards (such as teaching awards) (Joshi et al., 2020 ).

Individual factors

Sometimes teachers resisted institutional policies and employed instead other technologies of their own preference. Individual factors therefore play an important role in shaping teachers’ technology use. Despite the challenges posed by the pandemic, some teachers were tolerant of uncertainties, valiantly departing from their previous pedagogical praxis and forging ahead with ‘pedagogical agility’ (Kidd & Murray, 2020 )—the flexibility of adapting to the new teaching conditions in rapid yet meaningful ways. Resilient and adaptive, these teachers ‘rolled up their sleeves’ and worked around the clock to seek teaching solutions and countermeasures through constant, active self-exploration (Sales et al., 2020 ). Some music teachers, for instance, would make immediate remedies for the connection disruptions to synchronous lessons by providing students with recordings of their playing as examples (Akyürek, 2020 ). In an Israeli college, teacher educators incorporated topics like ‘distance learning’ into the teacher training curriculum to reflect the new circumstances of teaching (Hadar et al., 2021 ). One teacher educator even painted a wall at home with special paint to make it into a ‘blackboard’ where his writings were presented and screened to students (Hadar et al., 2021 ). These are just a few of the many manifestations of teachers’ agentic creativity and ongoing inventiveness in innovating their own use of technologies and resources despite the presence of severe constraints in ERT times.

In terms of readiness, despite receiving considerable institutional support in some cases, teachers often felt ill-prepared for ERT and doubtful of their abilities in using various technologies to teach (Scherer et al., 2021 ), and only a minority felt rather ready for ERT (Alqabbani et al., 2020 ). The studies reviewed discussed the variation in teachers’ readiness for ERT in relation to gender, academic discipline, and country context (Scherer et al., 2021 ). For example, in predominantly high-income economies teachers moved from a customary integration of technologies in pre-COVID teaching to fully-online ERT (Mideros, 2020 ; Sales et al., 2020 ). But not all teachers and students had had the opportunities to familiarise themselves with various technologies (including otherwise widely used applications like Word processing) prior to COVID-19 (Gyampoh et al., 2020 ). Whilst experienced online teachers felt more prepared and expected themselves to employ more frequently a wide array of technologies in teaching, across HE contexts many teachers had seriously limited prior experience in ‘online teaching’ and were apprehensive about using technologies for teaching purposes (Bailey & Lee, 2020 ). Besides, being experienced in ‘online teaching’ does not necessarily translate to successful handling of ERT, given the limited time frame and the stressful and even traumatising circumstances at the outset of the crisis.

Pedagogical factors

Across HE settings, teachers considered how to connect and engage dislocated groups of students through technologies, how to empower students to explore beyond the curriculum as students gained more control over what and how they study in the shifting context of teaching and learning (Mideros, 2020 ), and how to reconfigure spaces in ways that provide students with a nourishing, inter-connected intellectual environment despite being physically apart during the ERT period (Kidd & Murray, 2020 ). In Australia, teachers were especially concerned about first-year students, as the southern hemisphere’s Autumn 2020 was their very first term at the university. In addition to providing students with considered feedback, these teachers employed strategies such as the online polls and hand-raising functions on various EdTech platforms (Zeng, 2020 ), or made students the host of Blackboard Collaborate in order for teaching to be more engaging (Marshalsey & Sclater, 2020 ).

As coronavirus infections spread, teachers also attended to students’ emotional and educational well-being. Some teacher educators in the United Kingdom offered one-on-one tutorials online to establish personal connections with student teachers and monitor their progress (Kidd & Murray, 2020 ). A teacher in Pakistan went the extra mile to care for the students living in far-flung areas without internet access by sending them CD recordings of their lectures (Said et al., 2021 ). In Saudi Arabia, teachers of hard-of-hearing students used a special configuration of multiple spaces to enable the inclusion of synchronous sign-language translation in their online lectures (Alsadoon & Turkestani, 2020 ). In cases where the discrepancy between technology use by teachers and students was significant, teachers would often bridge the gap by adapting and adopting technologies (such as social media) that they were not always conversant with, but which were most used and preferred by students. As a teacher participant put it, teachers have ‘to go where [students] are, and not wait for [students] to come to where [they] are’ (Sales et al., 2020 , p. 13).

Often teachers would consider the compatibility of certain technologies with their teaching philosophies and practices within their disciplines. Teacher educators in Israel, for example, might feel additional pressure from the expectation that their pedagogical use of technologies has to set examples for their student teachers (Hadar et al., 2021 ). As another example, teaching translation/interpretation in Mainland China was especially challenging during the ERT period since teachers have to demonstrate to students the operation of simultaneous interpretation equipment and the use of dual-track recording function—which is not commonly found in existing online applications (Ren, 2020 ).

Peer factors

Teachers reported that they saw their colleagues as not only sources of inspiration for technology use, but also remedies for stress and uncertainty during the ERT period (Ren, 2020 ). Unlike in prior ‘online teaching’ where they could still meet in person to discuss technology use, many teachers struggled with technological learning-by-doing in relative isolation during the COVID-19 lockdown period (Cutri et al., 2020 ). In view of the absence of physical spaces for colleagues to informally exchange professional practices and channel their emotionality and empathy for one another (Cutri et al., 2020 ; Scherer et al., 2021 ), some teachers put in deliberate effort into organising new networking spaces to bring the academic community together online. In an attempt to alleviate the uncertainties brought by ERT and their adverse impact on psychological well-being, teachers worked together remotely as a team to explore solutions and share useful insights about technology use in teaching. They felt empowered by the constant encouragement and motivational texts from their peers (Ren, 2020 ). Teachers thrived on establishing connections with technology-proficient colleagues whose technical expertise and guidance were relied upon (Bailey & Lee, 2020 ; Mouchantaf, 2020 ) and whose ingenious engagement with technologies inspired and were even assimilated into their own teaching practices. As a mitigation strategy to ease teachers’ hasty migration into ERT, mutual empowerment through facilitated discussions amongst colleagues meaningfully shaped the ways technologies were used by teachers in ERT.

Interplay of factors

Whilst we have delineated potential factors shaping technology use in ERT in a linear, point-by-point fashion, this list of non-exhaustive items should not be conceived as separate, stand-alone factors since they interact in a complex and nuanced way across various contexts. For instance, having little institutional support and no access to LMS or students’ information, some teachers in public HEIs in Egypt resorted to reaching students through popular social media. Teachers then explored on their own the ways in which they could continue teaching activities via these platforms which were new to them (Sobaih et al., 2020 ). As for teachers in an Israeli college, upon realising some Arabic female students refused to appear online due to their cultural values, they made allowance for students’ decisions to keep their cameras off (Hadar et al., 2021 ). But the inability to read students’ expressions during class added to the teaching challenges during ERT and demanded additional flexibility and pedagogical adjustments from teachers. Therefore, technology use is influenced by the combined factors of students’ socio-cultural backgrounds and teachers’ resources and adaptability to changes. In addition to the complex interplay of these factors, these examples demonstrate that teachers’ technology use in ERT is heavily contextualised across HE settings and should therefore be understood in its wider cultural embedding and socio-economic contexts.

Implications of technology use in ERT for teachers

As for our second research question, the studies reviewed indicate that the implications of technology use in ERT for teachers are manifold. These findings are categorised into pedagogical, work-related, and cross-cutting implications, discussed below (see Table 9 for a summary table).

Pedagogical implications

With the paradoxical amalgam of being ‘together but (physically) apart’ (Marshalsey & Sclater, 2020 ) in the new COVID-19 context of teaching, the notions of space and time, as well as the dynamics of the classroom and teacher-student relationship, have undergone less palpable yet important changes.

Spatiality-wise, teachers realised the loss of important physical spaces and the erosion of values traditionally attached to these spaces during the transition to ERT. Marshalsey and Sclater ( 2020 ), for example, reason how a physical art and design studio embodies a distinctive set of values, resources, and the signature experiential hands-on pedagogical practice of their discipline. But when artworks are presented online, their materiality, colours, and texture may be diminished.

Temporality-wise, some teachers felt a strongly contorted notion of time which rendered futile any discussion on the ordinary longitudinal perception of ‘being ready for teaching’ (Cutri et al., 2020 ). Not only was the migration to ERT perceived as rushed and disorganised but teachers also felt time as short, discrete intervals when many changes could occur. Some even found it difficult to find ‘a point of reference for their sense of self as experienced professionals’ (Cutri et al., 2020 , p. 533). This new sense of temporality is perhaps most concisely summarised by a comment made by a teacher during ERT: ‘I always plan a month ahead. Now I live from one day to the next’ (Hadar et al., 2021 , p. 454).

Within this new spatial–temporal context, teachers often felt that student engagement in remote teaching and learning activities was superficial and unequally distributed (Joshi et al., 2020 ; Kidd & Murray, 2020 ). Deprived of in-person interaction, teachers can neither hear the voices nor see the expressions of all students, and find the classroom discourse to be dominated by students who are generally more confident in sharing their ideas in front of the whole class (Hadar et al., 2021 ; Marshalsey & Sclater, 2020 ). With the loss of informal physical spaces where students used to ask questions and interact further with teachers before and after class (Cutri et al., 2020 ), some teachers commented that both teachers and students were more likely to stay in their ‘echo chambers’ during the pandemic (Eringfeld, 2021 ).

Teachers adopted different strategies to navigate being outside the comfort zone of the physical classroom. Some attempted to retain or increase control over interactions in the remote ‘classroom’ (Mideros, 2020 ) such as by only letting students speak when allowed (Gyampoh et al., 2020 ) and shifting to a predominantly teacher-centric, didactic approach of lecturing because of the perceived difficulty of implementing hands-on training in an exclusively remote teaching environment (Cutri et al., 2020 ). The students, too, adopted their own strategies, often distinct from their teachers’ (Callo & Yazon, 2020 ; Sobaih et al., 2020 ). As some students generally adapted to ERT with relative ease (Mideros, 2020 ; Ren, 2020 ), sometimes they even used technology as a defensive wall to exclude teachers (who were in some cases less tech-savvy than their students) from being involved in their studies during the pandemic (Sales et al., 2020 ). Many teachers in the studies reviewed reported that the mandated use of various technologies in ERT puts a strain on pedagogy, the major implications of which may include an elevated feeling of detachment from the class, a heightened distance from students (Kidd & Murray, 2020 ), and a more pronounced gap in teacher-student interactions (Callo & Yazon, 2020 ; Sales et al., 2020 ).

Moreover, ERT is thought to have precipitated the collapse of ‘yishigan’ (仪式感)—a Chinese expression which, when applied to this context, refers to the sense that teaching is a special, ritualised occasion (Lu, 2020 ; Ren, 2020 ). As ‘yishigan’ abates in the context of ERT, so does the sense of formality and immediacy felt by teachers and students, both of whom may no longer view teaching and learning as a serious, formalised routine of life in the same way as before; some of the studies reviewed note that motivation and classroom engagement are lowered as a result of this change in perception (see examples in Joshi et al., 2020 ; Lu, 2020 ; Marshalsey & Sclater, 2020 ).

In contrast with the sense of limitation, hierarchy, and loss illustrated by the accounts summarised above, other teachers reported a sense of the ‘intimacy of distance’ and a less visible teacher-student hierarchy as a combined result of emergency technology use during the pandemic. Such teachers valued the creation of spaces for more student-oriented and student-empowering pedagogy. In Mainland China, for example, the classroom atmosphere was livened up as students were encouraged by teachers to engage in class via alternative forms of interaction online such as sending emojis, raising ‘hands’, and taking polls (Gao & Zhang, 2020 ; Zeng, 2020 ). In other contexts, teachers felt an idiosyncratic sense of closeness as they shared a screen and read the same text with students on their devices (Eringfeld, 2021 ). They also reported a better understanding of students’ personal circumstances, home environment, and even household responsibilities as students turned on their cameras in class (Hadar et al., 2021 ; Kidd & Murray, 2020 ). In many ways, teachers observed their students being more relaxed in class, which enabled teachers to build personal relationships with their students in ways that they had never envisioned before (Marshalsey & Sclater, 2020 ).

Because of the collapse of ‘yishigan’ and the resultant casual and more relaxed classroom dynamics in the new spatiality, some teachers adapt to the ‘online etiquette’ by using emojis and GIFs when communicating with students (Marshalsey & Sclater, 2020 ). Also, the fact that students may be more technology-competent than teachers meaningfully shifts the dynamic of the teacher-student relationship in the ERT classroom (Kidd & Murray, 2020 ), for teachers often solicited help from students on questions regarding technology use, and during this process teachers increasingly saw students as their partners in teaching rather than subordinates to themselves (Cutri et al., 2020 ). As Cutri et al. ( 2020 ) remark, ‘the negative connotations of risk-taking and making mistakes while learning to teach online seem to have been mitigated by a combination of affective factors such as humility, empathy, and even optimism’ (p. 523). As an experience of vulnerability, ERT has grounded and humbled teachers, allowing them to develop both more appreciation for self-care (Eringfeld, 2021 ), and more empathy for students (Khoza & Mpungose, 2020 ; Kidd & Murray, 2020 ).

Teachers realised the salience of exercising care for students and themselves and considering the emotionality of students, especially those in vulnerable states (Alqabbani et al., 2020 ; Sales et al., 2020 ). Pastoral care took priority during particularly distressing periods when students were most in need of emotional support (Sobaih et al., 2020 ; Tejedor et al., 2020 ). All these examples suggest that under the new spatial–temporal reorientation an intricate web of human relations has evolved and, to varying degrees, been revitalised.

Work-related implications

The task of transitioning teaching to an alternative mode is only one of the many challenges teachers face in the larger contexts of academia during the pandemic period (Cutri et al., 2020 ). Although the extra time seemingly freed up by, say, the lack of commutes is highly valued for student support, self-care or family care (Eringfeld, 2021 ; Kidd & Murray, 2020 ; Tejedor et al., 2020 ), there has also been an excessive intensification of workload in preparation for ERT (Khan et al., 2020 ; Lu, 2020 ; Mouchantaf, 2020 ; Said et al., 2021 ), and this is expected to last for a few years into the post-ERT era (Watermeyer et al., 2021 ). When working from home, teachers received as many as hundreds of students’ inquiries throughout the day via various applications (Alsadoon & Turkestani, 2020 ; Sobaih et al., 2020 ). Coupled with the pressure to prove that work has been conducted remotely (Kidd & Murray, 2020 ; Marshalsey & Sclater, 2020 ), some teachers report feeling compelled to be present online around the clock. The ‘timelessness’ of working remotely in a home setting has been succinctly summarised by a teacher: ‘it is too easy to “just send one more email”’ (Watermeyer et al., 2021 ). The praxis and boundaries of academic work were shifted and reconstructed in ways many perceived as intrusive into the personal life sphere and deteriorative to work-life balance and also teachers’ well-being and occupational welfare (Watermeyer et al., 2021 ).

In addition, with looming financial challenges to the HE sector, casualised and untenured staff reported an elevated feeling of job precarity because their extra commitment to teaching cuts into time for other academic work, such as publishing research—which they perceived as often prioritised over teaching efforts in HE career progression (Cutri et al., 2020 ). Some reported that these teachers’ vulnerability was compounded by the management’s misperception that teaching remotely during emergency lightens teachers’ workload, and by their misinterpretation that low scores given by students on evaluations of ERT are a marker of ‘teacher quality’ rather than a way for students to express disinclination towards ERT in general (Watermeyer et al., 2021 ).

Technology use in ERT was further complicated by the need for swift re-coordination of private routines and domestic spaces to make room for professional work. A teacher, for example, asked all household members to disconnect from the Wi-Fi when teaching (Kidd & Murray, 2020 ). Having a separate, free-of-disturbance workspace at home is a luxury that not many teachers could afford (Gyampoh et al., 2020 ; Joshi et al., 2020 ) especially in contexts like Pakistan where joint families may live together in a crowded household (Said et al., 2021 ). Due to the non-separation of home/workspaces, customary parameters between the private and public domains were being reconstituted, and the boundaries between teachers’ personal and professional identities became blurry (Khoza & Mpungose, 2020 ). Consequently, female academics with caring responsibilities were disproportionately affected, and increasingly teachers found themselves struggling to perform either role well (Watermeyer et al., 2021 ).

In the larger context of HE, teachers were also worried about the ‘placelessness’ of HE during lockdowns and that the role of HE as an embodied, communal space for teaching and learning, self-formation, and socialisation was being undermined (Eringfeld, 2021 ). In two studies based in the UK (Eringfeld, 2021 ; Watermeyer et al., 2021 ), the accounts of their teacher participants add up to a strong ‘dystopian’ rhetoric, reflecting their fears that the ERT migration epitomises the beginning of a prolonged contraction of HE as an on-campus experience and monetisation of part of the HE experience driven largely by massification but not quality, thereby undermining the core academic values and humanising aims of HE.

Not all studies reviewed painted a consistently gloomy picture of the work-related implications of ERT and technology use. Some studies note that the compulsory, emergency move to remote teaching may have offered multiple opportunities. For example, in some propitious circumstances, teachers were able to constitute their networking spaces online to channel mutual support and facilitate exchanges on technology use. There are also reports that more trust was placed on technology specialists, technicians, and younger faculty who were often seen as more technologically adept and relied upon during ERT (Watermeyer et al., 2021 ). Moreover, the infrastructural divisions that used to separate departments on a physical campus are largely dismantled with the migration to ERT, enabling possibilities of various forms of inter-departmental communication and cross-disciplinary collaboration (Tejedor et al., 2020 ) and thereby making HE a flatter-structured and less hierarchically-organised workplace for teachers (Eringfeld, 2021 ).

Cross-cutting implications

Some of the teachers in the studies reviewed commented on the potential of ERT to undermine the ethos of the academic profession and imperil the work of academics. They noted that ERT could be pedagogically regressive, as teachers’ role may be reduced to merely technical functions, such as uploading materials online. This challenged their beliefs about what good teaching entails and compromised their often long-established pedagogical practices (Watermeyer et al., 2021 ). Other teachers struggled with balancing depth in their teaching with what they saw as their students’ preference for over-simplified yet visually appealing inputs such as bite-sized explanations shared on TikTok and other social media (Sales et al., 2020 ). Some anticipate worrying trends of ‘dumbing down’ of HE if teaching continues to be impersonal, disembodied and mediated predominantly by digital technologies in the post-ERT era (Watermeyer et al., 2021 ).

We have discussed so far the changes to HE teaching due to the relocation to newly formed spaces, as reported in the studies reviewed. Yet, some principles and values that teachers apply to guide their teaching practices remained unchanged amidst the ongoing crisis. These include the upholding of integrity, academic transparency, privacy, and other ethical principles in teaching (Mouchantaf, 2020 ). For example, teachers were concerned about the potential collection of students’ data for third-party use without prior informed consent (Diningrat et al., 2020 ; Joshi et al., 2020 ). Others also recognise the importance for students of using technology responsibly (Gyampoh et al., 2020 ) and being equipped with critical and reflective thinking capacity to evaluate the accuracy and relevance of information online (Sales et al., 2020 ; Tejedor et al., 2020 ), including resisting the temptation to reuse others’ ideas as their own work (Dampson et al., 2020 ) and refraining from using improper language on social media (Ghounane, 2020 ; Sobaih et al., 2020 ). This was especially relevant during the absence of teacher’s in-person monitoring, when the responsibility to access and study educational materials was partially shifted to students (Gyampoh et al., 2020 ), many of whom were inclined to explore topics of interest on their own (Marshalsey & Sclater, 2020 ; Mideros, 2020 ; Sales et al., 2020 ).

For teachers themselves, their practical wisdom and professional deliberation to ‘consider when, why, and how to use technology properly’ (Diningrat et al., 2020 , p. 706) were put to the test during the emergency contexts of teaching. A teacher participant in the study by Cutri et al. ( 2020 ) shared his belated reflection on an inadvertent, frivolous ridicule he had made about a student’s slow internet speed in front of the entire class online. This anecdote alludes to two problems looming in the wider context of HE teaching: (1) the largely absent code of conduct that delineates appropriate practices and roles of teachers and students in the new spatiality (and this can be due partly to the short time horizon in ERT); and (2) the difficulty for teachers to create supportive yet private spaces to address equity issues and attend to students’ emotionality in strict confidence when being online (Cutri et al., 2020 ).

Teachers participating in the studies reviewed in this paper indicated a multiplicity of factors that interacted to shape their technology use during the ERT period. In line with Liu et al. ( 2020 )’s pre-pandemic work, we find strong evidence that technology use in teaching is a context-sensitive, socially-embedded topic of study and hence should be understood in the socio-political, cultural and material context in which academics and students are situated (Selwyn et al., 2020 ). For example, the label ‘technical issues’ could encompass a wide range of contextualised problems, from power outages to long commutes for Internet access, from material shortages to widespread hunger, from trenchant poverty to deep-seated structured inequalities, which afflict disproportionately relatively poor, underserved communities and the most disadvantaged segments of populations (Chan et al., 2022 ) but are also palpable within higher-income countries/regions [see, for example, Cullinan et al. ( 2021 ) for a study on broadband access disparities in Ireland].

The narrative account we constructed is indicative of the resourcefulness and resilience of teachers to continue teaching during the crisis, even those in marginalised communities where resources are limited. This view is also shared by Padilla Rodríguez et al. ( 2021 ) who study the changes teachers in rural Mexico have made to their teaching practice in response to the suspension of in-person classes without receiving much external support during the pandemic. Around the world, teachers forayed into ERT during times of uncertainty by seeking to empower themselves and exploring various technological artefacts in teaching on their own, on the one hand; and by endorsing mutual empowerment and drawing inspiration from amongst their peers, on the other. Their collective efforts in supporting one another in the wake of crisis created what Matthewman and Uekusa ( 2021 ) call ‘disaster communitas’, which temporarily served to support teachers when adapting to the hasty conversion to ERT. We concur with Hickling et al. ( 2021 ) that the creation of a supportive space and environment for HE teachers to commiserate, discuss experiences, and share insights and resources with colleagues helps advance teaching practices with technology.

In answering the second research question, we have discussed at length the implications of a more encompassing use of technology in ERT and how evolving notions of space and time combined to reconstitute teacher-student relationships and the nature of academics’ work (Williamson et al., 2020 ). The studies reviewed indicate that the rushed transition to ERT has affected the sense of professional identity of academics as HE teachers (Littlejohn et al., 2021 ) in ways that are as yet only partly explored. Echoing the findings of Ramlo ( 2021 ), we believe that teachers’ negotiation of the blurring home-workspace boundaries (Blumsztajn et al., 2022 ; Littlejohn et al., 2021 ) and attempts to rebalance their professional work and personal life have important implications for future HE teaching and merit further investigation (Gourlay et al., 2021 ).

As COVID-19 continues to take a toll on people’s lives, we draw on the studies reviewed to emphasise the importance of re-prioritising the value of social and emotional connections in HE teaching, as well as the overall well-being of both teachers and students (Baker et al., 2022 ; Yeung & Yau, 2021 ). ‘Networks of care’ between teachers and students as well as amongst teachers themselves may be constructed to ameliorate uncertainties brought by the pandemic (Czerniewicz et al., 2020 ; Joseph & Trinick, 2021 ). Elements of care can be developed by simple acts of kindness (Murray et al., 2020 ) and gestures to communicate approachability (Glantz et al., 2021 ), all of which contribute to constructing more supportive and less hierarchical teacher-student relationships in the digital context. We note, however, that evidence scattered across the studies reviewed indicates that academic recognition and reward systems have not accounted well for the creative efforts that academics (including casualised and untenured staff) have put into teaching and maintaining relationships with their colleagues and students in response to the ongoing challenges ensuing from the coronavirus crisis. This is another priority for HEIs and leadership teams. On a final note, future research may explore further, innovative ways in which HE teaching can be reconstituted in the presence and context of technology without undermining teachers’ professional identity or compromising the revitalisation of teaching as an embodied, communal, and humanising experience as campuses around the world re-open, in full or in part, for in-person activities in post-pandemic times.

Appendix 1. A detailed version of inclusion/exclusion criteria

Appendix 2. Search terms in English and Chinese (note that the search strategy varied slightly across databases due to the different limits they set on the length of search input)

Appendix 3. PRISMA 2020 flow diagram for systematic review (Page et al., 2021 )

Appendix 4. Quality and relevance assessment rubric and the average scores of the 32 included studies (adapted from Oancea et al., 2021 )

• a Score description: 4—criterion fully met; 3—criterion mostly met, though with some weaknesses; 2—criterion only partly met, with several or serious weaknesses; 1—criterion largely not met

Appendix 5. Data extraction grid

Appendix 6. Summary of characteristics of 32 reviewed studies

• a The references of four articles show the publication year of 2021. These four articles were published online ahead of print in 2020 and hence are included in this study

Availability of data and materials

All data generated or analysed during this study are included in this published article.

Adedoyin, O. B., & Soykan, E. (2020). Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments . https://doi.org/10.1080/10494820.2020.1813180

Article   Google Scholar  

Akyürek, R. (2020). The views of lecturers about distance music education process in the pandemic period. International Journal of Education Technology and Scientific Researches, 5 (13), 1790–1833. https://doi.org/10.35826/ijetsar.262

Alqabbani, S., Almuwais, A., Benajiba, N., & Almoayad, F. (2020). Readiness towards emergency shifting to remote learning during COVID-19 pandemic among university instructors. E-Learning and Digital Media, 18 (5), 460–479. https://doi.org/10.1177/2042753020981651

Alsadoon, E., & Turkestani, M. (2020). Virtual classrooms for hearing-impaired students during the COVID-19 pandemic. Romanian Journal for Multidimensional Education, 12 (1, Sup. 2), 1–8. https://doi.org/10.18662/rrem/12.1sup2/240

Amunga, J. (2021). Leveraging technology to enhance STEM education amidst the Covid-19 pandemic: An overview of pertinent concerns. Technium Social Sciences Journal, 18 (1), 40–55. https://doi.org/10.47577/tssj.v18i1.3044

An, T., & Oliver, M. (2021). What in the world is educational technology? Rethinking the field from the perspective of the philosophy of technology. Learning, Media and Technology, 46 (1), 6–19. https://doi.org/10.1080/17439884.2020.1810066

Bailey, D. R., & Lee, A. R. (2020). Learning from experience in the midst of covid-19: Benefits, challenges, and strategies in online teaching. Computer-Assisted Language Learning Electronic Journal, 21 (2), 176–196.

Google Scholar  

Baker, S., Anderson, J., Burke, R., De Fazio, T., Due, C., Hartley, L., Molla, T., Morison, C., Mude, W., Naidoo, L., & Sidhu, R. (2022). Equitable teaching for cultural and linguistic diversity: Exploring the possibilities for engaged pedagogy in post-COVID-19 higher education. Educational Review . https://doi.org/10.1080/00131911.2021.2015293

Blumsztajn, A., Koopal, W., Rojahn, P., Schildermans, H., Thoilliez, B., Vlieghe, J., & Wortmann, K. (2022). Offline memos for online teaching: A collective response to The manifesto for teaching online (Bayne et al. 2020). Postdigital Science and Education . https://doi.org/10.1007/s42438-022-00286-4

Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2021). Emergency remote teaching in higher education: Mapping the first global online semester. International Journal of Educational Technology in Higher Education, 18 , 50. https://doi.org/10.1186/s41239-021-00282-x

Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27 , 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007

Callo, E. C., & Yazon, A. D. (2020). Exploring the factors influencing the readiness of faculty and students on online teaching and learning as an alternative delivery mode for the new normal. Universal Journal of Educational Research, 8 (8), 3509–3518. https://doi.org/10.13189/ujer.2020.080826

Chakraborty, P., Mittal, P., Gupta, M. S., Yadav, S., & Arora, A. (2021). Opinion of students on online education during the COVID-19 pandemic. Human Behavior and Emerging Technologies, 3 (3), 357–365. https://doi.org/10.1002/hbe2.240

Chan, R. Y., Bista, K., & Allen, R. M. (Eds.). (2022). Online teaching and learning in higher education during COVID-19: International perspectives and experiences . Routledge.

Costa, C., Hammond, M., & Younie, S. (2019). Theorising technology in education: An introduction. Technology, Pedagogy and Education, 28 (4), 395–399. https://doi.org/10.1080/1475939X.2019.1660089

Crawford, J., Butler-Henderson, K., Rudolph, J., Malkawi, B., Glowatz, M., Burton, R., Magni, P. A., & Lam, S. (2020). COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. Journal of Applied Learning & Teaching, 3 (1), 9–28. https://doi.org/10.37074/jalt.2020.3.1.7

Cullinan, J., Flannery, D., Harold, J., Lyons, S., & Palcic, D. (2021). The disconnected: COVID-19 and disparities in access to quality broadband for higher education students. International Journal of Educational Technology in Higher Education, 18 , 26. https://doi.org/10.1186/s41239-021-00262-1

Cutri, R. M., Mena, J., & Whiting, E. F. (2020). Faculty readiness for online crisis teaching: Transitioning to online teaching during the COVID-19 pandemic. European Journal of Teacher Education, 43 (4), 523–541. https://doi.org/10.1080/02619768.2020.1815702

Czerniewicz, L., Agherdien, N., Badenhorst, J., Belluigi, D., Chambers, T., Chili, M., de Villiers, M., Felix, A., Gachago, D., Gokhale, C., Ivala, E., Kramm, N., Madiba, M., Mistri, G., Mgqwashu, E., Pallitt, N., Prinsloo, P., Solomon, K., Strydom, S., … Wissing, G. (2020). A wake-up call: Equity, inequality and Covid-19 emergency remote teaching and learning. Postdigital Science and Education, 2 (3), 946–967. https://doi.org/10.1007/s42438-020-00187-4

Dampson, D. G., Addai-Mununkum, R., Apau, S. K., & Bentil, J. (2020). COVID-19 and online learning: A SWOT analysis of users’ perspectives on learning management system of University of Education, Winneba, Ghana. International Journal of Learning, Teaching and Educational Research, 19 (9), 382–401. https://doi.org/10.26803/ijlter.19.9.20

Dedeilia, A., Sotiropoulos, M. G., Hanrahan, J. G., Janga, D., Dedeilias, P., & Sideris, M. (2020). Medical and surgical education challenges and innovations in the COVID-19 era: A systematic review. In Vivo, 34 (3 suppl), 1603–1611. https://doi.org/10.21873/invivo.11950

Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49 (1), 5–22. https://doi.org/10.1177/0047239520934018

Diningrat, S., Nindya, M., & Salwa, S. (2020). Emergency online teaching: Early childhood education lecturers’ perception of barrier and pedagogical competency. Jurnal Cakrawala Pendidikan, 39 (3), 705–719. https://doi.org/10.21831/cp.v39i3.32304

Eringfeld, S. (2021). Higher education and its post-coronial future: Utopian hopes and dystopian fears at Cambridge University during Covid-19. Studies in Higher Education, 46 (1), 146–157. https://doi.org/10.1080/03075079.2020.1859681

Ferdig, R. E., Baumgartner, E., Hartshorne, R., Kaplan-Rakowski, R., & Mouza, C. (Eds.). (2020). Teaching, technology, and teacher education during the COVID-19 pandemic: Stories from the field . Association for the Advancement of Computing in Education.

Gao, L. X., & Zhang, L. J. (2020). Teacher learning in difficult times: Examining foreign language teachers’ cognitions about online teaching to tide over COVID-19. Frontiers in Psychology, 11 , 549653. https://doi.org/10.3389/fpsyg.2020.549653

Gaur, U., Majumder, M. A. A., Sa, B., Sarkar, S., Williams, A., & Singh, K. (2020). Challenges and opportunities of preclinical medical education: COVID-19 crisis and beyond. SN Comprehensive Clinical Medicine, 2 (11), 1992–1997. https://doi.org/10.1007/s42399-020-00528-1

Ghounane, N. (2020). Moodle or social networks: What alternative refuge is appropriate to Algerian EFL students to learn during COVID-19 pandemic. Arab World English Journal, 11 (3), 21–41. https://doi.org/10.24093/awej/vol11no3.2

Glantz, E., Gamrat, C., Lenze, L., & Bardzell, J. (2021). Improved student engagement in higher education’s next normal. EDUCAUSE Review . https://er.educause.edu/articles/2021/3/improved-student-engagement-in-higher-educations-next-normal

Gordon, M., Patricio, M., Horne, L., Muston, A., Alston, S. R., Pammi, M., Thammasitboon, S., Park, S., Pawlikowska, T., Rees, E. L., Doyle, A. J., & Daniel, M. (2020). Developments in medical education in response to the COVID-19 pandemic: A rapid BEME systematic review: BEME Guide No. 63. Medical Teacher, 42 (11), 1202–1215. https://doi.org/10.1080/0142159X.2020.1807484

Gough, D., Oliver, S., & Thomas, J. (2017). Introducing systematic reviews. In D. Gough, S. Oliver, & J. Thomas (Eds.), An introduction to systematic reviews (2nd ed., pp. 1–18). Sage.

Gourlay, L., Littlejohn, A., Oliver, M., & Potter, J. (2021). Lockdown literacies and semiotic assemblages: Academic boundary work in the Covid-19 crisis. Learning, Media and Technology, 46 (4), 377–389. https://doi.org/10.1080/17439884.2021.1900242

Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50 (5), 2572–2593. https://doi.org/10.1111/bjet.12864

Gyampoh, A. O., Ayitey, H. K., Fosu-Ayarkwah, C., Ntow, S. A., Akossah, J., Gavor, M., & Vlachopoulos, D. (2020). Tutor perception on personal and institutional preparedness for online teaching-learning during the COVID-19 crisis: The case of Ghanaian Colleges of Education. African Educational Research Journal, 8 (3), 511–518. https://doi.org/10.30918/AERJ.83.20.088

Hadar, L. L., Alpert, B., & Ariav, T. (2021). The response of clinical practice curriculum in teacher education to the Covid-19 breakout: A case study from Israel. Prospects, 51 , 449–462. https://doi.org/10.1007/s11125-020-09516-8

Hickling, S., Bhatti, A., Arena, G., Kite, J., Denny, J., Spencer, N. L., & Bowles, D. C. (2021). Adapting to teaching during a pandemic: Pedagogical adjustments for the next semester of teaching during COVID-19 and future online learning. Pedagogy in Health Promotion, 7 (2), 95–102. https://doi.org/10.1177/2373379920987264

Higher Education Statistical Agency. (n.d.). The higher education classification of subjects (HECoS) . Retrieved from https://www.hesa.ac.uk/support/documentation/hecos

Hodges, C. B., Moore, S., Lockee, B. B., Trust, T., & Bond, M. A. (2020). The difference between emergency remote teaching and online learning. EDUCAUSE Review . https://er.educause.edu/articles/2020/3/the-difference-between-emergency-remote-teaching-and-online-learning

Jandrić, P., Hayes, D., Truelove, I., Levinson, P., Mayo, P., Ryberg, T., Monzó, L. D., Allen, Q., Stewart, P. A., Carr, P. R., Jackson, L., Bridges, B., Escaño, C., Grauslund, D., Mañero, J., Lukoko, H. O., Bryant, P., Fuentes-Martinez, A., Gibbons, A., & Hayes, S. (2020). Teaching in the age of Covid-19—1 year later. Postdigital Science and Education, 3 (3), 1073–1223. https://doi.org/10.1007/s42438-021-00243-7

Joseph, D., & Trinick, R. (2021). ‘Staying apart yet keeping together’: Challenges and opportunities of teaching during COVID-19 across the Tasman. New Zealand Journal of Educational Studies, 56 (2), 209–226. https://doi.org/10.1007/s40841-021-00211-6

Joshi, A., Vinay, M., & Bhaskar, P. (2020). Impact of coronavirus pandemic on the Indian education sector: Perspectives of teachers on online teaching and assessments. Interactive Technology and Smart Education, 18 (2), 205–226. https://doi.org/10.1108/ITSE-06-2020-0087

Khan, R., Bashir, A., Basu, B. L., & Uddin, M. E. (2020). Emergency online instruction at higher education in Bangladesh during COVID-19: Challenges and suggestions. The Journal of Asia TEFL, 17 (4), 1497–1506. https://doi.org/10.18823/asiatefl.2020.17.4.26.1497

Khoza, S. B., & Mpungose, C. B. (2020). Digitalised curriculum to the rescue of a higher education institution. African Identities . https://doi.org/10.1080/14725843.2020.1815517

Kidd, W., & Murray, J. (2020). The Covid-19 pandemic and its effects on teacher education in England: How teacher educators moved practicum learning online. European Journal of Teacher Education, 43 (4), 542–558. https://doi.org/10.1080/02619768.2020.1820480

Lee, J., & Jung, I. (2021). Instructional changes instigated by university faculty during the COVID-19 pandemic: The effect of individual, course and institutional factors. International Journal of Educational Technology in Higher Education, 18 , 52. https://doi.org/10.1186/s41239-021-00286-7

Littlejohn, A., Gourlay, L., Kennedy, E., Logan, K., Neumann, T., Oliver, M., Potter, J., & Rode, J. A. (2021). Moving teaching online: Cultural barriers experienced by university teachers during Covid-19. Journal of Interactive Media in Education, 1 (7), 1–15. https://doi.org/10.5334/jime.631

Liu, Q., Geertshuis, S., & Grainger, R. (2020). Understanding academics’ adoption of learning technologies: A systematic review. Computers & Education, 151 , 103857. https://doi.org/10.1016/j.compedu.2020.103857

Lu, X. C. (2020). Jiyu shiping huiyi pingtai de yuancheng tongbu kouyi jiaoxue: Yi beiwai gaofan tongsheng chuanyi kecheng wei li [Distance teaching of interpreting: Delivering simultaneous interpreting courses via video conferencing at Graduate School of Translation and Interpretation, Beijing Foreign Studies University]. Zhongguo Fanyi, 42 (4), 76–84.

Marshalsey, L., & Sclater, M. (2020). Together but apart: Creating and supporting online learning communities in an era of distributed studio education. International Journal of Art & Design, 39 (4), 826–840. https://doi.org/10.1111/jade.12331

Matthewman, S., & Uekusa, S. (2021). Theorizing disaster communitas. Theory and Society, 50 (6), 965–984. https://doi.org/10.1007/s11186-021-09442-4

Mercader, C., & Gairín, J. (2020). University teachers’ perception of barriers to the use of digital technologies: The importance of the academic discipline. International Journal of Educational Technology in Higher Education, 17 , 4. https://doi.org/10.1186/s41239-020-0182-x

Mideros, D. (2020). Out-of-class learning of Spanish during COVID-19: A case study in Trinidad and Tobago. Studies in Self-Access Learning Journal, 11 (3), 119–219. https://doi.org/10.37237/110308

Mittal, A., Mantri, A., Tandon, U., & Dwivedi, Y. K. (2021). A unified perspective on the adoption of online teaching in higher education during the COVID-19 pandemic. Information Discovery and Delivery . https://doi.org/10.1108/IDD-09-2020-0114

Mok, K. H., Xiong, W., & Bin Aedy Rahman, H. N. (2021). COVID-19 pandemic’s disruption on university teaching and learning and competence cultivation: Student evaluation of online learning experiences in Hong Kong. International Journal of Chinese Education . https://doi.org/10.1177/22125868211007011

Mouchantaf, M. (2020). The COVID-19 pandemic: Challenges faced and lessons learned regarding distance learning in Lebanese higher education institutions. Theory and Practice in Language Studies, 10 (10), 1259–1266. https://doi.org/10.17507/tpls.1010.11

Murray, C., Heinz, M., Munday, I., Keane, E., Flynn, N., Connolly, C., Hall, T., & MacRuairc, G. (2020). Reconceptualising relatedness in education in ‘distanced’ times. European Journal of Teacher Education, 43 (4), 488–502. https://doi.org/10.1080/02619768.2020.1806820

Na, S., & Jung, H. (2021). Exploring university instructors’ challenges in online teaching and design opportunities during the COVID-19 pandemic: A systematic review. International Journal of Learning, Teaching and Educational Research, 20 (9), 308–327. https://doi.org/10.26803/ijlter.20.9.18

Oancea, A., McDermott, T., Robson, J., Scutt, C., Xu, X., Mun, O., Nuseibeh, N. & Voss, M. (2021). The landscape of educational research in the UK. Report to the Royal Society and British Academy joint enquiry on educational research. London: Royal Society and British Academy.

Padilla Rodríguez, B. C., Armellini, A., & Traxler, J. (2021). The forgotten ones: How rural teachers in Mexico are facing the COVID-19 pandemic. Online Learning, 25 (1), 253–268. https://doi.org/10.24059/olj.v25i1.2453

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372 (71), n71. https://doi.org/10.1136/bmj.n71

Pokhrel, S., & Chhetri, R. (2021). A literature review on impact of COVID-19 pandemic on teaching and learning. Higher Education for the Future, 8 (1), 133–141. https://doi.org/10.1177/2347631120983481

Rajab, M. H., Gazal, A. M., & Alkattan, K. (2020). Challenges to online medical education during the COVID-19 pandemic. Cureus, 12 (7), e8966. https://doi.org/10.7759/cureus.8966

Ramlo, S. (2021). The coronavirus and higher education: Faculty viewpoints about universities moving online during a worldwide pandemic. Innovative Higher Education, 46 (3), 241–259. https://doi.org/10.1007/s10755-020-09532-8

Ren, W. (2020). Yiqing he hou yiqing shidai de kouyi jiaoxue: Jiyu jiaoshi shijiao de anli fenxi yu fansi [Interpretation studies in the pandemic and post-pandemic times: A case analysis and reflections based on teachers’ perspectives]. Zhongguo Fanyi, 42 (6), 69–74.

Resch, K., Alnahdi, G., & Schwab, S. (2022). Exploring the effects of the COVID-19 emergency remote education on students’ social and academic integration in higher education in Austria. Higher Education Research & Development . https://doi.org/10.1080/07294360.2022.2040446

Said, F., Ali, I., & Javed, T. (2021). An interpretative phenomenological analysis of challenges faced by the university teachers in Pakistan amid Covid-19. International Journal of Educational Research and Innovation, 15 , 260–272. https://doi.org/10.46661/ijeri.5256

Salas‐Pilco, S. Z., Yang, Y., Zhang, Z. (2022). Student engagement in online learning in Latin American higher education during the COVID‐19 pandemic: A systematic review. British Journal of Educational Technology, 53 (3), 593–619.

Sales, D., Cuevas-Cerveró, A., & Gómez-Hernández, J. A. (2020). Perspectives on the information and digital competence of social sciences students and faculty before and during lockdown due to Covid-19. Profesional De La Información, 29 (4), e290423. https://doi.org/10.3145/epi.2020.jul.23

Scherer, R., Howard, S. K., Tondeur, J., & Siddiq, F. (2021). Profiling teachers’ readiness for online teaching and learning in higher education: Who’s ready? Computers in Human Behavior, 118 , 106675. https://doi.org/10.1016/j.chb.2020.106675

Selwyn, N. (2022). Education and technology: Key issues and debates . Bloomsbury Publishing.

Book   Google Scholar  

Selwyn, N., Hillman, T., Eynon, R., Ferreira, G., Knox, J., Macgilchrist, F., & Sancho-Gil, J. M. (2020). What’s next for Ed-Tech? Critical hopes and concerns for the 2020s. Learning, Media and Technology, 45 (1), 1–6. https://doi.org/10.1080/17439884.2020.1694945

Singh, V., & Thurman, A. (2019). How many ways can we define online learning? A systematic literature review of definitions of online learning (1988–2018). American Journal of Distance Education, 33 (4), 289–306. https://doi.org/10.1080/08923647.2019.1663082

Singh-Pillay, A., & Naidoo, J. (2020). Context matters: Science, technology and mathematics education lecturers’ reflections on online teaching and learning during the COVID-19 pandemic. Journal of Baltic Science Education, 19 (6A), 1125–1136. https://doi.org/10.33225/jbse/20.19.1125

Sobaih, A. E. E., Hasanein, A. M., & Abu Elnasr, A. E. (2020). Responses to COVID-19 in higher education: Social media usage for sustaining formal academic communication in developing countries. Sustainability, 12 (16), 6520. https://doi.org/10.3390/su12166520

Stewart, W. H. (2021). A global crash-course in teaching and learning online: A thematic review of empirical emergency remote teaching (ERT) studies in higher education during year 1 of COVID-19. Open Praxis, 13 (1), 89–102.

Sum, M., & Oancea, A. (2021). Higher education teachers’ perspectives on technology use in emergency remote teaching during the global pandemic: A systematic literature review. In Proceedings of the Yidan Prize Doctoral Conference (pp. 103-124). Department of Education, University of Oxford. https://yidanprize.org/files/Proceedings-of-the-2021-Yidan-Prize-Doctoral-Conference.pdf .

Talib, M. A., Bettayeb, A. M., & Omer, R. I. (2021). Analytical study on the impact of technology in higher education during the age of COVID-19: Systematic literature review. Education and Information Technologies, 26 , 6719–6746. https://doi.org/10.1007/s10639-021-10507-1

Tang, C., Zhou, X. Y., & Qian, X. H. (2020). Yiqing fangkong qijian difang gaoxiao zaixian jiaoxue de shijian yu sikao – yi Chengdu shifan xueyuan ‘1-3-5-4’ fang’an weili [The path and practice of promoting online teaching in local undergraduate universities during the epidemic prevention and control period—Taking Chengdu Normal University ‘1-3-5-4’ scheme as an example]. Xiandai Jiaoyu Jishu, 27 (8), 120–126.

Tanga, P., Ndhlovu, G. N., & Tanga, M. (2020). Emergency remote teaching and learning during Covid-19: A recipe for disaster for social work education in the Eastern Cape of South Africa? African Journal of Social Work, 10 (3), 17–24.

Tartavulea, C. V., Albu, C. N., Albu, N., Dieaconescu, R. I., & Petre, S. (2020). Online teaching practices and the effectiveness of the educational process in the wake of the COVID-19 pandemic. Amfiteatru Economic, 22 (55), 920–936. https://doi.org/10.24818/EA/2020/55/920

Tejedor, S., Cervi, L., Tusa, F., & Parola, A. (2020). Education in times of pandemic: Reflections of students and teachers on virtual university education in Spain, Italy, and Ecuador. Latin Journal of Social Communication, 78 , 19–40. https://doi.org/10.4185/RLCS-2020-1466

UNESCO Institute for Statistics. (2012). International standard classification of education: ISCE, 2011. UNESCO Institute for Statistics. Retrieved from http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf

Watermeyer, R., Crick, T., Knight, C., & Goodall, J. (2021). COVID-19 and digital disruption in UK universities: Afflictions and affordances of emergency online migration. Higher Education, 81 (3), 623–641. https://doi.org/10.1007/s10734-020-00561-y

Williamson, B., Eynon, R., & Potter, J. (2020). Pandemic politics, pedagogies and practices: Digital technologies and distance education during the coronavirus emergency. Learning, Media and Technology, 45 (2), 107–114. https://doi.org/10.1080/17439884.2020.1761641

World Bank. (June 2020). List of economies (with classification of country development) [Database]. https://databank.worldbank.org/data/download/site-content/CLASS.xls

Wu, W., Yao, R., & Xie, Z. (2020). Gaoxiao jiaoshi zaixian jiaoxue jingli dui ziwo jiaoxue pingjia de yingxiang - jiyu quanguo 334 suo gaoxiao zaixian jiaoxue de diaocha fenxi [The influence of university teachers’ online teaching experience on their self-evaluation of teaching: A survey in 334 universities]. Gaodeng Jiaoyu Yanjiu, 41 (8), 63–72.

Yeung, M. W., & Yau, A. H. (2021). A thematic analysis of higher education students’ perceptions of online learning in Hong Kong under COVID-19: Challenges, strategies and support. Education and Information Technologies . https://doi.org/10.1007/s10639-021-10656-3

Zeng, L. W. (2020). Yiqing fangkong beijing xia xianshang jiaoxue shishi xiaoguo ji yingxiang yinsu fenxi – jiyu Guangdong sheng gaoxiao de diaocha [Analysis of the effectiveness and influencing factors of online teaching in the context of pandemic prevention and control—A survey based on universities in Guangdong province]. Gaoxiao Tansuo, 27 (7), 85–91.

Download references

Acknowledgements

The corresponding author gave a presentation on the preliminary findings of this systematic review at the 1st International Yidan Prize Doctoral Conference (online) organized by the University of Oxford on 27 May 2021. The insightful questions raised by the audience are gratefully acknowledged. We would like to thank Dr. Victoria Elliott, Ms. Renyu Jiang, Ms. Abbey Palmer, and Ms. Catherine Scutt who have directly and indirectly provided their support for this research project.

Additional information

The corresponding author is a doctoral candidate reading Education. This paper is an original work, conducted by the corresponding author in parallel to the preparation for submission of a thesis for a Doctor of Philosophy (DPhil) degree under the supervision of the second author. Preliminary findings of this systematic review have been published in the Proceedings of the Yidan Prize Doctoral Conference under the terms of a Creative Commons Attribution License (CC-BY) (see Sum & Oancea, 2021 ).

This work was generously supported by a scholarship jointly awarded by the Clarendon Fund and New College of the University of Oxford (2020–2023).

Author information

Authors and affiliations.

Department of Education, University of Oxford, 15 Norham Gardens, Oxford, OX2 6PY, UK

McQueen Sum & Alis Oancea

You can also search for this author in PubMed   Google Scholar

Contributions

Under the guidance and supervision of AO, MS performed all stages of the systematic review, from conceptualising the review project to writing the manuscript. Both authors worked collaboratively from late 2020 to mid 2022 on this project. MS and AO independently coded and analysed a selection of data excerpts at various stages to check for inter-rater reliability as mentioned in ‘ Methodology ’ section. The rubric for quality assessment was based on past work by AO. Communications between the authors were maintained throughout the research process. MS worked on drafting this paper, which was subsequently revised by the AO. Both authors read and approved the final manuscript.

Corresponding author

Correspondence to McQueen Sum .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Publisher's Note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/ .

Reprints and permissions

About this article

Cite this article.

Sum, M., Oancea, A. The use of technology in higher education teaching by academics during the COVID-19 emergency remote teaching period: a systematic review. Int J Educ Technol High Educ 19 , 59 (2022). https://doi.org/10.1186/s41239-022-00364-4

Download citation

Received : 27 June 2022

Accepted : 04 September 2022

Published : 14 December 2022

DOI : https://doi.org/10.1186/s41239-022-00364-4

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

• Systematic review
• Higher education
• Technology use
• Emergency remote teaching

Integration of Technology in Education and its Impact on Learning and Teaching

• Asian Journal of Education and Social Studies 47(2):54-63
• 47(2):54-63

• National Electronic Media Institute of South Africa

• University of KwaZulu-Natal

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

• Adrian Mwesigye
• Basil Tibanyendera
• Rahmi Syafriyeti
• Ribut Wahyu Eriyanti
• Ainur Rofiq
• Atok Miftahul Huda
• Naila Luna Az Zahra
• Sunarti Sunarti
• Yeni Rahmawati

• Sumita Saha

• Akademi Pengajian Bahasa

• John Loui Dizon
• Anabella Lao
• Delverden Bandoy

• Fatma Sukmawati

• George Bulman
• Robert W. Fairlie
• T. van Weert

• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

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: 09 September 2024

Navigating post-pandemic challenges through institutional research networks and talent management

• Muhammad Zada   ORCID: orcid.org/0000-0003-0466-4229 1 , 2 ,
• Imran Saeed 3 ,
• Jawad Khan   ORCID: orcid.org/0000-0002-6673-7617 4 &
• Shagufta Zada 5 , 6  

Humanities and Social Sciences Communications volume  11 , Article number:  1164 ( 2024 ) Cite this article

Metrics details

• Business and management

Institutions actively seek global talent to foster innovation in the contemporary landscape of scientific research, education, and technological progress. The COVID-19 pandemic underscored the importance of international collaboration as researchers and academicians faced limitations in accessing labs and conducting research experiments. This study uses a research collaboration system to examine the relationship between organizational intellectual capital (Human and structural Capital) and team scientific and technological performance. Further, this study underscores the moderating role of top management support. Using a time-lagged study design, data were collected from 363 participants in academic and research institutions. The results show a positive relationship between organizational intellectual capital (Human and structural Capital) and team scientific and technological performance using a research collaboration system. Moreover, top management support positively moderates the study’s hypothesized relationships. The study’s findings contribute significantly to existing knowledge in this field, with implications for academia, researchers, and government focused on technology transmission, talent management, research creative collaboration, supporting innovation, scientific research, technological progress, and preparing for future challenges.

Similar content being viewed by others

Towards understanding the characteristics of successful and unsuccessful collaborations: a case-based team science study

Remote collaboration fuses fewer breakthrough ideas

Interpersonal relationships drive successful team science: an exemplary case-based study

Introduction.

Global talent management and the talent hunt within research and educational institutions have become extensively discussed topics in international human resource management (HRM) (Al et al., 2022 ). Global talent management is intricately connected to the notion of finding, managing, and facilitating the fetch of research, skills, techniques, and knowledge among team members and progress in education and technology (Kwok, 2022 ; Sommer et al., 2017 ). This topic assumes a greater position when it is looked at through the lens of research, academicians, and educational institutions serving as a means of achieving scientific and technological advancement and performance (Kaliannan et al., 2023 ; Patnaik et al., 2022 ). Effective knowledge management and transfer occur between teams engaged in cross-border research collaborations (Davenport et al., 2002 ; Fasi, 2022 ). Effective team management, global talent recruitment, and the exchange of scientific knowledge across national boundaries face different challenges due to the swift growth of economic and political fanaticism. This is particularly evident in advanced economies that rely heavily on knowledge-based industries (Vaiman et al., 2018 ). Research and educational sectors are encountering significant challenges in effectively hunting and managing international talent, particularly in the aftermath of the COVID-19 pandemic, during which approximately half of the global workforce faced the possibility of job loss (Almeida et al., 2020 ; Radhamani et al., 2021 ). Due to the implementation of lockdown measures by governments, many research intuitions are facing significant issues, and the pandemic has changed the situation; work was stuck, and scientists around the globe are thinking to be prepared for this kind of situation, which is possible through the use scientific research collaboration platforms. These platforms serve as a means to exchange research and knowledge, which is crucial in the talent hunt and management (Haak-Saheem, 2020 ). In the situation above, wherein limitations exist regarding the exchange of research and knowledge within the institutions, it becomes imperative for the top management of institutions to incentivize employees to engage the team in knowledge sharing actively and achieve team-level scientific and technological advancement. It can be achieved by implementing a research collaboration system that facilitates knowledge exchange and contributes to effective talent hunt and management (Haider et al., 2022 ; Xu et al., 2024 ).

A research collaboration network is a tool for scientific and technological advancement and talent management encompassing various processes and practices to facilitate the sharing, integration, translation, and transformation of scientific knowledge (Biondi & Russo, 2022 ). During and after the COVID-19 era characterized by travel restrictions, research networking platforms serve as valuable tools for students and researchers located in variance regions to engage in the exchange of research knowledge and achieve team-level scientific and technological advancement (Yang et al., 2024 ). Enhancing intellectual capital (IC) within the organizations is imperative within this framework (Pellegrini et al., 2022 ; Vătămănescu et al., 2023 ). Intellectual capital (IC) is the intangible assets owned by an organization that has the potential to generate value (Stewart, 1991 ). An organization’s intellectual capital (IC) includes human and structural capital (Marinelli et al., 2022 ). According to Vătămănescu et al. ( 2023 ), the organization can effectively manage the skills and abilities of its team members across different countries by properly utilizing both human and structural capital and establishing a strong research collaboration system with the help of top management support. This capability remains intact even during and after the COVID-19 pandemic. This study emphasizes the importance of talent hunt and management within research and educational institutions in the post-COVID-19 pandemic because of every country’s following implementation of lockdown measures. Our study focuses on the implication of facilitating the exchange of research, knowledge, and techniques among team members during and after this period. The effective way to share research expertise and techniques in such a scenario is through a research collaboration network (O’Dwyer et al., 2023 ).

While previous research has extensively explored talent management in various industries (Al Ariss, Cascio, & Paauwe, 2014 ; Susanto, Sawitri, Ali, & Rony, 2023 ), a noticeable gap exists in the body of knowledge regarding the discussion of global talent acquisition and management within research and academic institutions, particularly within volatile environments and about scientific and technological advancements (Harsch & Festing, 2020 ). The objective of this research is to fill this research gap.1) To investigate the strategies of how research and educational institutions hunt and manage gobble talent. 2)To analyze the impact of human and structural capital and team scientific and technological performance using a research collaboration system. 3) To examine the moderating effect of top management support on the IC to use the research collation network among institution research teams and scientific and technological performance.

In addition, current research contributes significantly to the literature by elucidating the pivotal role of organizational intellectual capital in strengthening scientific and technological performance through research collaborative networks. This study advances our grip on how internal resources drive innovation and research outcomes by empirically demonstrating the positive association between human and structural capital and team-level scientific and technological performance. Furthermore, the current study highlights the moderating effect of top management support, suggesting that management commitment can amplify the benefits of intellectual capital (human and structural capital). These results show a subtle perspective on how organizations can influence their intellectual assets to foster higher levels of productivity and innovation. The study’s theoretical contributions lie in integrating resource-based views and organizational theory with performance metrics, while its practical implications provide actionable insights for institutions aiming to optimize their intellectual resources and management practices. This research also sets the stage for future inquiries into the dynamics of intellectual capital and management support in various collaborative contexts.

Research theories, literature review, and hypotheses development

Research theories.

The focus of the current study pertains to the challenges surrounding talent management within institutions during and after the COVID-19 pandemic(Fernandes et al., 2023 ). Global talent management is intently linked to the objective of enhancing the intellectual capital of the organization (Zada et al., 2023 ). Considering the COVID-19 pandemic, which raised much more attention toward scientific and technological advancement, the academic sector has noticed an observable shift towards utilizing research collaboration platforms to share scientific knowledge effectively and achieve scientific and technological performance. Intellectual capital encompasses five distinct resource categories, as identified by Roos and Roos ( 1997 ), comprising three immaterial and two touchable resources. Intangible resources such as human capital, structural capital, and customer capital are complemented by tangible resources, encompassing monetary and physical assets. Global talent management encompasses human and structural capital management (Felin & Hesterly, 2007 ). The enhancement of talent management capabilities within the institution can be achieved by cultivating institution-specific competencies in both human and structural capital (Al Ariss et al., 2014 ). This concept lines up with the theoretical background of the resource-based view (RBV) theory presented by Barney ( 1991 ). According to this theory, organizations should prioritize examining their core resources to recognize valuable assets, competencies, and capabilities that can contribute to attaining a sustainable competitive advantage (Barney, 1991 ).

During and after the COVID-19 scenario, virtual platforms are utilized by institutions to engage students and staff abroad in research and knowledge exchange, which is part of global talent management. Staff possessing adequate knowledge repositories will likely participate in knowledge exchange activities. Therefore, organizations must improve their internal resources to enhance talent management, as per the fundamental principle of the RBV theory (Barney, 1991 ). Enhancing internal resources entails strengthening an organization’s human capital, which refers to its staff’s scientific research and technical skills and knowledge and structural capital. Strengthening these two resources can facilitate the institution in effectively sharing knowledge through a research collaboration platform, consequently enhancing their global talent management endeavors and contributing to the team’s scientific and technological performance.

In this research, we also utilize institutional theory (Oliver, 1997 ) and Scott ( 2008 ) as a framework to examine the utilization of research collaboration social platforms by faculty of institutions. Our focus is on exchanging research and technical knowledge within the climate of global talent management during and after the COVID-19 epidemic. According to Scott ( 2008 ), “Institutional theory is a widely recognized theoretical framework emphasizing rational myths, isomorphism, and legitimacy (p. 78)”. For electronic data interchange, the theory has been utilized in technology adoption research (Damsgaard, Lyytinen ( 2001 )) and educational institutes (J. et al., 2007 ). In the pandemic situation, institutional theory provides researchers with a framework to analyze the motivations of employees within institutions to engage in teams to achieve team-level scientific and technological performance through a research collaboration system. According to institutional theory, organizations should utilize a research collaboration network to ensure that their staff do not need to compromise their established norms, values, and expectations. During the COVID-19 pandemic, numerous countries implemented limitations on international movement as a preventive measure. Consequently, there has been a growing identification of the potential importance of utilizing an institutional research collaboration platform for facilitating the online exchange of knowledge, skills, research techniques, and global talent management among employees of institutions operating across various countries. The active support of staff by the top management of an institution can play a key role in expediting the implementation of social networks for research collaboration within the institution (Zada et al., 2023 ).

Literature review

An institution’s scientific and technological advancement is contingent upon optimal resource utilization (Muñoz et al., 2022 ). Global talent hunt and management encompasses utilizing information and communication technologies (ICT) to provide a way for the exchange of research knowledge and techniques, thereby enabling the implementation of knowledge-based strategies (Muñoz et al., 2022 ). In a high research-level turbulent environment, it becomes imperative to effectively manage human capital (HUC) to facilitate the appropriate exchange of research knowledge and techniques (Salamzadeh, Tajpour, Hosseini, & Brahmi, 2023 ). Research shows that transferring research knowledge and techniques across national boundaries, exchanging best practices, and cultivating faculty skills are crucial factors in maintaining competitiveness (Farahian, Parhamnia, & Maleki, 2022 ; Shao & Ariss, 2020 ).

It is widely acknowledged in scholarly literature that there is a prevailing belief among individuals that talent possesses movability and that research knowledge and techniques can be readily transferred (Bakhsh et al., 2022 ; Council, 2012 ). However, it is essential to note that the matter is more complex than it may initially appear (Biondi & Russo, 2022 ). The proliferation of political and economic nationalism in developed knowledge-based economies poses a significant risk to exchanging research knowledge and techniques among faculty members in research and educational institutions worldwide (Arocena & Sutz, 2021 ). During and after COVID-19, knowledge transfer can be effectively facilitated by utilizing a research collaboration network platform (Duan & Li, 2023 ; Sulaiman et al., 2022 ). This circumstance is noticeable within the domain of international research and development, wherein academic professionals have the opportunity to utilize research collaboration platforms as a means of disseminating valuable research knowledge and techniques to their counterparts in various nations (Jain et al., 2022 ).

The scientific and technological advancement of institutions linked by intuition research and development level and research and development depend on the intuition’s quality of research, knowledge, and management (Anshari & Hamdan, 2022 ). However, there is a need to enhance the research team’s capacity to learn and transfer research knowledge and techniques effectively. Research suggests that institutional human capital (HUC) is critical in managing existing resources and hunting international talent, particularly after the COVID-19 pandemic (Sigala, Ren, Li, & Dioko, 2023 ). Human capital refers to the combined implicit and crystal clear knowledge of employees within an institution and their techniques and capabilities to effectively apply this knowledge to achieve scientific and technological advancements (Al-Tit et al., 2022 ). According to Baron and Armstrong ( 2007 ) Human capital refers to the abilities, knowledge, techniques, skills, and expertise of individuals, particularly research team members, that are relevant to the current task.

Furthermore, HUC encompasses the scope of individuals who can contribute to this reservoir of research knowledge, techniques, and expertise through individual learning. As the literature shows, the concept of IC encompasses the inclusion of structural capital (STC), which requires fortification through the implementation of a proper global talent acquisition and management system (Pak et al., 2023 ; Phan et al., 2020 ). STC encompasses various mechanisms to enhance an institution’s performance and productivity (Barpanda, 2021 ). STC is extensively acknowledged as an expedited framework for HUC, as discussed by Bontis ( 1998 ) and further explored by Gogan, Duran, and Draghici ( 2015 ). During and after the COVID-19 epidemic, a practical approach to global talent management involves leveraging research collaboration network platforms to facilitate knowledge exchange among research teams (Arslan et al., 2021 ). However, the crucial involvement of top management support is imperative to effectively manage talent by utilizing research collaboration network platforms for knowledge transfer (Zada et al., 2023 ). Nevertheless, the existing body of knowledge needs to adequately explore the topic of talent management about knowledge transfer on research collaboration platforms, particularly in the context of institution-active management support (Tan & Md. Noor, 2013 ).

Conceptual model and research hypothesis

By analyzing pertinent literature and theoretical frameworks, we have identified the factors influencing staff intention in research and academic institutions to utilize research collaboration networks after the COVID-19 pandemic and achieve scientific and technical performance. This study aims to explain the determinants. Additionally, this study has considered the potential influence of top management support as a moderator on the associations between education and research institution staff intention on IC to utilize research collaboration platforms in the post-COVID-19 era and predictors. Through this discourse, we shall generate several hypotheses to serve as the basis for constructing a conceptual model (see Fig. 1 ).

Relationships between study variables: human capital, structural capital, top management support, and team scientific and technological performance. Source: authors’ development.

Human capital and team scientific and technological performance

According to Dess and Picken ( 2000 ), HUC encompasses individuals’ capabilities, knowledge, skills, research techniques, and experience, including staff and supervisors, relevant to the specific task. Human capital also refers to the ability to pay to this reservoir of knowledge, techniques, and expertize through individual learning (Dess & Picken, 2000 ). HUC refers to the combinations of characteristics staff possess, including but not limited to research proficiency, technical aptitude, business acumen, process comprehension, and other similar competencies (Kallmuenzer et al., 2021 ). The HUC is considered an institutional repository of knowledge, as Bontis and Fitz‐enz ( 2002 ) indicated, with its employees serving as representatives. The concept of HUC refers to the combined abilities, research proficiency, and competencies that individuals possess to address and resolve operational challenges within an institutional setting (Barpanda, 2021 ; Yang & Xiangming, 2024 ). The human capital possessed by institutions includes crucial attributes that allow organizations to acquire significant internal resources that are valuable, difficult to replicate, scarce, and cannot be substituted. It aligns with the theoretical framework of the RBV theory, as suggested by Barney ( 1991 ). IC is extensively recognized as a main factor in revitalizing organizational strategy and promoting creativity and innovation. It is crucial to enable organizations to acquire and effectively disseminate knowledge among their employees, contribute to talent management endeavors, and achieve scientific and technological performance (Alrowwad et al., 2020 ; He et al., 2023 ). Human capital is linked to intrinsic aptitude, cognitive capabilities, creative problem-solving, exceptional talent, and the capacity for originality (Bontis & Fitz‐enz, 2002 ). In talent management, there is a focus on enhancing scientific and technological performance and development. According to Shao and Ariss ( 2020 ), HUC is expected to strengthen employee motivation to utilize research collaboration networks for scientific knowledge-sharing endeavors. Based on these arguments, we proposed that.

Hypothesis 1 Human capital (HUC) positively impacts team scientific and technological performance using a research collaboration system.

Structural capital and team scientific and technological Performance

According to Mehralian, Nazari, and Ghasemzadeh ( 2018 ) structural capital (STC) encompasses an organization’s formalized knowledge assets. It consists of the structures and mechanisms employed by the institution to enhance its talent management endeavors. The concept of STC is integrated within the framework of institutions’ programs, laboratory settings, and databases (Cavicchi & Vagnoni, 2017 ). The significance of an organization’s structural capital as an internal tangible asset that bolsters its human capital has been recognized by scholars such as Secundo, Massaro, Dumay, and Bagnoli ( 2018 ), and This concept also lines up with the RBV theory (J. Barney, 1991 ). The strategic assets of an organization encompass its capabilities, organizational culture, patents, and trademarks (Gogan et al., 2015 ).

Furthermore, Birasnav, Mittal, and Dalpati ( 2019 ) Suggested that these strategic assets promote high-level organizational performance, commonly called STC. Literature shows that STC encompasses an organization’s collective expertise and essential knowledge that remains intact even when employees depart (Alrowwad et al., 2020 ; Mehralian et al., 2018 ; Sarwar & Mustafa, 2023 ). The institution’s socialization, training, and development process facilitates the transfer of scientific research knowledge, skills, and expertise to its team (Arocena & Sutz, 2021 ; Marchiori et al., 2022 ). The STC is broadly recognized as having important potential and is a highly productive resource for generating great value. STC motivates its team member to share expertise with their counterparts at subordinate organizations by utilizing an institution’s research collaboration network and achieving team-level scientific and technological performance. This method remains effective even in challenging environments where traditional means of data collection, face-to-face meetings, and travel are not feasible (Secundo et al., 2016 ). In light of the above literature and theory, we propose the following hypothesis.

Hypothesis 2: Structural capital (STC) positively impacts team scientific and technological performance using a research collaboration system.

Top management support as a moderator

If the relationship between two constructs is not constant, the existence of a third construct can potentially affect this relationship by enhancing or diminishing its strength. In certain cases, the impact of a third construct can adjust the trajectory of the relationship between two variables. The variable in question is commonly called the “moderating variable.” According to Zada et al. ( 2023 ), top management support to leaders efficiently encourages team members within institutions to share research scientific knowledge with their counterparts in different countries through international research collaboration systems. Similarly, another study shows that the active endorsement of the top management significantly affects the development of direct associations, thereby influencing the team and organization’s overall performance (Biondi & Russo, 2022 ; Phuong et al., 2024 ). Different studies have confirmed that top management support is crucial in fostering a conducive knowledge-sharing environment by offering necessary resources (Ali et al., 2021 ; Lee et al., 2016 ; Zada et al., 2023 ). During and after the COVID-19 epidemic, numerous nations implemented nonessential travel restrictions and lockdown measures. In the given context, utilizing a research collaboration system would effectively facilitate the exchange of research, skills, and knowledge among staff belonging to various subsidiaries of an institution (Rådberg & Löfsten, 2024 ; Rasheed et al., 2024 ). However, it is common for researchers to exhibit resistance to adopting a novel research technique, often citing various justifications for their reluctance. To address the initial hesitance of employees at subsidiary institutes towards utilizing research collaborative networking within the institute, top management must employ strategies that foster motivation, encouragement, and incentives. These measures help create an atmosphere where team members feel empowered to engage with the new system freely. Institutional theory asserts that top management support is crucial for aligning talent management with institutional norms. Human and structural capital, pivotal within the institutional framework, contributes to an institution’s capacity to attract and retain talent, enhancing legitimacy. Adaptation to scientific and technological advancements is imperative for international institutional competitiveness, as institutional theory dictates (Oliver, 1997 ). Grounded on the above discussion, we have hypothesized.

Hypothesis 3a : Top management support moderates the relationship between human capital (HUC) and team scientific and technological performance. Specifically, this relationship will be stronger for those with higher top management support and weaker for those with lower top management support.

Hypothesis 3b : Top management support moderates the relationship between structural capital (STC) and team scientific and technological performance through the use of research collaboration network platforms. Specifically, this relationship will be stronger for those with higher top management support and weaker for those with lower top management support.

Methods data and sample

Sample and procedures.

To test the proposed model, we collected data from respondents in China’s research and academic sector in three phases to mitigate standard method variance (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003 ). In the first phase (T1-phase), respondents rated human capital, structural capital, and demographic information. After one month, respondents rated the team’s scientific and technological performance in the second phase (T2-phase). Following another one-month interval, respondents were asked to rate top management support in the third phase (T3-phase). In the first phase, after contacting 450 respondents, we received 417 usable questionnaires (92.66%). In the second phase, we received 403 usable questionnaires. In the third phase, we received 363 usable questionnaires (90.07%), constituting our final sample for interpreting the results. The sample comprises 63.4% male and 36.6% female respondents. The age distribution of the final sample was as follows: 25–30 years old (6.6%), 31–35 years old (57%), 36–40 years old (19.8%), and above 40 years old (16.5%). Regarding respondents’ experience, 45.7% had 1–5 years, 39.4% had 6–10 years, 11.3% had 11–15 years, and 3.6% had over 16 years. According to the respondents’ levels of education, 4.1% had completed bachelor’s degrees, 11.6% had earned master’s degrees, 78.8% were doctorate (PhD) scholars, and 5.5% were postdoctoral and above.

Measurement

To measure the variables, the current study adopted a questionnaire from previous literature, and age, gender, education, and experience were used as control variables. A five-point Likert scale was used (1 = strongly disagree to 5 = strongly agree). Human capital (HUC) was measured through an eight-item scale adopted by Kim, Atwater, Patel, and Smither ( 2016 ). The sample item is “The extent to which human capital of research and development department is competitive regarding team performance”. The self-reported scale developed by Nezam, Ataffar, Isfahani, and Shahin ( 2013 ) was adopted to measure structural capital. The scale consists of seven items. The sample scale item is “My organization emphasizes IT investment.” In order to measure top management support, a six-item scale was developed by Singh, Gupta, Busso, and Kamboj ( 2021 ), was adopted, and sample item includes “Sufficient incentives were provided by top management (TM) for achieving scientific and technological performance.” Finlay, the self-reported scale developed by Gonzalez-Mulé, Courtright, DeGeest, Seong, and Hong ( 2016 ) was adopted to gauge team scientific and technological performance and scales items are four. The sample item is “This team achieves its goals.”

Assessment of measurement model

In the process of employing AMOS for analysis, the initial step encompasses an assessment of the model to determine the strength and validity of the study variables. The evaluation of variable reliability conventionally revolves around two key aspects, which are indicator scale reliability and internal reliability. More precisely, indicator reliability is deemed to be recognized when factor loadings exceed the threshold of 0.60. In parallel, internal consistency reliability is substantiated by the attainment of values exceeding 0.70 for both Cronbach’s alpha and composite reliability, aligning with well-established and recognized guidelines (Ringle et al., 2020 ).

To gauge the reliability of construct indicators, we utilized two key metrics which are composite reliability (CR) and average variance extracted (AVE). The CR values for all variables were notably high, exceeding 0.70 and falling within the range of 0.882 to 0.955. This signifies a robust level of reliability for the indicators within each construct. Furthermore, the AVE values, which indicate convergent validity, exceeded the minimum threshold of 0.50, with each construct value varying from 0.608 to 0.653, thus affirming the presence of adequate convergent validity.

In addition to assessing convergent validity, we also examined discriminant validity by scrutinizing the cross-loadings of indicators on the corresponding variables and the squared correlations between constructs and AVE values. Our findings indicated that all measures exhibited notably stronger loadings on their intended constructs, thereby underscoring the measurement model’s discriminant validity.

Discriminant validity was recognized by observing average variance extracted (AVE) values that exceeded the squared correlations between constructs, as indicated in Table 1 . In conjunction with the Composite Reliability (CR) and AVE values, an additional discriminant validity assessment was conducted through a Heterotrait-Monotrait Ratio (HTMT) analysis. This analysis entailed a comparison of inter-construct correlations against a predefined upper threshold of 0.85. The results demonstrated that all HTMT values remained significantly below this threshold, affirming satisfactory discriminant validity for each variable (Henseler et al., 2015 ). Every HTMT value recorded was situated beneath the specified threshold, thereby supplying supplementary confirmation regarding the constructs’ discriminant validity. In summary, the results of the outer model assessment indicate that the variables showcased commendable levels of reliability and validity, with the discriminant validity being suitably and convincingly established.

Moreover, correlation Table 2 shows that human capital is significantly and positively correlated with structural capital ( r  = 0.594**), TMS ( r  = 0.456 **), and STP ( r  = 0.517**). Structural capital is also significantly and positively correlated with TMS ( r  = 0.893**) and STP ( r  = 0.853**). Furthermore, TMS is significantly and positively correlated with STP (0.859**).

Confirmatory factor analysis (CFA)

A comprehensive confirmatory factor analysis was estimated by employing the software AMOS version 24 to validate the distinctiveness of the variables. CFA shows the fitness of the hypothesized four factors model, including human capital, structural capital, top management support, and team scientific and technological performance, as delineated in Table 3 ; the results show that the hypothesized four-factor model shows fit and excellent alternative models. Consequently, The study variables demonstrate validity and reliability, which makes the dimension model appropriate for conducting a structural path analysis, as advocated by Hair, Page, and Brunsveld ( 2019 ).

Hypotheses testing

This study used the bootstrapping approach, which involves 5,000 bootstrap samples to test the proposed study model and assess the significance and strength of the structural correlations. Using this approach, bias-corrected confidence intervals and p-values were generated in accordance with Streukens and Leroi-Werelds ( 2016 ) guidelines. First, we did an analysis that entailed checking the path coefficients and their connected significance. The findings, as shown in Table 4 , validate Hypothesis 1, revealing a positive correlation between HUC and STP ( β  = 0.476, p  < 0.001). Additionally, the finding validates Hypothesis 2, highlighting a positive association between structural capital and STP ( β  = 0.877, p  < 0.001). For the moderation analysis, we utilized confidence intervals that do not encompass zero, per the guidelines that Preacher and Hayes ( 2008 ) recommended.

In our analysis, we found support for Hypothesis 3a, which posited that top management support (TMS) moderates the relationship between human capital (HUC) and team scientific and technological performance (STP). The results in Table 4 showed that the moderating role, more precisely, the interaction between HUC and TMS, was substantial and positive ( β  = −0.131, p  = 0.001). These results suggest that TMS enhances the positive association between HUC and STP, as shown in Fig. 2 . Consequently, we draw the conclusion that our data substantiates hypothesis 3a. Furthermore, Hypothesis 3b posited that TMS moderates the relationship between STC and STP. The results indicate that TMS moderates the association between STC and STP ( β  = −0.141, p  = 0.001, as presented in Table 4 and Fig. 3 ).

The moderating effect of top management support (TMS) on the relationship between human capital (HUC) and team scientific and technological performance (STP). Source: authors’ development.

The moderating effect of top management support (TMS) on the relationship between structural capital (SUC) and team scientific and technological performance (STP). Source: authors’ development.

The current study highlights the importance of research and academic institutions effectively enhancing their scientific and technological capabilities to manage their global talent within an international research collaboration framework and meet future challenges. Additionally, it underscores the need for these institutions to facilitate scientific knowledge exchange among their employees and counterparts in different countries. The enhancement of talent management through the exchange of scientific research knowledge can be most effectively accomplished by utilizing a collaborative research system between educational and research institutions (Shofiyyah et al., 2023 ), particularly in the context of the COVID-19 landscape. This study has confirmed that enhancing the higher education and research institutions’ human capital (HUC) and structural capital (STC) could attract and maintain global talent management and lead to more effective scientific and technological progress. The findings indicate that the utilization of human capital (HUC) has a significant and positive effect on scientific and technological term performance (STP) (Hypothesis 1), which is consistent with previous research (Habert & Huc, 2010 ). This study has additionally demonstrated that the implementation of s tructural capital (STC) has a significant and positive effect on team scientific and technological performance (STP), as indicated by hypothesis 2, which is also supported by the previous studies finding in different ways (Sobaih et al., 2022 ). This study has also shown that top management support moderates the association between human capital (HUC) and team scientific and technological performance hypothesis 3a and the association between structural capital (STC) and team scientific and technological performance hypothesis 3b. These hypotheses have garnered support from previous studies’ findings in different domains (Chatterjee et al., 2022 ). The study’s empirical findings also confirm the substantial moderating influence exerted by top management support on the relationships between HUC and STP described in hypothesis 3a and STC and STP described in hypothesis 3b, as evidenced by the results presented in Table 4 . Additionally, graphical representations are conducted to investigate the impacts on hypotheses 3a and 3b resulting from the application of high-top management support (TMS) and weak TMS.

The effect of high-top management support (TMS) and weak TMS on Hypothesis 3a is depicted in Fig. 2 . The solid line illustrates the effects of robust TMS on Hypothesis 3a, while the dashed line shows the effects of weak TMS on Hypothesis 3a. The graphic description validates that, as human capital (HUC) increases, team scientific and technological performance (STP) is more pronounced when influenced by robust TMS than weak TMS. This is evidenced by the steeper slope of the solid line in comparison to the dashed line. This finding suggests that employees within the research and academic sectors are more likely to utilize research collaboration networks when influenced by HUC and receive strong support from the organization’s top management.

The graph in Fig. 3 shows the impact of solid top management support (TMS) and weak TMS on Hypothesis 3b. The dotted lines continuous on the graph correspond to the effects of robust TMS and weak TMS, respectively. Figure 3 illustrates that, with increasing top management support (TMS), scientific and technological performance (STP) increase is more significant for robust TMS than weak TMS. This is evident from the steeper slope of the continuous line compared to the slope of the dotted line. This finding suggests that employees within universities and institutes are more likely to engage in research collaboration systems when they receive strong support from top management despite enhanced structural support.

Theoretical contribution

The current study makes significant contributions to the existing body of knowledge by exploring the intricate dynamics between organizational intellectual capital and team performance within scientific and technological research, especially during the unprecedented times brought about by the COVID-19 pandemic. Through its detailed examination of human and structural capital, alongside the moderating impact of top management support, the study provides a multi-faceted understanding of how these factors interact to enhance team outcomes.

This research enriches the literature on intellectual capital by providing empirical evidence on the positive association between HUC and STC and team performance. HUC, which includes employees’ skills, knowledge, and expertise, is a critical driver of innovation and productivity (Lenihan et al., 2019 ). The study highlights how a team’s collective intelligence and capabilities can lead to superior scientific and technological outputs. This finding aligns with and extends previous research that underscores the importance of skilled HR in achieving organizational success (Luo et al., 2023 ; Salamzadeh et al., 2023 ). Structural capital, encompassing organizational processes, databases, and intellectual property, contributes significantly to team performance(Ling, 2013 ). The study illustrates how well-established structures and systems facilitate knowledge sharing, streamline research processes, and ultimately boost the efficiency and effectiveness of research teams. This aspect of the findings adds depth to the existing literature by demonstrating the tangible benefits of investing in robust organizational infrastructure to support research activities.

Another essential contribution of this study is integrating a research collaboration network as a facilitating factor. This network, including digital platforms and tools that enable seamless communication and collaboration among researchers, has become increasingly vital in remote work and global collaboration (Mitchell, 2023 ). By examining how these systems leverage HUC and STC to enhance team performance, the study provides a practical understanding of the mechanisms through which technology can facilitate team scientific and technological performance.

One of the most novel contributions of this study is its emphasis on the moderating role of top management support. The findings suggest that when top management actively supports research initiatives, provides required resources, and fosters innovation, the positive effects of human and structural capital on team performance are amplified (Zada et al., 2023 ). This aspect of the study addresses a gap in the literature by highlighting the critical influence of top management on the success of intellectual capital investments. It underscores the importance of managerial involvement and strategic vision in driving research excellence and team scientific and technological performance.

Practical implications

The practical implications of the current study are weightage for organizations aiming to enhance their research and innovation capabilities and boost their scientific and technical progress. Organizations should prioritize recruiting, training, and retaining highly skilled and trained researchers and professionals globally. This can be achieved through targeted hiring practices, offering competitive compensation and retention, providing continuous professional development opportunities, and developing proper research collaboration networks. Organizations can leverage their expertize to drive innovative research and technological advancements by nurturing a global, talented workforce. Investing in robust organizational structures, processes, and systems is critical (Joseph & Gaba, 2020 ). This includes developing comprehensive databases, implementing efficient research processes, securing intellectual property, and strengthening collaborations. These factors support efficient knowledge sharing and streamline research activities, leading to higher productivity and quality research outcomes (Azeem et al., 2021 ). Organizations should ensure that their infrastructure is adaptable and can support remote and collaborative work environments.

The current study emphasizes the importance of digital platforms and tools facilitating research collaboration. Organizations should adopt advanced research collaboration networks that enable seamless communication, data sharing, and talent management. These systems are particularly crucial in a globalized research environment where team members may be geographically dispersed. Investing in such technology can significantly enhance research projects’ productivity in a sustainable way (Susanto et al., 2023 ). Top Management plays a vital role in the success of research initiatives and contributes to scientific and technological performance. Top management should actively support research teams by providing required resources, setting clear strategic directions, and fostering a culture of innovation. This includes allocating budgets for organizational research and development, encouraging cross-border collaboration, recognizing and rewarding research achievements, and enhancing overall performance. Effective Management ensures that the intellectual capital within the organization is fully utilized and aligned with organizational developmental goals (Paoloni et al., 2020 ). Organizations should create a working atmosphere that encourages research, creativity, and innovation. This can be done by establishing innovation labs, promoting interdisciplinary research, recruiting international talents, sharing research scholars, and encouraging the sharing of ideas across different departments globally. A research-oriented culture that supports innovation can inspire researchers to pursue groundbreaking work and contribute to the organization’s competitive edge.

Limitations and future research direction

The research presents numerous theoretical and practical implications; however, it has. The potential limitation of common method bias could impact the findings of this study. This concern arises because the data for the study variables were obtained from a single source and relied on self-report measures (Podsakoff, 2003 ). Therefore, it is recommended that future studies be conducted longitudinally to gain additional insights into organizations’ potential to enhance efficiency. Furthermore, it is essential to note that the sample size for this study was limited to 363 respondents who were deemed usable. These respondents were drawn from only ten research and academic institutions explicitly targeting the education and research sector.

Consequently, this restricted sample size may hinder the generalizability of the findings. Future researchers may employ a larger sample size and implement a more systematic approach to the organization to enhance the comprehensiveness and generalizability of findings in the context of global talent management and scientific and technological advancement. Furthermore, in future investigations, researchers may explore alternative boundary conditions to ascertain whether additional factors could enhance the model’s efficacy.

Numerous academic studies have emphasized the significance of examining talent management outcomes in global human resource management (HRM). The continuous international movement of highly qualified individuals is viewed as a driving force behind the development of new technologies, the dissemination of scientific findings, and the collaboration between institutions worldwide. Every organization strives to build a qualified and well-trained team, and the personnel department of the organization focuses on finding ways to transfer knowledge from experienced workers to new hires. This study uses a research collaboration system to examine the relationship between organizational intellectual capital (Human and structural Capital) and team scientific and technological performance. Further, this study underscores the moderating role of top management support. These findings offer a nuanced perspective on how organizations can leverage their intellectual assets to foster higher productivity and innovation, especially in emergencies.

Data availability

Due to respondents’ privacy concerns, data will not be publicly available. However, it can be made available by contacting the corresponding author at a reasonable request.

Al-Tit AA, Al-Ayed S, Alhammadi A, Hunitie M, Alsarayreh A, Albassam W (2022) The impact of employee development practices on human capital and social capital: the mediating contribution of knowledge management. J Open Innov 8(4):218

Article   Google Scholar  

Al Ariss A, Cascio WF, Paauwe J (2014) Talent management: current theories and future research directions. J World Bus 49(2):173–179

Al Jawali H, Darwish TK, Scullion H, Haak-Saheem W (2022) Talent management in the public sector: empirical evidence from the Emerging Economy of Dubai. Int J Hum Resour Manag 33(11):2256–2284

Ali M, Li Z, Khan S, Shah SJ, Ullah R (2021) Linking humble leadership and project success: the moderating role of top management support with mediation of team-building. Int J Manag Proj Bus 14(3):545–562

Almeida F, Santos JD, Monteiro JA (2020) The challenges and opportunities in the digitalization of companies in a post-COVID-19 World. IEEE Eng Manag Rev 48(3):97–103

Alrowwad AA, Abualoush SH, Masa’deh RE (2020) Innovation and intellectual capital as intermediary variables among transformational leadership, transactional leadership, and organizational performance. J Manag Dev 39(2):196–222

Anshari M, Hamdan M (2022) Understanding knowledge management and upskilling in Fourth Industrial Revolution: transformational shift and SECI model. VINE J Inf Knowl Manag Syst 52(3):373–393

Google Scholar  

Arocena R, Sutz J (2021) Universities and social innovation for global sustainable development as seen from the south. Technol Forecast Soc change 162:120399

Article   PubMed   Google Scholar  

Arslan A, Golgeci I, Khan Z, Al-Tabbaa O, Hurmelinna-Laukkanen P (2021) Adaptive learning in cross-sector collaboration during global emergency: conceptual insights in the context of COVID-19 pandemic. Multinatl Bus Rev 29(1):21–42

Azeem M, Ahmed M, Haider S, Sajjad M (2021) Expanding competitive advantage through organizational culture, knowledge sharing and organizational innovation. Technol Soc 66:101635

Bakhsh K, Hafeez M, Shahzad S, Naureen B, Faisal Farid M (2022) Effectiveness of digital game based learning strategy in higher educational perspectives. J Educ e-Learn Res 9(4):258–268

Barney J (1991) Firm resources and sustained competitive advantage. J Manag 17(1):99–120

Barney JB, Clark DN (2007) Resource-based theory: Creating and sustaining competitive advantage. Oup Oxford

Baron A, Armstrong M (2007) Human capital management: achieving added value through people. Kogan Page Publishers

Barpanda S (2021) Role of human and structural capital on performance through human resource practices in Indian microfinance institutions: a mediated moderation approach. Knowl Process Manag 28(2):165–180

Biondi L, Russo S (2022) Integrating strategic planning and performance management in universities: a multiple case-study analysis. J Manag Gov 26(2):417–448

Birasnav M, Mittal R, Dalpati A (2019) Integrating theories of strategic leadership, social exchange, and structural capital in the context of buyer–supplier relationship: an empirical study. Glob J Flex Syst Manag 20:219–236

Bontis N (1998) Intellectual capital: an exploratory study that develops measures and models. Manag Decis 36(2):63–76

Bontis N, Fitz‐enz J (2002) Intellectual capital ROI: a causal map of human capital antecedents and consequents. J Intellect Cap 3(3):223–247

Cavicchi C, Vagnoni E (2017) Does intellectual capital promote the shift of healthcare organizations towards sustainable development? Evidence from Italy. J Clean Prod 153:275–286

Chatterjee S, Chaudhuri R, Vrontis D (2022) Does remote work flexibility enhance organization performance? Moderating role of organization policy and top management support. J Bus Res 139:1501–1512

Council NR (2012) Education for life and work: developing transferable knowledge and skills in the 21st century. National Academies Press

Davenport S, Carr A, Bibby D (2002) Leveraging talent: spin–off strategy at industrial research. RD Manag 32(3):241–254

Dess GG, Picken JC (2000) Changing roles: Leadership in the 21st century. Organ Dyn 28(3):18–34

Duan W, Li C (2023) Be alert to dangers: collapse and avoidance strategies of platform ecosystems. J Bus Res 162:113869

Farahian M, Parhamnia F, Maleki N (2022) The mediating effect of knowledge sharing in the relationship between factors affecting knowledge sharing and reflective thinking: the case of English literature students during the COVID-19 crisis. Res Pract Technol Enhanc Learn 17(1):1–25

Fasi MA (2022) An overview on patenting trends and technology commercialization practices in the university Technology Transfer Offices in USA and China. World Pat Inf 68:102097

Felin T, Hesterly WS (2007) The knowledge-based view, nested heterogeneity, and new value creation: philosophical considerations on the locus of knowledge. Acad Manag Rev 32(1):195–218

Fernandes C, Veiga PM, Lobo CA, Raposo M (2023) Global talent management during the COVID‐19 pandemic? The Gods must be crazy! Thunderbird Int Bus Rev 65(1):9–19

Gogan LM, Duran DC, Draghici A (2015) Structural capital—a proposed measurement model. Procedia Econ Financ 23:1139–1146

Gonzalez-Mulé E, Courtright SH, DeGeest D, Seong J-Y, Hong D-S (2016) Channeled autonomy: the joint effects of autonomy and feedback on team performance through organizational goal clarity. J Manag 42(7):2018–2033

Haak-Saheem W (2020) Talent management in Covid-19 crisis: how Dubai manages and sustains its global talent pool. Asian Bus Manag 19:298–301

Habert B, Huc C (2010) Building together digital archives for research in social sciences and humanities. Soc Sci Inf 49(3):415–443

Haider SA, Akbar A, Tehseen S, Poulova P, Jaleel F (2022) The impact of responsible leadership on knowledge sharing behavior through the mediating role of person–organization fit and moderating role of higher educational institute culture. J Innov Knowl 7(4):100265

Hair JF, Page M, Brunsveld N (2019) Essentials of business research methods. Routledge

Harsch K, Festing M (2020) Dynamic talent management capabilities and organizational agility—a qualitative exploration. Hum Resour Manag 59(1):43–61

He S, Chen W, Wang K, Luo H, Wang F, Jiang W, Ding H (2023) Region generation and assessment network for occluded person re-identification. IEEE Trans Inf Forensic Secur 19:120–132

Henseler J, Ringle CM, Sarstedt M (2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci 43:115–135

Jain N, Thomas A, Gupta V, Ossorio M, Porcheddu D (2022) Stimulating CSR learning collaboration by the mentor universities with digital tools and technologies—an empirical study during the COVID-19 pandemic. Manag Decis 60(10):2824–2848

Joseph J, Gaba V (2020) Organizational structure, information processing, and decision-making: a retrospective and road map for research. Acad Manag Ann 14(1):267–302

Kaliannan M, Darmalinggam D, Dorasamy M, Abraham M (2023) Inclusive talent development as a key talent management approach: a systematic literature review. Hum Resour Manag Rev 33(1):100926

Kallmuenzer A, Baptista R, Kraus S, Ribeiro AS, Cheng C-F, Westhead P (2021) Entrepreneurs’ human capital resources and tourism firm sales growth: a fuzzy-set qualitative comparative analysis. Tour Manag Perspect 38:100801

Kim KY, Atwater L, Patel PC, Smither JW (2016) Multisource feedback, human capital, and the financial performance of organizations. J Appl Psychol 101(11):1569

Kwok L (2022) Labor shortage: a critical reflection and a call for industry-academia collaboration. Int J Contemp Hosp Manag 34(11):3929–3943

Lee J-C, Shiue Y-C, Chen C-Y (2016) Examining the impacts of organizational culture and top management support of knowledge sharing on the success of software process improvement. Comput Hum Behav 54:462–474

Lenihan H, McGuirk H, Murphy KR (2019) Driving innovation: public policy and human capital. Res policy 48(9):103791

Ling Y-H (2013) The influence of intellectual capital on organizational performance—knowledge management as moderator. Asia Pac J Manag 30(3):937–964

Luo J, Zhuo W, Xu B (2023) The bigger, the better? Optimal NGO size of human resources and governance quality of entrepreneurship in circular economy. Manag Decis (ahead-of-print) https://doi.org/10.1108/MD-03-2023-0325

Damsgaard J, Lyytinen K (2001) The role of intermediating institutions in the diffusion of electronic data interchange (EDI): how industry associations intervened in Denmark, Finland, and Hong Kong. Inf Soc 17(3):195–210

Marchiori DM, Rodrigues RG, Popadiuk S, Mainardes EW (2022) The relationship between human capital, information technology capability, innovativeness and organizational performance: an integrated approach. Technol Forecast Soc Change 177:121526

Marinelli L, Bartoloni S, Pascucci F, Gregori GL, Briamonte MF (2022) Genesis of an innovation-based entrepreneurial ecosystem: exploring the role of intellectual capital. J Intellect Cap 24(1):10–34

Mehralian G, Nazari JA, Ghasemzadeh P (2018) The effects of knowledge creation process on organizational performance using the BSC approach: the mediating role of intellectual capital. J Knowl Manag 22(4):802–823

Mitchell A (2023) Collaboration technology affordances from virtual collaboration in the time of COVID-19 and post-pandemic strategies. Inf Technol People 36(5):1982–2008

Muñoz JLR, Ojeda FM, Jurado DLA, Peña PFP, Carranza CPM, Berríos HQ, Vasquez-Pauca MJ (2022) Systematic review of adaptive learning technology for learning in higher education. Eurasia J Educ Res 98(98):221–233

Nezam MHK, Ataffar A, Isfahani AN, Shahin A (2013) The impact of structural capital on new product development performance effectiveness—-the mediating role of new product vision and competitive advantage. Int J Hum Resour Stud 3(4):281

O’Dwyer M, Filieri R, O’Malley L (2023) Establishing successful university–industry collaborations: barriers and enablers deconstructed. J Technol Transf 48(3):900–931

Oliver C (1997) Sustainable competitive advantage: combining institutional and resource‐based views. Strateg Manag J 18(9):697–713

Pak J, Heidarian Ghaleh H, Mehralian G (2023) How does human resource management balance exploration and exploitation? The differential effects of intellectual capital‐enhancing HR practices on ambidexterity and firm innovation. Human Resource Manag https://doi.org/10.1002/hrm.22180

Paoloni M, Coluccia D, Fontana S, Solimene S (2020) Knowledge management, intellectual capital and entrepreneurship: a structured literature review. J Knowl Manag 24(8):1797–1818

Patnaik S, Munjal S, Varma A, Sinha S (2022) Extending the resource-based view through the lens of the institution-based view: a longitudinal case study of an Indian higher educational institution. J Bus Res 147:124–141

Pellegrini L, Aloini D, Latronico L (2022) Open innovation and intellectual capital during emergency: evidence from a case study in telemedicine. Knowl Manag Res Pract 21(4), 765–776

Phan LT, Nguyen TV, Luong QC, Nguyen TV, Nguyen HT, Le HQ, Pham QD (2020) Importation and human-to-human transmission of a novel coronavirus in Vietnam. N Engl J Med 382(9):872–874

Article   PubMed   PubMed Central   Google Scholar  

Phuong QN, Le Ngoc M, Dong HT, Thao TLT, Tran T, Cac T (2024) Enhancing employment opportunities for people with disabilities in Vietnam: the role of vocational training and job placement centers. J Chin Hum Resour Manag 15(3):64–75

Podsakoff N (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 885(879):10.1037

Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88(5):879

Preacher KJ, Hayes AF (2008) Asymptotic and resampling strategies for assessing and comparing indirect effects inmultiple mediator models. Behav Res Methods 40(3):879–891

Rådberg KK, Löfsten H (2024) The entrepreneurial university and development of large-scale research infrastructure: Exploring the emerging university function of collaboration and leadership. J Technol Transf 49(1):334–366

Radhamani R, Kumar D, Nizar N, Achuthan K, Nair B, Diwakar S (2021) What virtual laboratory usage tells us about laboratory skill education pre-and post-COVID-19: Focus on usage, behavior, intention and adoption. Educ Inf Technol 26(6):7477–7495

Rasheed MH, Khalid J, Ali A, Rasheed MS, Ali K (2024) Human resource analytics in the era of artificial intelligence: Leveraging knowledge towards organizational success in Pakistan. J Chin Hum Resour Manag 15:3–20

Ringle CM, Sarstedt M, Mitchell R, Gudergan SP (2020) Partial least squares structural equation modeling in HRM research. Int J Hum Resour Manag 31(12):1617–1643

Roos G, Roos J (1997) Measuring your company’s intellectual performance. Long Range Plan 30(3):413–426

Salamzadeh A, Tajpour M, Hosseini E, Brahmi MS (2023) Human capital and the performance of Iranian Digital Startups: the moderating role of knowledge sharing behaviour. Int J Public Sect Perform Manag 12(1-2):171–186

Sarwar A, Mustafa A (2023) Analysing the impact of green intellectual capital on environmental performance: the mediating role of green training and development. Technol Anal Strateg Manag 1–14. https://doi.org/10.1080/09537325.2023.2209205

Scott WR (2008) Institutions and organizations: ideas and interests. Sage

Secundo G, Dumay J, Schiuma G, Passiante G (2016) Managing intellectual capital through a collective intelligence approach: an integrated framework for universities. J Intellect Cap 17(2):298–319

Secundo G, Massaro M, Dumay J, Bagnoli C (2018) Intellectual capital management in the fourth stage of IC research: a critical case study in university settings. J Intellect Cap 19(1):157–177

Shao JJ, Ariss AA (2020) Knowledge transfer between self-initiated expatriates and their organizations: research propositions for managing SIEs. Int Bus Rev 29(1):101634

Shofiyyah NA, Komarudin TS, Hasan MSR (2023) Innovations in Islamic Education Management within the University Context: addressing challenges and exploring future prospects. Nidhomul Haq 8(2):193–209

Sigala M, Ren L, Li Z, Dioko LA (2023) Talent management in hospitality during the COVID-19 pandemic in Macao: a contingency approach. Int J Contemp Hosp Manag 35(8):2773–2792

Singh SK, Gupta S, Busso D, Kamboj S (2021) Top management knowledge value, knowledge sharing practices, open innovation and organizational performance. J Bus Res 128:788–798

Sobaih AEE, Hasanein A, Elshaer IA (2022) Higher education in and after COVID-19: the impact of using social network applications for e-learning on students’ academic performance. Sustainability 14(9):5195

Article   CAS   Google Scholar  

Sommer LP, Heidenreich S, Handrich M (2017) War for talents—how perceived organizational innovativeness affects employer attractiveness. RD Manag 47(2):299–310

Stewart T (1991) Brainpower: how intellectual capital is becoming America’s most valuable. Fortune

Streukens S, Leroi-Werelds S (2016) Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. Eur Manage J 34(6):618–632

Sulaiman F, Uden L, Eldy EF (2022) Online Learning in Higher Education Institution During COVID-19: A Review and the Way Forward. Paper presented at the International Workshop on Learning Technology for Education Challenges

Susanto P, Sawitri NN, Ali H, Rony ZT (2023) Employee performance and talent management impact increasing construction company productivity. Int J Psychol Health Sci 1(4):144–152

Tan CN-L, Md. Noor S (2013) Knowledge management enablers, knowledge sharing and research collaboration: a study of knowledge management at research universities in Malaysia. Asian J Technol Innov 21(2):251–276

Vaiman V, Sparrow P, Schuler R, Collings DG (2018) Macro talent management: a global perspective on managing talent in developed markets. Routledge

Vătămănescu E-M, Cegarra-Navarro J-G, Martínez-Martínez A, Dincă V-M, Dabija D-C (2023) Revisiting online academic networks within the COVID-19 pandemic–From the intellectual capital of knowledge networks towards institutional knowledge capitalization. J Intellect Cap 24(4):948–973

Wang Y, Lee L-H, Braud T, Hui P (2022) Re-shaping Post-COVID-19 teaching and learning: A blueprint of virtual-physical blended classrooms in the metaverse era. Paper presented at the 2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW)

Wang Z, Wang N, Liang H (2014) Knowledge sharing, intellectual capital and firm performance. Manag Decis 52(2):230–258

Xu A, Li Y, Donta PK (2024) Marketing decision model and consumer behavior prediction with deep learning. J Organ End Use Comput (JOEUC) 36(1):1–25

Yang G, Xiangming L (2024) Graduate socialization and anxiety: insights via hierarchical regression analysis and beyond. Stud High Educ 1–17. https://doi.org/10.1080/03075079.2024.2375563

Zada M, Khan J, Saeed I, Zada S, Jun ZY (2023) Linking public leadership with project management effectiveness: mediating role of goal clarity and moderating role of top management support. Heliyon 9(5)

Download references

Author information

Authors and affiliations.

School of Economics and Management, Hanjiang Normal University, Shiyan, 442000, China

Muhammad Zada

Facultad de Administración y Negocios, Universidad Autónoma de Chile, Santiago, 8320000, Chile

School of Law, Huazhong University of Science and Technology, Wuhan, Hubie, China

Imran Saeed

College of Management, Shenzhen University, Shenzhen, China

Department of Business Administration, Faculty of Management Sciences, Ilma University, Karachi, Pakistan

Shagufta Zada

Business School Henan University, Kaifeng, Henan, China

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: Muhammad Zada and Imran Saeed. Methodology: Jawad Khan. Software: Shagufta Zada. Data collection: Muhammad Zada, Shagufta Zada and Jawad Khan. Formal analysis: Imran Saeed and Jawad Khan. Resources: Muhammad Zada. Writing original draft preparation: Muhammad Zada and Imran Saeed. Writing review and editing: Jawad Khan, Shagufta Zada. All authors have read and agreed to the published version of the paper.

Corresponding author

Correspondence to Muhammad Zada .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

The author sought and received ethical approval from the Research Ethical Committee School of Economics and Management at Hanjiang Normal University, China, with approval number 2023REC001, and the study complied with ethical standards.

Informed consent statement

Informed consent was obtained from all subjects involved in the study. All the participants were accessed with the support of the HR Department employed in China’s research and academia sector. Response Participants were provided with comprehensive information regarding the study’s purpose and procedures. Confidentiality and privacy were strictly implemented throughout the research process. Using the time lag data collection approach, we collected from 393 employees employed in China’s research and academic sector.

Additional information

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

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.

Zada, M., Saeed, I., Khan, J. et al. Navigating post-pandemic challenges through institutional research networks and talent management. Humanit Soc Sci Commun 11 , 1164 (2024). https://doi.org/10.1057/s41599-024-03697-9

Download citation

Received : 28 February 2024

Accepted : 30 August 2024

Published : 09 September 2024

DOI : https://doi.org/10.1057/s41599-024-03697-9

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

Quick links

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