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Communication

Factor Analysis of Croatian Secondary School Teachers’ Readiness for Digital Transformation

by
Višeslav Kirinić
*,
Darijo Čerepinko
* and
Iva Rosanda Žigo
*
Department for Public Relations, University North, 42000 Varaždin, Croatia
*
Authors to whom correspondence should be addressed.
Soc. Sci. 2023, 12(12), 650; https://doi.org/10.3390/socsci12120650
Submission received: 17 October 2023 / Revised: 16 November 2023 / Accepted: 19 November 2023 / Published: 22 November 2023
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Abstract

:
Based on the fact that digitization of education and culture is one of the fundamental strategic objectives of the European Commission and based on the analysis of key documents published by the European Commission in recent years, it can be said that infrastructure, digital competences, and the use of digital content in the educational process are fundamental guidelines that will guide the transformation of educational systems in all EU Member States in the coming years. The aim of this article is to identify the factors, based on a survey of the respondents’ attitudes, that could drive digital transformation in secondary educational institutions in Croatia. Within the theoretical background of the technology acceptance model (TAM), the results of the survey of 185 teachers and subsequent factor analysis show that the material support of institutions is mandatory as a base for change, while individual factors such as fear of technology and digital enthusiasm could govern the teachers’ response to acceptance of the new technologies.

1. Introduction

As stated in the opening sentence in the conclusion of the document Shaping Europe’s Digital Future, digital technologies, as advanced as they may be, are just a tool (European Commission 2020). The document, representing the umbrella framework of the European Union’s digital strategy, and aiming to prepare Europe for the digital age and the digital decade—the period until 2030—is primarily characterized by the digitization of all segments of social life and social transactions. A similar claim, uttered by Steve Jobs in his interview with Rolling Stone magazine in June 1994, stated that “[t]echnology is nothing. What’s important is that you have a faith in people, that they’re basically good and smart, and if you give them tools, they’ll do wonderful things with them. It’s not the tools that you have faith in—tools are just tools. They work, or they don’t work. It’s people you have faith in or not” (The Rolling Stone 1994). It was at a time when the Internet was still a space reserved for geeks and scientists, and a time when social network sites, big data, algorithms, and AI chatbots were not even on the radar, let alone the primary social issues.
Putting people at the center of any kind of social change is pivotal. Without people as the real key to digital transformation (Kane 2019), such transformation is destined to fail (Verina and Titko 2019). Since transformation implies a paradigm change, Europe must harness digitization to drive alteration in how citizens, public administrations, and democratic institutions interact, ensuring interoperability across all levels of government and across public services (European Commission 2020). One of the key issues of the whole process is the readiness of those whose role is utterly essential in preparing European citizens to do so, namely elementary and secondary schools teachers.
Hinings et al. (2018) define digital transformation (DT) as “the combined effect—s of several digital innovations bringing about novel actors (and actor constellations), structures, practices, values, and beliefs that change, threaten, replace, or complement existing rules of the game within organizations, ecosystems, industries, or fields.” DT has been gaining research interest since the beginning of the new millennium (Kraus et al. 2021) and is well researched in organizational science, economics, and related fields (e.g., Chen and Grossklags 2022; Orlova 2021; Vial 2019). Digital transformation in education is mainly researched in higher education institutions (Abad-Segura et al. 2020; Benavides et al. 2020), often connected to other societal processes (Türkeli and Schophuizen 2019) or compelled from the perspective of student performance (Garcez et al. 2022; Zain 2021; Castellví et al. 2020; Bilyalova et al. 2020), focusing on the teaching process that provides the most satisfactory outcomes, the use of technology as a new tool or resource, and curriculum enhancements that should include rapid changes in students’ daily lives. Research from the teachers’ perspective is mostly conducted from the perspective of the technology acceptance model (TAM) and its variations (Granić and Marangunić 2019).
Following the theoretical foundations explained in the subsequent section, namely that attitudes toward technology should be considered as a starting point for any kind of practical or policy proposal, the aim of this article is to identify the position of secondary education teachers toward the change in the teaching process due to the digitalization of educational practices and environments. For that reason, collecting data was administered through the use of an appropriate survey, thoroughly presented in the methodology section.

2. Theoretical Background

The nature of user acceptance of new technologies is relatively well researched theoretically, and one of the most commonly used theoretical frameworks (Scherer et al. 2019) is the technology acceptance model (TAM), first defined by Fred Davis in 1985 (Davis 1985) and later extended by a number of new studies and findings (Marangunić and Granić 2015; Scherer et al. 2019). It was based on user motivation, which consisted of three basic elements: perceived usefulness, perceived ease of use, and attitude toward using technology, but was later improved to include behavioral characteristics. New elements such as subjective norm, image, job relevance, output quality, and result demonstrability were added to the original model (Venkatesh and Davis 2000). External factors were also included in the further development of the model, namely personality traits, demographic characteristics, and computer self-efficacy, as well as external predictors such as technology anxiety, self-efficacy, and confidence in technology (Marangunić and Granić 2015).
Park et al. (2007) found that ease of use, perceived usefulness, and motivation to use the technology all had a significant impact on technology acceptance among university instructors, just as the model predicted, and suggested integrating TAM and the uses-and-gratification approach when introducing a new technology to faculty. Teo et al. (2008) conducted a survey of pre-service teachers, focusing on social norms and facilitating conditions as external variables, and found “that perceived usefulness, perceived ease of use and subjective norm are significant determinants of pre-service teachers’ attitudes toward computers.” Straub (2009) states that technology acceptance is a “complex, inherently social, developmental process” influenced by various internal and external factors, and cites factors such as prior experience, beliefs about specific and general abilities, stable personality traits, and mandated versus voluntary use of technology as the most important elements of individuals’ beliefs about technology acceptance.
O’Bannon and Thomas (2014) showed that age matters in faculty technology acceptance, as did Scherer et al. (2015) and to some extent Siddiq and Scherer (2016), which is consistent with other similar findings at the general level (Hauk et al. 2018). Almerich et al. (2016) showed that factors such as gender, frequency of using a computer at home, level of education taught, and having access to a computer classroom were most important to teachers’ readiness for technology adoption. In terms of gender, they reported negative effects of gender on the technological competencies of the teachers surveyed, noting that the female respondents were less tech-savvy than their male colleagues, but also asserting that this does not affect their pedagogical competencies. Similar results were found by Lucas et al. (2021) and are consistent with findings in the general population (Cai et al. 2017; Gefen and Straub 1997). However, there is a body of work that demonstrates that age and/or gender are not predictors of technology acceptance, and finds these claims inconclusive (e.g., Daniali et al. 2022; Scherer and Teo 2019).
Koehler et al. (2014) suggest that effective technology acceptance by faculty must be a “systematic, long-term educational experience in which participants can engage fruitfully in all three of these knowledge bases in an integrated manner”. The three knowledge bases mentioned above were content knowledge, pedagogical knowledge, and technology knowledge. Koehler and his co-authors proposed a new framework for integrating technology into teachers’ skills called Technological Pedagogical Content Knowledge (TPACK), which emphasizes the need to develop new teaching methods, enhance learners’ understanding of knowledge, and apply different ways of representing content that they see as a feature or benefit of a particular technology. Fraillon et al. (2020) conducted a survey of more than 26,000 teachers in 12 countries and found that more than two-thirds of the teachers surveyed had an average of at least five years of experience using information and communication technology (ICT), but less than half of them used ICT frequently in their teaching.
In their meta-analysis of TAM in education, Scherer et al. (2019) found “strong relations between perceived ease of use and perceived usefulness, larger effects of perceived usefulness on behavioral intention than of perceived ease of use on behavioral intention and mediocre to strong attitudes toward technology– behavioral intention and behavioral intention– technology use relations” suggesting that training teachers, primarily to improve their self-efficacy in using technology, could be an important practical use of their study. Finally, Granić (2022) states that “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”.
Several studies (e.g., Huang et al. 2021; Nistor et al. 2013; Göğüş et al. 2012) have found over time that cultural factors may play an important role in technology adoption, citing a number of cultural dimensions that should be considered. Methodologically, though, their work was based on Hofstede’s cultural values theory, which has received reasoned criticism from several authors in the social sciences and anthropology (e.g., Baskerville 2003). However, Zhao et al. (2021) conducted a meta-analysis of 45 empirical studies and, including culture as an important factor in their model, found “that the influence of subjective norms and self-efficacy on users’ behavior intention is more salient in the collectivistic culture, whereas perceived usefulness is more important for online learners in an individualistic culture.” In this context, it is important to mention several studies on the topic of teachers’ technology acceptance from a Croatian perspective.
Vukovac (2012) found that the self-assessment of college teachers surveyed showed that they rated themselves as above average in terms of computer literacy but their knowledge of learning management systems was only slightly above average (3.12 for the whole sample). Divković (2013) stated that a “lack of new technologies” is one of the main factors for the insufficient success of the introduction of a new civic education program. Vrdoljak (2016) studied the attitudes of primary school teachers and students towards the introduction of certain social network sites into the teaching process, and found that students were several orders of magnitude more interested in the proposal than their teachers.
Kolić-Vehovec (2020) presented the results of a pilot project on the introduction of the “E-School” program in 150 elementary and secondary schools in Croatia, and concluded overall that teachers who had positive expectations recognized the benefits of ICT and considered themselves digitally competent to use digital tools, expressing the intention to use ICT and actually use ICT more in their work. This intention and behavior were influenced by a positive ICT climate in the schools, which is an important prerequisite. These findings are consistent with elements of the Unified Theory of Acceptance and Use of Technology (UTAUT), another model of technology acceptance that is consistent in many details with TAM (Scherer et al. 2019). Berc (2021) also found that secondary education teachers were prone to new technology and found, as a main obstacle, lack of financial support for technological advancement.
Svalina (2022) found some level of traditionalism among elementary school teachers regarding the inclusion of technology in the classroom but did not find high levels of technology anxiety. She concluded that the sample preferred traditional teaching methods, although she found intrinsic motivation for incorporating more e-learning materials among the teachers who better understood the changes happening around them. The biggest barrier to adopting more e-learning, she reported, was a lack of institutional support.

3. Materials and Methods

The survey was conducted over a two-week period in 167 different schools of secondary education throughout Croatia (44.91% gymnasiums1, 48.5% vocational/professional school, 1.8% art schools, 4.3% private gymnasiums, 0.6% schools for special education). The convenience sample consisted of 185 respondents working in secondary educational institutions in the Republic of Croatia. The questionnaire, with 29 closed and two open questions, was based on previous surveys of Croatian teachers mentioned in the theoretical background (Vukovac 2012; Divković 2013; Vrdoljak 2016; Kolić-Vehovec 2020; Berc 2021; Svalina 2022) and also on other surveys conducted globally (e.g., Koehler et al. 2014). It consisted of four sets of questions that captured (i) socio-demographic data, (ii) attitudes toward digital transformation and the use of digital technologies, (iii) teachers’ professional development, and (iv) use of digital sources. Responses to the closed-ended questions were in the form of a 5-point categorical Likert scale. Questions in the sets ii, iii, and iv are shown in Table 1.
The Kaiser–Meyer–Olkin (KMO) test of sample adequacy was conducted to check whether the collected data were acceptable. Victor et al. (2018) suggest that “if the KMO value is greater than 0.6 and Bartlett’s test of sphericity is significant, then the factorability of the correlation matrix can be assumed. In other words, that means the dataset is suitable for factor analysis.” The value of KMO is 0.722 (Table 2), which implies that the dataset could be used for factor analysis. It can also be seen that Bartlett’s test of sphericity is statistically significant. The p-value for the Bartlett test was below 0.05, “confirming that the data frame under consideration was not an identity matrix” (Victor et al. 2018).
The exploratory factor analysis (EFA) applied in the analysis is well explained in the literature (e.g., Osborne 2008; Osborne 2015).
Extracted factors were checked for internal consistency and reliability. “Cronbach’s alpha, as one of the most commonly used methods to test the reliability and internal consistency of the test items” (Trochim and Donnelly 2006; cf. Victor et al. 2018) was used to check the correlation within the items and show “how well the given items fit to a conceptual model” (Nunnally and Bernstein 1994; Devon et al. 2007; cf. Victor et al. 2018).

4. Results

The results of the applied factor analysis are shown in the Table 3 and Table 4, while component loadings are presented in Figure 1.
In Table 3, factors are marked by a three-digit alphanumeric code (PC1 to PC6) and items (questions), loading the factors by the three-digit alphanumeric code (Q1 to 19). The factor loading matrix (Table 3) indicates that six listed factors contained the observed questions, namely (PC1) Digital enthusiasm, (PC2) Digital sources, (PC3) Fear of technology, (PC4) Encouraging the use of technology and providing ICT resources in schools, (PC5) Promoting identity through digital sources, and (PC6) Familiarity with strategy.
The items with insignificant loadings were eliminated, and 19 out of 31 items were retained for further analysis. For Factor PC5, three items were loaded, and for Factor PC6, only two. Victor et al. (2018) suggest that “if the scale uses only one factor, a minimum of four items should be loaded, whereas scales with more than one factor identified with as little as two items are considered acceptable, in accordance with the type of the study conducted”. In this study, the loadings of Factors PC5 and PC6 were omitted due to their insignificance for the theoretical framework of the article. The naming of the factors was influenced by the items loading each factor. The first, Factor PC1, had the highest load and explained 23.2% of the total variance. The factor was loaded by items Q2, Q4, Q7, Q8, and Q17. Items with significant loading indicated high regard for digital transformation of education and a highly positive attitude toward the application of contemporary digital technologies in the education process. The factor therefore indicated digital enthusiasm among second grade teachers, and was called accordingly throughout the article.
PC2 (explaining 11.4% of the total variance) was loaded by items indicating teachers’ preparedness for the use of various digital sources and online collections of digitalized objects, and their readiness to use them and to find digital online sources sufficient for the needs of the digital transformation of education.
PC3 (10.1% of the total variance) was loaded by items indicating teachers’ self-assessment of their digital skills, indicating that they were not completely convinced about their digital competences and needed additional training in the use of contemporary technology and digital pedagogy. Although loaded with just two items, the significance of the items (>800) loading PC4 (7.2% of the total variance) was strong enough to make the factor acceptable. Items indicated that the management of the institutions included in the survey highly encouraged the use of digital technology and provided the teachers with technology necessary for the implementation of digital transformation goals. The significance of the items loading Factor PC5 (Promoting identity through digital sources) and Factor PC6 (Familiarity with EU digitalization strategy) were not presented in this paper, as explained above.
Nunnally and Bernstein (1994); cf. Victor et al. (2018) suggest that “if there are two or more subscales in an instrument, Cronbach’s alpha should be calculated for the individual subscales, as well as the entire scale as a whole”. The closer the Cronbach Alpha coefficient is to 1, the more reliable the measurement scale is, so a reliability coefficient (including the Cronbach Alpha coefficient) of around 0.9 indicates an excellent correlation (Kline 1998). Each set of questions was checked for internal consistency and reliability. Table 5 shows Cronbach’s alpha for test items.
The Cronbach’s Alpha values for the components Digital enthusiasm, Digital sources, Fear of technology, and Encouraging the use of technology and providing ICT resources in schools were >0.700, which represents a high reliability value for the observed scale. The components Promoting identity through digital sources and Familiarity with strategy were <500, indicating a lower but still positive reliability value of the mentioned scale.

5. Discussion

If teachers are assigned the role of novel actors, as Hinings et al. (2018) suggest, and new structures and practices are put in place to create new values and beliefs, then the findings presented in this article indicate two paths that could be taken to achieve the goal of digital transformation. The first path is that of infrastructural and organizational support, which is evident through Factors 2 and 4, namely “Digital sources” and “Encouraging the use of technology and providing ICT resources in schools”. These findings suggest that the provision of the necessary infrastructure, regardless of individual characteristics that are usually the focus of TAM, as in Davis (1985) and others, should be a basis on which actors could be encouraged to overcome their personal limitations. This may be in line with previous findings (e.g., Almerich et al. 2016; Fraillon et al. 2020) suggesting that access to ICT is in itself an important determinant of technology adoption.
At the personal level, Factors 1 and 3 of this analysis show that specific values and beliefs should be considered when planning processes to implement digital transformation, as also shown in the literature review above (see Marangunić and Granić 2015; Scherer et al. 2019). Factor 3, which we referred to as the “Fear of technology”, is one of the previously identified factors (e.g., Koehler et al. 2014; Granić 2022), but Factor 1 (Digital enthusiasm) is to some extent a novelty, as it shows the elements that could produce a state of enthusiasm that may improve all of the requirements identified in earlier research (e.g., Venkatesh and Davis 2000). Due to time limitations, data sets and ensuing factors were not yet tested for socio-demographic differences among respondents, and no further inquiry into beliefs and attitudes of the respondents was explored, which is a next step in furthering this research, along with the examination of the teachers’ positions through the use of qualitative methods such as in-depth interviews and focus groups.

6. Conclusions

The findings presented in this article suggest that, in order to successfully start the process of digital transformation within educational institutions, before any action to enable and enhance digital transformation is taken or considered, two sets of requirements need to be implemented on an institutional level. The first is strong material support regarding equipment supply, which should provide a foundation for the process of digitalizing educational practices. Without it, most of the elements described both in TAM and other models would be fruitless and potentially meaningless. Second, it would be wise to test individual readiness, considering the factors of enthusiasm and fear, which should provide an individualized approach that is tailor-made for each staff member. Identifying a specific instrument for measuring both factors is a goal yet to be achieved by the extension of the presented research. Also, as mentioned in the previous section, more qualitative methods should be applied in the clarification of all the presented factors. This remains a goal for the prospective expansion of this topic.

Author Contributions

Conceptualization, V.K., D.Č. and I.R.Ž.; methodology, V.K. and D.Č.; validation, D.Č. and I.R.Ž.; formal analysis, V.K.; writing—original draft preparation, V.K.; writing—review and editing, D.Č. and I.R.Ž.; visualization, V.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the University North Grant for Research UNIN-HUM-23-1-2 and UNIN-DRUŠ-23-1-10.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of University North (Assessment nr. K: 602-04/20-03/17/MIK/12 on 20. March 2017).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Conflicts of Interest

The authors declare no conflict of interest.

Note

1
Croatian equivalent to Preparatory High School or Lyceum (in other European systems).

References

  1. Abad-Segura, Emilio, Mariana-Daniela González-Zamar, Juan C. Infante-Moro, and Germán Ruipérez García. 2020. Sustainable management of digital transformation in higher education: Global research trends. Sustainability 12: 2107. [Google Scholar] [CrossRef]
  2. Almerich, Gonzalo, Natividad Orellana, Jesús Suárez-Rodríguez, and Isabel Díaz-García. 2016. Teachers’ information and communication technology competences: A structural approach. Computers & Education 100: 110–25. [Google Scholar]
  3. Baskerville, Rachel F. 2003. Hofstede never studied culture. Accounting Organizations and Society 28: 1–14. [Google Scholar] [CrossRef]
  4. Benavides, Lina María Castro, Johnny Alexander Tamayo Arias, Martín Darío Arango Serna, John William Branch Bedoya, and Daniel Burgos. 2020. Digital transformation in higher education institutions: A systematic literature review. Sensors 20: 3291. [Google Scholar] [CrossRef] [PubMed]
  5. Berc, Tatjana. 2021. Novi trendovi i tehnologije poučavanja iz perspektive nastavnika srednjih škola u Hrvatskoj New Trends and Teaching Technologies from the Perspective of High School Teachers in Croatia. EDUvision 2021: 24. [Google Scholar]
  6. Bilyalova, A. A., D. A. Salimova, and T. I. Zelenina. 2020. Digital transformation in education. In Integrated Science in Digital Age: ICIS 2019. Berlin/Heidelberg: Springer International Publishing, pp. 265–76. [Google Scholar]
  7. Cai, Zhihui, Xitao Fan, and Jianxia Du. 2017. Gender and attitudes toward technology use: A meta-analysis. Computers & Education 105: 1–13. [Google Scholar]
  8. Castellví, Jordi, María-Consuelo Díez-Bedmar, and Antoni Santisteban. 2020. Pre-service teachers’ critical digital literacy skills and attitudes to address social problems. Social Sciences 9: 134. [Google Scholar] [CrossRef]
  9. Chen, Mo, and Jens Grossklags. 2022. Social control in the digital transformation of society: A case study of the Chinese Social Credit System. Social Sciences 11: 229. [Google Scholar] [CrossRef]
  10. Daniali, Sara Mehrab, Sergey Evgenievich Barykin, Marzieh Zendehdel, Olga Vladimirovna Kalinina, Valeriia Vadimovna Kulibanova, Tatiana Robertovna Teor, Irina Anatolyevna Ilyina, Natalia Sergeevna Alekseeva, Anton Lisin, Nikita Moiseev, and et al. 2022. Exploring UTAUT Model in Mobile 4.5 G Service: Moderating Social–Economic Effects of Gender and Awareness. Social Sciences 11: 187. [Google Scholar] [CrossRef]
  11. Davis, Fred D. 1985. A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Doctoral dissertation, Massachusetts Institute of Technology, Cambridge, MA, USA. [Google Scholar]
  12. Devon, Holli A., Michelle E. Block, Patricia Moyle-Wright, Diane M. Ernst, Susan J. Hayden, Deborah J. Lazzara, Suzanne M. Savoy, and Elizabeth Kostas-Polston. 2007. A psychometric Toolbox for testing Validity and Reliability. Journal of Nursing Scholarship 39: 155–64. [Google Scholar] [CrossRef]
  13. Divković, Marina. 2013. Ključne kompetencije učitelja u odgoju i obrazovanju za građanstvo. Život i Škola: Časopis za Teoriju i Praksu Odgoja i Obrazovanja 59: 326–40. [Google Scholar]
  14. European Commission. 2020. Shaping_Europes_Digital_Future_en. Available online: https://ec.europa.eu/commission/presscorner/detail/en/fs_20_278 (accessed on 21 June 2023).
  15. Fraillon, Julian, John Ainley, Wolfram Schulz, Tim Friedman, and Daniel Duckworth. 2020. Preparing for Life in a Digital World: IEA International Computer and Information Literacy Study 2018 International Report. Berlin/Heidelberg: Springer Nature, p. 297. [Google Scholar]
  16. Garcez, Ana, Ricardo Silva, and Mário Franco. 2022. The Hard Skills Bases in Digital Academic Entrepreneurship in Relation to Digital Transformation. Social Sciences 11: 192. [Google Scholar] [CrossRef]
  17. Gefen, David, and Detmar W. Straub. 1997. Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly 21: 389–400. [Google Scholar] [CrossRef]
  18. Göğüş, Aytac, Nicolae Nistor, Richard W. Riley, and Thomas Lerche. 2012. Educational Technology Acceptance across Cultures: A Validation of the Unified Theory of Acceptance and Use of Technology in the Context of Turkish National Culture. Turkish Online Journal of Educational Technology-TOJET 11: 394–408. [Google Scholar]
  19. Granić, Andrina. 2022. Educational technology adoption: A systematic review. Education and Information Technologies 27: 9725–44. [Google Scholar] [CrossRef]
  20. Granić, Andrina, and Nikola Marangunić. 2019. Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology 50: 2572–93. [Google Scholar] [CrossRef]
  21. Hauk, Nathalie, Joachim Hüffmeier, and Stefan Krumm. 2018. Ready to be a silver surfer? A meta-analysis on the relationship between chronological age and technology acceptance. Computers in Human Behavior 84: 304–19. [Google Scholar] [CrossRef]
  22. Hinings, Bob, Thomas Gegenhuber, and Royston Greenwood. 2018. Digital innovation and transformation: An institutional perspective. Information and Organization 28: 52–61. [Google Scholar] [CrossRef]
  23. Huang, Fang, José Carlos Sánchez-Prieto, Timothy Teo, Francisco J. García-Peñalvo, Susana Olmos-Migueláñez, and Chen Zhao. 2021. A cross-cultural study on the influence of cultural values and teacher beliefs on university teachers’ information and communications technology acceptance. Educational Technology Research and Development 69: 1271–97. [Google Scholar] [CrossRef]
  24. Kane, Gerald. 2019. The Technology Fallacy: People are the real key to digital transformation. Research-Technology Management 62: 44–49. [Google Scholar] [CrossRef]
  25. Kline, Rex B. 1998. Principles and Practic of Structural Equation Modeling. New York: The Guiford Press. [Google Scholar]
  26. Koehler, Matthew J., Punya Mishra, Kristen Kereluik, Tae Seob Shin, and Charles R. Graham. 2014. The technological pedagogical content knowledge framework. In Handbook of Research on Educational Communications and Technology. New York: Springer, pp. 101–11. [Google Scholar]
  27. Kolić-Vehovec, Svjetlana. 2020. Uvođenje Suvremenih Tehnologija u Učenje i Poučavanje: Istraživanje Učinaka Pilot-Projekta e-Škole. Rijeka: Sveučilište u Rijeci. Available online: http://izdavastvo.ffri.hr/wp-content/uploads/2021/02/Uvodjenje_suvremen_tehnologija_u_ucenje_i_poucavanje_Istraziv_ucinaka_pilot-projekta_e-Skole_E-IZDANJE_17.2.21.pdf (accessed on 21 June 2023).
  28. Kraus, Sascha, Paul Jones, Norbert Kailer, Alexandra Weinmann, Nuria Chaparro-Banegas, and Norat Roig-Tierno. 2021. Digital transformation: An overview of the current state of the art of research. Sage Open 11: 21582440211047576. [Google Scholar] [CrossRef]
  29. Lucas, Margarida, Pedro Bem-Haja, Fazilat Siddiq, António Moreira, and Christine Redecker. 2021. The relation between in-service teachers’ digital competence and personal and contextual factors: What matters most? Computers & Education 160: 104052. [Google Scholar]
  30. Marangunić, Nikola, and Andrina Granić. 2015. Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society 14: 81–95. [Google Scholar] [CrossRef]
  31. Nistor, Nicolae, Aytaç Göğüş, and Thomas Lerche. 2013. Educational technology acceptance across national and professional cultures: A European study. Educational Technology Research and Development 61: 733–49. [Google Scholar] [CrossRef]
  32. Nunnally, Jum C., and H. I. Bernstein. 1994. Psychometric Theory, 3rd ed. New York: McGraw-Hill. [Google Scholar]
  33. O’Bannon, Blanche W., and Kevin Thomas. 2014. Teacher perceptions of using mobile phones in the classroom: Age matters! Computers & Education 74: 15–25. [Google Scholar]
  34. Orlova, Ekaterina V. 2021. Design of personal trajectories for employees’ professional development in the knowledge society under Industry 5.0. Social Sciences 10: 427. [Google Scholar] [CrossRef]
  35. Osborne, Jason, ed. 2008. Best Practices in Quantitative Methods. Thousand Oaks: SAGE Publications, Inc. [Google Scholar]
  36. Osborne, Jason W. 2015. What is Rotating in Exploratory Factor Analysis? Practical Assessment, Research, and Evaluation 20: 2. [Google Scholar]
  37. Park, Namkee, Kwan Min Lee, and Pauline Hope Cheong. 2007. University instructors’ acceptance of electronic courseware: An application of the technology acceptance model. Journal of Computer-Mediated Communication 13: 163–86. [Google Scholar] [CrossRef]
  38. Scherer, Ronny, and Timothy Teo. 2019. Unpacking teachers’ intentions to integrate technology: A meta-analysis. Educational Research Review 27: 90–109. [Google Scholar] [CrossRef]
  39. Scherer, Ronny, Fazilat Siddiq, and Jo Tondeur. 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. [Google Scholar]
  40. Scherer, Ronny, Fazilat Siddiq, and Timothy Teo. 2015. Becoming more specific: Measuring and modeling teachers’ perceived usefulness of ICT in the context of teaching and learning. Computers & Education 88: 202–14. [Google Scholar]
  41. Siddiq, Fazilat, and Ronny Scherer. 2016. The relation between teachers’ emphasis on the development of students’ digital information and communication skills and computer self-efficacy: The moderating roles of age and gender. Large-Scale Assessments in Education 4: 1–21. [Google Scholar] [CrossRef]
  42. Straub, Evan T. 2009. Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research 79: 625–49. [Google Scholar] [CrossRef]
  43. Svalina, Vlasta. 2022. Stavovi učitelja i nastavnika prema e-učenju. In Proceedings of The First Academic Colloquium of the Postgraduate University Study Programme Pedagogy and Contemporary School Culture. Edited by Marija Sablić Senka Žižanović. Osijek: University of Osijek, pp. 206–22. [Google Scholar]
  44. Teo, Timothy, Chwee Beng Lee, and Ching Sing Chai. 2008. Understanding pre-service teachers’ computer attitudes: Applying and extending the technology acceptance model. Journal of Computer Assisted Learning 24: 128–43. [Google Scholar] [CrossRef]
  45. The Rolling Stone. 1994. Available online: https://www.rollingstone.com/culture/culture-news/steve-jobs-in-1994-the-rolling-stone-interview-231132/ (accessed on 21 June 2023).
  46. Trochim, William M. K., and James P. Donnelly. 2006. The Research Methods Knowledge Base, 3rd ed. Cincinnati: Atomic Dog. [Google Scholar]
  47. Türkeli, Serdar, and Martine Schophuizen. 2019. Decomposing the complexity of value: Integration of digital transformation of education with circular economy transition. Social Sciences 8: 243. [Google Scholar] [CrossRef]
  48. Venkatesh, Viswanath, and Fred D. Davis. 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science 46: 186–204. [Google Scholar] [CrossRef]
  49. Verina, Natalja, and Jelena Titko. 2019. Digital Transformation: Conceptual Framework. Paper presented at the Scientific Conference “Contemporary Issues in Business, Management and Economics Engineering, Vilnius, Lithuania, May 9–10. [Google Scholar] [CrossRef]
  50. Vial, Gregory. 2019. Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems 28: 118–44. [Google Scholar] [CrossRef]
  51. Victor, Vijay, Jose Joy Thoppan, Robert Jeyakumar Nathan, and Fekete Farkas Maria. 2018. Factors Influencing Consumer Behavior and Prospective Purchase Decisions in a Dynamic Pricing Environment—An Exploratory Factor Analysis Approach. Social Sciences 7: 153. [Google Scholar] [CrossRef]
  52. Vrdoljak, Marijana. 2016. Stavovi učenika i učitelja prema uvođenju društvene mreže edmodo u nastavu. Školski Vjesnik: Časopis za Pedagogijsku Teoriju i Praksu 65: 369–79. [Google Scholar]
  53. Vukovac, Dijana Plantak. 2012. Students and teachers’ usage of e-learning artifacts in tertiary education in Croatia. Paper presented at the 35th International Convention MIPRO, Opatija, Croatia, May 21–25; pp. 1264–69. [Google Scholar]
  54. Zain, Sayeda. 2021. Digital transformation trends in education. In Future Directions in Digital Information. Witney: Chandos Publishing, pp. 223–34. [Google Scholar]
  55. Zhao, Yang, Ning Wang, Yixuan Li, Ruoxin Zhou, and Shuangshuang Li. 2021. Do cultural differences affect users’e-learning adoption? A meta-analysis. British Journal of Educational Technology 52: 20–41. [Google Scholar] [CrossRef]
Socsci 12 00650 g001
Figure 1. Rotated component loadings.
Figure 1. Rotated component loadings.
Socsci 12 00650 g001
Table 1. Items used in the questionnaire.
Table 1. Items used in the questionnaire.
ItemsQuestions/Measurement
1I am familiar with the guidelines of the EU Digital Transformation Strategy
2The digital transformation of education is an extremely positive change in education
3The guidelines of the EU Digital Transformation Strategy are incorporated into the curricula of secondary school education classes in the Republic of Croatia
4Digital transformation of education is of crucial importance for improving the quality of the teaching process
5The school where I work is fully technically equipped for the use of digital tools in the teaching process (computers and other equipment, speed and stability of the Internet connection...)
6The school where I work strongly encourages the implementation of digital transformation of the teaching process
7After the coronavirus pandemic, I will further intensify the use of digital tools in the teaching process
8Digital competences (competences in information and communication technology) have become a prerequisite for successful achievement of the goals of modern education
9I consider myself competent to work with digital tools in class
10I need additional training for the use of digital tools in teaching
11I need additional training in digital pedagogy (new teaching methods with the application of modern technologies)
12Training programs for the use of digital tools available to secondary school teachers meet their training needs
13The use of digital(ized) cultural heritage (online collections of texts, images, music) is strongly represented in the teaching process
14The use of digital(ized) cultural heritage is strongly encouraged in classes curricula
15The quantity of online archives is sufficient for the needs of the teaching process
16I use digitial(ized) online collections because of speed and availability
17Students prefer a digital tour of museums and collections to a physical tour
18I encourage students to “reinterpret” heritage through tools (e.g., create a blog, groups on social networks, shoot short films, process downloaded digital(ized) cultural objects with available software...)
19Knowledge about cultural heritage is essential for building, maintaining and strengthening of the identity of both individual and community
Table 2. KMO and Bartlett’s Test.
Table 2. KMO and Bartlett’s Test.
Kaiser–Meyer–Olkin Measure of Sampling Adequacy 0.722
Bartlett’s Test of SphericityApprox. Chi-Square1079.186
Df171
Sig.0.000
Table 3. Component loadings.
Table 3. Component loadings.
PC1PC2PC3PC4PC5PC6Uniqueness
Q20.784 0.241
Q40.761 0.394
Q80.740 0.429
Q70.735 0.371
Q170.471 0.494
Q14 0.837 0.263
Q13 0.784 0.320
Q15 0.662 0.543
Q12 0.469 0.535
Q10 0.874 0.204
Q11 0.830 0.254
Q9 −0.587 0.412
Q5 0.829 0.265
Q6 0.828 0.221
Q16 0.579 0.330
Q19 0.687 0.392
Q18 0.597 0.442
Q1 0.6400.453
Q3 0.5890.346
Note. Applied rotation method is varimax.
Table 4. Component characteristics.
Table 4. Component characteristics.
Unrotated SolutionRotated Solution
EigenvalueProportion var.CumulativeSumSq. LoadingsProportion var.Cumulative
Component 14.4070.2320.2322.8910.1520.152
Component 22.1640.1140.3462.6580.1400.292
Component 31.9140.1010.4472.0570.1080.400
Component 41.3730.0720.5191.7740.0930.494
Component 51.1790.0620.5811.4500.0760.570
Component 61.0520.0550.6361.2600.0660.636
Table 5. Cronbach’s alpha.
Table 5. Cronbach’s alpha.
ComponentsCronbach’s AlphaN of Items
Digital enthusiasm0.7565
Digital sources0.7354
Fear of technology0.7183
Encouraging the use of technology and providing ICT resources in schools0.7522
Promoting identity through digital sources0.4973
Familiarity with strategy0.4202
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Kirinić, V.; Čerepinko, D.; Rosanda Žigo, I. Factor Analysis of Croatian Secondary School Teachers’ Readiness for Digital Transformation. Soc. Sci. 2023, 12, 650. https://doi.org/10.3390/socsci12120650

AMA Style

Kirinić V, Čerepinko D, Rosanda Žigo I. Factor Analysis of Croatian Secondary School Teachers’ Readiness for Digital Transformation. Social Sciences. 2023; 12(12):650. https://doi.org/10.3390/socsci12120650

Chicago/Turabian Style

Kirinić, Višeslav, Darijo Čerepinko, and Iva Rosanda Žigo. 2023. "Factor Analysis of Croatian Secondary School Teachers’ Readiness for Digital Transformation" Social Sciences 12, no. 12: 650. https://doi.org/10.3390/socsci12120650

APA Style

Kirinić, V., Čerepinko, D., & Rosanda Žigo, I. (2023). Factor Analysis of Croatian Secondary School Teachers’ Readiness for Digital Transformation. Social Sciences, 12(12), 650. https://doi.org/10.3390/socsci12120650

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