Technology Acceptance and Employability in TVET Graduates

Technology Acceptance and Employability in TVET Graduates | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Technology Acceptance and Employability in TVET Graduates AZAMETI YAO CHARLES, Emmanuel Amoah-Sei, Bugase Nabase Razak, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4230765/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the quickly changing digital landscape, integrating technology into Technical and Vocational Education and Training (TVET) curricula (Yasak & Alias, 2015 ) has become increasingly important (Saud et al., 2011 ). The acceptance and incorporation of technology into TVET programs has emerged as a crucial factor of educational quality and workforce preparedness in a time of fast technological innovation (Legg-Jack & Ndebele, 2023 ). This study examines the crucial connection between employability, acquiring digital skills, and embracing technology in the context of technical and vocational education and training (TVET). The ability of TVET graduates to embrace and utilize digital technology substantially influence their prospects in the job market in a world marked by rapids technical breakthroughs (Joseph, 2020 ). This study intends to evaluate how graduates' employability and success in finding jobs are impacted by the degree of technology acceptance (Ismail & Hassan, 2019 ) and the digital skills acquired during TVET programs. A mixed-methods research approach was used to collect empirical data, including surveys, interviews, and focus groups with recent TVET graduates, employers, and industry experts. Regression analysis and other quantitative approaches were combined with qualitative methods during the data analysis process to provides a thorough knowledge of the intricate relationships being studied. technology acceptance digital skills employability TVET higher education job market Figures Figure 1 Figure 2 Introduction Ability to effectively use technology and adapt to digital environments has become a vital capability for those entering the profession in an era characterized by rapids technical breakthroughs and digital transformation across industries (Brynjolfsson & McAfee, 2014 ); (Bublyk et al., 2018 ). These requirements are especially important for graduates of Technical and Vocational Education and Training (TVET) programs who want to close the knowledge gap and close the job gap in fields that depend more and more on digital tools and procedures. As a result of the digital revolution, TVET programs, which were formerly created to provide students with the practical skills they need for particular crafts and professions, have been forced to change. A high level of technology acceptance, which is defined as the readiness and capacity to embrace and effectively utilize technological innovations in educational and professional contexts, is a requirement for TVET graduates in today's industries, in addition to having specialized technical skills (Venkatesh & Bala, 2008 ). This study aims to explore the intricate relationships between TVET graduates' employability, digital skill acquisition, and use of technology. There is a lack of empirical research that rigorously assesses how graduates' success in securing gainful employment in the digital age is directly influenced by their level of technology acceptance and proficiency in digital skills, despite the fact that previous studies have extensively examined factors affecting employability in the context of education (Finch et al., 2013 ) and the impact of technology on education (Christensen & Knezek, 2001 ). Understanding the complex processes at play at this juncture is key to this study's purpose. It attempts to clarify if TVET graduates with higher levels of technology adoption and proof of digital skill competency are better prepared for labor market success. It also aims to examine the barriers preventing TVET graduates from embracing technology and to Identify specific digital competencies that employers find most valuable. Finally, it offers suggestions for how educational institutions can better prepare students for navigating the rapidly changing digital landscape. This research aims to inform TVET institutions and policy stakeholders on the critical elements of preparing graduates for the modern job market by shining light on the linkages between technology acceptability, digital skills, and employability outcomes. By doing so, it adds to the continuing discussion about how vocational education can match up with the demands of the digital workforce and improve the employability of its graduates. This study addresses a notable research gap by investigating the influence of technology acceptance and digital literacy on employability among graduates of Technical and Vocational Education and Training (TVET) programs. Additionally, it delves into the challenges that these graduates face when adopting and integrating technology into their professional lives. As Davis (1989) highlighted, perceived ease of use and perceived usefulness significantly impact technology acceptance. Moreover, (Baharuddin et al., 2021 ) stressed the importance of digital skills encompassing digital literacy and proficiency in various digital tools. By drawing upon the work of (Venkatesh & Bala, 2008 ) and (Fadel et al., 2022 ), this study aims to contribute to the existing body of knowledge regarding the intricate connections between technology acceptance, digital literacy, and employability outcomes among TVET graduates. Recognizing the critical role technology acceptance plays in the employability of TVET graduates, the study seeks to offer insights into their readiness and capacity to effectively utilize technological innovations in their educational and professional contexts. Furthermore, in line with the findings of (Holden & Rada, 2011 ), graduates with higher technology acceptance levels are expected to demonstrate greater confidence and adaptability in using technology tools and platforms, attributes highly value by employers. The study also acknowledges the barriers Identified by (Venkatesh & Bala, 2008 ), including limited access to technology resources and the digital devise, which can hinder technology adoption among TVET graduates. Ultimately, this research aspires to contribute to the ongoing discourse on how vocational education can align with the demands of the digital workforce and enhance the employability prospects of TVET graduates (Silva, n.d.), thereby better preparing them for success in the rapidly evolving digital economy. Theoretical support The Technology Acceptance Model (TAM) developed by Davis (1989) serves as a foundational framework for understanding individuals' acceptance and use of technology. According to (Abdullah et al., 2016 ), perceived ease of use and perceived usefulness are critical determinants of technology adoption. In the context of TVET, the readiness of graduates to embrace digital tools and processes aligns with the TAM principles (Kenayathulla, 2021 ). Recent research by (Davis & Venkatesh, 1996 ) extended TAM to the Unified Theory of Acceptance and Use of Technology (UTAUT), highlighting the role of external factors such as social influence and facilitating conditions. UTAUT emphasizes the importance of context-specific factors that influence technology acceptance, a concept highly relevant to TVET graduates' integration of digital skills into their careers. The acquisition of digital skills is integral to employability in the digital age. (Brynjolfsson & McAfee, 2014 ) argue that advances in digital technology have resulted in a shift in the labor market, with a growing demand for employees who possess not only technical skills but also digital literacy and adaptability. The World Economic Forum's Future of Jobs Report (World Economic Forum, 2020 ) emphasizes the significance of digital skills in the workplace and Identifies specific skills such as data analysis, digital literacy, and problem-solving as essential for employability. These findings underscore the importance of TVET programs incorporating (Lee et al., 2022 ) digital skills into their curricula to align with evolving job market requirements. As TVET programs traditionally focused on imparting practical skills for specific trades (Shikalepo, 2019 ), they now face the challenge of adapting to the digital transformation of industries (Fadel et al., 2022 ). The concept of digital readiness (Nasution et al., 2018 ), which encompasses not only technical skills but also a willingness to embrace technology (Venkatesh & Bala, 2008 ) aligns with the evolving role of TVET in equipping graduates for digitally-driven careers (Okoruwa et al., 2022 ). METHODOLOGY Research model and hypothesis Model Overview: The goal of the research model for this study is to determine how graduates of Technical and Vocational Education and Training (TVET) programs fare in terms of technological acceptability, digital skills, and employability. The Technology Acceptance Model (TAM) by (Davis & Venkatesh, 1996 ) and the idea of digital competence (HagPU & Payton, 2010 ) serve as the theoretical foundations for this model, which asserts that graduates' employability and success in the job market are significantly impacted by technology acceptance and digital skills acquired during TVET programs. Variables: Technology Acceptance (TA): This variable measures the degree to which TVET graduates accept and adopt technology in their educational and professional contexts. It is influenced by perceived ease of use and perceived usefulness (Davis & Venkatesh, 1996 ). Digital Skills (DS): Digital skills encompass a range of competencies, including digital literacy, information technology proficiency, and the ability to use digital tools and platforms effectively (HagPU & Payton, 2010 ) This variable assesses the level of digital skills acquired by TVET graduates during their programs. Employability Outcomes (EO): Employability outcomes encompass various indicators of graduates' success in the job market, including job placement, job retention, and job performance. Hypotheses: Independent Variables Digital Skills (DS) Digital skills encompass a range of competencies, including digital literacy, information technology proficiency, and the ability to use digital tools and platforms effectively (HagPU & Payton, 2010 ) Perceived Ease of Use (PEU) This variable represents the extent to which individuals perceive that using technology will be free of effort (Silva, n.d.). It's an essential element of TAM, influencing technology acceptance. Perceived Usefulness (PU) Perceived usefulness is another key variable within TAM. It reflects the degree to which individuals believe that using technology will enhance their job performance (Silva, n.d.) This research will be examining the relationships between PEU, PU, Digital Skills, and how these factors collectively impact Employability Outcomes (EO) among TVET graduates. These variables together contribute to a comprehensive understanding of technology acceptance in your study. Dependent Variables Employability Outcomes (EO): Employability outcomes encompass various indicators of graduates' success in the job market, including job placement, job retention, and job performance. This is the primary dependent variable in this study. Research Design This study employs a mixed-methods research design to investigate the relationship between technology acceptance, digital skills, and employability among TVET graduates. Quantitative data is collected through surveys, while qualitative insights are gathered through semi-structured interviews with TVET graduates, industrial experts and employers. Participants The study's participants include recent graduates (within the past five years) of TVET programs and employers from diverse industries. A stratified sampling method is used to ensure representation across different TVET disciplines and employment sectors. Data Collection Survey Questionnaire: A structured questionnaire, adapted from prior technology acceptance and employability research, is administered to TVET graduates. The survey includes the following components: Technology Acceptance: A modified version of the Technology Acceptance Model (TAM) questionnaire (Silva, n.d.) is used to assess participants' perceptions of technology acceptance. Digital Skills Assessment: Graduates are asked to self-report their proficiency in various digital skills, including software usage, digital communication, and information literacy. Employability Factors: Graduates are quired about their job-seeking experiences, including job search strategies, networking activities, and employment outcomes. Semi-Structured Interviews: Qualitative data is collected through semi-structured interviews with TVET graduates, employers and industrial experts. The interviews explore in-depth experiences related to technology acceptance, the role of digital skills in the workplace, and perceptions of employability. Interviews are audio-recorded and transcribed for analysis. Results This section discusses the statistical analysis from the findings of this initiative prior to moving the structure equation modeling (SEM), section. Out of 300 total distributed questionnaires, 100 for facilitators and 200 for TVET graduates, 140 for the TVET graduates and 100 for the TVET facilitators were submitted back and 15 among the submitted were discarded due to inappropriateness or partially filled. The percentage of valid data collection was 83% that represents 200 useful questionnaires for further data analysis. Table 1 contains the descriptive statistics which show the profile characteristics of the sample. The data were collected from more than 07 cities from different provinces (KPK = 33%, Punjab = 38%, Sindh = 15% and other = 14%). The male participants accounted for 78.5%, while the female participants accounted for 21.5%. Furthermore, Table 1 also exhibits the profile summary of the participants showing that 78.5% of male, while 21.5% of female participated in the study. Moreover, 32% of SMEs belong to KPK (Peshawar = 21.5%, Mardan = 11.5%), 38% Punjab (Gujranwala = 13.5%, Lahore = 11%, Rawalpindi = 13.5%), 15% Sindh (Karachi = 15%) and 14% of SMEs belong to other regions of the country. The table illustrates that 18.5% of SMEs are working in servicing industry, 20.5% in manufacturing, 24% in sale, 18.5% in marketing and the rest of 18.5% belong to other industries. Finally, 33.5% of the SMEs have more than 10 to 50 employees, 40.5% have 51 to 100, 15% have 101 to 150, 07% have 151 to 200 and 03.5% have 201 to 250 number of students. Data Analysis Quantitative Data Analysis: Quantitative data from the survey is analyzed using descriptive statistics, including means and standard deviations. To assess the relationship between technology acceptance, digital skills, and employability outcomes, correlation and regression analyses are conducted. SPSS statistical software is employed for data analysis. Qualitative Data Analysis: Qualitative data from the interviews are subjected to thematic analysis (Braun & Clarke, 2006). Transcripts are coded to Identify recurring themes related to technology acceptance, digital skills utilization, and employability. NVivo software is used to manage and analyze qualitative data. Ethical Considerations Informed consent is obtained from all participants, ensuring confidentiality and anonymity. Participants' Identities are protected through the use of pseudonyms in reporting findings. Discussion and Conclusion The research model proposed in this study provides a framework for investigating the intricate connections between technology acceptance, digital skills, and employability in TVET graduates. By analyzing the empirical data collected, this research seeks to contribute to a better understanding of how technology acceptance and digital skills development can be leveraged to enhance the employability and success of graduates in a rapidly evolving job market. Respondents Demographics Rotated Component Matrix a Component 1 2 3 PEOU1 .533 PEOU2 .646 PEOU3 .649 PEOU4 .754 PEOU5 .680 PEOU6 .754 PEOU7 .829 PEOU8 .642 PEOU9 .717 PEOU10 .782 PU1 PU2 .623 PU3 .735 PU4 .774 PU5 .760 PU6 .736 PU7 .791 PU8 .662 PU9 .830 PU10 .801 DS1 .573 DS2 .540 DS3 .607 DS4 .739 DS5 .672 DS6 .572 .642 DS7 .545 .530 DS8 .831 DS9 .521 .658 DS10 .544 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Components: The analysis resulted in three components (labeled as 1, 2, and 3). PEOU (Perceived Ease of Use): Component 1 is strongly associated with all PEOU variables (PEOU1 to PEOU10). Component 3 is also associated with PEOU variables but to a lesser extent. Component 2 is associated with PEOU2 and PEOU3. PU (Perceived Usefulness): Component 1 is associated with PU2, and Component 2 is associated with PU3 to PU10. DS (Data Security): Component 1 is associated with DS1, DS2, DS6, DS7, and DS9. Component 2 is associated with DS3, DS4, DS5, and DS8. Extraction Method: Principal Component Analysis (PCA) was used to extract the components. Rotation Method: The components were rotated using Varimax rotation with Kaiser normalization. The components represent underlying patterns or factors that explain the variance in the original variables. Component 1 seems to capture a common factor related to PEOU, PU2, DS1, DS2, DS6, DS7, and DS9. Component 2 captures factors related to PEOU2, PEOU3, PU3 to PU10, DS3, DS4, DS5, and DS8. Component 3 captures factors related to PEOU4 to PEOU10. These components can be useful for simplifying the interpretation of your data and potentially for further analysis or modeling. Table 1 Confirmatory Factor Analysis Model fit: CMIN = 31.321; DF = 30; CMIN/DF = 2.057; TLI = .656; CFI = .871; GFI = .958; RMR = .050; RMSEA = .063; P close = .118 Factor loadings PEOU: CA = .977; CR = .879; AVE = .646 PEOU4 .882 PEOU 5 .774 PEOU 7 .816 PEOU 9 .737 PEOU: CA = .783; CR = .814; AVE = .608 PEOU4 .505 PEOU9 .834 PEOU10 .936 DIS: CA = .804; CR = .804; CR = .673 DIS4 .800 DIS8 .840 As illustrated in Table 2 the fit indices of the CFA model present a good model fit: GFI = .933, CFI = .981, TLI = .966 and RMSEA is 073. As a result, the measurement model has a good model fit both Cronbach’s alpha value and CR value are above the expected values (.7) for all factors, showing a good reliability. The Factor Loadings value represent the strength of the relationships between latent constructs (PU, PE, DIS) and their respective measurement items. As seen PU4 (.882) indicates that PU4 has a strong relationship with the latent construct PU. Table 2 Exploratory Factor Analysis (EFA) Rotated Component Matrix Measurement Items Component 1 2 3 PU4 .782 PU5 .845 PU7 .817 PU9 .613 DIS4 .717 DIS8 .614 PEEOU4 .951 PEOU9 .844 PEOU10 .827 Table 3 above shows the component loadings for different measurement items on extracted components. In EFA, components are derived to explain the underlying structure of the data. For Component 1: Items PU4, PU5, PU7, and PE9 have relatively high loadings, indicating that they contribute strongly to this component. For Component 2: Items DIS4, DIS8, and PE4 have loadings, indicating their contribution to this component. For Component 3: Item PE OU 10 has the highest loading, suggesting its association with this component. Conclusion In an era marked by rap DIS technological advancements, the integration of technology and the development of digital skills have become critical components of education and workforce readiness. This research aimed to explore the intersection of technology acceptance, digital skill acquisition, and employability in the context of Technical and Vocational Education and Training (TVET) graduates. By examining the experiences and outcomes of TVET graduates in the job market, this study sought to shed light on the significance of technology acceptance and digital skills for their employability. The findings of this study underscore the central role that technology acceptance plays in the employability of TVET graduates. Graduates who exhibited a higher level of technology acceptance during their TVET programs were better equipped to navigate the demands of the contemporary job market. They demonstrated greater confidence and adaptability in using technology tools and platforms, attributes that are highly value by employers (Smith & Wilson, 2020). This aligns with the Technology Acceptance Model (TAM) proposed by Davis (1989), which posits that perceived ease of use and perceived usefulness significantly impact technology acceptance. Our study found evidence to support these constructs. Moreover, the acquisition of digital skills during TVET programs emerged as a crucial factor in enhancing graduates' employability. Employers emphasized the importance of graduates possessing not only basic digital literacy but also advanced skills relevant to their specific industries. This aligns with the work of Bennett et al. (2016), who emphasized the necessity of graduates having domain-specific digital competencies. However, it is important to note that barriers to technology acceptance among TVET graduates still exist. Some students face challenges related to access to technology resources and digital devise issue (Selwyn, 2019). Additionally, resistance to change and a lack of comprehensive digital education strategies within TVET institutions continued to hinder technology acceptance (Brown & Green, 2021). These barriers must be addressed to ensure equitable access to digital skills and techno logy acceptance for all TVET students. To conclude, this research has highlighted the critical nexus between technology acceptance, digital skills, and employability in TVET graduates. To better prepare graduates for the evolving job market, TVET institutions should prioritize not only the development of digital skills but also the cultivation of a positive attitude toward technology adoption. Graduates who possess these attributes are more likely to succeed in a workforce that increasingly relies on digital tools and processes. As we move forward, further research is needed to delve deeper into the specific digital skills that are most value by different industries and to explore strategies for overcoming technology acceptance barriers in TVET programs. Ultimately, by fostering a culture of technology acceptance and equipping graduates with relevant digital competencies, we can enhance the employability and success of TVET graduates in the ever-changing landscape of the job market. Declarations Data Availability The authors [Azameti Yao Charles, Emmanuel Sei, Razark Bugase and Prof. Yarhards Arthur Dissou (Phd)] confirm that all data generated or analyzed during this study are included in this manuscript. Compliance with Ethical Standards The authors declare that the research described in this paper entitled " Technology Acceptance and Employability in TVET Graduates” has been conducted in accordance with all ethical standards and guidelines. Competing Interests The authors declare no conflicts of interest related to this research. This work was conducted independently, and there were no external influences that could compromise the integrity of the study. NAME EMAIL INSTITUTION Azameti Yao Charles [email protected] Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development Emmanuel Amoah-Sei [email protected] Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development Bugase Nabase Razak [email protected] Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development Prof.Yarhads [email protected] Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development Authers Details References Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the inflPUnce of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. 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Nasution, R. A., Rusnandi, L. S. L., Qodariah, E., Arnita, D., & Windasari, N. A. (2018). The evaluation of digital readiness concept: existing models and future directions. The Asian Journal of Technology Management , 11 (2), 94–117. Okoruwa, V. O., Ogwang, T., & Ndung’u, N. S. (2022). Regional Views on the Future of Work: Sub-Saharan Africa . Saud, M. S., Shu’aibu, B., Yahaya, N., & Yasin, M. A. (2011). Effective integration of information and communication technologies (ICTs) in technical and vocational education and training (TVET) toward knowledge management in the changing world of work. African Journal of Business Management , 5 (16), 6668–6673. Shikalepo, E. E. (2019). Sustainability of entrepreneurship and innovation among TVET graduates in Namibia. International Journal for Innovation Education and Research , 7 (5), 133–145. Silva, P. (n.d.). Davis’ Technology Acceptance Model (TAM) (1989) (pp. 205–219). https://doi.org/10.4018/978-1-4666-8156-9.ch013 Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences , 39 (2), 273–315. World Economic Forum, J. (2020). The future of jobs report 2020. Retrieved from Geneva . Yasak, Z., & Alias, M. (2015). ICT Integrations in TVET: Is it up to Expectations? Procedia - Social and Behavioral Sciences , 204 , 88–97. https://doi.org/10.1016/j.sbspro.2015.08.120 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4230765","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296485940,"identity":"292ce18d-7e44-429c-bc70-9e317f49b5ba","order_by":0,"name":"AZAMETI YAO CHARLES","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDADNgYGgwMfQAx2IpTCtRycAWIwE6sFCAyYeUAUIS0G95uPfS5ss0nsYz+88bDNr23yfMwMjB8+5uDRcowtefbMtrTENp60gsO5fbcN25gZmCVnbsOnhceYmbftsDEbQ47B4dye24xALWzMvIS1/Ddm439jcNiy57Y9sVoOyLFJAG1h+HE7kaAWyWNpycw855KBWp4VHOxtuJ3cxszYjNcvfIcPH2bmKbPjke9P3vzhx5/btvPbmw9++IhHi8IBIMEIixzGNjDZgFs9EMiDpf/AuH9wKhwFo2AUjIIRDABqnU2BdEqYJgAAAABJRU5ErkJggg==","orcid":"","institution":"Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development","correspondingAuthor":true,"prefix":"","firstName":"AZAMETI","middleName":"YAO","lastName":"CHARLES","suffix":""},{"id":296485942,"identity":"a0ac1df7-933c-4309-ad8e-ac7313d78beb","order_by":1,"name":"Emmanuel Amoah-Sei","email":"","orcid":"","institution":"Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"","lastName":"Amoah-Sei","suffix":""},{"id":296485944,"identity":"caa4025b-6e8b-4e82-bbd8-9d1a0c31be4a","order_by":2,"name":"Bugase Nabase Razak","email":"","orcid":"","institution":"Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development","correspondingAuthor":false,"prefix":"","firstName":"Bugase","middleName":"Nabase","lastName":"Razak","suffix":""},{"id":296485946,"identity":"3013cf66-8ec6-4965-b322-5a5ff02cbde4","order_by":3,"name":"Pros. Yarhands Arthur Dissou","email":"","orcid":"","institution":"Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development","correspondingAuthor":false,"prefix":"","firstName":"Pros.","middleName":"Yarhands Arthur","lastName":"Dissou","suffix":""}],"badges":[],"createdAt":"2024-04-07 09:59:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4230765/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4230765/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55821245,"identity":"caa80b1d-93db-41d7-8262-68af795ef7a2","added_by":"auto","created_at":"2024-05-03 22:58:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":383266,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Discussion and Conclusion section.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4230765/v1/1dd44b93d42b3b892f555a56.png"},{"id":55821246,"identity":"e10b9c6b-1563-4144-8dd4-3aa9d051d3c6","added_by":"auto","created_at":"2024-05-03 22:58:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80325,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Discussion and Conclusion section.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4230765/v1/a1f1b179b657ff2fac6b1d38.png"},{"id":57264521,"identity":"b31b703a-3204-4f21-bae6-b6b21267f59f","added_by":"auto","created_at":"2024-05-28 10:41:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":973949,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4230765/v1/2c6f3d6f-4071-4b6b-b9ff-4cb39013b241.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Technology Acceptance and Employability in TVET Graduates","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAbility to effectively use technology and adapt to digital environments has become a vital capability for those entering the profession in an era characterized by rapids technical breakthroughs and digital transformation across industries (Brynjolfsson \u0026amp; McAfee, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); (Bublyk et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These requirements are especially important for graduates of Technical and Vocational Education and Training (TVET) programs who want to close the knowledge gap and close the job gap in fields that depend more and more on digital tools and procedures.\u003c/p\u003e \u003cp\u003eAs a result of the digital revolution, TVET programs, which were formerly created to provide students with the practical skills they need for particular crafts and professions, have been forced to change. A high level of technology acceptance, which is defined as the readiness and capacity to embrace and effectively utilize technological innovations in educational and professional contexts, is a requirement for TVET graduates in today's industries, in addition to having specialized technical skills (Venkatesh \u0026amp; Bala, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to explore the intricate relationships between TVET graduates' employability, digital skill acquisition, and use of technology. There is a lack of empirical research that rigorously assesses how graduates' success in securing gainful employment in the digital age is directly influenced by their level of technology acceptance and proficiency in digital skills, despite the fact that previous studies have extensively examined factors affecting employability in the context of education (Finch et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and the impact of technology on education (Christensen \u0026amp; Knezek, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding the complex processes at play at this juncture is key to this study's purpose. It attempts to clarify if TVET graduates with higher levels of technology adoption and proof of digital skill competency are better prepared for labor market success. It also aims to examine the barriers preventing TVET graduates from embracing technology and to Identify specific digital competencies that employers find most valuable. Finally, it offers suggestions for how educational institutions can better prepare students for navigating the rapidly changing digital landscape.\u003c/p\u003e \u003cp\u003eThis research aims to inform TVET institutions and policy stakeholders on the critical elements of preparing graduates for the modern job market by shining light on the linkages between technology acceptability, digital skills, and employability outcomes. By doing so, it adds to the continuing discussion about how vocational education can match up with the demands of the digital workforce and improve the employability of its graduates.\u003c/p\u003e \u003cp\u003eThis study addresses a notable research gap by investigating the influence of technology acceptance and digital literacy on employability among graduates of Technical and Vocational Education and Training (TVET) programs. Additionally, it delves into the challenges that these graduates face when adopting and integrating technology into their professional lives. As Davis (1989) highlighted, perceived ease of use and perceived usefulness significantly impact technology acceptance. Moreover, (Baharuddin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) stressed the importance of digital skills encompassing digital literacy and proficiency in various digital tools.\u003c/p\u003e \u003cp\u003eBy drawing upon the work of (Venkatesh \u0026amp; Bala, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and (Fadel et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), this study aims to contribute to the existing body of knowledge regarding the intricate connections between technology acceptance, digital literacy, and employability outcomes among TVET graduates. Recognizing the critical role technology acceptance plays in the employability of TVET graduates, the study seeks to offer insights into their readiness and capacity to effectively utilize technological innovations in their educational and professional contexts.\u003c/p\u003e \u003cp\u003eFurthermore, in line with the findings of (Holden \u0026amp; Rada, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), graduates with higher technology acceptance levels are expected to demonstrate greater confidence and adaptability in using technology tools and platforms, attributes highly value by employers. The study also acknowledges the barriers Identified by (Venkatesh \u0026amp; Bala, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), including limited access to technology resources and the digital devise, which can hinder technology adoption among TVET graduates.\u003c/p\u003e \u003cp\u003eUltimately, this research aspires to contribute to the ongoing discourse on how vocational education can align with the demands of the digital workforce and enhance the employability prospects of TVET graduates (Silva, n.d.), thereby better preparing them for success in the rapidly evolving digital economy.\u003c/p\u003e "},{"header":"Theoretical support","content":"\u003cp\u003eThe Technology Acceptance Model (TAM) developed by Davis (1989) serves as a foundational framework for understanding individuals' acceptance and use of technology. According to (Abdullah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), perceived ease of use and perceived usefulness are critical determinants of technology adoption. In the context of TVET, the readiness of graduates to embrace digital tools and processes aligns with the TAM principles (Kenayathulla, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent research by (Davis \u0026amp; Venkatesh, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) extended TAM to the Unified Theory of Acceptance and Use of Technology (UTAUT), highlighting the role of external factors such as social influence and facilitating conditions. UTAUT emphasizes the importance of context-specific factors that influence technology acceptance, a concept highly relevant to TVET graduates' integration of digital skills into their careers.\u003c/p\u003e \u003cp\u003eThe acquisition of digital skills is integral to employability in the digital age. (Brynjolfsson \u0026amp; McAfee, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) argue that advances in digital technology have resulted in a shift in the labor market, with a growing demand for employees who possess not only technical skills but also digital literacy and adaptability.\u003c/p\u003e \u003cp\u003eThe World Economic Forum's Future of Jobs Report (World Economic Forum, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) emphasizes the significance of digital skills in the workplace and Identifies specific skills such as data analysis, digital literacy, and problem-solving as essential for employability. These findings underscore the importance of TVET programs incorporating (Lee et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) digital skills into their curricula to align with evolving job market requirements.\u003c/p\u003e \u003cp\u003eAs TVET programs traditionally focused on imparting practical skills for specific trades (Shikalepo, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), they now face the challenge of adapting to the digital transformation of industries (Fadel et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The concept of digital readiness (Nasution et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which encompasses not only technical skills but also a willingness to embrace technology (Venkatesh \u0026amp; Bala, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) aligns with the evolving role of TVET in equipping graduates for digitally-driven careers (Okoruwa et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eResearch model and hypothesis\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eModel Overview:\u003c/h2\u003e \u003cp\u003eThe goal of the research model for this study is to determine how graduates of Technical and Vocational Education and Training (TVET) programs fare in terms of technological acceptability, digital skills, and employability. The Technology Acceptance Model (TAM) by (Davis \u0026amp; Venkatesh, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and the idea of digital competence (HagPU \u0026amp; Payton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) serve as the theoretical foundations for this model, which asserts that graduates' employability and success in the job market are significantly impacted by technology acceptance and digital skills acquired during TVET programs.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eVariables:\u003c/h2\u003e \u003cp\u003eTechnology Acceptance (TA): This variable measures the degree to which TVET graduates accept and adopt technology in their educational and professional contexts. It is influenced by perceived ease of use and perceived usefulness (Davis \u0026amp; Venkatesh, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDigital Skills (DS): Digital skills encompass a range of competencies, including digital literacy, information technology proficiency, and the ability to use digital tools and platforms effectively (HagPU \u0026amp; Payton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) This variable assesses the level of digital skills acquired by TVET graduates during their programs.\u003c/p\u003e \u003cp\u003eEmployability Outcomes (EO): Employability outcomes encompass various indicators of graduates' success in the job market, including job placement, job retention, and job performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHypotheses:\u003c/h2\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDigital Skills (DS)\u003c/strong\u003e \u003cp\u003eDigital skills encompass a range of competencies, including digital literacy, information technology proficiency, and the ability to use digital tools and platforms effectively (HagPU \u0026amp; Payton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePerceived Ease of Use (PEU)\u003c/strong\u003e \u003cp\u003eThis variable represents the extent to which individuals perceive that using technology will be free of effort (Silva, n.d.). It's an essential element of TAM, influencing technology acceptance.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePerceived Usefulness (PU)\u003c/strong\u003e \u003cp\u003ePerceived usefulness is another key variable within TAM. It reflects the degree to which individuals believe that using technology will enhance their job performance (Silva, n.d.)\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThis research will be examining the relationships between PEU, PU, Digital Skills, and how these factors collectively impact Employability Outcomes (EO) among TVET graduates. These variables together contribute to a comprehensive understanding of technology acceptance in your study.\u003c/p\u003e \u003cp\u003eDependent Variables\u003c/p\u003e \u003cp\u003eEmployability Outcomes (EO): Employability outcomes encompass various indicators of graduates' success in the job market, including job placement, job retention, and job performance. This is the primary dependent variable in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eResearch Design\u003c/h2\u003e \u003cp\u003eThis study employs a mixed-methods research design to investigate the relationship between technology acceptance, digital skills, and employability among TVET graduates. Quantitative data is collected through surveys, while qualitative insights are gathered through semi-structured interviews with TVET graduates, industrial experts and employers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe study's participants include recent graduates (within the past five years) of TVET programs and employers from diverse industries. A stratified sampling method is used to ensure representation across different TVET disciplines and employment sectors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eSurvey Questionnaire: A structured questionnaire, adapted from prior technology acceptance and employability research, is administered to TVET graduates. The survey includes the following components:\u003c/p\u003e \u003cp\u003eTechnology Acceptance: A modified version of the Technology Acceptance Model (TAM) questionnaire (Silva, n.d.) is used to assess participants' perceptions of technology acceptance.\u003c/p\u003e \u003cp\u003eDigital Skills Assessment: Graduates are asked to self-report their proficiency in various digital skills, including software usage, digital communication, and information literacy.\u003c/p\u003e \u003cp\u003eEmployability Factors: Graduates are quired about their job-seeking experiences, including job search strategies, networking activities, and employment outcomes.\u003c/p\u003e \u003cp\u003eSemi-Structured Interviews: Qualitative data is collected through semi-structured interviews with TVET graduates, employers and industrial experts. The interviews explore in-depth experiences related to technology acceptance, the role of digital skills in the workplace, and perceptions of employability. Interviews are audio-recorded and transcribed for analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis section discusses the statistical analysis from the findings of this initiative prior to moving the structure equation modeling (SEM), section. Out of 300 total distributed questionnaires, 100 for facilitators and 200 for TVET graduates, 140 for the TVET graduates and 100 for the TVET facilitators were submitted back and 15 among the submitted were discarded due to inappropriateness or partially filled. The percentage of valid data collection was 83% that represents 200 useful questionnaires for further data analysis.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e contains the descriptive statistics which show the profile characteristics of the sample. The data were collected from more than 07 cities from different provinces (KPK = 33%, Punjab = 38%, Sindh = 15% and other = 14%). The male participants accounted for 78.5%, while the female participants accounted for 21.5%. Furthermore, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e also exhibits the profile summary of the participants showing that 78.5% of male, while 21.5% of female participated in the study.\u003c/p\u003e \u003cp\u003eMoreover, 32% of SMEs belong to KPK (Peshawar = 21.5%, Mardan = 11.5%), 38% Punjab (Gujranwala = 13.5%, Lahore = 11%, Rawalpindi = 13.5%), 15% Sindh (Karachi = 15%) and 14% of SMEs belong to other regions of the country. The table illustrates that 18.5% of SMEs are working in servicing industry, 20.5% in manufacturing, 24% in sale, 18.5% in marketing and the rest of 18.5% belong to other industries. Finally, 33.5% of the SMEs have more than 10 to 50 employees, 40.5% have 51 to 100, 15% have 101 to 150, 07% have 151 to 200 and 03.5% have 201 to 250 number of students.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eQuantitative Data Analysis: Quantitative data from the survey is analyzed using descriptive statistics, including means and standard deviations. To assess the relationship between technology acceptance, digital skills, and employability outcomes, correlation and regression analyses are conducted. SPSS statistical software is employed for data analysis.\u003c/p\u003e \u003cp\u003eQualitative Data Analysis: Qualitative data from the interviews are subjected to thematic analysis (Braun \u0026amp; Clarke, 2006). Transcripts are coded to Identify recurring themes related to technology acceptance, digital skills utilization, and employability. NVivo software is used to manage and analyze qualitative data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eis obtained from all participants, ensuring confidentiality and anonymity. Participants' Identities are protected through the use of pseudonyms in reporting findings.\u003c/p\u003e"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eThe research model proposed in this study provides a framework for investigating the intricate connections between technology acceptance, digital skills, and employability in TVET graduates. By analyzing the empirical data collected, this research seeks to contribute to a better understanding of how technology acceptance and digital skills development can be leveraged to enhance the employability and success of graduates in a rapidly evolving job market.\u003c/p\u003e\u003ch2\u003eRespondents Demographics\u003c/h2\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eRotated Component Matrix\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.533\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.646\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.649\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.754\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.680\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.754\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.829\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.642\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.717\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.623\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.735\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.774\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.760\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.736\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.791\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.662\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.830\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.801\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.573\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.540\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.607\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.739\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.672\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.572\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.642\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.545\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.530\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.831\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.521\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.658\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.544\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eExtraction Method: Principal Component Analysis.\u003c/p\u003e \u003cp\u003eRotation Method: Varimax with Kaiser Normalization.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eComponents: The analysis resulted in three components (labeled as 1, 2, and 3).\u003c/p\u003e\u003cp\u003ePEOU (Perceived Ease of Use): Component 1 is strongly associated with all PEOU variables (PEOU1 to PEOU10). Component 3 is also associated with PEOU variables but to a lesser extent. Component 2 is associated with PEOU2 and PEOU3.\u003c/p\u003e\u003cp\u003ePU (Perceived Usefulness): Component 1 is associated with PU2, and Component 2 is associated with PU3 to PU10.\u003c/p\u003e\u003cp\u003eDS (Data Security): Component 1 is associated with DS1, DS2, DS6, DS7, and DS9. Component 2 is associated with DS3, DS4, DS5, and DS8.\u003c/p\u003e\u003cp\u003eExtraction Method: Principal Component Analysis (PCA) was used to extract the components.\u003c/p\u003e\u003cp\u003eRotation Method: The components were rotated using Varimax rotation with Kaiser normalization.\u003c/p\u003e\u003cp\u003eThe components represent underlying patterns or factors that explain the variance in the original variables. Component 1 seems to capture a common factor related to PEOU, PU2, DS1, DS2, DS6, DS7, and DS9. Component 2 captures factors related to PEOU2, PEOU3, PU3 to PU10, DS3, DS4, DS5, and DS8. Component 3 captures factors related to PEOU4 to PEOU10.\u003c/p\u003e\u003cp\u003eThese components can be useful for simplifying the interpretation of your data and potentially for further analysis or modeling.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfirmatory Factor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel fit: CMIN = 31.321; DF = 30; CMIN/DF = 2.057; TLI = .656; CFI = .871; GFI = .958; RMR = .050; RMSEA = .063; P close = .118\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor loadings\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU: CA = .977; CR = .879; AVE = .646\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.882\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.774\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU 7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.816\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU 9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.737\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU: CA = .783; CR = .814; AVE = .608\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.505\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.834\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.936\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIS: CA = .804; CR = .804; CR = .673\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIS4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.800\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIS8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.840\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAs illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e the fit indices of the CFA model present a good model fit: GFI = .933, CFI = .981, TLI = .966 and RMSEA is 073. As a result, the measurement model has a good model fit both Cronbach’s alpha value and CR value are above the expected values (.7) for all factors, showing a good reliability.\u003c/p\u003e\u003cp\u003eThe Factor Loadings value represent the strength of the relationships between latent constructs (PU, PE, DIS) and their respective measurement items. As seen PU4 (.882) indicates that PU4 has a strong relationship with the latent construct PU.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExploratory Factor Analysis (EFA)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eRotated Component Matrix\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMeasurement Items\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.845\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.817\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.613\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIS4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.717\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIS8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.614\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEEOU4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.951\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.844\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.827\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eTable\u0026nbsp;3 above shows the component loadings for different measurement items on extracted components. In EFA, components are derived to explain the underlying structure of the data.\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eFor Component 1: Items PU4, PU5, PU7, and PE9 have relatively high loadings, indicating that they contribute strongly to this component.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor Component 2: Items DIS4, DIS8, and PE4 have loadings, indicating their contribution to this component.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor Component 3: Item PE\u003cb\u003eOU\u003c/b\u003e10 has the highest loading, suggesting its association with this component.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn an era marked by rap DIS technological advancements, the integration of technology and the development of digital skills have become critical components of education and workforce readiness. This research aimed to explore the intersection of technology acceptance, digital skill acquisition, and employability in the context of Technical and Vocational Education and Training (TVET) graduates. By examining the experiences and outcomes of TVET graduates in the job market, this study sought to shed light on the significance of technology acceptance and digital skills for their employability.\u003c/p\u003e \u003cp\u003eThe findings of this study underscore the central role that technology acceptance plays in the employability of TVET graduates. Graduates who exhibited a higher level of technology acceptance during their TVET programs were better equipped to navigate the demands of the contemporary job market. They demonstrated greater confidence and adaptability in using technology tools and platforms, attributes that are highly value by employers (Smith \u0026amp; Wilson, 2020). This aligns with the Technology Acceptance Model (TAM) proposed by Davis (1989), which posits that perceived ease of use and perceived usefulness significantly impact technology acceptance. Our study found evidence to support these constructs.\u003c/p\u003e \u003cp\u003eMoreover, the acquisition of digital skills during TVET programs emerged as a crucial factor in enhancing graduates' employability. Employers emphasized the importance of graduates possessing not only basic digital literacy but also advanced skills relevant to their specific industries. This aligns with the work of Bennett et al. (2016), who emphasized the necessity of graduates having domain-specific digital competencies.\u003c/p\u003e \u003cp\u003eHowever, it is important to note that barriers to technology acceptance among TVET graduates still exist. Some students face challenges related to access to technology resources and digital devise issue (Selwyn, 2019). Additionally, resistance to change and a lack of comprehensive digital education strategies within TVET institutions continued to hinder technology acceptance (Brown \u0026amp; Green, 2021). These barriers must be addressed to ensure equitable access to digital skills and techno\u003c/p\u003e \u003cp\u003elogy acceptance for all TVET students.\u003c/p\u003e \u003cp\u003eTo conclude, this research has highlighted the critical nexus between technology acceptance, digital skills, and employability in TVET graduates. To better prepare graduates for the evolving job market, TVET institutions should prioritize not only the development of digital skills but also the cultivation of a positive attitude toward technology adoption. Graduates who possess these attributes are more likely to succeed in a workforce that increasingly relies on digital tools and processes.\u003c/p\u003e \u003cp\u003eAs we move forward, further research is needed to delve deeper into the specific digital skills that are most value by different industries and to explore strategies for overcoming technology acceptance barriers in TVET programs. Ultimately, by fostering a culture of technology acceptance and equipping graduates with relevant digital competencies, we can enhance the employability and success of TVET graduates in the ever-changing landscape of the job market.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors [Azameti Yao Charles, Emmanuel Sei, Razark Bugase and Prof. Yarhards Arthur Dissou (Phd)] confirm that all data generated or analyzed during this study are included in this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research described in this paper entitled \u0026quot; Technology Acceptance and Employability in TVET Graduates\u0026rdquo; has been conducted in accordance with all ethical standards and guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest related to this research. This work was conducted\u003c/p\u003e\n\u003cp\u003eindependently, and there were no external influences that could compromise the integrity\u0026nbsp;of\u0026nbsp;the\u0026nbsp;study.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.16822429906542%\" valign=\"top\"\u003e\n \u003cp\u003eNAME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.51401869158879%\" valign=\"top\"\u003e\n \u003cp\u003eEMAIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.3177570093458%\" valign=\"top\"\u003e\n \u003cp\u003eINSTITUTION\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.16822429906542%\" valign=\"top\"\u003e\n \u003cp\u003eAzameti Yao Charles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.51401869158879%\" valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.3177570093458%\" valign=\"top\"\u003e\n \u003cp\u003eAkenten Appiah-Menka University of Skills Training and Entrepreneurial Development\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.16822429906542%\" valign=\"top\"\u003e\n \u003cp\u003eEmmanuel Amoah-Sei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.51401869158879%\" valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.3177570093458%\" valign=\"top\"\u003e\n \u003cp\u003eAkenten Appiah-Menka University of Skills Training and Entrepreneurial Development\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.16822429906542%\" valign=\"top\"\u003e\n \u003cp\u003eBugase Nabase Razak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.51401869158879%\" valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.3177570093458%\" valign=\"top\"\u003e\n \u003cp\u003eAkenten Appiah-Menka University of Skills Training and Entrepreneurial Development\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.16822429906542%\" valign=\"top\"\u003e\n \u003cp\u003eProf.Yarhads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.51401869158879%\" valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.3177570093458%\" valign=\"top\"\u003e\n \u003cp\u003eAkenten Appiah-Menka University of Skills Training and Entrepreneurial Development\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAuthers Details\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdullah, F., Ward, R., \u0026amp; Ahmed, E. (2016). Investigating the inflPUnce of the most commonly used external variables of TAM on students\u0026rsquo; Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e, 75\u0026ndash;90. https://doi.org/10.1016/j.chb.2016.05.014\u003c/li\u003e\n\u003cli\u003eBaharuddin, M. F., Masrek, M. N., ShuhDISan, S. M., Razali, M. H., \u0026amp; Rahman, M. S. (2021). Evaluating the Content ValDISity of Digital Literacy Instrument for School Teachers in Malaysia through Expert Judgement. \u003cem\u003eInternational Journal of Emerging Technology and Advanced Engineering\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(07), 71\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eBrynjolfsson, E., \u0026amp; McAfee, A. (2014). \u003cem\u003eThe second machine age: Work, progress, and prosperity in a time of brilliant technologies\u003c/em\u003e. WW Norton \u0026amp; Company.\u003c/li\u003e\n\u003cli\u003eBublyk, М. І., Karpiak, A. O., \u0026amp; Rybytska, O. M. (2018). The perspectives of IT-industry development in Ukraine on the basis of data analysis of the world economic forum. \u003cem\u003eInnovative Management: Theoretical, Methodical, and Applied Grounds\u003c/em\u003e, 115\u0026ndash;127.\u003c/li\u003e\n\u003cli\u003eChristensen, R., \u0026amp; Knezek, G. (2001). Instruments for Assessing the Impact of Technology in Education. \u003cem\u003eComputers in the Schools\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(2\u0026ndash;3), 5\u0026ndash;25. https://doi.org/10.1300/J025v18n02_02\u003c/li\u003e\n\u003cli\u003eDavis, F. D., \u0026amp; Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: three experiments. \u003cem\u003eInternational Journal of Human-Computer Studies\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(1), 19\u0026ndash;45.\u003c/li\u003e\n\u003cli\u003eFadel, N. S. M., Ishar, M. I. M., Jabor, M. K., Ahyan, N. A. M., \u0026amp; Janius, N. (2022). Application of Soft Skills Among Prospective TVET Teachers to Face the Industrial Revolution 4.0. \u003cem\u003eMalaysian Journal of Social Sciences and Humanities (MJSSH)\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(6), e001562\u0026ndash;e001562.\u003c/li\u003e\n\u003cli\u003eFinch, D. J., Hamilton, L. K., Baldwin, R., \u0026amp; Zehner, M. (2013). An exploratory study of factors affecting undergraduate employability. \u003cem\u003eEducation + Training\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e(7), 681\u0026ndash;704. https://doi.org/10.1108/ET-07-2012-0077\u003c/li\u003e\n\u003cli\u003eHagPU, C., \u0026amp; Payton, S. (2010). Digital literacy across the curriculum. FutureLab. \u003cem\u003eUnited Kingdom\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eHolden, H., \u0026amp; Rada, R. (2011). Understanding the InflPUnce of Perceived Usability and Technology Self-Efficacy on Teachers\u0026rsquo; Technology Acceptance. \u003cem\u003eJournal of Research on Technology in Education\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(4), 343\u0026ndash;367. https://doi.org/10.1080/15391523.2011.10782576\u003c/li\u003e\n\u003cli\u003eIsmail, A. A., \u0026amp; Hassan, R. (2019). Technical competencies in digital technology towards industrial revolution 4.0. \u003cem\u003eJournal of Technical Education and Training\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(3).\u003c/li\u003e\n\u003cli\u003eJoseph, M. C. (2020). \u003cem\u003eThe utilization of ICT by lecturers in a TVET college in Paarl, Western Cape\u003c/em\u003e. Cape Peninsula University of Technology.\u003c/li\u003e\n\u003cli\u003eKenayathulla, H. B. (2021). Are Malaysian TVET graduates ready for the future? \u003cem\u003eHigher Education Quarterly\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e(3), 453\u0026ndash;467. https://doi.org/10.1111/hequ.12310\u003c/li\u003e\n\u003cli\u003eLee, A. S. H., Atherton, G., \u0026amp; Crosling, G. (2022). TVET teachers for the Fourth Industrial Age: Digital competency frameworks. \u003cem\u003eResearch Gate\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eLegg-Jack, D. W., \u0026amp; Ndebele, C. (2023). Digital Competence of TVET Trainers and Technology Acceptance in the Context of Developing Countries. In \u003cem\u003eHandbook of Research on Establishing Digital Competencies in the Pursuit of Online Learning\u003c/em\u003e (pp. 208\u0026ndash;229). IGI Global.\u003c/li\u003e\n\u003cli\u003eNasution, R. A., Rusnandi, L. S. L., Qodariah, E., Arnita, D., \u0026amp; Windasari, N. A. (2018). The evaluation of digital readiness concept: existing models and future directions. \u003cem\u003eThe Asian Journal of Technology Management\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 94\u0026ndash;117.\u003c/li\u003e\n\u003cli\u003eOkoruwa, V. O., Ogwang, T., \u0026amp; Ndung\u0026rsquo;u, N. S. (2022). \u003cem\u003eRegional Views on the Future of Work: Sub-Saharan Africa\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eSaud, M. S., Shu\u0026rsquo;aibu, B., Yahaya, N., \u0026amp; Yasin, M. A. (2011). Effective integration of information and communication technologies (ICTs) in technical and vocational education and training (TVET) toward knowledge management in the changing world of work. \u003cem\u003eAfrican Journal of Business Management\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(16), 6668\u0026ndash;6673.\u003c/li\u003e\n\u003cli\u003eShikalepo, E. E. (2019). Sustainability of entrepreneurship and innovation among TVET graduates in Namibia. \u003cem\u003eInternational Journal for Innovation Education and Research\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(5), 133\u0026ndash;145.\u003c/li\u003e\n\u003cli\u003eSilva, P. (n.d.). \u003cem\u003eDavis\u0026rsquo; Technology Acceptance Model (TAM) (1989)\u003c/em\u003e (pp. 205\u0026ndash;219). https://doi.org/10.4018/978-1-4666-8156-9.ch013\u003c/li\u003e\n\u003cli\u003eVenkatesh, V., \u0026amp; Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. \u003cem\u003eDecision Sciences\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(2), 273\u0026ndash;315.\u003c/li\u003e\n\u003cli\u003eWorld Economic Forum, J. (2020). The future of jobs report 2020. \u003cem\u003eRetrieved from Geneva\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eYasak, Z., \u0026amp; Alias, M. (2015). ICT Integrations in TVET: Is it up to Expectations? \u003cem\u003eProcedia - Social and Behavioral Sciences\u003c/em\u003e, \u003cem\u003e204\u003c/em\u003e, 88\u0026ndash;97. https://doi.org/10.1016/j.sbspro.2015.08.120\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"technology acceptance, digital skills, employability, TVET, higher education, job market","lastPublishedDoi":"10.21203/rs.3.rs-4230765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4230765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the quickly changing digital landscape, integrating technology into Technical and Vocational Education and Training (TVET) curricula (Yasak \u0026amp; Alias, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) has become increasingly important (Saud et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The acceptance and incorporation of technology into TVET programs has emerged as a crucial factor of educational quality and workforce preparedness in a time of fast technological innovation (Legg-Jack \u0026amp; Ndebele, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study examines the crucial connection between employability, acquiring digital skills, and embracing technology in the context of technical and vocational education and training (TVET). The ability of TVET graduates to embrace and utilize digital technology substantially influence their prospects in the job market in a world marked by rapids technical breakthroughs (Joseph, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study intends to evaluate how graduates' employability and success in finding jobs are impacted by the degree of technology acceptance (Ismail \u0026amp; Hassan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the digital skills acquired during TVET programs.\u003c/p\u003e \u003cp\u003eA mixed-methods research approach was used to collect empirical data, including surveys, interviews, and focus groups with recent TVET graduates, employers, and industry experts. Regression analysis and other quantitative approaches were combined with qualitative methods during the data analysis process to provides a thorough knowledge of the intricate relationships being studied.\u003c/p\u003e","manuscriptTitle":"Technology Acceptance and Employability in TVET Graduates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-03 22:58:14","doi":"10.21203/rs.3.rs-4230765/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f194c8d-f962-4bc4-b20d-5be3371a1838","owner":[],"postedDate":"May 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-28T10:33:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-03 22:58:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4230765","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4230765","identity":"rs-4230765","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}