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Task-Technology Fit of Fourth Industrial Revolution (4IR) Education Technology for Inquiry-Based Learning (IBL)

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Title: Task-Technology Fit of Fourth Industrial Revolution (4IR) Education Technology for Inquiry-Based Learning (IBL)
Language: English
Authors: Segun Michael Ojetunde (ORCID 0000-0002-2333-8632), Umesh Ramnarain (ORCID 0000-0003-4548-5913), Timothy Teo (ORCID 0000-0002-7552-8497)
Source: Educational Media International. 2025 62(1):29-53.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 25
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Educational Technology, Active Learning, Inquiry, Technological Advancement, Futures (of Society), Industry, Influence of Technology, Electronic Learning, Task Analysis, Science Education, Teacher Attitudes, Social Influences, Science Teachers
DOI: 10.1080/09523987.2024.2441139
ISSN: 0952-3987
1469-5790
Abstract: The acceptance of Fourth Industrial Revolution technology for teaching and learning during the pandemic lockdown suggests that a significant portion of educational interactions will shift online shortly. However, the suitability of this technology for inquiry-based learning requires further investigation. This study aimed to assess the effectiveness of Fourth Industrial Revolution educational technology in supporting inquiry-based learning. A cross-sectional study was conducted, and the data were analyzed. The results indicated that task and technology characteristics do not influence science teachers' intentions to adopt online platforms for inquiry-based learning. In contrast, teacher social constructs -- such as perceived ease of use, perceived usefulness, and attitude -- do have an impact. Therefore, teachers' willingness to adopt online platforms could facilitate education stakeholders' efforts in training and adapting curricula for online teaching and learning activities.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1467739
Database: ERIC
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  Value: <anid>AN0183372528;5b101mar.25;2025Mar04.04:13;v2.2.500</anid> <title id="AN0183372528-1">Task-technology fit of Fourth Industrial Revolution (4IR) education technology for inquiry-based learning (IBL) </title> <p>The acceptance of Fourth Industrial Revolution technology for teaching and learning during the pandemic lockdown suggests that a significant portion of educational interactions will shift online shortly. However, the suitability of this technology for inquiry-based learning requires further investigation. This study aimed to assess the effectiveness of Fourth Industrial Revolution educational technology in supporting inquiry-based learning. A cross-sectional study was conducted, and the data were analyzed. The results indicated that task and technology characteristics do not influence science teachers' intentions to adopt online platforms for inquiry-based learning. In contrast, teacher social constructs – such as perceived ease of use, perceived usefulness, and attitude – do have an impact. Therefore, teachers' willingness to adopt online platforms could facilitate education stakeholders' efforts in training and adapting curricula for online teaching and learning activities.</p> <p>Keywords: Inquiry-based learning; online platforms; task-technology fit; Fourth Industrial Revolution</p> <hd id="AN0183372528-2">1. Background</hd> <p>Teaching and learning are two essential, interconnected activities in any educational setting. Effective teaching is defined by its ability to bring about expected changes across various domains of learning. Consequently, effective teaching should lead to improvements in learners' attitudes, knowledge, and skills over time (Babalola & Jaiyeoba, [<reflink idref="bib8" id="ref1">8</reflink>]). Teaching is deemed valuable only when it results in quality learning. Thus, for teaching to be effective, facilitating learning must be central to the teacher's role (Popoola et al., [<reflink idref="bib61" id="ref2">61</reflink>]).</p> <p>Effective teaching relies on several key factors, one of which is choosing suitable teaching methods that align with the subject being taught. The inquiry-based method is especially favored in science education (Ramnarain, [<reflink idref="bib63" id="ref3">63</reflink>]). A common framework supporting inquiry-based learning is the 5E Inquiry-Based Instructional Model, which takes learners through the stages of Engage, Explore, Explain, Elaborate, and Evaluate (R. W. Bybee et al., [<reflink idref="bib12" id="ref4">12</reflink>]). The inquiry approach has been integral to science teaching and learning for many years. It is grounded in the philosophies of early educators like Bruner and Dewey, who emphasized the importance of collaborative learning experiences and knowledge reconstruction. This learning process involves both retrospective thinking (such as adapting schemas, managing data, and exploring meanings) and prospective thinking (formulating new hypotheses), which are essential features of Inquiry-Based Learning (Filipiak, [<reflink idref="bib20" id="ref5">20</reflink>]).</p> <p>Over time, the effectiveness of traditional classroom environments in promoting inquiry-based teaching and learning has been widely discussed. However, contemporary educational activities increasingly necessitate the use of educational technologies, particularly online platforms, reducing reliance on traditional classrooms. This shift helps overcome barriers related to distance and space.</p> <p>The evolution of educational technology has progressed through various stages, beginning with the First and Second Industrial Revolutions. The Third Industrial Revolution integrated classical learning environments with remote learning platforms. In contrast, the Fourth Industrial Revolution (4IR) has ushered in an era of smart technologies in education (Mehta & Hamke, [<reflink idref="bib48" id="ref6">48</reflink>]), featuring remote learning platforms, artificial intelligence (AI), and robotics. This revolution stands apart from its predecessors by technologically merging the physical, digital, and biological realms (Schwab, [<reflink idref="bib69" id="ref7">69</reflink>]).</p> <p>It is important to note that the technological characteristics of the Third Industrial Revolution (3IR) differ significantly from those of the Fourth Industrial Revolution (4IR). In the 3IR, technology primarily aimed to automate learning materials to facilitate teaching and learning activities, with traditional classrooms serving as the primary learning environment. In contrast, the 4IR positions technology as an integral learning platform, allowing for the utilization of previously automated materials (S. M. Ojetunde & Ramnarain, [<reflink idref="bib55" id="ref8">55</reflink>]). In the 4IR, there is also a trend towards the humanization of classroom objects and learning materials, alongside the objectification of learners and educators. Many teaching activities are increasingly managed by robots or conducted online, resulting in minimal teacher-learner interaction (S. M. Ojetunde & Ramnarain, [<reflink idref="bib54" id="ref9">54</reflink>]). Consequently, this study examined the effectiveness of online platforms as a component of 4IR educational technology for inquiry-based learning (IBL).</p> <p>Numerous studies have explored the use and effectiveness of technologies in education across Africa, tracing a timeline that began with visual learning materials, which emphasized the production and use of low-cost instructional resources prior to 1940. This period was followed by the rise of radio media in the early 1940s and 1950s. The adoption of radio media laid the groundwork for the successful development of Information and Communication Technology (ICT) in education. This era of radio media preceded the emergence of audio-visual learning, enabling students to replay recorded lessons (Ajani & Ojetunde, [<reflink idref="bib2" id="ref10">2</reflink>]). Subsequently, the linear learning phase emerged, where educational content was delivered to computers or other electronic devices, allowing learners to study at their own pace, exemplified by computer-based testing (B. N. Lee, [<reflink idref="bib40" id="ref11">40</reflink>]). This was succeeded by collaborative learning, which encourages students to work together in groups to complete tasks, fostering critical thinking (Laal & Ghodsi, [<reflink idref="bib38" id="ref12">38</reflink>]). Finally, mobile learning developed as a pedagogical approach that utilizes mobile wireless technologies to support students and educators across diverse geographical locations (Kert, [<reflink idref="bib37" id="ref13">37</reflink>]). The era of collaborative learning paved the way for gamification, which incorporates game dynamics and frameworks into teaching and learning activities. This approach aims to make learning enjoyable and educational, motivating students in ways that align with their existing preferences and experiences (Dicheva et al., [<reflink idref="bib18" id="ref14">18</reflink>]).</p> <p>Mobile learning serves as the precursor to Mobile Instant Messaging (MIM), enabling two-way communication via Internet protocols (Yoon et al., [<reflink idref="bib79" id="ref15">79</reflink>]). The academic use of MIM is considered important as it fosters brainstorming, teamwork, community building, addresses technical issues, and extends classroom discussions (Chen Wang & Morgan, [<reflink idref="bib14" id="ref16">14</reflink>]). The evolution from MIM has resulted in the adoption of both asynchronous and synchronous online platforms in Africa, which now represent the primary educational technologies of the present day. A study by Rambe and Bere ([<reflink idref="bib62" id="ref17">62</reflink>]) involving higher education students in South Africa found that the use of educational technologies in teaching and learning activities enables students to be more active participants in the learning process, as they can adequately prepare and engage their ideas.</p> <p>Research by Pineteh ([<reflink idref="bib60" id="ref18">60</reflink>]) further supports the effectiveness of various technologies across different subjects in classroom interactions, indicating that emerging technologies are well-suited for teaching and learning tasks. According to Bere ([<reflink idref="bib11" id="ref19">11</reflink>]), the concept of task-technology fit suggests that new technologies are more likely to positively impact individual learning and be utilized effectively when their capabilities align with the tasks users need to perform. A study by Gu and Wang ([<reflink idref="bib27" id="ref20">27</reflink>]) explored the relationship between task fit, technology, and individual characteristics, revealing that the characteristics of individual users are essential for determining the appropriate fit of technology in teaching and learning activities.</p> <p>However, there is a lack of studies establishing the efficacy of online learning platforms associated with the Fourth Industrial Revolution (4IR) for inquiry-based learning (IBL). Furthermore, there are few, if any, known studies in the literature that address the task-technology fit of online learning platforms specifically for IBL. Previous research on task-technology fit has generally focused on how the alignment of technology with tasks predicts effective task performance. Yet, the interplay between users' characteristics, task characteristics, and technology characteristics in influencing users' intentions to use technology for learning activities, particularly IBL, has not been thoroughly investigated. This gap in the literature serves as the primary motivation for the present study.</p> <hd id="AN0183372528-3">2. Literature review</hd> <p>A study on Task-Technology Fit (TTF) was first conducted by Goodhue ([<reflink idref="bib23" id="ref21">23</reflink>]) in an industrial setting, highlighting that the features of tasks and technology are crucial to organizational performance. Since then, the TTF model has gained popularity in predicting outcomes in both business and educational contexts. For example, Muchenje and Seppänen ([<reflink idref="bib50" id="ref22">50</reflink>]) utilized TTF to assess the business value of big data analytics (BDA). Their findings indicated that the reconfigurability of tasks and the editability of BDA significantly influence the fit between tasks and BDA. Similarly, Kamdjoug et al. ([<reflink idref="bib35" id="ref23">35</reflink>]) examined the TTF of information and communication technology (ICT) usage in remote workplaces during the COVID-19 pandemic. Their study found that user satisfaction has the most substantial impact on individual performance, organizational performance, and the intention to continue using information systems. Additionally, Shahzad et al. ([<reflink idref="bib70" id="ref24">70</reflink>]) explored the roles of blockchain-enabled traceability, TTF, and user self-efficacy in mobile food delivery applications. The study revealed that TTF positively affects user attitudes and their intention to continue using the service, with user attitudes further enhancing this intention.</p> <p>The Task-Technology Fit (TTF) model has been widely applied in the field of education. For instance, B. Wu and Chen ([<reflink idref="bib77" id="ref25">77</reflink>]) explored TTF in the context of Massive Open Online Courses (MOOCs) and users' intentions for continued use. Their findings indicated that perceived ease of use and social influence did not significantly affect attitudes, and individual characteristics had no impact on perceived usefulness. Similarly, Alyoussef ([<reflink idref="bib7" id="ref26">7</reflink>]) examined TTF in the acceptance of e-learning within higher education. This study revealed a positive relationship between TTF and the perceived benefits of e-learning, with students expressing high satisfaction with e-learning systems. Additionally, research by Al-Rahmi et al. ([<reflink idref="bib6" id="ref27">6</reflink>]) investigated the application of TTF in adopting online digital media. This study found that TTF concepts enhanced students' active learning and facilitated effective knowledge exchange, data sharing, and discussions. Despite the TTF model's effectiveness in predicting user satisfaction, performance, and technology usage intentions, its versatility in predicting the adoption of technology for inquiry-based learning remains underexplored. There is also a growing consensus that the TTF model may not be robust enough to fully explain users' performance and intentions. For example, a study by Howard and Hair ([<reflink idref="bib32" id="ref28">32</reflink>]) advocated for a more comprehensive theory to better describe the relationship between technology and tasks. Considering this, the present study aims to investigate the efficacy of TTF in predicting teachers' use of Fourth Industrial Revolution (4IR) technologies for inquiry-based learning, employing a theory triangulation approach.</p> <hd id="AN0183372528-4">2.1. Theoretical background</hd> <p>The study is based on the suitability of task technology and theories for process virtualization. The theory of suitability in task technology posits that the hammer is used more than the screwdriver because the hammer is better suited to the task than the screwdriver and that people using the hammer insert nails more effectively than people using the screwdriver (Goodhue et al., [<reflink idref="bib24" id="ref29">24</reflink>]). This implies that the user-friendliness or effectiveness of either the conventional classroom or online inquiry-based learning platforms determines their perceived usefulness, intention to use, or adoption for teaching-learning tasks. Process virtualization theory, on the other hand, seeks to explain and predict whether processes traditionally performed in physical environments can be migrated to virtual or online environments, particularly those based on information technology. Process virtualization theory assumes that process properties (sensory requirements, relational requirements, synchronicity requirements, and identification and control requirements) and information technology properties (representation, reach, and monitorability) influence how suitable a process is to be performed virtually (Overby, [<reflink idref="bib57" id="ref30">57</reflink>]). The theory of process virtualization clarifies that the good match between teaching-learning activities (task) and the ease of using technology to carry them out determines the intention to migrate such activities to a virtual environment via online platforms. Both task-technology fit, and process virtualization theories relate technology, individual, and task characteristics to predict intent to use the computer to perform a specific task. It denotes the interdependence between tasks, technology, and people with their intention to use technology to accomplish future tasks (Yüce et al., [<reflink idref="bib80" id="ref31">80</reflink>]).</p> <hd id="AN0183372528-5">2.1.1. Theoretical framework</hd> <p>While Task-Technology Fit (TTF) theory focuses on how the nature of tasks and technology influences users' intentions to adopt a technology, Process Virtualization (PV) highlights the characteristics of individuals in the technology adoption process. In this study, users' characteristics in the adoption process are defined as teachers' attitudes, perceived ease of use of technology, and perceived usefulness.</p> <p>Figure 1 illustrates relationship among the constructs of Task-Technology Fit (TTF) and Process Virtualization (PV) theories. This framework indicates that the relationship between task and technology characteristics, as well as expected task performance, may be mediated by users' characteristics. It is an integrated version of TTF (Goodhue et al., [<reflink idref="bib24" id="ref32">24</reflink>]), PV (Overby, [<reflink idref="bib57" id="ref33">57</reflink>]), and the Technology Acceptance Model (TAM) (Venkatesh et al., [<reflink idref="bib75" id="ref34">75</reflink>]). While TTF and PV focus on predicting users' actual performance, TAM emphasizes users' intentions, positing that intention is strongly correlated with actual behavior or performance. Therefore, the present study hypothesizes that the fit among task characteristics, technology, and users' characteristics will determine the effective use of online platforms for inquiry-based learning (IBL).</p> <p>Graph: Figure 1. Framework for explaining task-technology fit of 4IR technology for IBL.</p> <hd id="AN0183372528-6">2.2. Task technology fit</hd> <p>The concept of task-technology fit asserts that the nature of teaching and learning activities, along with technology characteristics, are key antecedents to learners' outcomes. Tasks are defined as the teaching and learning activities undertaken by individuals to transform inputs (teaching) into outputs (learning) (Park, [<reflink idref="bib59" id="ref35">59</reflink>]). Technology is characterized as "computer systems (hardware, software, and data) and user support services (such as training, helplines, and internet networks) provided to assist users of online platforms in their teaching or learning tasks." Thus, task-technology fit refers to the extent to which technology aids individuals in performing their teaching and learning tasks (Park, [<reflink idref="bib59" id="ref36">59</reflink>]). Various models have been developed in the literature to explain task-technology fit, with Goodhue and Thompson's model ([<reflink idref="bib25" id="ref37">25</reflink>]) and Gu and Wang's model ([<reflink idref="bib27" id="ref38">27</reflink>]) being particularly well-known. Goodhue and Thompson's model posits that task and technology characteristics are key determinants of the usefulness of new technology for specific tasks. In contrast, Gu and Wang ([<reflink idref="bib27" id="ref39">27</reflink>]) introduced individual characteristics as an additional construct that influences the fit of new technology within a teaching and learning environment. They hypothesized that a strong fit among tasks, technology, and individual characteristics will enhance the effective performance of particular teaching and learning tasks.</p> <p>According to Aljukhadar et al. ([<reflink idref="bib5" id="ref40">5</reflink>]), the original Task-Technology Fit theory proposed by Goodhue ([<reflink idref="bib23" id="ref41">23</reflink>]) aimed to determine individual or organizational task performance using technology without considering the individual characteristics of users. However, with the emergence of smart technologies, particularly those associated with the Fourth Industrial Revolution (4IR), user characteristics have become increasingly relevant to the theory of task-technology fit (Aljukhadar et al., [<reflink idref="bib5" id="ref42">5</reflink>]).</p> <p>Moving beyond the original framework, Liu et al. ([<reflink idref="bib44" id="ref43">44</reflink>]) argued that fit can be more accurately assessed by evaluating the alignment between individual, task, and technology characteristics. Similarly, Lu and Yang ([<reflink idref="bib45" id="ref44">45</reflink>]) contended that task and technology adaptation, without considering users' social constructs, may not yield a comprehensive understanding of the fit. Consequently, recent studies, such as Bere ([<reflink idref="bib11" id="ref45">11</reflink>]), have incorporated user social constructs – specifically perceived usefulness, perceived ease of use, and social impact – to provide a more robust assessment of task and technology suitability. In this study, it is assumed that task-technology fit cannot be adequately assessed without incorporating individual technology-related characteristics, such as attitude and technology (computer) self-efficacy. Previous research has extensively examined the adoption of task-technology fit in contexts such as e-learning (Alyoussef, [<reflink idref="bib7" id="ref46">7</reflink>]), MOOCs (B. Wu & Chen, [<reflink idref="bib77" id="ref47">77</reflink>]), and online digital media (Al-Rahmi et al., [<reflink idref="bib6" id="ref48">6</reflink>]). However, there has been little focus on the task-technology fit of Fourth Industrial Revolution (4IR) educational technology for inquiry-based learning (IBL). Additionally, models developed in prior studies have typically viewed task performance as the outcome variable. Therefore, this study utilizes teachers' intention to use online 4IR technologies for IBL as a measure of task-technology fit, based on the premise that intention is a strong predictor of actual behavioral performance (Venkatesh et al., [<reflink idref="bib75" id="ref49">75</reflink>]).</p> <hd id="AN0183372528-7">2.3. Task characteristics</hd> <p>Task characteristics for inquiry-based learning (IBL) necessitate that students construct their own knowledge and meaning from personal experiences (Tamim & Grant, [<reflink idref="bib72" id="ref50">72</reflink>]). Guido ([<reflink idref="bib28" id="ref51">28</reflink>]) further explores task characteristics in inquiry-based teaching and learning from both student and teacher perspectives. The author notes that IBL requires students to focus on investigating open questions or problems, while teachers guide students beyond basic curiosity into critical thinking and deeper understanding. Additionally, students' engagement in learning activities through an inquiry-based approach demands skills such as analysis, problem-solving, discovery, and creative thinking (Saunders-Stewart et al., [<reflink idref="bib68" id="ref52">68</reflink>]).</p> <p>However, the extent to which technology, particularly online learning platforms, can effectively substitute for classroom interaction during inquiry-based teaching and learning tasks has not been well established. Furthermore, the literature has yet to sufficiently address how well online learning platforms facilitate the development of analysis, problem-solving, discovery, and critical thinking – key components of IBL.</p> <hd id="AN0183372528-8">2.4. Technology characteristics</hd> <p>Virtual teaching and learning tasks differ significantly from traditional classroom activities prevalent in Nigeria, where teacher-led instruction dominated by talk and chalk methods is common. In these settings, teachers primarily use books and boards, and learning occurs through face-to-face interaction in a participatory manner. In contrast, online platforms provide diverse learning options, allowing students who learn better through auditory means to engage with audio clips, while those who prefer reading can access learning materials in text form (Bere, [<reflink idref="bib11" id="ref53">11</reflink>]).</p> <p>Additionally, online platforms facilitate various forms of communication – such as texting and video – while accommodating numerous learners from different locations. These platforms have proven to be crucial tools for ensuring continued access to education during crises over the past decade (Basilaia & Kvavadze, [<reflink idref="bib10" id="ref54">10</reflink>]). Moreover, the successful adoption of technology in education relies not only on its perceived usefulness but also on its affordances. Norman ([<reflink idref="bib51" id="ref55">51</reflink>]) defined affordances as the potential interactions between users and objects, emphasizing the role of user intentions and tool design in shaping perceptions of affordance (McGrenere & Ho, [<reflink idref="bib47" id="ref56">47</reflink>]). For example, prior to the introduction of tablets, iPod Touches facilitated significant interactivity between teachers and students, enabling easier presentation of audio and visual materials (Reid & Ostashewski, [<reflink idref="bib64" id="ref57">64</reflink>]). With the arrival of tablets, Haßler et al. ([<reflink idref="bib30" id="ref58">30</reflink>]) found that teachers positively influenced group discussions and learning outcomes by utilizing tablets in many-to-one scenarios. Additionally, Clarke and Abbott ([<reflink idref="bib16" id="ref59">16</reflink>]) reported that the use of iPads transformed pedagogy by alleviating a considerable technical burden on teachers, allowing teaching and learning to proceed uninterrupted. However, while online platforms have been employed before, during, and after the COVID-19 pandemic, the extent of their usage and effectiveness remains to be fully explored by educational stakeholders and researchers.</p> <p>During the initial adoption of online platforms for learning activities at various educational levels in Nigeria, particularly during the COVID-19 pandemic, several technology-related characteristics – such as internet network availability and differences in internet-connected devices – were identified as significant barriers to effective teaching and learning (S. Ojetunde et al., [<reflink idref="bib52" id="ref60">52</reflink>]). This situation explains why many teachers in developing countries did not prioritize online platforms for instruction prior to the pandemic (Baez-Hernandez, [<reflink idref="bib9" id="ref61">9</reflink>]).</p> <p>In Africa, commonly used online platforms include Zoom, Skype, WhatsApp, Microsoft Teams, and various internet websites (Mhlanga & Moloi, [<reflink idref="bib49" id="ref62">49</reflink>]). In contrast, Europe and the United States typically favor platforms such as Google Classroom, Google Hangouts, Skype, Facebook Groups, Messenger, Microsoft Teams, and Zoom (Labad, [<reflink idref="bib39" id="ref63">39</reflink>]). Meanwhile, in some Asian countries, Microsoft Teams, Zoom, Google Meet, and institutionally developed platforms have been utilized (Basilaia & Kvavadze, [<reflink idref="bib10" id="ref64">10</reflink>]).</p> <p>The use of these platforms is linked to the broader social issue of digital inequality, which refers to a situation where only privileged individuals can pursue their education without interruption (Aldama, [<reflink idref="bib4" id="ref65">4</reflink>]). This concept is akin to what Kelly ([<reflink idref="bib36" id="ref66">36</reflink>]) described as the "homework gap" in the United States, highlighting the challenges students face when they lack access to a high-speed internet connection. A similar situation has been reported in the UK, where approximately 1.9 million households do not have stable internet access (Kelly, [<reflink idref="bib36" id="ref67">36</reflink>]). In Nigeria, the situation is even more dire, compounded by intermittent electricity supply and the inability of many students to afford the costs of connecting devices and remote internet subscriptions (Akinyemi & Ojetunde, [<reflink idref="bib3" id="ref68">3</reflink>]).</p> <p>Another common issue is the challenge of demonstrating research-based teaching and learning activities on online platforms. For instance, online labs, such as those available on Golabz (<ulink href="http://www.golabz.eu">http://www.golabz.eu</ulink>), were established to transition traditional laboratory activities to an online format. However, studies like Mawn et al. ([<reflink idref="bib46" id="ref69">46</reflink>]) have questioned their effectiveness in providing authentic laboratory experiences. Additionally, Timmis et al. ([<reflink idref="bib74" id="ref70">74</reflink>]) noted that while technology has supported teaching and learning in the past, the assessment component is often underdeveloped. This can result in unequal student performance, particularly in situations where assessments may be unfair or provide undue advantages among examinees, as it is challenging to monitor online testing activities effectively (S. Ojetunde et al., [<reflink idref="bib52" id="ref71">52</reflink>]).</p> <hd id="AN0183372528-9">2.5. Individuals (teachers') characteristics</hd> <p>Furthermore, the suitability of using technology in education significantly depends on teachers' ability to integrate technology, pedagogy, and content knowledge (often referred to as TPC) in designing teaching and learning activities (Akinyemi & Ojetunde, [<reflink idref="bib3" id="ref72">3</reflink>]). These skills are closely linked to teachers' computer self-efficacy and related constructs, such as attitudes toward online platform use, perceived ease of use, and perceived usefulness of online learning platforms, as highlighted in fit studies (Goodhue, [<reflink idref="bib23" id="ref73">23</reflink>]; Strong et al., [<reflink idref="bib71" id="ref74">71</reflink>]).</p> <p>Evidence suggests that individuals who are uncomfortable using computers are less equipped for technology-driven tasks (C.-C. Lee et al., [<reflink idref="bib41" id="ref75">41</reflink>]). Additionally, users' social variables, such as perceived ease of use and perceived usefulness, are central to the Technology Acceptance Model (TAM) and the Theory of Reasoned Action (TRA). These theories posit that perceived usefulness and perceived ease of use are critical determinants of users' attitudes, intentions, and actual usage behaviors when it comes to information technology devices (Oluwole, [<reflink idref="bib56" id="ref76">56</reflink>]). In addition to the role of attitude in predicting learners' and facilitators' intentions to use online platforms for learning, computer self-efficacy is crucial in determining how technology can be adapted for specific tasks. Computer self-efficacy has been identified as a significant factor influencing computer-related tasks, including online learning activities (Robertson & Al-Zahrani, [<reflink idref="bib66" id="ref77">66</reflink>]). Johnson ([<reflink idref="bib34" id="ref78">34</reflink>]) noted that computer self-efficacy plays a vital role in a user's decision to utilize computers and related technologies in teaching and learning contexts. Typically, studies focused on task and technology suitability evaluate task performance as a means of assessing this fit (Chongwoo, [<reflink idref="bib15" id="ref79">15</reflink>]; Elçi & Abubakar, [<reflink idref="bib19" id="ref80">19</reflink>]). In the context of this study, the intention to use an online platform for teaching and learning interactions is viewed as a measure of task-technology fit, as it directly influences the actual use of technology for teaching and learning engagement (Davis, [<reflink idref="bib17" id="ref81">17</reflink>]).</p> <hd id="AN0183372528-10">2.6. Development of hypotheses and research questions</hd> <p>According to task-technology fit, virtualization theories, and the Technology Acceptance Model, personal characteristics such as computer self-efficacy and attitudes toward using technology for specific tasks are critical determinants of the intention to use that technology. This intention, in turn, influences actual usage behavior for the specified task. The rationale is that individuals make decisions rationally and systematically based on their abilities and attitudes regarding task performance (Aboelmaged, [<reflink idref="bib1" id="ref82">1</reflink>]).</p> <p>Additionally, it has been observed that low self-efficacy in using computers often stems from the attitudes exhibited by students, which also affects their willingness to engage with online platforms for learning (Oyewusi et al., [<reflink idref="bib58" id="ref83">58</reflink>]). Previous studies indicate that an individual's attitude has a direct and significant impact on their behavioral intention to use technology for educational tasks (Gribbins et al., [<reflink idref="bib26" id="ref84">26</reflink>]). In education, attitude can function as both a process and an outcome variable, influenced by other factors. The use of technology to perform electronic or online tasks can have a direct or indirect impact on attitudes (Widianto et al., [<reflink idref="bib76" id="ref85">76</reflink>]). A study by Renny et al. ([<reflink idref="bib65" id="ref86">65</reflink>]) found that perceived ease of use and perceived usefulness significantly affect attitudes toward usability, which in turn shapes the intention to use technology. Additionally, Davis ([<reflink idref="bib17" id="ref87">17</reflink>]) reported that perceived usefulness directly influences the intention to use technology. Therefore, in Figure 2, it is hypothesized that attitude may serve as an influential mediating variable within the study.</p> <p>Graph: Figure 2. Hypothesized model for the study.</p> <p>The background and the reviewed literature led to the investigation of the following research questions:</p> <p></p> <ulist> <item> is there a significant direct influence of technology characteristics, task characteristics, perceived usefulness, perceived ease of use of 4IR technology, and teacher's attitude on intention to use 4IR education technology for inquiry-based learning?</item> <p></p> <item> Do the technology characteristics, task characteristics, perceived usefulness of 4IR technology, and its perceived ease of use indirectly influence the use of 4IR education technology through teacher's attitudes?</item> </ulist> <hd id="AN0183372528-11">3. Methodology</hd> <p>This section outlines the procedures followed to achieve the outcomes of the study. The steps are itemized as follows:</p> <hd id="AN0183372528-12">3.1. Research context and participants</hd> <p>The study was conducted in Ibadan Municipal, Nigeria. Ibadan is the third-largest city in the country, following Kano, and is located in Oyo State in the southwestern region. As of 2022, it has a population of approximately 4,000,000 people. The city comprises 11 Local Government Areas, with 5 situated in the urban metropolis and 6 in rural areas. As the capital of Oyo State, the education administration in Ibadan differs significantly from that of other southwestern states. Notably, students in Oyo State benefit from free education, which covers both school fees and learning materials. This unique educational policy creates a distinct context for studying the use of technology in teaching and learning within the region.</p> <p>The study adhered to the ethical guidelines established by the Oyo State Ministry of Education (MoE) and the Local Education Authority (LEA). Informed consent was sought and obtained from both the school authorities and the individuals involved before the commencement of the study. During the data collection period, there was no mandatory policy requiring teachers in the state to adopt online platforms for teaching and learning activities. In fact, official learning management systems incorporating Fourth Industrial Revolution (4IR) educational technology were not yet established at that time. Teachers were using online platforms voluntarily. Additionally, no training, workshops, or professional development programs related to the use of online learning platforms had been conducted for teachers in the state prior to data collection. Teachers were attempting to transition to online platforms due to lockdown restrictions from COVID-19, which made face-to-face teaching and learning activities impossible.</p> <p>According to the Oyo State Teaching Service Commission (TESCOM, [<reflink idref="bib73" id="ref88">73</reflink>]), there are 165 secondary schools in the Ibadan metropolis, while the less urbanized areas of Ibadan have 172 secondary schools. Each school is expected to have at least one teacher for Chemistry, Biology, Physics, and Mathematics. These teachers are often the ones who employ an inquiry-based approach in their teaching and learning activities. For the study, a survey instrument was developed and distributed in both printed and electronic formats. The printed version was taken to schools and administered to the sampled teachers. However, due to the heavy academic and administrative workload during the data collection period, an electronic version was also sent to some teachers via WhatsApp. This allowed them to respond at their convenience. Only science teachers who were using online platforms were invited to complete the survey, whether through electronic or printed versions.</p> <p>A total of 100 science and mathematics teachers completed the survey. The participants included 38.83% from rural secondary schools and 61.17% from urban schools. Among urban participants, 62.14% were from public schools, while 37.86% were from private schools. In terms of gender distribution, 32.04% of respondents were male and 40.78% were female. Regarding age, 32.04% were between 18–30 years old, 40.78% were aged 31–40 years, and 27.18% were 41 years and older.</p> <p>Additionally, the educational qualifications of the participants revealed that 94.17% hold a Bachelor's degree (B.Sc), while 65.05% have obtained a Master's degree. Only 5.83% of the respondents possess a Ph.D. degree, as illustrated in Figure 3.</p> <p>Graph: Figure 3. Proportion of teachers sampled by location, subject area, and education level.</p> <hd id="AN0183372528-13">3.2. Survey instrument</hd> <p>The instrument used for the study was both adapted and developed. The adapted aspects were taken from Behavioural Intention Scale (I. Wu & Chen, [<reflink idref="bib78" id="ref89">78</reflink>]) and the Perceived Ease of Use and Usefulness Scale (Saidu & Al Mamun, [<reflink idref="bib67" id="ref90">67</reflink>]). The survey is made up of seven constructs: Teachers' Attitude (ATT), Teachers' computer Self-Efficacy (TCS), Perceived Ease of Use of Online Platforms (PEU), Perceived Usefulness of Online Platforms (PUO), Task Characteristic (TC), Technology Characteristics (TEC) and the Intention to Use Online Platforms (IT). The survey was scaled after Likert's 4-point modified format.</p> <p>At leastfive items were pooled for each of the constructs in the survey respectively. The feedback from a trial test of the survey instrument with teachers as well as from some experts in the field of measurement and evaluation led to the elimination of some items except for teachers' computer self-efficacy. The remaining items for each of the construct are as follows: (PEU = 4), (TEC = 3), (PUO = 4), (ATT = 4), (TCS = 5), (TC = 4), and (IT = 4). The final instrument comprises 28 items for both adapted and developed (TCS, ATT, TEC, and TC) constructs. The negatively worded items were reversed during data analysis.</p> <hd id="AN0183372528-14">3.3. Analytical procedures</hd> <p>The data collected was analyzed using descriptive statistics, specifically frequency counts, percentages, and bar charts to illustrate the demographic characteristics of the respondents, as shown in Figure 2. The developed model was analyzed sequentially using Smart Partial Least Square Structural Equation Modeling (Smart PLS-SEM). The first procedure involved assessing the measurement model using the path algorithm. This step aimed to estimate reliability coefficients (Cronbach's Alpha and composite reliability), as well as Average Variance Extracted (AVE) and discriminant validity of the constructs in the model, as detailed in Tables 1 and 2. The second procedure focused on assessing the structural model through bootstrapping. Bootstrapping, as described by Hair and Alamer ([<reflink idref="bib29" id="ref91">29</reflink>]), allows for statistical testing of significance without relying on parametric assumptions, providing standard errors and t-values for the estimates. The purpose of bootstrapping in this study is to estimate the coefficients of direct and indirect effects to address the research questions outlined, as shown in Tables 3 and 4. The indicators of the quality of the developed model are discussed in section 3.5.</p> <p>Table 1. Statistics for assessing reliability.</p> <p> <ephtml> <table><thead><tr><td>Construct</td><td>Range of Factor Loadings</td><td>Cronbach Alpha</td><td>Composite Reliability</td><td>AVE</td></tr></thead><tbody><tr><td>ATT</td><td>0.72–0.91</td><td>0.796</td><td>0.867</td><td>0.621</td></tr><tr><td>IT</td><td>0.79–0.91</td><td>0.895</td><td>0.927</td><td>0.761</td></tr><tr><td>PEU</td><td>0.52–0.82</td><td>0.695</td><td>0.595</td><td>0.450</td></tr><tr><td>PUO</td><td>0.69–0.85</td><td>0.733</td><td>0.833</td><td>0.557</td></tr><tr><td>TC</td><td>0.73–0.86</td><td>0.795</td><td>0.865</td><td>0.617</td></tr><tr><td>TCS</td><td>0.79–0.90</td><td>0.918</td><td>0.937</td><td>0.750</td></tr><tr><td>TEC</td><td>0.56–0.85</td><td>0.576</td><td>0.759</td><td>0.519</td></tr></tbody></table> </ephtml> </p> <p>Table 2. Assessing validity using Fornell–Larcker approach.</p> <p> <ephtml> <table><thead><tr><td>Construct</td><td>ATT</td><td>IT</td><td>PEU</td><td>PUO</td><td>TC</td><td>TCS.</td><td>TEC</td></tr></thead><tbody><tr><td>ATT</td><td><bold>0.788</bold></td><td /><td /><td /><td /><td /><td /></tr><tr><td>IT</td><td>0.633</td><td><bold>0.873</bold></td><td /><td /><td /><td /><td /></tr><tr><td>PEU</td><td>0.383</td><td>0.344</td><td><bold>0.592</bold></td><td /><td /><td /><td /></tr><tr><td>PUO</td><td>0.583</td><td>0.382</td><td>0.448</td><td><bold>0.746</bold></td><td /><td /><td /></tr><tr><td>TC</td><td>0.290</td><td>0.149</td><td>0.400</td><td>0.366</td><td><bold>0.786</bold></td><td /><td /></tr><tr><td>TCS</td><td>0.486</td><td>0.663</td><td>0.268</td><td>0.384</td><td>0.171</td><td><bold>0.866</bold></td><td /></tr><tr><td>TEC</td><td>0.252</td><td>0.137</td><td>0.163</td><td>0.165</td><td>0.412</td><td>0.131</td><td><bold>0.721</bold></td></tr></tbody></table> </ephtml> </p> <p>1 The bold values are the square root of the average variance extract (AVE) for each construct.</p> <p>Table 3. Direct effects of task, technology, and users' characteristics on the adoption of 4IR education technology for IBL.</p> <p> <ephtml> <table><thead><tr><td>Ho</td><td>Path</td><td>Beta</td><td>Mean</td><td>Std. Error</td><td>t</td><td>p-value</td><td>Sig</td></tr></thead><tbody><tr><td>Ho<sub>1</sub></td><td>TC</td><td>→</td><td>IT</td><td>–0.033</td><td>–0.031</td><td>0.035</td><td>0.243</td><td>0.808</td><td>NS</td></tr><tr><td>Ho<sub>2</sub></td><td>TEC</td><td>→</td><td>IT</td><td>−0.012</td><td>−0.011</td><td>0.123</td><td>0.100</td><td>0.920</td><td>NS</td></tr><tr><td>Ho<sub>3</sub></td><td>ATT</td><td>→</td><td>IT</td><td>0.645</td><td>0.647</td><td>0.094</td><td>6.845</td><td>0.00</td><td>S</td></tr></tbody></table> </ephtml> </p> <p>Table 4. Indirect effects of task, technology and users' characteristics on the adoption of 4IR education technology for IBL.</p> <p> <ephtml> <table><thead><tr><td>Ho</td><td>Path (mediating relationship)</td><td>Beta</td><td>Std. Error</td><td>t</td><td>p-value</td><td>Sig</td></tr></thead><tbody><tr><td>Ho<sub>4</sub></td><td>TC</td><td>→</td><td>ATT</td><td>→</td><td>IT</td><td /><td /><td /><td /><td>0.017</td><td>0.097</td><td>0.190</td><td>0.850</td><td>NS</td></tr><tr><td>Ho<sub><bold>5</bold></sub></td><td>PUO</td><td>→</td><td>ATT</td><td>→</td><td>IT</td><td /><td /><td /><td /><td>0.354</td><td>0.069</td><td>5.146</td><td>0.000</td><td>S</td></tr><tr><td>Ho<sub><bold>6</bold></sub></td><td>TEC</td><td>→</td><td>ATT</td><td>→</td><td>IT</td><td /><td /><td /><td /><td>0.010</td><td>0.080</td><td>1.22'</td><td>0.223</td><td>NS</td></tr><tr><td>Ho<sub><bold>7</bold></sub></td><td>PEU</td><td>→</td><td>PUO</td><td>→</td><td>ATT</td><td>→</td><td>IT</td><td /><td /><td>0.158</td><td>0.047</td><td>3.345</td><td>0.001</td><td>S</td></tr></tbody></table> </ephtml> </p> <hd id="AN0183372528-15">3.4. Model validation/assessment</hd> <p>The fit indices of the hypothesized model were examined to know the fitness of the model. The fit indexes of the model are SRMR = 0.084, Exact fit criteria d_ULS = 3.591 and d_G = 1.398, NFI = 0.792, Chi<sups>2</sups> = 722.87. For a model to be considered fit the value of SRMR < 0.08 and NFI > 0.90 was recommended (Byrne & Byrne, [<reflink idref="bib13" id="ref92">13</reflink>]). The fit indexes for the model in this study show a relatively fit model. The model, path coefficients, coefficients of determination (<emph>R</emph>), and factor loadings are shown in Figure 4.</p> <p>Graph: Figure 4. Measurement and structure model of the study.</p> <p>From the model, it can be observed that the constructs reliably predict 40.2% of the total variance in science teachers' intention to use online platforms for inquiry-based learning activities, with an R<sups>2</sups> value of 0.402. This indicates a good fit for the model, suggesting that the included constructs are significant contributors to understanding teachers' intentions in this context.</p> <hd id="AN0183372528-16">3.5. Assessing reliability</hd> <p>Different statistics were employed to establish the reliability of the survey instrument apart from the factor loading (λ). Cronbach Alpha reliability coefficients and composite reliability alongside Average variance extract were estimated and presented in Table 1.</p> <p>The factor loadings indicate that all coefficients are above the acceptable cutoff of λ ≥ 0.707, except for TEC3, PEU2, and PEU1, which fall below this threshold. The Cronbach's Alpha reliability coefficients range from 0.5 to 0.9, demonstrating that the consistency of the constructs is moderate to high. Composite reliability for all constructs exceeds the cutoff of 0.7, except for PEU, suggesting that the items within the instrument are generally reliable. The Average Variance Extracted (AVE), which measures construct validity, ranges from 0.51 to 0.75, except for PEU, which has an AVE of 0.45–again falling below the cutoff of 0.5. Based on these indicators, it can be inferred that the instrument is reliable overall.</p> <hd id="AN0183372528-17">3.6. Assessing validity</hd> <p>The Fornell–Larcker approach was employed to assess the validity of the instrument, as illustrated in Table 2. This method allows for the evaluation of discriminant validity by comparing the square root of the Average Variance Extracted (AVE) for each construct with the correlations between constructs. The results indicate that each construct's square root of AVE is greater than its correlations with other constructs, supporting the validity of the instrument in measuring the intended constructs.</p> <p>In this approach, the Fornell–Larcker criterion is considered. This states that for a construct to be said to have discriminant validity, the coefficient of relationship among the constructs in the model should not be up to the square root of the average variance extract (AVE) of the constructs in question (Fornell & Larcker, [<reflink idref="bib21" id="ref93">21</reflink>]). From the result, the square root of AVE (0.621) for the ATT in Table 2 is greater (0.788) than its correlation with other constructs (in the column) in the model. Also, the square root of AVE (0.761) for IT in Table 2 is greater (0.873) than its correlation coefficient with other constructs (in the column). The same condition was fulfilled for PEU (0.592), PUO (0.746), TC (0.786), TCS (0.866) and TEC (0.721). Based on this result, the discriminant validity of the construct in the model was confirmed.</p> <hd id="AN0183372528-18">4. Findings</hd> <p></p> <hd id="AN0183372528-19">4.1. Answers to the research questions through hypotheses testing</hd> <p>The direct effects were determined by testing hypotheses H0<subs>1</subs> to H0<subs>3</subs>, which examined the relationships among the core constructs in the study. Additionally, the indirect effects were calculated to assess the mediating effects of certain constructs on others, as hypothesized in H0<subs>4</subs> to H0<subs>7</subs>. This comprehensive analysis aimed to address the research questions raised in the study, providing insights into the dynamics of how various factors influence teachers' intentions to use online platforms for inquiry-based learning.</p> <hd id="AN0183372528-20">Research Question 1:</hd> <p>Is there a significant direct influence of technology characteristics, task characteristics, perceived usefulness, perceived ease of use of 4IR technology and teacher's attitude on intention to use 4IR education technology for inquiry-based learning?</p> <p>Table 3 presents the result of the analysis on the direct influence of the dependent variables on the criterion variable (IT). The result shows that task characteristics have no significant (direct) influence on science teachers' intention to employ online platforms for inquiry-based learning (β = 0.027, <emph>p</emph> > 0.05) which implies that there is no significant direct influence of task characteristics (TC) on teachers' intention to use (IT) online learning platform. Hypothesis 2 investigates the direct influence of technology characteristics on teachers' intention to use the online platform, the result shows that technology Characteristics (TEC) have no significant direct effect on teachers' intention (IT) (β = -0.012, <emph>p</emph> > 0.05). However, teachers' attitude to use online platforms has a significant direct effect on their intention to online platforms for inquiry-based learning (β = -0.033, <emph>p</emph> < 0.05).</p> <hd id="AN0183372528-21">Research Question 2:</hd> <p>Do the technology characteristics, task characteristics, perceived usefulness of 4IR technology, and its perceived ease of use indirectly influence the use of 4IR education technology through teacher's attitudes?</p> <p>The result in Table 4 shows the mediated relationship among the constructs in the model. It could be a hypothetical relationship proposed by Ho<subs><bold>4</bold></subs> is not significant (β = 0.017, <emph>p</emph> > 0.05), which implies that both direct and mediating effects of task characteristics on teachers' intention to use online platforms for inquiry-based learning are not significant. Also, the mediated effect of TEC on IT proposed by hypothesis Ho<subs><bold>6</bold></subs> was not significant (β = 0.010, <emph>p</emph> > 0.05), which shows that both direct and mediated effects of technology characteristics on their intention to use online platforms for inquiry-based learning were not significant. However, the mediated effect of PUO on IT was significant (β = 0.354, <emph>p</emph> < 0.05) which is an indication that attitude goes a long way in helping science teachers to consider the usefulness of online platforms and their intention to use them for teaching-learning purposes. In the same vein, the effect of PEU on IT mediated by PUO and ATT proposed by Ho<subs><bold>7</bold></subs> was significant (β = 0.158, <emph>p</emph> < 0.05), this implies that the effect of personal characteristics of the teachers whether PEU or PUO on IT could be significantly mediated by ATT whereas the effect of TEC as proposed by Ho<subs><bold>8</bold></subs> was not significantly mediated by any teachers' personal characteristics (β = 0.043, <emph>p</emph> > 0.05). From the result, it could be observed that the person of the teachers is the major determinant of their intention to use online platforms for inquiry-based teaching-learning activities.</p> <hd id="AN0183372528-22">5. Discussion</hd> <p>Based on the results of the study, it could be observed that neither technology characteristics nor task characteristics nor teachers' computer self-efficacy influenced the intention of science teachers to use online platforms for inquiry-based teaching-learning activities. The result showed that science teachers' personal characteristics, such as perceived ease of use, perceived usefulness of online platforms, and attitude have an impact on science teachers' intention to use online platforms for inquiry-based learning. In contrast to the fact that task-technology-fit connotes the interdependence among tasks, technology, and individual characteristics (Yüce et al., [<reflink idref="bib80" id="ref94">80</reflink>]), the finding shows that individual teacher characteristics speak volumes when it comes to the adoption of online platforms for inquiry-based teaching-learning activities. This finding aligns with the work of Alyoussef ([<reflink idref="bib7" id="ref95">7</reflink>]) and Al-Rahmi et al. ([<reflink idref="bib6" id="ref96">6</reflink>]), who reported that user characteristics are essential in determining the appropriate technology for a given task. However, the finding contradicts the report of B. Wu and Chen ([<reflink idref="bib77" id="ref97">77</reflink>]) who found that perceived ease of use and social influence did not significantly affect attitudes, and individual characteristics had no impact on perceived usefulness of technology for tasks. This could presumably be because the notion of technological tools in contemporary times has moved beyond mainstream conceptualization, with routine apparatuses/instruments being programmed as technological tools and individuals being described as being born with digital DNA (S. M. Ojetunde & Nweze, [<reflink idref="bib53" id="ref98">53</reflink>]). However, Lin and Huang ([<reflink idref="bib42" id="ref99">42</reflink>]) argued that technology characteristics such as remote network availability and quality of connecting devices, and task characteristics such as tactfulness affect the suitability and acceptance of new technologies which in this study were not considered as technology or task characteristics.</p> <p>The ineffectiveness of technology characteristics in predicting teachers' intention to use online platforms for inquiry-based activities has also been envisaged due to the reason that many teachers in Nigeria have limited access to the computer-based Internet for the reason in part due to socio-economic status and poor funding of science education (L. Hsu, [<reflink idref="bib33" id="ref100">33</reflink>]) and mobile technologies which enhance cost-effective interactions on smartphones, tablets, and iPads are popular tools for online interaction. Also, the proliferation of online teaching resources may make the task of teaching on an online platform a trivial task as the present-day offers a wide range of didactic resources ranging from materials, apps, webs (Gladun & Buchynska, [<reflink idref="bib22" id="ref101">22</reflink>]), and online laboratory (web site address: <ulink href="http://www.golabz.eu">http://www.golabz.eu</ulink>). The foregoing shows that there is a great mutation in the dimension and configuration of initially conceived task and technology characteristics which invariably has ceded the fitness of technology for a task on other constructs.</p> <p>It is noteworthy that the task-technology fit theory has been criticized for the exclusion of individual perceptions in determining choices concerning technology by some scholars (Hong et al., [<reflink idref="bib31" id="ref102">31</reflink>]; Strong et al., [<reflink idref="bib71" id="ref103">71</reflink>]). The results of the study are also in alliance with such scholars. The present study found that teachers' attitudes, perceived ease of use, and perceived usefulness are major constructs that could influence the fitness of technology for tasks most especially teaching-learning activities. This result corroborates the findings of Liu et al. ([<reflink idref="bib43" id="ref104">43</reflink>]) and is also in alliance with the technology acceptance model (Zhou et al., [<reflink idref="bib81" id="ref105">81</reflink>]) and unified theory of acceptance and use of technology (Venkatesh et al., [<reflink idref="bib75" id="ref106">75</reflink>]). The foregoing is a strong indication that the task-technology fits model may be considered insufficient for explaining the fit for choice (intention to use) of teaching-learning technology as initially proposed (Goodhue et al., [<reflink idref="bib24" id="ref107">24</reflink>]) but the fitness of purpose (task performance) as the power of choice belongs to the users of technology tools. This is probably because every industrial revolution/era comes with its technology which is programmed in an appealing manner to the users and not the task. Therefore, as reported by Howard and Hair ([<reflink idref="bib32" id="ref108">32</reflink>]), the present study revealed that individual characteristics better describe the fit between technology and tasks for optimum performance most especially in human oriented activities most especially IBL which requires learners to engage, explore, explain, and evaluate.</p> <hd id="AN0183372528-23">5.1. Contributions to theory</hd> <p>Prominent among task-technology fit models are Goodhue and Thompson's model ([<reflink idref="bib25" id="ref109">25</reflink>]) and Gu and Wang's model ([<reflink idref="bib27" id="ref110">27</reflink>]). These models suggest that the fit between task and technology should be reflected in the actual performance of the task, particularly in terms of learning satisfaction and effectiveness among students in teaching-learning interactions. In contrast to the traditional focus on actual task performance, this study hypothesized – and found – that the intention to use technology plays a crucial role in determining the goodness of fit between task and technology. This is particularly relevant because task performance in teaching-learning activities cannot be directly measured. Moreover, the study revealed that certain personal and social constructs, such as users' attitudes toward using the technology and their perceived usefulness of it, enhance the fit of technology for a given task. These findings underscore the importance of considering both individual user factors and the context of technology use when evaluating task-technology fit in educational settings.</p> <hd id="AN0183372528-24">5.2. Implications for practice</hd> <p>The finding that technology characteristics had no influence on science teachers' intention to use online platforms for inquiry-based learning (IBL) provides valuable insights for education stakeholders and researchers. It suggests that the fit of technology for teaching practices, particularly IBL, may not necessarily depend on extensive hardware or technological tools typically found in conventional classrooms. Instead, success may rely more on having well-equipped and knowledgeable individuals who can effectively leverage smart technology along with available online learning resources. Additionally, the results highlight that many current teachers possess inherent technological skills that require minimal or no training to integrate the latest technologies into their teaching practices, especially in inquiry-based learning. This suggests that the transition to using online platforms for IBL is now more accessible, as it demands less training and is facilitated by the wealth of learning materials available online. The broader implication of the study emphasizes the importance of considering teacher social constructs – such as perceived ease of use, perceived usefulness, and attitudes – when selecting online platforms for facilitating inquiry-based teaching and learning activities. These factors should be prioritized to enhance the effectiveness of technology integration in education.</p> <hd id="AN0183372528-25">5.3. Limitations of the study</hd> <p>There are several limitations in the study, particularly regarding the small sample size. In this research, 100 science teachers were sampled from two local governments (one rural and one urban) out of a total of 11 in the Ibadan municipal area. Sample size limitations may pose a threat to the external validity of the findings. Therefore, future researchers are encouraged to use a more representative sample of teachers in Ibadan Municipal or to extend the study's scope beyond Oyo State, Nigeria. Additionally, the study employed a quantitative approach, which limits the depth of opinions gathered from participants compared to a qualitative approach that allows for more nuanced and extended responses. Researchers interested in investigating similar constructs are advised to consider using a mixed-methods design, incorporating both quantitative and qualitative approaches. This triangulation can provide a more comprehensive understanding of participants' perspectives on the issues being studied.</p> <hd id="AN0183372528-26">6. Conclusion</hd> <p>The use of online platforms has gained global acceptance among stakeholders in various sectors, with education being no exception. This study aimed to address the concerns of stakeholders who doubt the effectiveness of online platforms for managing activities associated with teaching and learning in the sciences, particularly through an inquiry-based approach. Based on the findings, it was concluded that the intention to use online platforms for inquiry-based learning is not determined by the types of technology or platforms used, nor by the specific activities within science education. Instead, it is influenced by teachers' perceived ease of use, perceived usefulness, and their attitudes toward the technology or online platforms involved. This suggests that stakeholders in science and technology education should redirect their focus towards the social constructs that influence teachers' adoption of technology for teaching and learning activities. When planning to transition from traditional classrooms to online environments, the emphasis should be placed on understanding and supporting these social constructs, rather than solely concentrating on acquiring extensive hardware or technological tools.</p> <hd id="AN0183372528-27">Disclosure statement</hd> <p>No potential conflict of interest was reported by the author(s).</p> <ref id="AN0183372528-28"> <title> References </title> <blist> <bibl id="bib1" idref="ref82" type="bt">1</bibl> <bibtext> Aboelmaged, G. M. (2010). Predicting e-procurement adoption in a developing country. Industrial Management & Data Systems, 110 (3), 392 – 414. https://doi.org/10.1108/02635571011030042</bibtext> </blist> <blist> <bibl id="bib2" idref="ref10" type="bt">2</bibl> <bibtext> Ajani, M. O., & Ojetunde, S. M. (2021). Effects of education budget and enrolment on science, technology, engineering and mathematics (STEM) education in Nigeria. International Journal of Educational Management, 19 (2), 30 – 41. https://doi.org/10.52589/BJCE-SMUJBDB1</bibtext> </blist> <blist> <bibl id="bib3" idref="ref68" type="bt">3</bibl> <bibtext> Akinyemi, A. L., & Ojetunde, S. M. (2020). Techno-pedagogical models and influence of adoption of remote learning platforms on classical variables of education inequality during COVID-19 pandemic in Africa. Journal of Positive Psychology and Counselling, 7, 72 – 82. https://doi.org/10.1038/d41586-020-00896-7will</bibtext> </blist> <blist> <bibl id="bib4" idref="ref65" type="bt">4</bibl> <bibtext> Aldama, P. (2020). What will happen to poor students when schools go online? Retrieved July 24, from https://rappler.com/voices/ispeak/opinion-poorstudents-schools-online-coronavirus</bibtext> </blist> <blist> <bibl id="bib5" idref="ref40" type="bt">5</bibl> <bibtext> Aljukhadar, M., Senecal, S., & Nantel, J. (2014). Is more always better? Investigating the task- technology fit theory in an online user context. Information & Management, 51 (4), 391 – 397. https://doi.org/10.1016/j.im.2013.10.003</bibtext> </blist> <blist> <bibl id="bib6" idref="ref27" type="bt">6</bibl> <bibtext> Al-Rahmi, W. M., Al-Adwan, A. S., Al-Maatouk, Q., Othman, M. S., Alsaud, A. R., Almogren, A. S., & Al-Rahmi, A. M. (2023). Integrating communication and task-technology fit theories: The adoption of digital media in learning. Sustainability, 15 (10), 8144. https://doi.org/10.3390/su15108144</bibtext> </blist> <blist> <bibl id="bib7" idref="ref26" type="bt">7</bibl> <bibtext> Alyoussef, I. Y. (2023). Acceptance of e-learning in higher education: The role of task-technology fit with the information systems success model. Heliyon, 9 (3), 1 – 19. https://doi.org/10.1016/j.heliyon.2023.e13751</bibtext> </blist> <blist> <bibl id="bib8" idref="ref1" type="bt">8</bibl> <bibtext> Babalola, J. B., & Jaiyeoba, A. O. (2008). Curriculum development for effective learning in higher education during knowledge and digital revolutions. Awemark Publishers.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref61" type="bt">9</bibl> <bibtext> Baez-Hernandez, R. A. (2019). Impact of instructional alignment workshop on teachers' self-efficacy and perceived instruction performance. Education Reform Journal, 4 (1), 1 – 13. https://doi.org/10.22596/erj2019.04.01.1.13</bibtext> </blist> <blist> <bibtext> Basilaia, G., & Kvavadze, D. (2020). Transition to online education in schools during a SARS- CoV-2 coronavirus (COVID-19) pandemic in Georgia. Journal of Pedagogical Research, 5 (4), 43 – 56. https://doi.org/10.29333/pr/7937</bibtext> </blist> <blist> <bibtext> Bere, A. (2018). Applying an extended task-technology fit for establishing determinants of mobile learning: An instant messaging initiative. Journal of Information Systems Education, 29 (4), 239 – 252. <ulink href="http://jise.org/Volume29/n4/JISEv29n4p239.html">http://jise.org/Volume29/n4/JISEv29n4p239.html</ulink></bibtext> </blist> <blist> <bibtext> Bybee, R. W., Taylor, J. A., Gardner, A., Van Scotter, P., Carlson Powell, J., Westbrook, A., & Landes, N. (2006). The BSCS 5E instructional model: Origins, effectiveness and applications. BSCS.</bibtext> </blist> <blist> <bibtext> Byrne, B. M., & Byrne, B. M. (2006). Structural equation modeling with EQS: Basic concepts, applications, and programming (2nd ed., pp. 1–454). Routledge. https://doi.org/10.4324/9780203726532</bibtext> </blist> <blist> <bibtext> Chen Wang, L.-C., & Morgan, W. R. (2008). Student perceptions of using instant messaging software to facilitate synchronous online class interaction in a graduate teacher education course. Journal of Computing in Teacher Education, 25 (1), 15 – 21.</bibtext> </blist> <blist> <bibtext> Chongwoo, P. (2018). Exploring a new determinant of task technology fit: Content characteristics. Journal of International Technology and Information Management, 27 (3), 100 – 118. https://doi.org/10.58729/1941-6679.1385</bibtext> </blist> <blist> <bibtext> Clarke, L., & Abbott, L. (2016). Young pupils', their teacher's and classroom assistants' experiences of iPads in a Northern Ireland school: "Four and five years old, who would have thought they could do that?" British Journal of Educational Technology, 47 (6), 1051 – 1064. https://doi.org/10.1111/bjet.12266</bibtext> </blist> <blist> <bibtext> Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319 – 340. https://doi.org/10.2307/249008</bibtext> </blist> <blist> <bibtext> Dicheva, D. D., Agre, D., G, C., & Angelova, G. (2015). Gamification in education: A systematic mapping study. Journal of Educational Technology & Society, 18 (3), 75 – 88.</bibtext> </blist> <blist> <bibtext> Elçi, A., & Abubakar, A. M. (2021). The configurational effects of task-technology fit, technology-induced engagement and motivation on learning performance during covid- 19 pandemic: An FsQCA approach. Education and Information Technologies, 26 (6), 7259 – 7277. https://doi.org/10.1007/S10639-021-10580-6</bibtext> </blist> <blist> <bibtext> Filipiak, E. (2011). Z Wygotskim i Brunerem w tle: Słownik pojęć kluczowych. Wydawnictwo Uniwersytetu Kazimierza Wielkiego.</bibtext> </blist> <blist> <bibtext> Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18 (1), 39 – 50.</bibtext> </blist> <blist> <bibtext> Gladun, M., & Buchynska, D. (2017). Tools for inquiry-based learning in primary school. Open Educational E-Environment of Modern Universities, 3 (3), 43 – 54. https://doi.org/10.28925/2414-0325.2017.3.4354</bibtext> </blist> <blist> <bibtext> Goodhue, D. L. (1995). Understanding user evaluations of information systems. Management Science, 41 (12), 1827 – 1844.</bibtext> </blist> <blist> <bibtext> Goodhue, D. L., Klein, B. D., & March, S. T. (2000). User evaluations of is as surrogates for objective performance. Information & Management, 38 (2), 87 – 101. https://doi.org/10.1016/S0378-7206(00)00057-4</bibtext> </blist> <blist> <bibtext> Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual peformance. MIS Quarterly, 19 (2), 213 – 236.</bibtext> </blist> <blist> <bibtext> Gribbins, M., Shaw, M., & Gebauer, J. (2003). An investigation into employees' acceptance of integrating mobile commerce into organizational processes. Proceedings of the 9th Americas Conference on Information Systems, Tampa, FL (pp. 77 – 87).</bibtext> </blist> <blist> <bibtext> Gu, L., & Wang, J. (2015). A task technology fit model on e-learning. Issues in Information Systems, 16 (I), 163 – 169. https://doi.org/10.48009/1_iis_2015_163-169</bibtext> </blist> <blist> <bibtext> Guido, M. (2017). Inquiry-Based Learning Definition, benefits & strategies. Retrieved October 26, from https://<ulink href="http://www.prodigygame.com/blog/inquiry-basedl-learning-defintiionbenefits-strategies/">www.prodigygame.com/blog/inquiry-basedl-learning-defintiionbenefits-strategies/</ulink></bibtext> </blist> <blist> <bibtext> Hair, J. F., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied examples. Research Methods in Applied Linguistics, 1 (3), 100027. https://doi.org/10.1016/j.rmal.2022.100027</bibtext> </blist> <blist> <bibtext> Haßler, B., Major, L., & Hennessy, S. (2016). Tablet use in schools: A critical review of the evidence for learning outcomes. Journal of Computer Assisted Learning, 32 (2), 139 – 156. https://doi.org/10.1111/jcal.12123</bibtext> </blist> <blist> <bibtext> Hong, W., Thong, J. Y., Wai-ManWong, K. T., & Tam, K.-Y. (2002). Determinants of user acceptance of digital libraries: An empirical examination of individual differences and system characteristics. Journal of Management Information Systems, 18 (3), 97 – 124. https://doi.org/10.1080/07421222.2002.11045692</bibtext> </blist> <blist> <bibtext> Howard, M. C., & Hair, J. F. (2023). Integrating the expanded task-technology fit theory and the technology acceptance model: A multi-wave empirical analysis. AIS Transactions on Human-Computer Interaction, 15 (1), 83 – 110. https://doi.org/10.17705/1thci.00184</bibtext> </blist> <blist> <bibtext> Hsu, L. (2013). English as a foreign language learners' perception of mobile assisted language learning: A cross-national study. Computer Assisted Language Learning, 26 (3), 197 – 213. https://doi.org/10.1080/09588221.2011.649485</bibtext> </blist> <blist> <bibtext> Johnson, R. D. (2005). An empirical investigation of sources of application-specific computer-self-efficacy and mediators of the efficacy--performance relationship. International Journal of Human-Computer Studies, 62 (6), 737 – 758. https://doi.org/10.1016/j.ijhcs.2005.02.008</bibtext> </blist> <blist> <bibtext> Kamdjoug, J. R., Tchana, P. B., Wamba, S. F., & Teutio, A. O. (2023). Task-technology fit and ICT use in remote work practice during the COVID-19 pandemic. Journal of Global Information Management (JGIM), 31 (1), 1 – 24. https://doi.org/10.4018/JGIM.324097</bibtext> </blist> <blist> <bibtext> Kelly, R. (2020). Results from top hat's covid-19 faculty survey about online teaching. Retrieved December 17, from https://onedtech.philhillaa.com/p/results-from-top-hats-covid-19-faculty-survey-about-online-teaching</bibtext> </blist> <blist> <bibtext> Kert, S. B. (2011). The use of SMS support in programming education. The Turkish Online Journal of Educational Technology, 10 (2), 268 – 273.</bibtext> </blist> <blist> <bibtext> Laal, M., & Ghodsi, S. M. (2012). Benefits of collaborative learning. Procedia-Social and Behavioral Sciences, 31, 486 – 490. https://doi.org/10.1016/j.sbspro.2011.12.091</bibtext> </blist> <blist> <bibtext> Labad, M. (2020). How metro schools continue lessons amid coronavirus threat. Retrieved July 12, from https://rappler.com/newsbreak/iq/how-metro-manila-schoolscontinue-lessons-coronavirus-threat</bibtext> </blist> <blist> <bibtext> Lee, B. N. (2016). Supportive learning: Linear learning and collaborative learning. The Turkish Online Journal of Educational Technology, 15 (2), 97 – 105.</bibtext> </blist> <blist> <bibtext> Lee, C.-C., Cheng, H. K., & Cheng, H.-H. (2007). An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences. Decision Support Systems, 43 (1), 95 – 110. https://doi.org/10.1016/j.dss.2005.05.008</bibtext> </blist> <blist> <bibtext> Lin, H. C., & Huang, C. C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45 (6), 410 – 417. https://doi.org/10.1016/j.im.2008.06.004</bibtext> </blist> <blist> <bibtext> Liu, Y., Lee, Y., & Chen, A. N. (2011). Evaluating the effects of task–individual–technology fit in multi-dss models context: A two-phase view. Decision Support Systems, 51 (3), 688 – 700. https://doi.org/10.1016/j.dss.2011.03.009</bibtext> </blist> <blist> <bibtext> Liu, Y., Lee, Y., & Chen, A. N. (2011). Evaluating the effects of task–individual–technology fit in multi-dss models context: A two-phase view. Decision Support Systems, 51 (3), 688 – 700.</bibtext> </blist> <blist> <bibtext> Lu, H.-P., & Yang, Y.-W. (2014). Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34, 323 – 332. https://doi.org/10.1016/j.chb.2013.10.020</bibtext> </blist> <blist> <bibtext> Mawn, M. V., Carrico, P., Charuk, K., Stote, K. S., & Lawrence, B. (2011). Hands‐on and online: Scientific explorations through distance learning. Open Learning: The Journal of Open, Distance and E-Learning, 26 (2), 135 – 146. https://doi.org/10.1080/02680513.2011.567464</bibtext> </blist> <blist> <bibtext> McGrenere, J., & Ho, W. (2000, May). Affordances: Clarifying and evolving a concept. Proceedings of Graphics Interface 2000 (pp. 179 – 186), Montreal.</bibtext> </blist> <blist> <bibtext> Mehta, D., & Hamke, A. K. (2019, August). In-depth: Industry 4.0 2019 – statista digital market outlook. https://<ulink href="http://www.statista.com/study/66974/in-depth-industry-40/">www.statista.com/study/66974/in-depth-industry-40/</ulink></bibtext> </blist> <blist> <bibtext> Mhlanga, D., & Moloi, T. (2020). COVID-19 and the digital transformation of education: What are we learning on 4IR in South Africa? Education Sciences, 10 (7), 180 – 197. https://doi.org/10.3390/educsci10070180</bibtext> </blist> <blist> <bibtext> Muchenje, G., & Seppänen, M. (2023). Unpacking task-technology fit to explore the business value of big data analytics. International Journal of Information Management, 69, 1 – 8. https://doi.org/10.1016/j.ijinfomgt.2022.102619</bibtext> </blist> <blist> <bibtext> Norman, D. (1999). Affordance, convention and design. Interactions, 6 (3), 38 – 42. https://doi.org/10.1145/301153.301168</bibtext> </blist> <blist> <bibtext> Ojetunde, S., Ademidun, A., & Sangodoyin, T. (2021). Online learning platforms' induced education inequalities and special education students' learning attitude during COVID- 19 pandemic homestay in the University of Ibadan. Journal of Education and Practice, 12 (23), 61 – 67.</bibtext> </blist> <blist> <bibtext> Ojetunde, S. M., & Nweze, C. A. (2021). Digital nativity: Curriculum implications of artificial intelligence, computer programing language and data science in Africa. Journal of Positive Psychology and Counselling, 9 (2), 179 – 188.</bibtext> </blist> <blist> <bibtext> Ojetunde, S. M., & Ramnarain, U. (2023a). Applying 4IRs education technology to science pedagogy: Effects and students' experience. Journal of Smart Learning Environment, 10 (32), 1 – 18. https://doi.org/10.1186/s40561-023-00251-z</bibtext> </blist> <blist> <bibtext> Ojetunde, S. M., & Ramnarain, U. (2023b). Contextual and personal determinants of Nigerian science teachers' intention to use online platforms for inquiry-based learning. In U. Ramnarain & M. Ndlovu (Eds.), Information and communications technology in STEM education (pp. 82 – 98). Routledge. https://doi.org/10.4324/9781003279310</bibtext> </blist> <blist> <bibtext> Oluwole, D. (2016). Technology acceptance model as a predictor of using information system to acquire information literacy skills. Library Philosophy & Practice, 1450, 1 – 27. <ulink href="http://digitalcommons.unl.edu/libphilprac/1450">http://digitalcommons.unl.edu/libphilprac/1450</ulink></bibtext> </blist> <blist> <bibtext> Overby, E. (2008). Process virtualization theory and the impact of information technology. Organization Science, 19 (2), 277 – 291. https://<ulink href="http://www.jstor.org/stable/25146179">www.jstor.org/stable/25146179</ulink></bibtext> </blist> <blist> <bibtext> Oyewusi, F. O., Sokoya, A. A., & Aramide, K. A. (2016). Computer self-efficacy and computer attitude as correlates of internet use of secondary school students' in Nigeria. Journal of Information & Knowledge Management, 7 (2), 114 – 123. https://doi.org/10.4314/iijikm.v7i2.11</bibtext> </blist> <blist> <bibtext> Park, C. (2019). Exploring a new determinant of task technology fit: Content characteristics. Journal of International Technology and Information Management, 27 (3), 100 – 118. https://doi.org/10.58729/1941-6679.1385</bibtext> </blist> <blist> <bibtext> Pineteh, E. A. (2012). Using virtual interactions to enhance the teaching of communication skills to information technology students. British Journal of Education Technology, 43 (1), 85 – 96. https://doi.org/10.1111/j.1467-8535.2011.01193.x</bibtext> </blist> <blist> <bibtext> Popoola, B. I., Ajibade, Y. A., Etim, J. S., Oloyede, E. O., & Adeleke, M. A. (2010). Teaching effectiveness and attitude to reading of secondary school teachers in Osun State, Nigeria. The African Symposium: An On-Line Journal of the African Educational Research Network, 10 (2), 142 – 154.</bibtext> </blist> <blist> <bibtext> Rambe, P., & Bere, A. (2013). Using mobile instant messaging to leverage learner participation and transform pedagogy at a South African university of technology. British Journal of Educational Technology, 44 (4), 544 – 561. https://doi.org/10.1111/bjet.12057</bibtext> </blist> <blist> <bibtext> Ramnarain, U. (2014). Understanding the influence of intrinsic and extrinsic factors on inquiry- based science education at township schools in South Africa. Journal of Research in Science Teaching, 53 (4), 598 – 619. https://doi.org/10.1002/tea.21315</bibtext> </blist> <blist> <bibtext> Reid, D., & Ostashewski, N. (2011). iPads in the classroom - new technologies, old issues: Are they worth the effort? A paper presented at AACE (Association for the Advancement of Computing in Education), Proceedings of ED-MEDIA 2011-World Conference on Educational Multimedia, Hypermedia & Telecommunications. Lisbon, Portugal, Association for the Advancement of Computing in Education, Waynesville, NC, 27 June - 1 July 2011.</bibtext> </blist> <blist> <bibtext> Renny, G., Siringoringo, S., & Siringoringo, H. (2013). Perceived usefulness, ease of use, and attitude towards online shopping usefulness towards online airlines ticket purchase. Social & Behavioral Sciences, 81, 212 – 216. https://doi.org/10.1016/j.sbspro.2013.06.415</bibtext> </blist> <blist> <bibtext> Robertson, M. J., & Al-Zahrani, A. M. (2012). Self-efficacy and ICT integration into initial teacher education in Saudi Arabia: Matching policy with practice. Australasian Journal of Educational Technology, 28 (7), 1136 – 1151.</bibtext> </blist> <blist> <bibtext> Saidu, M. K., & Al Mamun, M. A. (2022). Exploring the factors affecting behavioural intention to use google classroom: University teachers' perspectives in Bangladesh and Nigeria. Tech Trends, (2022), 1 – 16. https://doi.org/10.1007/s11528-022-00704-1</bibtext> </blist> <blist> <bibtext> Saunders-Stewart, K. S., Gyles, P. D. T., & Shore, B. M. (2012). Student outcomes in inquiry instruction: A literature-derived inventory. Journal of Advanced Academics, 23 (1), 5 – 31.</bibtext> </blist> <blist> <bibtext> Schwab, K. (2017). The fourth industrial revolution (pp. 45 – 63). Crown Publishing Group.</bibtext> </blist> <blist> <bibtext> Shahzad, H., Zhang, Q., Ullah Zafar, A., Ashfaq, M., & Ur Rehman, S. (2023). The role of blockchain-enabled traceability, task technology fit, and user self-efficacy in mobile food delivery applications. Journal of Retailing & Consumer Services, 73, 1 – 13. https://doi.org/10.1016/j.jretconser.2023.103331</bibtext> </blist> <blist> <bibtext> Strong, D. M., Dishaw, M. T., & Bandy, D. B. (2006). Extending task technology fit with computer self-efficacy. The Data Base for Advances in Information Systems, 37 (2–3), 96 – 107. https://doi.org/10.1145/1161345.1161358</bibtext> </blist> <blist> <bibtext> Tamim, S. R., & Grant, M. M. (2013). Definitions and uses: Case study of teachers implementing project-based learning. Interdisciplinary Journal of Problem-Based Learning, 7 (2), 75 – 99. https://doi.org/10.7771/1541-5015.1323</bibtext> </blist> <blist> <bibtext> TESCOM. (2017). Oyo State Schools, Teachers and the Degree. Oyo : Teaching Service Commission (pp. 4 – 9).</bibtext> </blist> <blist> <bibtext> Timmis, S., Broadfoot, P., Sutherland, R., & Oldfield, A. (2016). Rethinking assessment in a digital age: Opportunities, challenges and risks. British Educational Research Journal, 42 (3), 454 – 476. https://doi.org/10.1002/berj.3215</bibtext> </blist> <blist> <bibtext> Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), 425 – 478. https://doi.org/10.2307/30036540</bibtext> </blist> <blist> <bibtext> Widianto, F. Y., Haryadi H. A., & Devani, L. I. (2020). Technology acceptance model on gojek applications in Purwokerto. Journal Akuntansi, Manajemen Dan Ekonomi, 22 (1), 12 – 20.</bibtext> </blist> <blist> <bibtext> Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221 – 232. https://doi.org/10.1016/j.chb.2016.10.028</bibtext> </blist> <blist> <bibtext> Wu, I., & Chen, J. (2005). An extension of trust and TAM model with TPB in the initial adoption of online tax: An empirical study. International Journal of Human-Computer Studies, 62 (6), 784 – 808. https://doi.org/10.1016/j.ijhcs.2005.03.003</bibtext> </blist> <blist> <bibtext> Yoon, C., Jeong, C., & Rolland, E. (2015). Understanding individual adoption of mobile instant messaging: A multiple perspectives approach. Information Technology and Management, 16 (2), 139 – 151. https://doi.org/10.1007/s10799-014-0202-4</bibtext> </blist> <blist> <bibtext> Yüce, A., Abubakar, A. M., & İlkan, M. (2019). Intelligent tutoring systems and learning performance: Applying task-technology fit and is success model. Online Information Review, 43 (4), 600 – 616. https://doi.org/10.1108/OIR-11-2017-0340</bibtext> </blist> <blist> <bibtext> Zhou, L., Xue, S., & Li, R. (2022). Extending the technology acceptance model to explore students' intention to use an online education platform at a university in China. SAGE Open. 12(1), 1–13. https://doi.org/10.1177/21582440221085259</bibtext> </blist> </ref> <aug> <p>By Segun Michael Ojetunde; Umesh Ramnarain and Timothy Teo</p> <p>Reported by Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib61" firstref="ref2"></nolink> <nolink nlid="nl2" bibid="bib63" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib12" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib20" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib48" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib69" firstref="ref7"></nolink> <nolink nlid="nl7" bibid="bib55" firstref="ref8"></nolink> <nolink nlid="nl8" bibid="bib54" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib40" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib38" firstref="ref12"></nolink> <nolink nlid="nl11" bibid="bib37" firstref="ref13"></nolink> <nolink nlid="nl12" bibid="bib18" firstref="ref14"></nolink> <nolink nlid="nl13" bibid="bib79" firstref="ref15"></nolink> <nolink nlid="nl14" bibid="bib14" firstref="ref16"></nolink> <nolink nlid="nl15" bibid="bib62" firstref="ref17"></nolink> <nolink nlid="nl16" bibid="bib60" firstref="ref18"></nolink> <nolink nlid="nl17" bibid="bib11" firstref="ref19"></nolink> <nolink nlid="nl18" bibid="bib27" firstref="ref20"></nolink> <nolink nlid="nl19" bibid="bib23" firstref="ref21"></nolink> <nolink nlid="nl20" bibid="bib50" firstref="ref22"></nolink> <nolink nlid="nl21" bibid="bib35" firstref="ref23"></nolink> <nolink nlid="nl22" bibid="bib70" firstref="ref24"></nolink> <nolink nlid="nl23" bibid="bib77" firstref="ref25"></nolink> <nolink nlid="nl24" bibid="bib32" firstref="ref28"></nolink> <nolink nlid="nl25" bibid="bib24" firstref="ref29"></nolink> <nolink nlid="nl26" bibid="bib57" firstref="ref30"></nolink> <nolink nlid="nl27" bibid="bib80" firstref="ref31"></nolink> <nolink nlid="nl28" bibid="bib75" firstref="ref34"></nolink> <nolink nlid="nl29" bibid="bib59" firstref="ref35"></nolink> <nolink nlid="nl30" bibid="bib25" firstref="ref37"></nolink> <nolink nlid="nl31" bibid="bib44" firstref="ref43"></nolink> <nolink nlid="nl32" bibid="bib45" firstref="ref44"></nolink> <nolink nlid="nl33" bibid="bib72" firstref="ref50"></nolink> <nolink nlid="nl34" bibid="bib28" firstref="ref51"></nolink> <nolink nlid="nl35" bibid="bib68" firstref="ref52"></nolink> <nolink nlid="nl36" bibid="bib10" firstref="ref54"></nolink> <nolink nlid="nl37" bibid="bib51" firstref="ref55"></nolink> <nolink nlid="nl38" bibid="bib47" firstref="ref56"></nolink> <nolink nlid="nl39" bibid="bib64" firstref="ref57"></nolink> <nolink nlid="nl40" bibid="bib30" firstref="ref58"></nolink> <nolink nlid="nl41" bibid="bib16" firstref="ref59"></nolink> <nolink nlid="nl42" bibid="bib52" firstref="ref60"></nolink> <nolink nlid="nl43" bibid="bib49" firstref="ref62"></nolink> <nolink nlid="nl44" bibid="bib39" firstref="ref63"></nolink> <nolink nlid="nl45" bibid="bib36" firstref="ref66"></nolink> <nolink nlid="nl46" bibid="bib46" firstref="ref69"></nolink> <nolink nlid="nl47" bibid="bib74" firstref="ref70"></nolink> <nolink nlid="nl48" bibid="bib71" firstref="ref74"></nolink> <nolink nlid="nl49" bibid="bib41" firstref="ref75"></nolink> <nolink nlid="nl50" bibid="bib56" firstref="ref76"></nolink> <nolink nlid="nl51" bibid="bib66" firstref="ref77"></nolink> <nolink nlid="nl52" bibid="bib34" firstref="ref78"></nolink> <nolink nlid="nl53" bibid="bib15" firstref="ref79"></nolink> <nolink nlid="nl54" bibid="bib19" firstref="ref80"></nolink> <nolink nlid="nl55" bibid="bib17" firstref="ref81"></nolink> <nolink nlid="nl56" bibid="bib58" firstref="ref83"></nolink> <nolink nlid="nl57" bibid="bib26" firstref="ref84"></nolink> <nolink nlid="nl58" bibid="bib76" firstref="ref85"></nolink> <nolink nlid="nl59" bibid="bib65" firstref="ref86"></nolink> <nolink nlid="nl60" bibid="bib73" firstref="ref88"></nolink> <nolink nlid="nl61" bibid="bib78" firstref="ref89"></nolink> <nolink nlid="nl62" bibid="bib67" firstref="ref90"></nolink> <nolink nlid="nl63" bibid="bib29" firstref="ref91"></nolink> <nolink nlid="nl64" bibid="bib13" firstref="ref92"></nolink> <nolink nlid="nl65" bibid="bib21" firstref="ref93"></nolink> <nolink nlid="nl66" bibid="bib53" firstref="ref98"></nolink> <nolink nlid="nl67" bibid="bib42" firstref="ref99"></nolink> <nolink nlid="nl68" bibid="bib33" firstref="ref100"></nolink> <nolink nlid="nl69" bibid="bib22" firstref="ref101"></nolink> <nolink nlid="nl70" bibid="bib31" firstref="ref102"></nolink> <nolink nlid="nl71" bibid="bib43" firstref="ref104"></nolink> <nolink nlid="nl72" bibid="bib81" firstref="ref105"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1467739
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PubType: Academic Journal
PubTypeId: academicJournal
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Task-Technology Fit of Fourth Industrial Revolution (4IR) Education Technology for Inquiry-Based Learning (IBL)
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Segun+Michael+Ojetunde%22">Segun Michael Ojetunde</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2333-8632">0000-0002-2333-8632</externalLink>)<br /><searchLink fieldCode="AR" term="%22Umesh+Ramnarain%22">Umesh Ramnarain</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4548-5913">0000-0003-4548-5913</externalLink>)<br /><searchLink fieldCode="AR" term="%22Timothy+Teo%22">Timothy Teo</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7552-8497">0000-0002-7552-8497</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Educational+Media+International%22"><i>Educational Media International</i></searchLink>. 2025 62(1):29-53.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 25
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Educational+Technology%22">Educational Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Active+Learning%22">Active Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Inquiry%22">Inquiry</searchLink><br /><searchLink fieldCode="DE" term="%22Technological+Advancement%22">Technological Advancement</searchLink><br /><searchLink fieldCode="DE" term="%22Futures+%28of+Society%29%22">Futures (of Society)</searchLink><br /><searchLink fieldCode="DE" term="%22Industry%22">Industry</searchLink><br /><searchLink fieldCode="DE" term="%22Influence+of+Technology%22">Influence of Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Task+Analysis%22">Task Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Science+Education%22">Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Attitudes%22">Teacher Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Social+Influences%22">Social Influences</searchLink><br /><searchLink fieldCode="DE" term="%22Science+Teachers%22">Science Teachers</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/09523987.2024.2441139
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0952-3987<br />1469-5790
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The acceptance of Fourth Industrial Revolution technology for teaching and learning during the pandemic lockdown suggests that a significant portion of educational interactions will shift online shortly. However, the suitability of this technology for inquiry-based learning requires further investigation. This study aimed to assess the effectiveness of Fourth Industrial Revolution educational technology in supporting inquiry-based learning. A cross-sectional study was conducted, and the data were analyzed. The results indicated that task and technology characteristics do not influence science teachers' intentions to adopt online platforms for inquiry-based learning. In contrast, teacher social constructs -- such as perceived ease of use, perceived usefulness, and attitude -- do have an impact. Therefore, teachers' willingness to adopt online platforms could facilitate education stakeholders' efforts in training and adapting curricula for online teaching and learning activities.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1467739
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1467739
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/09523987.2024.2441139
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 25
        StartPage: 29
    Subjects:
      – SubjectFull: Educational Technology
        Type: general
      – SubjectFull: Active Learning
        Type: general
      – SubjectFull: Inquiry
        Type: general
      – SubjectFull: Technological Advancement
        Type: general
      – SubjectFull: Futures (of Society)
        Type: general
      – SubjectFull: Industry
        Type: general
      – SubjectFull: Influence of Technology
        Type: general
      – SubjectFull: Electronic Learning
        Type: general
      – SubjectFull: Task Analysis
        Type: general
      – SubjectFull: Science Education
        Type: general
      – SubjectFull: Teacher Attitudes
        Type: general
      – SubjectFull: Social Influences
        Type: general
      – SubjectFull: Science Teachers
        Type: general
    Titles:
      – TitleFull: Task-Technology Fit of Fourth Industrial Revolution (4IR) Education Technology for Inquiry-Based Learning (IBL)
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Segun Michael Ojetunde
      – PersonEntity:
          Name:
            NameFull: Umesh Ramnarain
      – PersonEntity:
          Name:
            NameFull: Timothy Teo
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0952-3987
            – Type: issn-electronic
              Value: 1469-5790
          Numbering:
            – Type: volume
              Value: 62
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Educational Media International
              Type: main
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