**Technology Acceptance Model in Islamic Education (TAMISE) for Digitally Enhanced Learning: Validity and Reliability Testing** **Author 11\*, Author 22, Author 33** * * Received Date: \*date. Accepted Date: \*date. Revised Date: \*date Published Date: \*date. **HIGHLIGHTS** - The TAMISE model indicated a strong fit with pilot data collected based on various fit indices through CFA. - Strong validity and reliability of the TAMISE model were observed in this pilot study based on CCA. - The validated TAMISE model offers insights into the factors influencing the acceptance of digital technologies in Islamic education and can guide future research and practice in this domain. **ABSTRACT** *Digital technologies (DT) have revolutionised various sectors, including education. In Islamic education, integrating DT can potentially enhance the teaching and learning processes. However, integrating these technologies into Islamic education requires careful consideration due to the unique nature of the subject matter and cultural sensitivities. Thus, this pilot study assessed the validity and reliability of the TAMISE (Technology Acceptance Model in Islamic Education) through confirmatory factor and composite analyses. The TAMISE model extends the Technology Acceptance Model (TAM) with perceived Islamic education compatibility to address the acceptance of digital technologies in Islamic education. The study employed a survey questionnaire administered to a sample (N = 65) of Islamic education students from Indonesia and Malaysia. The data collected were analysed using confirmatory factor analysis (CFA) and confirmatory composite analysis (CCA). The results indicated that the TAMISE model demonstrated adequate validity and reliability, thus supporting its applicability as a theoretical framework for understanding the acceptance of digital technologies in Islamic education. Furthermore, the findings theoretically contribute to Islamic education and technology acceptance by providing insights into factors influencing students' adoption and use of digital technologies.* ***Keywords** Digital technology, Technology acceptance, CFA, Islamic education, TAM, Digital learning.* # INTRODUCTION Digital technologies (DT) have undoubtedly transformed the way people live, socialise, work, and learn. Consequently, DT has become an integral part of teaching and learning processes in the education sector, particularly in digital learning (García-Martínez et al., 2020). Digital learning refers to using electronic devices and multimedia to facilitate learning (Wheeler, 2012). The usage of digital learning is prevalent across different educational contexts, including Islamic education. Islamic education is a religious education that emphasises Islamic values, beliefs, and practices (Waghid, 2014). Recently, Islamic education in some countries, like Indonesia and Saudi Arabia, has embraced technology to enhance the quality and accessibility of education (Al-Harbi, 2019; Jamaluddin et al., 2019). Islamic education emphasises the teachings of the Quran and the Sunnah of the Prophet Muhammad (Arjmand, 2018). It aims to provide individuals with the knowledge and skills to live righteous lives and serve society's needs (Daun & Arjmand, 2021). In recent years, the use of technology in Islamic education has gained significant attention (Alsharbi et al., 2021; Subiyakto et al., 2022). Various technologies, such as online learning platforms, multimedia content, and mobile applications, have been developed to facilitate teaching and learning activities (Alsharbi et al., 2021). The usage of digital technologies in Islamic education has the potential to enhance the quality of education and reach a wider audience (Shan-a-alahi & Huda, 2017). Generally, DT integration in various education systems has become increasingly prevalent (ITU et al., 2019). Islamic education, which emphasises the teachings and principles of Islam, also stands to benefit from the effective utilisation of digital resources. However, the acceptance and adoption of digital technologies in Islamic education remain understudied (Al-rahmi et al., 2017) and require a specific framework to comprehend the unique factors influencing its acceptance (Abubakari et al., 2023; Abubakari & Priyanto, 2021). In this article, we propose a conceptual model of digital technology acceptance in Islamic education, built upon the foundation of the well-established Technology Acceptance Model (TAM) but tailored to Islamic education's specific needs and characteristics. Technology acceptance is a critical factor in the success of digital learning culture. The Technology Acceptance Model (TAM) is a widely implemented model that can explain digital technology acceptance behaviour (Davis, 1989). The model proposed that perceived usefulness and ease of use are the primary factors influencing user behaviour toward adopting digital technologies. Despite the usefulness of TAM in predicting technology acceptance, it has limitations in explaining DT acceptance in Islamic education. TAM does not consider the compatibility of technology with the cultural and religious values of Islamic education. Islamic education has unique cultural and religious characteristics that influence the acceptance of digital technologies. Therefore, there is a need to integrate the factor of perceived Islamic education compatibility into TAM to develop a Technology Acceptance Model in Islamic Education (TAMISE) for digitally enhanced learning. This article aims to review related literature on technology acceptance in Islamic education and verify the validity of the TAMISE model. Thus, the primary objectives of this pilot study are as follows: 1. To develop and validate the Technology Acceptance Model in Islamic Education (TAMISE) for digitally enhanced learning. 2. To examine the reliability of TAMISE in explaining the acceptance and adoption of digital technologies in Islamic educational settings. # LITERATURE REVIEW ## Digital Technologies in Education Digital technologies offer numerous benefits in education. First, they promote student engagement, as interactive and multimedia resources capture students' attention and cater to various learning styles (Alamri et al., 2020; Surjono, 2014). Further, digital technologies enable access to vast information and educational resources (Serin, 2022), expanding learning opportunities beyond the traditional classroom (Daniela, 2019), primarily through online learning (Abubakari et al., 2022; Abubakari & Mashoedah, 2021). Finally, teachers also benefit from digital technologies, as they can leverage online platforms for instructional planning, assessment, and collaboration with colleagues (UNESCO, 2021). In general, the introduction of digital technologies (DT) in classrooms has transformed teaching and learning practices. Interactive digital resources, such as online textbooks, multimedia presentations, and educational apps, have made learning more engaging and interactive (Haleem et al., 2022). Digital technologies also enable personalised learning experiences, allowing students to learn at their own pace and explore topics in greater depth (Alshammari & Qtaish, 2019). Moreover, digital tools facilitate collaboration among students and encourage active participation in the learning process (Wheeler, 2012). Even though various digital resources are established in educational institutions, most individuals do not optimally use them to capture their full benefits (Abubakari et al., 2021; Islam et al., 2019). DT underutilisation and factors influencing its adoption, especially in the Islamic education context, are under-explored in the literature. Therefore, explaining how DT is accepted and utilised in different contexts is an intriguing topic. Several theories based on social-technical and psychology have been founded to demonstrate this phenomenon of DT acceptance in various contexts. The technology acceptance model (TAM) is among those theories which this conceptual study will adapt and modify to match the context under investigation. ## The TAM Model Overview TAM is a theoretical model that explains the probable factors that influence user acceptance and adoption of digital technologies. In 1986, Davis developed the TAM model based on previous behavioural theories, especially the theory of reasoned action and the theory of planned behaviour (Davis, 1989). TAM posited that perceived usefulness (PU) and perceived ease of use (PEOU) are the key driving factors of digital technology adoption (Davis, 1989). PU refers to the degree to which a user believes that digital technology will improve their performance, while PEOU refers to the degree to which a user perceives that digital technology is easy to use. TAM proposed that if users perceive a particular technology as valuable and easy to use, they are more likely to adopt it. Despite the broad adaptation of the TAM model in multiple contexts, there are limitations that require further empirical investigations (Scherer & Teo, 2019). Moreover, regardless of the praise TAM receives for its simplicity and ability to understand DT adoption, researchers insist that it is crucial to conduct further studies to improve the external validity of the model (Dishaw & Strong, 1999). Furthermore, the literature revealed that numerous studies on TAM were primarily explored in Western settings and rarely studied in non-Western contexts (Teo & van Schaik, 2012), especially in Islamic education. This fact implies that more investigations into TAM should be conducted in different contexts and cultures. Accordingly, while TAM will be a foundational framework for the proposed study, suitable individual and religious constructs are considered to formulate the proposed TAMISE model and test its validity and reliability. ## Proposed TAMISE Model The proposed conceptual model extends the original TAM by incorporating two more constructs: perceived Islamic education compatibility (PIC) and digital self-efficacy (DSE), to reflect the unique context of Islamic education. Originally, TAM posited that DT acceptance is primarily influenced by perceived usefulness (PU) and perceived ease of use (PEU). However, the model should include an added unique construct in the Islamic education context: perceived Islamic education compatibility (values and pedagogical needs compatibilities) to contextualise the Islamic education setting. ![A diagram of a diagram Description automatically generated with low confidence](6476dd642ce55_media/media/image1.png) **Figure 1:** Research Conceptual Model Figure 1 shows five constructs in the proposed TAMISE model, two external factors (PIC and DSE) and four constructs from TAM. The following is the theoretical explanation of each construct. ***Perceived Islamic Education Compatibility (PIC):*** Since DT utilisation in an Islamic context is sensitive, careful examination of the matter is vital for education sustainability. Earlier researchers (Abubakari et al., 2023; Al-rahmi et al., 2017) insisted on studying DT adoption from Muslims’ perspectives by considering contextualised religious-related factors. Some scholars (Eid & El-Gohary, 2015) found that religious-related factors influenced physical and non-physical values in Muslim’s adoption of technology. Religious values tend to influence religious individuals' acceptance of technological innovations as they influence their perspective on deciding what technology to adopt (Ateeq-ur-Rehman & Shabbir, 2010). Thus, the religious perception of a person plays a critical cultural force on an individual’s behavioural intention (El-Gohary & Eid, 2013; Zamani-Farahani & Musa, 2012). Therefore, the TAM theory should be polished from different research contextual findings to understand better individuals’ intention to use DT (Nabavi et al., 2016). Based on earlier arguments, in this proposed study, based on the construct of perceived compatibility from innovation diffusion theory (IDT), perceived Islamic education compatibility (PIC) is formulated to suit the Islamic education perspective. In IDT, perceived compatibility is explained as the extent to which technology adopter perceives that a particular technological innovation is consistent with pre-existing values and needs (Rogers, 1995, 2003). Accordingly, for the sake of the present study’s context, PIC is described as the perception of a Muslim individual on the relevance and compatibility of using digital technologies in an Islamic education context. Thus, PIC is hypothesised to capture the feeling of potential DT adopters concerning the consistency between technological innovation and Islamic education values. Scholars (Amaro & Duarte, 2015; Li & Buhalis, 2006) discovered that perceived compatibility significantly influenced consumer behavioural intention to use various innovations. ***Digital Self-Efficacy (DSE):*** Psychology-based research focused on the cognitive aspect (Bandura, 1977, 2001) indicated that self-efficacy is crucial in predicting human behaviour. Some researchers (Peiffer et al., 2020) argue that subjective beliefs about an individual’s skills are as important as objective skills in using digital technologies effectively. Self-efficacy related to DT refers to a person’s belief in their capability to interact with and comfortably use a specific information technology (Compeau & Higgins, 1995). Thus, the self-efficacy of a person in using digital systems is crucial in predicting the efficient use of DT (Ulfert-Blank & Schmidt, 2022; Ulfert et al., 2022). Literature shows that self-efficacy beliefs can determine whether an individual is willing to utilise DT (Venkatesh & Bala, 2008). Other researchers (Janssen et al., 2013) view digital self-efficacy (DSE) as a foundational factor of a person’s digital competencies. Further, previous literature (Compeau et al., 1999) observed that DSE influenced both intention and actual behavioural usage of digital systems. ***Perceived Usefulness (PU):*** PU is considered the most profound factor in predicting and explaining an individual’s intention to utilise a specific technology in TAM (Davis, 1989). PU refers to an individual’s belief that a particular DT system is beneficial and provides advantages in using it (Davis, 1989). PU is synonymous with some variables used in other studies like job fit variable (Davis et al., 1992), performance expectance (Venkatesh et al., 2003), expected outcome (Compeau et al., 1999), and technology relative advantage (Moore & Benbasat, 1991). All those variables target the same purpose, explaining to what extent an individual believes a given technology benefits them and their task in a particular context. ### *Perceived Ease of Use (PEU):* PEU is the second influential factor after the PU factor in explaining the user’s intention to use DT in the TAM theory (Davis et al., 1989; Scherer et al., 2019). PEU is the extent to which an individual believes that a particular technology does not require much effort and time when using it (Davis, 1989; Davis et al., 1989). Earlier studies argued that even if a person perceives that particular technology is beneficial, they might still not use it if they perceive it will take a lot of time and effort to utilise it (Davis et al., 1989). PEU can intervene in the effect of PU on an individual’s intention to use DT. Some studies found PEU was insignificant in affecting users’ intentions directly but significantly affected PU (Scherer et al., 2019). ### *Behavioural Intention (BI) and Use Behaviour (UB):* Research on behaviour psychology indicates that behavioural intention (BI) determines a particular behaviour (Ajzen & Fishbein, 1977; Fishbein & Ajzen, 1975), such as DT usage. An intention highly predicts human behaviour (Ajzen & Fishbein, 2002; Fishbein & Ajzen, 2015). BI is the subjective probability of carrying out the behaviour and the cause of particular usage behaviour (Yi et al., 2006). The intention is theorised as a motivational factor that strongly affects a particular usage behaviour (UB), such as DT usage, and indicates to what extent a person will exert effort to perform an act (Ajzen, 1991). Therefore, BI is a variable intended to explore to what extent an individual is willing to use DT in the actual sense. ## Related Studies on DT Acceptance in Islamic Education Findings from earlier literature on technology acceptance, especially from an Islamic education perspective, have demonstrated the validity of the TAM model adaptation. To illustrate, a study conducted in Malaysia extended TAM by additional external factors, including technology familiarity and computer competency (CC), to assess students’ BI to utilise e-learning (Rahman et al., 2022). The study observed that all validity and reliability criteria of the research model were adequately established, including average variance extracted (AVE), discriminant validity, composite reliability (CR), and Cronbach’s alpha. However, one item of the CC construct was removed as its AVE’s loading was below the 0.5 threshold value (Hair, Risher, et al., 2019). Similarly, other studies have also found extended TAM to be valid and reliable in their investigations (Ali et al., 2016; Haris et al., 2022; Ngabiyanto et al., 2021; Subiyakto et al., 2022). However, these studies have no consistency regarding the effects of some constructs of their research models. For instance, a study by (Ngabiyanto et al., 2021) assessed online learning acceptance in Indonesia and found a negative but significant effect of PEU on BI. Another study added two constructs: Basic Islamic Knowledge and Self-Efficacy, into TAM to assess blog acceptance for learning Islam and discovered that PEU had significant effects on BI, and PU was insignificant on BI (Ali et al., 2016), unsimilar to other researchers (Haris et al., 2022) who found that PU significantly affected BI; meanwhile, PEU had no substantial effect on BI. Further, other researchers applied TAM while extending it with perceived validity (PV) and trust (PT) in using electronic resources in Islamic education (Subiyakto et al., 2022). They uncovered that both PT and PV significantly affected PU, while PEU and PU had considerable influences on BI to utilise e-resources in Islamic education. Similarly, other studies (Nuryanna et al., 2021; Wardayanti et al., 2022) found that PU and PEU are significantly associated with BI. Therefore, despite previous findings having comparable results in the reliability and validity of their adapted TAM models, there are discrepancies in some variables’ effects, varying from one study to another. Some scholars (Scherer et al., 2019) have argued that these similar yet different findings might be due to the effects of varying samples, contexts, and technologies across studies; however, these unsimilar findings might not enlighten the educational community properly. This fact warranties the significance of technology specificity, contextual variables, and sample types on intervening TAM effects on DT acceptance (Dimitrijević & Devedžić, 2021). Accordingly, the present study validates the TAMISE model to verify its validity and reliability in Islamic education. # METHODOLOGY ## Research Design and Sampling The pilot study implemented a quantitative design approach based on an online survey using Google Forms to gather data from students at Islamic education universities. Convenience sampling was used for data gathering and obtained a sample of Sixty-five (65) university students from Malaysia (11) and Indonesia (54), the majority of which were females (41), while males were twenty-four (24). Further, the education level of the majority was undergraduate (44), followed by postgraduate (12) and diploma (9). The age of many participants (46) ranged between 18 and 25 years, followed by 26-35 years (15); few were below eighteen years (2) and above 35 years (2). It is worth noting that this pilot study is part of an ongoing PhD project. ## Instrumentation and Analysis Approach The survey instrument was developed based on the TAMISE framework in which four constructs were adapted from TAM’s items (Davis, 1989), while DSE and PIC items were modified from previous research (Kuo et al., 2014; Moore & Benbasat, 1991; Xie et al., 2022). A five-point Likert scale (from 1: Strongly disagree to 5: Strongly agree) was used to measure every research item. Data analysis was done using Confirmatory Factor Analysis (CFA) through the JASP software (JASP Team, 2023) to assess the model fit, and Smart-PLS v4 (Ringle et al., 2022) was used for analysing the validity and reliability of TAMISE. # RESULTS AND DISCUSSIONS ## Multicollinearity and Multivariate Normality Since the analysis is Multivariate in nature, excess Kurtosis and Skewness were used to assess the data normality. The scores of Kurtoses of each item ranged from –0.87 to 2.97, while that of Skewness was between –1.32 and 0.38, indicating that there was no severe violation of the Multivariate normality test whose scores should be between –3 and +3 (Kline, 2016). As for Multicollinearity, the variance inflation factor (VIF) values of individual items ranged from 1.3 to 4.3, and that of between construct relationships ranged from 1.0 to 2.2, fulfilling the required cut-off value of 5 (Hair, Black, et al., 2019; Kock, 2022), hence, no redundancy issues among indicators (Kline, 2016). Thus, this indicates that no common method bias and Multicollinearity issues were observed in the model and survey design (Kock, 2022). ## Model Fitting Results Before testing the reliability and validity of the TAMISE model, the model fit was evaluated based on CFA using diagonally-weighted least squares (DWLS) for parameter estimation, as all data scores were ordinal (Forero et al., 2009). The results indicated that the chi-square test was significant (*χ*2 = 452.654, df = 390, p \< .05), suggesting that the proposed model did not fit the data perfectly. However, note that the chi-square test is susceptible to sample size, and therefore, alternative fit indices were examined to provide a more comprehensive assessment of the model fit (Hu & Bentler, 1999). Thus, six fit indices were selected to analyse the model’s goodness-of-fit: root mean square error of approximation (RMSEA); goodness of fit index (GFI); comparative fit index (CFI); Tucker-Lewis Index (TLI); Normed Fit Index (NFI); and the Chi-square (*χ*²) to its degree of freedom (*df*) ratio. Table 1 portrays the score of each fit index after running the CFA. **Table 1:** Measurement Model Fit Indices | Fit Index | Recommended Value | Measurement Model Value | | ------------------ | ----------------- | ----------------------- | | RMSEA | \< 0.08 | 0.05 | | GFI | \> 0.95 | 0.979 | | CFI | \> 0.95 | 0.997 | | TLI | \> 0.95 | 0.996 | | NFI | \> 0.95 | 0.976 | | *χ*2 | N/A | 452.654 (*P =* 0.016) | | *df* | N/A | 390 | | χ2/*df* | \< 3.0 | 1.16 | As Table 1 shows, all fit indices fulfilled the fitting criteria; the CFI, GFI, TLI and NFI scores are above the 0.95 minimum thresholds, indicating an acceptable fit to the data. Similarly, the χ2/*df* ratio (1.16) is below 3, confirming an acceptable fit. Additionally, the RMSEA value was computed, resulting in a value of 0.05, indicating a reasonable fit to the data (Hu & Bentler, 1999). Finally, note that the model was fitted without any modifications. ## Measurement Model Analysis Confirmatory composite analysis (CCA) was applied through the Smart-PLS software to examine the validity and reliability of the measurement model and the individual constructs. In addition, Cronbach’s alpha (CA) and composite reliability (CR) were used to assess each construct's measurement reliability. The results revealed a high level of internal consistency for the constructs under investigation. As Table 2 portrays, the CA values ranged from 0.786 to 0.932, while that of CR ranged from 0.814 to 0.933, which are all above 0.7, indicating strong reliability (Hair, Risher, et al., 2019). Both convergent and discriminant validity were examined to assess the validity of the measures used in the study. First, convergent validity was evaluated by examining each latent construct's average variance extracted (AVE) and indicator loadings (IL). Table 2 shows that all constructs demonstrated AVE values above the recommended threshold of 0.50, ranging from 0.582 to 0.830, and IL scores ranged from 0.477 to 0.928, indicating satisfactory convergent validity. Note that the strictest criteria for indicator loadings is a minimum of 0.7, as proposed by scholars, and the relaxed criterion is between 0.5 and 0.6 (Hair, Black, et al., 2019; Hair Jr et al., 2017). Therefore, as indicated in Table 2, four items (PEU1, PIC4, PU4, and DSE2) should be rephrased in future studies as their values are below 0.7. Nevertheless, any value above 0.3 is still considered relevant for the model’s structure interpretation (Hair, Black, et al., 2019). **Table 2:** Convergent Validity, Item Loadings, and Reliability Results | Construct | Item | IL | CA | CR | AVE | | ----------------------------------------------- | ---- | ----- | ----- | ----- | ----- | | Perceived Ease of Use (PEU) | PEU1 | 0.601 | 0.786 | 0.814 | 0.615 | | | PEU2 | 0.879 | | | | | | PEU3 | 0.841 | | | | | | PEU4 | 0.788 | | | | | Perceived Islamic Education Compatibility (PIC) | PIC1 | 0.843 | 0.869 | 0.907 | 0.602 | | | PIC2 | 0.899 | | | | | | PIC3 | 0.756 | | | | | | PIC4 | 0.477 | | | | | | PIC5 | 0.780 | | | | | | PIC6 | 0.827 | | | | | Perceived Usefulness (PU) | PU1 | 0.853 | 0.839 | 0.865 | 0.677 | | | PU2 | 0.880 | | | | | | PU3 | 0.859 | | | | | | PU4 | 0.682 | | | | | Digital Self-Efficacy (DSE) | DSE1 | 0.752 | 0.855 | 0.870 | 0.582 | | | DSE2 | 0.606 | | | | | | DSE3 | 0.767 | | | | | | DSE4 | 0.818 | | | | | | DSE5 | 0.802 | | | | | | DSE6 | 0.812 | | | | | Behavioural Intention (BI) | BI1 | 0.900 | 0.932 | 0.933 | 0.830 | | | BI2 | 0.928 | | | | | | BI3 | 0.924 | | | | | | BI4 | 0.893 | | | | | Use Behaviour (UB) | UB1 | 0.739 | 0.914 | 0.923 | 0.702 | | | UB2 | 0.842 | | | | | | UB3 | 0.869 | | | | | | UB4 | 0.896 | | | | | | UB5 | 0.887 | | | | | | UB6 | 0.784 | | | | Further, Discriminant validity was verified based on Fornell-Larcker Criterion (FLC) and Heterotrait-Monotrait Ratio (HTMT). For FLC, the results (in Table 3) revealed that the square root of the AVE for each construct was bigger than the correlation coefficients between the construct and other constructs in the model (Hair, Risher, et al., 2019). The HTMT ratio provides a measure of discriminant validity by comparing the correlation between constructs to the correlation between constructs and their indicators. As shown in Table 3, the analysis revealed HTMT values (italicised in brackets) below the recommended threshold of 0.85, indicating satisfactory discriminant validity (Hair, Risher, et al., 2019). This finding confirms the adequate discriminant validity of the constructs. **Table 3:** Fornell-Larcker Criterion and HTMT Results
Construct BI DSE PEU PIC PU UB
Behavioural Intention (BI)

0.911

(0)
Digital Self-Efficacy (DSE)

0.614

(0.672)

0.763

(0)
Perceived Ease of Use (PEU)

0.592

(0.697)

0.675

(0.797)

0.784

(0)
Perceived Islamic Education Compatibility (PIC)

0.409

(0.398)

0.548

(0.610)

0.461

(0.501)

0.776

(0)
Perceived Usefulness (PU)

0.724

(0.808)

0.600

(0.655)

0.611

(0.712)

0.499

(0.517)

0.823

(0)
Use Behaviour (UB)

0.766

(0.825)

0.611

(0.653)

0.571

(0.673)

0.417

(0.414)

0.664

(0.742)

0.838

(0)
The analysis results demonstrate the strong validity and reliability of the measurement model and individual constructs of the TAMISE model in this study. Overall, the model fit indices suggest an acceptable fit of the proposed model to the data. Moreover, the measures demonstrated satisfactory validity and reliability, indicating they are suitable for future research. The satisfactory validity and reliability of the modified TAM models were similarly observed in previous studies in Islamic education (Haris et al., 2022; Ngabiyanto et al., 2021; Nuryanna et al., 2021; Subiyakto et al., 2022). # CONCLUSION AND RECOMMENDATIONS This study contributes to the existing literature by providing empirical evidence for the validity and reliability of the TAMISE model through confirmatory factor and composite analyses. The results confirm the importance of incorporating factors specific to Islamic education when assessing the acceptance and adoption of digital technologies. The validated TAMISE model offers insights into the factors influencing the acceptance of digital technologies in Islamic education and can guide future research and practice in this domain. The study is limited to pilot testing the validity and reliability of the TAMISE model due to sample size limitations. Thus, using adequate samples, future research should further validate the TAMISE model by testing various hypotheses based on the constructs’ relationships to explore factors that may influence digital technology acceptance in Islamic education. We propose future studies to implement structural equation modelling (SEM) for testing hypotheses based on the TAMISE model. # CONFLICT OF INTEREST DISCLOSURE The authors declare that there are not any conflicts of interest to disclose. # REFERENCES Abubakari, M. S., & Mashoedah. (2021). Online Learning Engagement Model for International Students in Indonesia amid Covid-19 Period: A Conceptual Model Proposal. *International Journal of Distance Education and E-Learning*, *6*(2), 15–30. https://doi.org/10.36261/ijdeel.v6i2.1859Abubakari, M. S., Nurkhamid, & Hungilo, G. (2021). Evaluating an e-Learning Platform at Graduate School Based on User Experience Evaluation Technique. *Journal of Physics: Conference Series*, *1737*(1). https://doi.org/10.1088/1742-6596/1737/1/012019Abubakari, M. S., Nurkhamid, N., & Priyanto, P. (2022). Factors Influencing Online Learning Engagement: International Students’ Perspective and the Role of Institutional Support. *Turkish Online Journal of Distance Education*, *23*(3), 118–136. https://doi.org/10.17718/tojde.1137253Abubakari, M. S., & Priyanto. (2021). Information and Communication Technology Acceptance in Madrasa Education: Religious’ Perspective in Tanzania. *International Journal of Social Sciences & Educational Studies*, *8*(3), 129–148. https://doi.org/10.23918/ijsses.v8i3p129Abubakari, M. S., Zakaria, G. A. N., Priyanto, P., & Triantini, D. T. (2023). Analysing technology acceptance for digital learning in Islamic education: the role of religious perspective on ICT. *Journal of Computing Research and Innovation*, *8*(1), 1–16. https://doi.org/10.24191/jcrinn.v8i1.344Ajzen, I. (1991). The theory of planned behavior. *Organizational Behavior and Human Decision Processes*, *50*(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-TAjzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. *Psychological Bulletin*, *84*(5), 888–918. https://doi.org/10.1037/0033-2909.84.5.888Ajzen, I., & Fishbein, M. (2002). *Understanding attitudes and predicting social behavior*. Prentice-Hall.Al-Harbi, B. A. (2019). The Attitudes of Islamic Education Teachers towards the Use of Social Media in Teaching and Learning. In *International Education Studies* (Vol. 12, Issue 11, pp. 154–163).Al-rahmi, W. M., Zeki, A. M., Alias, N., & Saged, A. A. (2017). Information Technology Usage in the Islamic Perspective: A Systematic Literature Review. *The Anthropologist*, *29*(1), 27–41. https://doi.org/10.1080/09720073.2017.1335758Alamri, M. M., Almaiah, M. A., & Al-Rahmi, W. M. (2020). Social media applications affecting students’ academic performance: A model developed for sustainability in higher education. *Sustainability (Switzerland)*, *12*(16), 1–14. https://doi.org/10.3390/su12166471Ali, A. H., Idris, M. R., & Rahman, M. N. A. (2016). Technology Influence and Self-aspect on Blog Acceptance as a Teaching Medium for Islamic Education in Muslim Y Generation at IPTA. *International Journal of Academic Research in Business and Social Sciences*, *6*(12), 197–210. https://doi.org/10.6007/IJARBSS/v6-i12/2486Alshammari, M. T., & Qtaish, A. (2019). Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level. *Journal of Information Technology Education: Research*, *18*, 529–547. https://doi.org/10.28945/4459Alsharbi, B. M., Mubin, O., & Novoa, M. (2021). Quranic Education and Technology: Reinforcement learning System for Non-Native Arabic Children. *Procedia Computer Science*, *184*(2019), 306–313. https://doi.org/10.1016/j.procs.2021.04.007Amaro, S., & Duarte, P. (2015). An integrative model of consumers’ intentions to purchase travel online. *Tourism Management*, *46*, 64–79. https://doi.org/10.1016/j.tourman.2014.06.006Arjmand, R. (2018). Introduction to Part I: Islamic Education: Historical Perspective, Origin, and Foundation. In *Handbook of Islamic Education* (pp. 3–31). https://doi.org/10.1007/978-3-319-64683-1\_3Ateeq-ur-Rehman, & Shabbir, M. S. (2010). The relationship between religiosity and new product adoption. *Journal of Islamic Marketing*, *1*(1), 63–69. https://doi.org/10.1108/17590831011026231Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. *Psychological Review*, *84*(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191Bandura, A. (2001). Social Cognitive Theory: An Agentic Perspective. *Annual Review of Psychology*, *52*(1), 1–26. https://doi.org/10.1146/annurev.psych.52.1.1Compeau, D., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. *MIS Quarterly: Management Information Systems*, *19*(2), 189–210. https://doi.org/10.2307/249688Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. *MIS Quarterly: Management Information Systems*, *23*(2), 145–158. https://doi.org/10.2307/249749Daniela, L. (2019). Smart Pedagogy for Technology-Enhanced Learning. In *Didactics of Smart Pedagogy* (pp. 3–21). Springer International Publishing. https://doi.org/10.1007/978-3-030-01551-0\_1Daun, H., & Arjmand, R. (2021). Globalisation and Islamic Education. In J. Zajda (Ed.), *Third International Handbook of Globalisation, Education and Policy Research* (pp. 451–463). Springer International Publishing. https://doi.org/10.1007/978-3-030-66003-1\_25Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. *MIS Quarterly*, *13*(3), 319–340. https://doi.org/10.2307/249008Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. *Journal of Applied Social Psychology*, *22*(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.xDimitrijević, S., & Devedžić, V. (2021). Utilitarian and experiential aspects in acceptance models for learning technology. *Educational Technology Research and Development*, *69*(2), 627–654. https://doi.org/10.1007/s11423-021-09970-xDishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. *Information & Management*, *36*(1), 9–21. https://doi.org/10.1016/S0378-7206(98)00101-3Eid, R., & El-Gohary, H. (2015). The role of Islamic religiosity on the relationship between perceived value and tourist satisfaction. *Tourism Management*, *46*, 477–488. https://doi.org/10.1016/j.tourman.2014.08.003El-Gohary, H., & Eid, R. (2013). Leadership teaching impact on tourism students’ attitudes and perceptions toward leadership in developing economies: The case of egypt. *Journal of Hospitality and Tourism Education*, *25*(4), 180–192. https://doi.org/10.1080/10963758.2013.850297Fishbein, M., & Ajzen, I. (1975). *Belief, attitude, intention, and behavior : an introduction to theory and research*. Addison-Wesley Pub. Co.Fishbein, M., & Ajzen, I. (2015). *Predicting and changing behavior : the reasoned action approach*.Forero, C. G., Maydeu-Olivares, A., & Gallardo-Pujol, D. (2009). Factor Analysis with Ordinal Indicators: A Monte Carlo Study Comparing DWLS and ULS Estimation. *Structural Equation Modeling: A Multidisciplinary Journal*, *16*(4), 625–641. https://doi.org/10.1080/10705510903203573García-Martínez, J.-A., Rosa-Napal, F.-C., Romero-Tabeayo, I., López-Calvo, S., & Fuentes-Abeledo, E.-J. (2020). Digital Tools and Personal Learning Environments: An Analysis in Higher Education. *Sustainability*, *12*(19), 8180. https://doi.org/10.3390/su12198180Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., Black, W. C., & Anderson, R. E. (2019). *Multivariate Data Analysis*. Cengage Learning EMEA. https://doi.org/10.1002/9781119409137.ch4Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. *European Business Review*, *31*(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203Hair Jr, J., Hult, G. T., Ringle, C., & Sarstedt, M. (2017). *A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)*. SAGE Publications Inc.Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. *Sustainable Operations and Computers*, *3*(May), 275–285. https://doi.org/10.1016/j.susoc.2022.05.004Haris, A., Asnawi, N., & Fanani, M. A. (2022). Expanding the Technology Acceptance Model ( TAM ) to investigate e-learning usage behavior during the COVID-19 pandemic : Islamic Higher Education Institution ( IHEI ) context. *Baltic Journal of Law & Politics*, *15*(1), 1885–1903. https://doi.org/10.2478/bjlp-2022-00120Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. *Structural Equation Modeling: A Multidisciplinary Journal*, *6*(1), 1–55. https://doi.org/10.1080/10705519909540118Islam, A. Y. M. A., Mok, M. M. C., Gu, X., Spector, J., & Hai-Leng, C. (2019). ICT in Higher Education: An Exploration of Practices in Malaysian Universities. *IEEE Access*, *7*(c), 16892–16908. https://doi.org/10.1109/ACCESS.2019.2895879ITU, UNESCO, & UNICEF. (2019). *The Digital Transformation of Education: Connecting Schools, Empowering Learners* (Issue September). https://www.repsol.com/imagenes/global/en/digital\_transformation\_dossier\_tcm14-144286.pdfJamaluddin, D., Ramdhani, M. A., Priatna, T., & Darmalaksana, W. (2019). Techno University to increase the quality of islamic higher education in Indonesia. *International Journal of Civil Engineering and Technology*, *10*(1), 1264–1273.Janssen, J., Stoyanov, S., Ferrari, A., Punie, Y., Pannekeet, K., & Sloep, P. (2013). Experts’ views on digital competence: Commonalities and differences. *Computers & Education*, *68*, 473–481. https://doi.org/10.1016/j.compedu.2013.06.008JASP Team. (2023). *JASP (Version 0.17.2) \[Computer software\]* (0.17.2). JASP Team. https://jasp-stats.org/Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling. In *The Guilford Press* (Fourth Edi). A Division of Guilford Publications, Inc.Kock, N. (2022). Model-Driven Data Analytics: Applications with WarpPLS. In *ScriptWarp Systems*. ScriptWarp Systems.Kuo, Y. C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. *Internet and Higher Education*, *20*, 35–50. https://doi.org/10.1016/j.iheduc.2013.10.001Li, L., & Buhalis, D. (2006). E-Commerce in China: The case of travel. *International Journal of Information Management*, *26*(2), 153–166. https://doi.org/10.1016/j.ijinfomgt.2005.11.007Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. *Information Systems Research*, *2*(3), 192–222. https://doi.org/10.1287/isre.2.3.192Nabavi, A., Taghavi-Fard, M. T., Hanafizadeh, P., & Taghva, M. R. (2016). Information Technology Continuance Intention. *International Journal of E-Business Research*, *12*(1), 58–95. https://doi.org/10.4018/IJEBR.2016010104Ngabiyanto, Nurkhin, A., Widiyanto, Saputro, I. H., & Kholid, A. M. (2021). Teacher’s intention to use online learning; an extended technology acceptance model (TAM) investigation. *Journal of Physics: Conference Series*, *1783*(1), 012123. https://doi.org/10.1088/1742-6596/1783/1/012123Nuryanna, N., Halim, A., Mahzum, E., & Hamid, A. (2021). Acceptance of Technology by Islamic Boarding School Students Based on the TAM Model. *Jurnal Penelitian Pendidikan IPA*, *7*(Special Issue), 194–198. https://doi.org/10.29303/jppipa.v7iSpecialIssue.825Peiffer, H., Schmidt, I., Ellwart, T., & Ulfert, A.-S. (2020). Digital competences in the workplace: Theory, terminology, and training. In *Vocational education and training in the age of digitization: Challenges and opportunities* (pp. 157–181). Verlag Barbara Budrich. https://doi.org/https://doi.org/10.2307/j.ctv18dvv1c.11Rahman, A. H. A., Samad, N. S. A., Abdullah, A., Yasoa’, M. R., Muhamad, S. F., Bahari, N., & Mohamad, S. R. (2022). E-Learning and Sustainability of Pondok Schools: A Case Study on Post-COVID-19 E-Learning Implementation among Students of Pondok Sungai Durian, Kelantan, Malaysia. *Sustainability*, *14*(18), 11385. https://doi.org/10.3390/su141811385Ringle, C. M., Wende, S., & Becker, J.-M. (2022). *SmartPLS 4* (4.0.8.9). SmartPLS GmbH. https://www.smartpls.comRogers, E. M. (1995). Diffusion of Innovations: Modifications of a Model for Telecommunications. In *Die Diffusion von Innovationen in der Telekommunikation* (pp. 25–38). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-79868-9\_2Rogers, E. M. (2003). *Diffusion of Innovations, 5th Edition*.Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. *Computers & Education*, *128*(0317), 13–35. https://doi.org/10.1016/j.compedu.2018.09.009Scherer, R., & Teo, T. (2019). Unpacking teachers’ intentions to integrate technology: A meta-analysis. *Educational Research Review*, *27*(0317), 90–109. https://doi.org/10.1016/j.edurev.2019.03.001Serin, H. (2022). Challenges and Opportunities of E-Learning in Secondary School in Iraq. *International Journal of Social Sciences & Educational Studies*, *9*(3), 305–318. https://doi.org/10.23918/ijsses.v9i3p305Shan-a-alahi, A., & Huda, M. N. (2017). Role of Information Technology on Preaching Islam (Da’wah). *American International Journal of Research in Humanities, Arts and Social Sciences (AIJRHASS)*, *17*(1), 1–5.Subiyakto, A., Sekarningtyas, R., Aini, Q., Hakiem, N., Muslimin, J. M., Subchi, I., & Ahlan, A. R. (2022). The Impacts of Perceived Trust and Perceived Validity on the Religious Electronic Resource Acceptance. *ICIC Express Letters*, *16*(9), 1019–1028. https://doi.org/10.24507/icicel.16.09.1019Surjono, H. D. (2014). The Evaluation of a Moodle Based Adaptive e-Learning System. *International Journal of Information and Education Technology*, 89–92. https://doi.org/10.7763/IJIET.2014.V4.375Teo, T., & van Schaik, P. (2012). Understanding the Intention to Use Technology by Preservice Teachers: An Empirical Test of Competing Theoretical Models. *International Journal of Human-Computer Interaction*, *28*(3), 178–188. https://doi.org/10.1080/10447318.2011.581892Ulfert-Blank, A.-S., & Schmidt, I. (2022). Assessing digital self-efficacy: Review and scale development. *Computers & Education*, *191*(August), 104626. https://doi.org/10.1016/j.compedu.2022.104626Ulfert, A.-S., Antoni, C. H., & Ellwart, T. (2022). The role of agent autonomy in using decision support systems at work. *Computers in Human Behavior*, *126*, 106987. https://doi.org/10.1016/j.chb.2021.106987UNESCO. (2021). *Information and Communication Technology Use in Education*. United Nations Educational, Scientific and Cultural Organization (UNESCO).Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. *Decision Sciences*, *39*(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.xVenkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. *MIS Quarterly: Management Information Systems*, *27*(3), 425–478. https://doi.org/10.2307/30036540Waghid, Y. (2014). Islamic Education and Cosmopolitanism: A Philosophical Interlude. *Studies in Philosophy and Education*, *33*(3), 329–342. https://doi.org/10.1007/s11217-013-9390-3Wardayanti, Y. P., Amin, F. M., & Permadi, A. (2022). Analysis of Measurement of Readiness and Success of E-Learning Using the Method of Technology Readiness and Acceptance Model (TRAM) At the Islamic University of Darul Ulum Lamongan. *International Journal of Service Science, Management, Engineering*, *2*(1), 5–14.Wheeler, S. (2012). e-Learning and Digital Learning. In *Encyclopedia of the Sciences of Learning* (pp. 1109–1111). Springer US. https://doi.org/10.1007/978-1-4419-1428-6\_431Xie, T., Zheng, L., Liu, G., & Liu, L. (2022). Exploring structural relations among computer self-efficacy, perceived immersion, and intention to use virtual reality training systems. *Virtual Reality*, *26*(4), 1725–1744. https://doi.org/10.1007/s10055-022-00656-0Yi, M. Y., Fiedler, K. D., & Park, J. S. (2006). Understanding the Role of Individual Innovativeness in the Acceptance of IT-Based Innovations: Comparative Analyses of Models and Measures. *Decision Sciences*, *37*(3), 393–426.Zamani-Farahani, H., & Musa, G. (2012). The relationship between Islamic religiosity and residents’ perceptions of socio-cultural impacts of tourism in Iran: Case studies of Sare’in and Masooleh. *Tourism Management*, *33*(4), 802–814. https://doi.org/10.1016/j.tourman.2011.09.003