This study was conducted to discover instructors’ behavioral intention in adopting e-learning during the Covid-19 pandemic, on the basis of the combination of two classical theories of UTAUT and TTF. This study investigated the effect of technology and task characteristics on task- technology fit and also examined the effect of task- technology fit, SI, EE, PE and FC as external factors on faculty members’ intention in adopting e-learning.
Concerning the association between technology characteristics with task-technology fit, the standard coefficient of technology characteristics and task- technology fit was found to be 0.652 (p = 0.001). Thus, H1 was supported, indicating that technology characteristics affect task-technology fit in adopting e- learning. The results also showed that the standard coefficient of task characteristics and task- technology fit was found to be 0.525 (P = 0.001); thus, H2 was supported, showing task characteristics affect task-technology fit in adopting e-learning. In line with the results of the current study, Zhou et al.  found that both technology and task characteristics have significant effect on task-technology fit, thereby determining user adoption. This finding supports previous research results. Pal and Patra [[[]]] conducted a study in which a combination of two classical theories, TAM and TTF, was used to elucidate university students’ perception of video-based learning in times of COVID-19. They found that technology characteristics has direct and significant effect on task technology fit. Also, they acknowledged that task technology fit has direct and significant effect on actual use of video-based learning by effecting on both perceived ease of use and perceived usefulness . Thus, technology and task characteristics should be considered in adopting a new technology, in this case e-learning. This is based on the assumption that users will adopt a new technology according to the fit between the technology characteristics and task requirements .
In terms of the relationship between task- technology fit with intention to adopt e-learning, the standard coefficient of task- technology fit and intention to adopt e-learning was found to be 0.244 (P = 0.001); thus, H3 was supported, indicating task- technology fit affects instructors’ intentions to adopt e-learning. Wu and Chen  found that task-technology fit has a direct and significant effect on perceived usefulness, which further affects user attitude and the intention to adopt Massive Open Online Courses (MOOCs). Similar with the results of this study, Zhou et al.  acknowledged that task-technology fit had significant effect on end-user adoption. Task-technology fit is defined as a matter of how the competency of a new technology matches with the tasks that the person must perform, which is a determining factor in explaining job performance levels . Furthermore, based on the finding of this study and other similar studies, task- technology fit is a major factor in adopting new technologies such as e-learning. Users welcome new technologies that can help them perform their tasks easily; however, the technology should be simple and comprehensible and effective. Thus, this issue should be addressed when successful and comprehensive implementation of e-learning is in progress.
As for the relationship between SI and intention to adopt e-learning, the results show that SI has a direct and significant effect on intention to adopt e-learning (β = 0/262, p = 0.001); thus, H4 was supported. This findings is in line with the studies of Maldonado, Alrawashdeh et al., Tan, Usoro et al., and Abdekhoda et al. [16,17,18,19,20], who have reported that SI has a direct and significant effect on new technology adoption. The ways people of a society exert effort to form values, beliefs, perception, intention, attitude and behavior are Sis . Thus, values, beliefs, perception, intention, attitude and behavior in the society affect the adoption of e-learning.
Regarding the association between EE and the intention to adopt e-learning, the standard coefficient of EE and the intention to adopt e-learning was found to be 0.646 (P = − 0.001); thus, H5 was supported; suggesting that EE affects instructors’ intention to adopt e-learning. Likewise, Alrawashdeh et al., Tan, Nassuora, Abdekhoda et al., Abdekhoda et al., and Zhou et al. [6,16,18,19,21] found that EE had a direct and significant effect on end-users’ intention in adopting e-learning. “Effort expectancy is defined as user perception of how they can use a technology easily” . Thus, ease of use of e-learning should be considered by designers and mangers, because the findings of the current study and pervious research suggest that users adopt systems which have considerable efficacy and can be used easily.
Concerning the relationship between PE and intention to adopt e-learning, the results show that PE has a direct and significant effect on the intention to adopt e-learning (β = 0/679, p = 0.001); thus, H6 was supported. This finding supports the findings of previous studies, which have shown that PE has a direct and significant effect on users’ intention behavior in adopting a new technology [6,13,16,18,19,22]. Performance expectancy is defined as “the expected impact of a technology’s functional advantage even in uncertain conditions” . Users’ acceptance of any new system such as e-learning takes place, when users perceive that the new system has considerable advantages and promotes their performance.
Finally, regarding the association between FCs and intention to adopt e-learning, the standard coefficient of FCs and intention to adopt e-learning was found to be 0.423 (P = 0.001; thus, H7 was supported, indicating that FCs affect instructors’ intention to adopt e-learning. Similarity, Alrawashdeh et al. , Echeng et al. , Nassuora , Abdekhoda et al. , and Zhou et al. (2017) have reported that FCs have a significant effect on end users’ perception when applying a new technology is considered.
This was a descriptive analytical study to investigate the determining factors in adopting e-learning in Covid-19 pandemic. This study presented a comprehensive scenario of the determinant factors in successful adoption of e-learning, based on an integration model of UTAUT and TTF. The combination of these two models to elucidate the faculty members’ intention toward e-learning adoption in Covid-19 pandemic, is the distinction of this research from other similar studies, indicating the combination of UTAUT and TTF can be a comprehensive and powerful model to identify the determinant factors of e-learning. This idea can be used in futures studies regarding new technology adoption.
Also, this study clearly identified effective and prominent factors in adopting e-learning which should be considered as the second distinctive feature of this study. We found that technology and task characteristics have a direct and significant effect on task- technology fit, which further determines instructors’ intention to adopt e-learning. This finding can help service providers to improve the task-technology fit of e-learning. Meanwhile, we found that SIs, EE, PE and FCs have considerable effects on instructors’ intention to adopt e-learning. These factors should be taken into consideration by providers and policy makers to promote e-learning in developing countries.
However, this study has the fallowing limitations that should be addressed in future studies. First, the setting of this study is limited to faculty members of Ahwaz University of Medical Sciences, and due to some barriers we were not able to include all faculty members of Iran’s ministry of health. If we had included faculty members of all Iranian universities of medical sciences as the study population, we would have been able to provide a more comprehensive picture of the current situation. Second, the intention to adopt e-learning is dynamic and may change constantly, but this study was carried out as a cross-sectional study. Finally, the items of the questionnaire were selected on the basis of instructors’ selection which might have a self-selection bias.