# The Extended Information Systems Success Measurement Model: e-Learning Perspective
## 6 Discussion  
Beholding that COVID-19 has, so far, changed every traditional way of processing activities in the past two years [129; 130; 131; 132; 133], higher education institutions also seek this change to keep the learning process ongoing [134; 135; 136; 137; 138; 139; 140; 141; 142; 143; 144]. Therefore, online learning has become the new normal. However, the question of this transition's success arose, highlighting the need to understand what factors are crucial across the needed change. The most accurate software, i.e., ISs, have enabled the digital transformation of the learning environment, but have the teachers adapted to the new way of working? That is the question we found essential when measuring IS success. In the vast e-learning IS success literature, no previous research observed the influence explained above. Consequently, this study investigated the potential of combining two widely accepted theories, shaped in a manner to fulfill the research gap and widen the IS success horizon. Figure 3: PICB–SEM model. The conceptual model was hypothesized with seven main and six auxiliary hypotheses in two region-different higher education institutions. The results confirmed five main and four auxiliary hypotheses for the UNS, while for the PICB, five primary and six auxiliary hypotheses were confirmed. The main contribution of this article was to test the given instrument in diverse countries, emphasizing the quest for workforce agility toward IS success in various learning environments. The UTAUT model has been widely recognized and utilized in previous IS success and acceptance testing [3; 4; 5; 109; 110; 111; 112; 113; 114; 115; 116]. Our research found that performance expectancy was still one of the most decisive behavioral intention predictors [4; 5; 6; 109; 110; 111; 112; 113; 114; 115; 116] in both observed institutions. Moreover, effort expectancy was widely shown as essential for behavioral intention [4; 5; 6; 103; 104; 109; 110; 111; 112; 113; 114; 115; 116]; however, with \(\beta=-0.07\) for the UNS, it was shown negative. At the UNS, 63% of respondents had experience using such an IS for a year or more, so it could be assumed that this is why the expected effort in using the IS did not influence the intention to use the system. Although social influence was the strongest predictor of behavioral intention in the UNS, this relationship resulted in a hostile and statistically insignificant influence \(\beta=-0.01\) at the PICB. In the previously observed studies, the facilitating conditions towards the use behavior path coefficient ranged from \(-0.23\) to \(0.40\)[3; 4; 5; 6; 103; 104; 109; 110; 111; 113; 115; 116; 117]. Within this study, the facilitating conditions factor negatively influenced use behavior in both observed institutions. A possible explanation for these results is that the respondents needed to consider the facilitating conditions in this IS exclusive because they started using it as an addition to the existing digital work environment and the e-services they already use. In this regard, respondents are very likely to perceive facilitating conditions before using the given IS. The relation between behavioral intention to use the system and use behavior was found to be statistically significant and positive in almost 90% of the previous studies [3; 4; 6; 103; 110; 111; 112; 115; 117], which was also found in this study. Finally, to evidence the importance of workforce agility's influence on the overall IS success and overcome research shortcomings, this paper's authors hypothesized WA with two main hypotheses. Seeking more extensive outputs on two main WA hypotheses, an additional six were set for assessing each WA aspect (proactivity, adaptability, and resilience). Observing the first quested factor, behavioral intention, its dependent factor proactivity demonstrated a statistically non-significant influence on it at UNS (\(\beta=-0.33\)). However, at PICB, it was found to be strong and statistically significant (\(\beta=0.18\); \(p<0.05\)).