# Integrative Factors of E-Health Laboratory Adoption: A Case of Indonesia
## 5 Results and Data Analysis
### Analysis of the Structural Model  
Two conditions need to be considered in evaluating the structural model that is formed to gain the path coefficient (\(\beta\)) and the coefficient of determination (R\({}^{2}\)). The first condition in the PLS is that there is no significance test or estimation of the confidence interval, and the second condition, namely that is the coefficient of determination from the PLS analysis is identical to that found in multiple regression analysis. Figure 5 indicates the output of the estimated path coefficient (\(\beta\)) and explanatory power (R\({}^{2}\)). Therefore, the task-driven variable with both the construction of task characteristics and technology characteristics through task-technology fit simultaneously explained 49.2% of the variables in perceived usefulness (R\({}^{2}\) = 0.492). Bootstrap procedures were used to obtain path coefficients, t-statistics, statistical significance, and relevant parameters such as averages, standard errors, and loading items [114; 117]. All relationships in the conceptual model were supported by data. Task-driven variables were driven by task characteristics, technology characteristics, and task-technology fit. The modeling of task characteristics and technology characteristics had a significant influence on task-technology fit (\(\beta\) = 0.238 and 0.642). Thus, Hypotheses 1 and 2 were supported. Task-driven variables consisting of the task characteristics and the technology characteristics of e-health laboratories had a positive impact on the task-technology fit. The two constructs simultaneously accounted for 54.7% of the variance in task-technology fit (R\({}^{2}\) = 0.547). Task-technology fit in e-health laboratories was very important in determining perceived usefulness (\(\beta\) = 0.183). Hypothesis 3 prop  
Figure 5: Result of the structural model. the task-driven variable with both the construction of task characteristics and technology characteristics through task-technology fit simultaneously explained 49.2% of the variables in perceived usefulness (R\({}^{2}\) = 0.492). The results of data analysis for Hypothesis 4 were rejected because the task-technology fit of the e-health laboratory system did not have a positive and significant result on perceived ease of use. The relationships were negatively correlated (\(\beta\) = \(-\)0.048). The relationships that occurred were negatively correlated, so the higher the task technology fit indicator in an e-health laboratory, the lower the perceived ease of use felt by the user, or vice versa. Technology-driven variables were driven by information quality, accessibility, and design. Hypotheses 5 and 6 were also supported by the data. Information quality had a positive and significant impact on perceived usefulness (\(\beta\) = 0.257) and perceived ease of use (\(\beta\) = 0.240). Accessibility was the construct of technology driven variables and was an important precursor of perceived usefulness (\(\beta\) = 0.261) and perceived ease of use (\(\beta\) = 0.220). Thus, Hypotheses 7 and 8 were supported. For Hypotheses 9 and 10, the design of the e-health laboratory system negatively affected perceived usefulness (\(\beta\) = \(-\)0.001). The relationships that occurred were negatively correlated, so the higher the design indicators in an e-health laboratory, the lower the perceived usefulness felt by the user, or vice versa. The results of data analysis for Hypothesis 9 were rejected because the design of the e-health laboratory system did not have a positive and significant effect on perceived usefulness. The relationships were negatively correlated (\(\beta\) = \(-\)0.001) while the design of the e-health laboratory system had a positive impact on the perceived ease of use (\(\beta\) = 0.427). The relationships that occurred were positively correlated, so Hypothesis 10 was supported. Design in an e-health laboratory system positively influenced perceived ease of use.