International Journal of Cyber and IT Service Management (IJCITSM) Vol.
No.
October 2025, pp.
214Oe222 E-ISSN: 2808-554X | P-ISSN: 2797-1325.
DOI:10.
ye Evaluation of Oracle NetSuite Adoption Using UTAUT Model at PT Austin Engineering Indonesia Sri Zuliarni1* .
Dwi Kartikasari2 1, 2 Dept.
of Management and Business.
Batam State Polytechnic.
Indonesia 1 sri.
zuliarni@polibatam.
id, 2 dwi@polibatam.
*Corresponding Author
Article Info
ABSTRACT
Article history:
The purpose of this study is to evaluate the extent of acceptance and use of the Oracle NetSuite system at PT Austin Engineering Indonesia based on the Unified Theory of Acceptance and Use of Technology (UTAUT) approach.
an effort to improve operational efficiency and effectiveness, many companies have begun to adopt cloud-based Enterprise Resource Planning (ERP) systems such as Oracle NetSuite.
However, the success of its implementation is highly dependent on the level of acceptance from users.
This study is a quantitative approach, involving 50 respondents taken through purposive sampling, namely active employees who use Oracle NetSuite.
The questionnaire used was designed with a Likert scale of 1-5 and analyzed using SmartPLS software.
The results of the analysis showed that Effort Expectancy and Facilitating Conditions had a significant influence on Behavioral Intention and Use Behavior.
Meanwhile.
Performance Expectancy and Social Influence did not show a significant influence on Behavioral Intention.
The results of this study offer insights for the development of ERP technology adoption strategies in the corporate environment.
Submission July 10, 2025 Revised August 19, 2025 Accepted August 28, 2025 Published September 16, 2025 Keywords:
Oracle NetSuite UTAUT Behavioral Intentions Use Behavior This is an open access article under the CC BY 4.
0 license.
DOI: https://doi.
org/10.
34306/ijcitsm.
This is an open-access article under the CC-BY license .
ttps://creativecommons.
org/licenses/by/4.
AAuthors retain all copyrights INTRODUCTION In a rapidly growing business world, companies are required to continuously improve productivity and operational efficiency.
The increasing complexity of business processes and the risk of misinformation make integrated information systems an important requirement to support strategic decisions .
The various risks faced by companies, such as human error, business process irregularities, and potential fraud, require a system that is able of produce valid data and manage information efficiently .
Enterprise Resource Planning (ERP) is present as the main solution by integrating all business units into one centralized platform to increase company efficiency and productivity.
Currently, cloud-based ERP implementation is trending because it offers flexibility and cost efficiency .
In addition, the implementation of ERP systems such as Oracle NetSuite is also in line with the Sustainable Development Goals (SDG.
, particularly SDG 8 (Decent Work and Economic Growt.
and SDG 9 (Industry.
Innovation, and Infrastructur.
, which emphasize sustainable industrial innovation, operational efficiency, and the strengthening of digital infrastructure to support inclusive economic growth .
Furthermore, by integrating business processes into a centralized and transparent system.
ERP adoption also contributes to Journal homepage: https://iiast.
iaic-publisher.
org/ijcitsm/index.
php/IJCITSM/index International Journal of Cyber and IT Service Management (IJCITSM) ye creating resilient organizations that are better prepared to face global challenges, while supporting long-term sustainability agendas in both economic and technological dimensions .
PT Austin Engineering Indonesia is a heavy equipment manufacturing company that has implemented Oracle NetSuite as a cloud-based ERP system, to integrate financial, manufacturing, and supply chain management functions.
It is expected that this implementation will be able to increase efficiency and transparency in the companyAos operations .
However.
ERP implementation often faces challenges, especially in the post-implementation stage.
Several factors such as lack of training for users, inconsistency of business processes, and weak internal communication are often the causes of system implementation failure .
Therefore, evaluating the success of Oracle NetSuite implementation at PT Austin Engineering Indonesia is important, especially regarding system acceptance by users .
In addition.
ERP adoption presents nuanced challenges across different industries and geographical For example, service-based industries often emphasize customer interaction and flexibility, while manufacturing industries focus on operational efficiency and supply chain integration .
Similarly.
ERP implementations in Southeast Asia may face cultural and infrastructure-related challenges distinct from those in Western countries.
Positioning this study within such a comparative perspective not only strengthens originality but also provides broader insights into how Oracle NetSuite adoption can be contextualized across diverse business environments .
To further enrich this research, the study also highlights that ERP adoption in manufacturing industries, such as PT Austin Engineering Indonesia, differs significantly from service-based or logistics industries where customer engagement and flexibility dominate .
In addition, geographical factors such as cultural acceptance, infrastructure readiness, and government digital policies in Southeast Asia may create unique challenges and opportunities when compared to implementations in Western contexts.
Including these nuances not only enhances the comparative depth of the research but also strengthens its originality by situating the Oracle NetSuite adoption within a broader, cross-industry and cross-regional framework .
, .
UTAUT model as a framework for evaluating the level of technology acceptance.
The main objective of this study is to identify the main factors that contribute to the success of system adoption and provide strategic recommendations to support the sustainability of system use .
This study is also expected to be able to fill the literature gap related to ERP system evaluation, especially in the aspect of sustainable adoption in the corporate environment .
RESEARCH METHODS
The Oracle NetSuite system serves as the research object in this study.
The Likert scale used ranges from 1 .
trongly disagre.
to 5 .
trongly agre.
The UTAUT model was applied to design the questionnaire.
The questionnaire used for data collection was analyzed using SmartPLS software .
The respondents of this study were Oracle NetSuite system users employed at PT Austin Engineering Indonesia.
A total of 50 active Oracle NetSuite users participated, and their data was processed.
Purposive sampling was used, where only employees who regularly use the Oracle NetSuite system were included in the sample .
Although this study is limited to 50 respondents from a single company, it acknowledges the potential limitation in generalizing the findings .
Future research is recommended to increase the sample size and include multiple organizations across various industries to provide a more comprehensive perspective on ERP Furthermore, the use of SEM-PLS in this study is justified due to its suitability for small to medium sample sizes and complex models.
Assumptions such as non-normal data distribution and multicollinearity were taken into account, while robustness was ensured through convergent validity, discriminant validity, and reliability testing.
These aspects enhance the replicability and credibility of the model applied in this research .
, .
Additionally, the study explicitly validated the measurement model by reporting Average Variance Extracted (AVE).
Composite Reliability, and CronbachAos Alpha to ensure internal consistency and convergent Discriminant validity was further assessed using the Fornell-Larcker criterion and cross-loadings to confirm construct independence.
Bootstrapping with 5,000 resamples was employed to evaluate the stability of path coefficients and significance levels .
These methodological details not only improve the transparency of the analysis but also provide a solid reference framework for researchers who aim to replicate or extend the model in similar ERP adoption contexts.
These elements enhance the replicability and credibility of the model applied in this research .
E-ISSN: 2808-554X | P-ISSN: 2797-1325
RESULT AND DISCUSSION
Descriptive Statistics of Respondents Table 1.
Respondent data Gender Man Woman Age < 20 years 20Ae29 years old 30Ae39 years old Ou 40 years Experience < 6 months 6Ae12 months 1Ae2 years > 2 years Usage Status Required Voluntary Respondents Usage Status Based on the data collected, the majority of respondents were male employees .
%) who were in the productive age range, namely between 30 and 39 years .
%).
Most respondents .
%) have experience using the Oracle NetSuite system for 6 to 12 months, and the use of this system is mandatory for 92% of respondents according to company policy as shown in Table 1 .
This shows that the system implementation has been carried out comprehensively through a top-down approach, requiring adaptation from all employee levels.
The predominance of male and young to middle-aged groups indicates adoption by core workers with strategic roles in daily operations .
Although most usage is mandatory, a small number of voluntary users reflects a relatively high acceptance of the technology.
Overall, this demonstrates a strong adoption level, though still in the adaptation stage of user experience .
The demographic composition and usage characteristics thus provide a crucial foundation for evaluating the measurement model and ensuring continuity between descriptive findings and the analysis of validity and reliability .
, .
Evaluation of Measurement Model A convergent validity and reliability Figure 1.
Path diagram of the UTAUT model The Figure 1 of the SmartPLS analysis show that most indicators have outer loading values above 0.
International Journal of Cyber and IT Service Management (IJCITSM).
Vol.
No.
October 2025, pp.
214Ae222 ye International Journal of Cyber and IT Service Management (IJCITSM) indicating strong convergent validity.
Several indicators with values below 0.
uch as SI1.
FC2.
FC3.
FC.
are still considered feasible to use because their AVE values and construct reliability are within acceptable limits .
Ae Average Variance Extracted (AVE) Table 2.
Results of validity and reliability tests Construct CronbachAos Alpha Composite Reliability Average Variance Extracted (AVE) All constructs in the model have AVE values greater than 0.
As shown in Table 2, all variable values obtained exceed 0.
In other words, the outer loadings are considered to meet the required standards, allowing the measurement process to proceed to the next stage .
Ae Construct Reliability The Composite Reliability values of all constructs exceeded the threshold of 0.
70, even most of them were above 0.
80, reflecting high internal consistency.
Although the CronbachAos Alpha value on the FC construct was slightly below the minimum limit .
, its Composite Reliability value .
still met the reliability requirements.
A Discriminant validity test Ae Fornell-Larcker Criterion Table 3.
Fornell-Larcker Criterion A construct is considered valid when the root AVE value is compared with the correlation values between latent variables.
If the root AVE value exceeds the correlation between other constructs, it indicates a clear distinction between the constructs, thus meeting the criteria for discriminant validity .
As shown in Table 3, no value exceeds the diagonal value being compared.
Therefore, it can be concluded that the data in the Fornell-Larcker calculation is valid .
Structural Model Evaluation A R-square value (R2 ) R-square is used to assess how much the independent variables contribute to predicting the dependent variable and how much of the effect variable can be explained by the causal variables .
Table 4.
Results of R-square calculations R-square R-square adjusted The table 4 above shows the R2 value for endogenous variables:
Ae Behavioral Intention (BI): R2 = 0.
This indicates that 47.
4% of the variation in Behavioral Intention can be accounted for by Performance Expectancy.
Effort Expectancy, and Social Influence .
E-ISSN: 2808-554X | P-ISSN: 2797-1325
ye Ae Use Behavior (UB): R2 = 0.
This suggests that 63.
9% of the variation in Use Behavior can be explained by Behavioral Intention and Facilitating Conditions, reflecting a relatively strong predictive power of the model .
A F-square Table 5.
F-square Based on the calculation results of the effect size .
2 ), it can be observed that Effort Expectancy (EE) exerts a strong influence on Behavioral Intention (BI) with a value of 0.
253, indicating a significant contribution to usersAo intention to adopt the system .
In contrast.
Social Influence (SI) shows only a minor effect on BI with a value of 0.
026, suggesting that peer or organizational pressure plays a limited role in shaping user intention.
Likewise.
Performance Expectancy (PE) demonstrates a very weak effect on BI .
2 = 0.
, which can practically be considered negligible as shown in Table 5 .
Furthermore.
Behavioral Intention (BI) has a moderate effect on Use Behavior (UB), as indicated by a value of 0.
This highlights that usersAo intention to use the system contributes meaningfully to actual system utilization .
On the other hand.
Facilitating Conditions (FC) show a strong effect on UB, with a value of 0.
680, underscoring the critical importance of adequate infrastructure, technical support, and resources in driving actual system use .
A Path Coefficients and Significance Table 6.
Hypothesis test results Path BI Ie UB EE Ie BI FC Ie UB PE Ie BI SI Ie BI Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (AiO/STDEVA.
P values Based on the results from testing the relationship between variables using the Structural Equation ModelingPartial Least Squares (SEM-PLS) method, it was found that Behavioral Intention (BI) has a positive and significant effect on Use Behavior (UB), as shown in Table 6.
This is supported by a path coefficient 255, a t-statistic value of 2.
881, and a p-value of 0.
< 0.
This indicates that the greater the userAos intention to use the system, the higher the probability of actual system usage.
Furthermore.
Effort Expectancy (EE) also has a positive and significant effect on Behavioral Intention (BI), with a coefficient value of 0.
567, a t-statistic of 3.
336, and a p-value of 0.
< 0.
This suggests that the systemAos ease of use plays a significant role in shaping usersAo intention to adopt it .
In addition.
Facilitating Conditions (FC) are shown to have a positive and significant impact on Use Behavior (UB), with a coefficient value of 0.
620, a t-statistic of 7.
126, and a p-value of 0.
< 0.
This emphasizes the critical role of sufficient infrastructure, facilities, and IT support in promoting actual system usage .
On the other hand.
Performance Expectancy (PE) does not show a significant effect on Behavioral Intention (BI) .
oefficient = -0.
041, t-statistic = 0.
216, p-value = 0.
Similarly.
Social Influence (SI) has no significant effect on Behavioral Intention (BI) .
oefficient = 0.
195, t-statistic = 1.
012, pvalue = 0.
Thus, neither performance expectations nor social pressure appear to play a critical role in shaping behavioral intentions in this study context .
International Journal of Cyber and IT Service Management (IJCITSM).
Vol.
No.
October 2025, pp.
214Ae222 International Journal of Cyber and IT Service Management (IJCITSM) ye From a practical perspective, these findings imply that ERP implementation strategies should prioritize ease of use and facilitating conditions rather than relying heavily on performance perceptions or peer influence .
Organizations can improve ERP adoption by providing targeted training programs, simplifying workflows, and ensuring rapid-response IT support.
Such measures help transform behavioral intention into consistent system use, reducing user resistance and maximizing the value of ERP investments .
From a practical perspective, these findings indicate that organizations must prioritize user-friendly system design and strong technical support rather than relying heavily on performance perceptions or peer influence .
, .
For example, when implementing ERP systems like Oracle NetSuite, companies can enhance adoption rates by providing hands-on training, simplifying workflows, and ensuring responsive IT support.
This creates a smoother transition from behavioral intention to actual system usage, making the research findings directly applicable to practitioners who manage ERP rollouts in real world environments .
MANAGERIAL IMPLICATION
The results of this study emphasize that effort expectancy is key in influencing usersAo behavioral This suggests that managers should focus on the systemAos ease of use when implementing ERP Practical actions include designing intuitive interfaces, offering thorough training, and providing ongoing support to help employees transition smoothly and confidently into using the system.
The strong influence of facilitating conditions on actual system use also emphasizes the need for reliable infrastructure and responsive technical support.
Managers should ensure adequate IT facilities, establish a dedicated helpdesk, and implement clear operational guidelines.
These efforts will not only reduce resistance but also encourage consistent system utilization, making ERP adoption more sustainable within the organization.
On the other hand, the insignificant effects of performance expectancy and social influence suggest that management should not overly rely on perceived usefulness or peer encouragement to drive adoption.
Instead, greater emphasis should be placed on hands-on support and workflow simplification that employees can directly experience as tangible benefits in their daily tasks.
From a managerial perspective.
ERP adoption plays a crucial role in enhancing operational efficiency and strengthening digital infrastructure.
By improving system performance and promoting sustainable innovation.
ERP implementation not only helps achieve operational excellence but also supports long-term organizational resilience and sustainable business practices.
CONCLUSION
This study assessed the acceptance and utilization of the Oracle NetSuite system at PT Austin Engineering Indonesia using the UTAUT model.
The findings revealed that effort expectancy and facilitating conditions significantly impact both behavioral intention and use behavior, whereas performance expectancy and social influence do not.
This suggests that the ease of use and available support facilities are more critical factors in driving ERP adoption than perceived usefulness or peer influence.
The findings also highlight the importance of strengthening technical training, user assistance, and internal IT support to optimize ERP utilization.
Technology acceptance is not solely determined by system performance, but also by how simple and well-supported the system feels to users.
Therefore, companies are advised to provide sustainable training and support strategies so that ERP adoption can deliver maximum benefits for operational performance.
Furthermore, future ERP strategies should consider integration with emerging technologies such as Artificial Intelligence (AI) and blockchain to enhance predictive analytics, decision-making, transparency, and data integrity.
However, this study has limitations in terms of a small sample size and focus on a single company, which reduces generalizability.
Future research is encouraged to expand the scope across industries and regions to provide a more comprehensive view of ERP adoption and capture variations influenced by organizational culture and regional context.
E-ISSN: 2808-554X | P-ISSN: 2797-1325
DECLARATIONS
About Authors Sri Zuliarni (SZ) https://orcid.
org/0009-0008-9925-396X Dwi Kartikasari (DK) https://orcid.
org/0000-0002-3222-4426 Author Contributions Conceptualization: SZ.
Methodology: DK.
Software: DK.
Validation: SZ and DK.
Formal Analysis:
DK and SZ.
Investigation: SZ.
Resources: DK.
Data Curation: DK.
Writing Original Draft Preparation: SZ and DK.
Writing Review and Editing: SZ and DK.
Visualization: SZ.
All authors.
SZ, and DK, have read and agreed to the published version of the manuscript.
Data Availability Statement The data presented in this study are available on request from the corresponding author.
Funding The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interest The authors declare that they have no conflicts of interest, known competing financial interests, or personal relationships that could have influenced the work reported in this paper.
REFERENCES