Jurnal Manajemen Indonesia (Vol 25.
, pp.
118 -130, 2.
Online ISSN: 2502-3713 | Print ISSN: 1411-7835 This Journal is available in Telkom University Online Journals Jurnal Manajemen Indonesia Journal homepage: journals.
id/ijm The Influence of TOE Framework on the Readiness of Toll Road Business Entities (BUJT) in Implementing Multi Lane Free Flow (MLFF) Technology Maulana Nur Hidayatullah1.
Anton Mulyono Aziz2 Faculty of Economy and Business.
Telkom University.
Indonesia Abstract The toll payment system in Indonesia has evolved using e-money since 2017.
However, increasing vehicle populations and economic activities have caused congestion at toll gates, resulting in economic losses of up to IDR 4.
4 trillion annually.
To address this, the government plans to implement Multi Lane Free Flow (MLFF) technology, enabling transactions without stopping.
The implementation requires thorough readiness, facing challenges such as recording accuracy between 80-99%, potential revenue loss for BUJT, regulatory uncertainties, delayed planning, and infrastructure needs.
This study evaluates BUJT readiness using the Technology-Organization-Environment (TOE) framework, focusing on seven variables: Compatibility.
Advantage.
Complexity.
Organization Readiness.
Top Management Support.
Government Support, and Vendor Quality.
Using Partial Least Squares (PLS) and Structural Equation Modeling (SEM), data from purposive sampling of BUJT were analyzed.
Results show only Advantage significantly affects readiness .
=2.
p=0.
, with an RA of 0.
24 indicating weak predictive power.
The study contributes to understanding factors influencing BUJT readiness and recommends enhancing government support, stakeholder coordination, and technical and organizational preparedness evaluation.
Keywords: Toll Collection.
Multi Lane Free Flow.
TOE Framework.
Partial Least Squares.
Structural Equation Modeling Abstrak Sistem pembayaran tol di Indonesia telah berkembang menggunakan uang elektronik sejak tahun 2017.
Namun, peningkatan jumlah kendaraan dan aktivitas ekonomi menyebabkan kemacetan di gerbang tol, yang mengakibatkan kerugian ekonomi hingga mencapai Rp 4,4 triliun per tahun.
Untuk mengatasi hal ini, pemerintah berencana mengimplementasikan teknologi Multi Lane Free Flow (MLFF), yang memungkinkan transaksi tanpa harus berhenti.
Pelaksanaan teknologi ini memerlukan kesiapan yang matang dan menghadapi berbagai tantangan seperti akurasi pencatatan yang berkisar antara 80-99%, potensi kerugian pendapatan bagi Badan Usaha Jalan Tol (BUJT), ketidakpastian regulasi, perencanaan yang tertunda, serta kebutuhan infrastruktur.
Penelitian ini mengevaluasi kesiapan BUJT menggunakan kerangka Technology-Organization-Environment (TOE), dengan fokus pada tujuh variabel: Kesesuaian (Compatibilit.
Keunggulan (Advantag.
Kompleksitas (Complexit.
Kesiapan Organisasi (Organization Readines.
Dukungan Manajemen Puncak (Top Management Suppor.
Dukungan Pemerintah (Government Suppor.
, dan Kualitas Vendor (Vendor Qualit.
Dengan menggunakan Partial Least Squares (PLS) dan Structural Equation Modeling (SEM), data dari purposive sampling BUJT Hasil penelitian menunjukkan hanya variabel Advantage yang berpengaruh signifikan terhadap kesiapan .
=2,496.
p=0,.
, dengan nilai RA sebesar 0,24 yang menunjukkan kekuatan prediksi yang lemah.
Penelitian ini memberikan kontribusi dalam memahami faktor-faktor yang memengaruhi kesiapan BUJT dan merekomendasikan peningkatan dukungan pemerintah, koordinasi antar pemangku kepentingan, serta evaluasi kesiapan teknis dan organisasi.
Kata kunci: Toll Collection.
Multi Lane Free Flow.
TOE Framework.
Partial Least Squares.
Structural Equation Modeling Article info Received .
/05/2.
Revised .
/06/2.
Accepted .
/10/2.
maulanaalan@student.
DOI: 10.
34818/jmi.
Copyright@2025.
Published by School of Economics and Business Ae Telkom University Hidayatullah.
& Aziz.
Jurnal Manajemen Indonesia (Vol 25.
, pp.
118 -130, 2.
INTRODUCTION
Toll roads are a critical component of IndonesiaAos transportation infrastructure, serving as vital arteries for economic activities and mobility (Kamiliah & Wijaya, 2.
According to Government Regulation (PP) No.
2005, toll roads are public roads that are part of the national road network and require users to pay toll fees.
These roads are characterized by dedicated lanes, toll collection facilities, enhanced security, and maintenance standards.
The operation and management of toll roads are entrusted to Toll Road Business Entities (Badan Usaha Jalan Tol.
BUJT), which are limited liability companies established through competitive bidding to manage toll road concessions under the Toll Road Operation Agreement (PPJT) as regulated by the Ministry of Public Works (Permen PU No.
13, 2.
BUJTs are specialized entities responsible for the development, operation, and maintenance of toll roads in Indonesia.
BUJTs generate revenue primarily from toll fees based on vehicle class and distance traveled, regulated by the government (Aditya, 2.
Additional income may come from commercial areas adjacent to toll roads, such as rest areas and fuel stations.
Problem Statement Despite the adoption of electronic money for toll payments since 2017 (Permen PU 16/PRT/M/2.
, congestion at toll gates remains a significant problem due to increasing vehicle volumes and economic activities (Santosa et , 2.
This congestion results in substantial economic losses estimated at IDR 4.
4 trillion annually (Roatex Ltd Zrt, 2.
The government aims to implement Multi Lane Free Flow (MLFF) technology, which allows toll transactions without stopping, to alleviate congestion and improve traffic flow.
This system eliminates the need for vehicles to stop at toll booths by utilizing technologies such as Radio Frequency Identification (RFID).
Global Navigation Satellite Systems (GNSS), and automatic number plate recognition.
While the benefits of MLFF are widely recognized including reduced congestion, and lower emissions (Budiharjo & Margarani, 2.
Despite being designated as a National Strategic Project (PSN) under the Coordinating Ministerial Regulation (Permenk.
No.
6 of 2024, the implementation of the Multi-Lane Free Flow (MLFF) toll collection system in Indonesia has faced significant delays and uncertainties (Parikesit et al.
, 2.
Initially targeted for completion in mid-2022, the system has yet to be operational as of 2025 (Santosa et al.
, 2.
The Indonesian Supreme Audit Agency (BPK) has raised concerns regarding regulatory noncompliance and recommended a review of toll road management agreements and MLFF implementation (Yatun et al.
, 2.
From a technological standpoint, the application of GNSS-based MLFF via smartphones poses unresolved challenges related to system accuracy, with reported error margins ranging from 0.
1% to 1%, which potentially translates to revenue losses for toll road enterprises (Parikesit, 2.
Moreover, the lack of physical barriers and enforcement mechanisms under MLFF increases the risk of toll evasion and revenue leakage, while full responsibility for toll data resides with government-appointed operators (Santosa et al.
, 2.
In addition to technological and regulatory risks, concerns remain regarding government support, system control, and the financial implications of inaccurate traffic data.
Although public perception of MLFF is generally positive (Hermawan, 2.
, toll road operators (BUJT) must evaluate their readiness from multiple dimensions technological, organizational, and governmental to ensure effective implementation.
Research Objectives This study aims to evaluate the readiness of BUJTs in implementing MLFF technology using the TechnologyOrganization-Environment (TOE) framework.
Specifically, it investigates the influence of seven variables:
Compatibility.
Advantage.
Complexity.
Organization Readiness.
Top Management Support.
Government Support, and Vendor Quality (Mahirah et al.
, 2.
on BUJT readiness.
II.
LITERATURE REVIEW
Technology Adoption Theories Technology adoption research has been extensively developed through various theoretical models.
The Technology Acceptance Model (TAM) posits that perceived usefulness and ease of use influence technology adoption decisions (Davis, 1.
The Unified Theory of Acceptance and Use of Technology (UTAUT) integrates multiple models, emphasizing performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al.
, 2.
The Theory of Planned Behavior (TPB) explains behavioral intentions based on attitudes, subjective norms, and perceived behavioral control (Ajzen, 1.
These models provide foundational insights into individual and organizational technology adoption behaviors.
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Technology-Organization-Environment (TOE) Framework The TOE framework, developed by Tornatzky and Fleischer .
, offers a comprehensive perspective on organizational technology adoption by considering three contexts: technological, organizational, and The technological context includes the internal and external technologies relevant to the firm.
The organizational context encompasses the firmAos size, structure, and resources.
The environmental context involves industry characteristics, regulatory environment, and external support.
This framework is particularly suitable for studying complex technology adoption in organizations such as BUJTs.
Multi Lane Free Flow (MLFF) Technology MLFF technology enables toll collection without requiring vehicles to stop or slow down, using advanced sensors, cameras, and communication systems (Santosa et al.
, 2.
This technology promises to reduce congestion, enhance traffic flow, and improve revenue collection accuracy.
However.
MLFF implementation involves challenges such as ensuring data accuracy, infrastructure readiness, regulatory clarity, and stakeholder coordination (Parikesit et al.
, 2.
Previous Studies on Technology Adoption in Toll Roads and MLFF Prior research has examined technology adoption, highlighting factors such as perceived benefits, organizational readiness, and external support (Min & Kim, 2.
Studies on MLFF adoption emphasize the importance of technological compatibility, management support, and government policies.
However, there is a research gap regarding the comprehensive assessment of BUJT readiness using the TOE framework in the Indonesian context.
Research Gap and Hypothesis Development This study addresses a significant research gap by employing the Technology-Organization-Environment (TOE) framework to assess the readiness of Badan Usaha Jalan Tol (BUJT) in implementing the Multi-Lane Free Flow (MLFF) toll collection system.
While various studies have applied the TOE framework to technology adoption in multiple industries, limited research specifically targets toll road operators in the Indonesian context.
The TOE framework, introduced by Tornatzky and Fleischer .
, provides a multidimensional approach to evaluating technological adoption based on technological, organizational, and environmental factors.
Compatibility refers to the degree to which the MLFF technology fits with the existing values, infrastructure, and workflows of BUJT.
A high degree of compatibility reduces uncertainty and resistance, thereby increasing the likelihood of adoption.
In the context of toll road systems, technologies that align with current operational procedures are more readily accepted and integrated (Rogers, 2.
Therefore, it is hypothesized that compatibility have a significant affects on BUJT readiness for MLFF implementation.
H1: Compatibility affects BUJT readiness in implementing MLFF significantly.
Perceived advantage represents the extent to which the MLFF system is seen to offer improvements over the existing system, such as reduced congestion, operational efficiency, and enhanced user convenience.
The higher the perceived benefits, the greater the organizational motivation to adopt the technology (Min & Kim, 2.
this study, perceived advantage is expected to serve as a significant affects predictor of BUJT readiness.
H2: Advantage affects BUJT readiness in implementing MLFF significantly.
Complexity describes how difficult the system is perceived to be in terms of understanding, usage, and integration (Qatawneh, 2.
Technologies that are perceived as too complex tend to face resistance from organizations due to the anticipated learning curve, training requirements, and implementation risks (Rogers, 2.
Hence, complexity is hypothesized to have a significant affects on BUJT readiness.
H3: Complexity affects BUJT readiness in implementing MLFF significantly.
Organizational readiness includes factors such as financial resources, technical infrastructure, and human capital available within the BUJT to support MLFF adoption.
Organizations with greater readiness are better positioned to manage the transition and overcome challenges during implementation (Oliveira & Martins, 2.
Therefore, it is proposed that organizational readiness significantly affects BUJT readiness.
H4: Organizational readiness affects BUJT readiness in implementing MLFF significantly.
Top management support refers to the degree of commitment and involvement of leadership in the adoption of MLFF.
In cases such as the adoption of Artificial Intelligence within companies, it has been found that Hidayatullah.
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management support and organizational readiness are the most crucial factors within an organization (Min & Kim.
Consequently, top management support is expected to significantly affects BUJT readiness.
H5: Top management support affects BUJT readiness in implementing MLFF significantly.
Government support includes regulatory guidance, policy incentives, and strategic direction provided by relevant authorities to facilitate MLFF implementation.
In the toll road sector, government alignment is essential due to the public-private nature of operations.
Supportive government interventions have been shown to enhance organizational adoption of new technologie (Naeem et al.
, 2.
, and thus, government support is hypothesized to significantly affects BUJT readiness.
H6: Government support affects BUJT readiness in implementing MLFF significantly.
Vendor quality refers to the capabilities and reliability of the technology providers involved in MLFF A high-quality vendor offers strong technical support, system reliability, and postimplementation services, which reduce the perceived risk and uncertainty (Gui et al.
, 2.
Therefore, vendor quality is proposed to significantly affects BUJT readiness.
H7: Vendor quality affects BUJT readiness in implementing MLFF significantly.
Technology Compatibility Advantage Complexity Organization Organization Readiness H2A
Top Management Support
H2B
Implementation MLFF Environment Vendor Quality Environment Uncertainly Government Support Figure 1 Research Framework i.
RESEARCH METHODOLOGY
Research Design This study employs a quantitative research design to empirically test the relationships between TOE framework variables .
ee Table .
and BUJT readiness for MLFF implementation.
The approach allows for statistical analysis and hypothesis testing.
Table 1 Research Characteristic Characteristic Method Purpose Strategy Paradigm Unit of Analysis Time Frame Research Involvement Type Quantitative Evaluative Case Study & Survey Positivism Organization Cross-Sectional & Prospective Non-intervention in data collection Population and Sample The population in this study refers to all entities sharing specific characteristics relevant to the research (Amin et , 2.
According to data from the Toll Road Regulatory Agency (BPJT) as of January 2024, there are a total Hidayatullah.
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of 58 BUJT.
The sample is defined as a subset of the population that serves as the actual source of data for the study (Fadilah Amin et al.
, 2.
This research employs a non-probability sampling technique, meaning not all members of the population have an equal chance of being selected.
Specifically, purposive .
sampling is used, where the selection is based on specific criteria directly related to the research objectives (Azis & Irjayanti.
To determine the minimum sample size required.
G*Power software was utilized, the required minimum sample size is 77 respondents from the selected BUJT.
Data Collection Methods Data were collected through structured questionnaires distributed to BUJT managers.
The questionnaire items were developed based on operational definitions of the TOE variables and readiness constructs.
Additionally, case studies were conducted to provide qualitative insights into BUJT challenges and practices.
Data Analysis Techniques Partial Least Squares (PLS) and Structural Equation Modeling (SEM) were employed to analyze the data.
PLS is selected for its flexibility and its suitability for studies with relatively small sample sizes.
These techniques allow for testing complex relationships among latent variables and assessing model fit, validity, and reliability.
The data analysis in this study comprises three main components.
First, descriptive analysis is conducted using percentage scores from 77 respondents on a Likert scale to categorize responses into five levels: Very Poor to Very Good.
Second, statistical testing using PLS-SEM includes evaluation of the measurement model .
uter mode.
through indicators such as loading factor.
AVE.
HTMT.
CR, and CronbachAos Alpha, and the structural model .
nner mode.
using R-square.
SRMR, and NFI to assess model fit and explanatory power.
Lastly, hypothesis testing is carried out via bootstrapping to determine the statistical significance of relationships between variables, using t-statistics and p-values.
Table 2 PLS-SEM Model Evaluation Criteria
Test
Convergent Validity Discriminant Validity Reliability Parameter Loading Factor
Average Variance Extracted (AVE) Heterotrait-Monotrait Ratio
(HTMT)
Rule / Threshold > 0.
Source Hair et al.
, 2017
> 0.
Hair et al.
, 2017
< 0.
Kline, 2016 Fornell-Larcker Criterion OoAVE > correlation with other constructs Composite Reliability (CR) CronbachAos Alpha > 0.
> 0.
RA < 0.
02 = Very Weak, 0.
02 O RA < 0.
13 = Weak, 0.
13 O RA
< 0.
26 = Moderate.
RA > 0.
26 = Substantial RA < 0.
25 = Very Weak, 0.
25 O RA < 0.
50 = Weak, 0.
50 O RA
< 0.
75 = Moderate.
RA > 0.
75 = Substantial < 0.
90 = Poor Fit.
Ou 0.
90 = Good Fit.
Ou 0.
95 = Excellent Fit O 0.
08 = Good Fit.
O 0.
10 = Acceptable Fit, > 0.
10 = Poor Fit > 1.
96 = Significant < 0.
05 = Significant R-Square (Cohen, 1.
Structural Model Hypothesis Testing R-Square (Hair et al.
, 2.
Normed Fit Index (NFI) Standardized Root Mean Square Residual (SRMR) t-statistic ( = 5%) p-value Fornell & Larcker.
Hair et al.
, 2017
Hair et al.
, 2017
Cohen, 1988 Hair et al.
, 2017
Hu & Bentler, 1999 Kline, 2016 Hair et al.
, 2017
Hair et al.
, 2017
IV.
RESULT/FINDING
Respondent Characteristics The data shows that the majority of respondents have a relatively high educational background, with most holding a Bachelor's degree .
73%), followed by high school/vocational school .
58%).
Master's degree .
49%), and a small portion holding a Diploma.
This educational level supports their understanding of MLFF technology and the TOE Framework.
In terms of position, the majority are operational staff .
45%), followed by low-level management .
97%), mid-level management .
08%), and top-level management .
49%).
With 54.
involved in management roles, it indicates strong engagement in both strategic and operational aspects of technology implementation within BUJTs.
Regarding work experience, most respondents have 5Ae10 years .
86%) or more than 10 years .
56%) of experience in toll road operations, while 15.
58% have less than 5 This suggests that the respondents possess deep knowledge of toll operations and are well-positioned to assess BUJT readiness for MLFF adoption.
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Table 3 Respondents Characteristics Characteristic Education Position Work Experience Category High School / Equivalent D1/D2/D3 TOTAL
Top Level Management Middle Level Management Low Level Management Staff
TOTAL
< 5 years 5-10 years > 10 years TOTAL Number Percentage (%) Descriptive Statistics of Variables Descriptive analysis showed varying perceptions of TOE variables, with Advantage and Top Management Support rated relatively high, while Complexity was perceived as moderate.
Table 4 Descriptive Analysis Variable Compatibility Advantage Complexity Organization Readiness Top Management Support Government Support Vendor Quality Readiness Mean Std.
Dev.
Measurement Model Evaluation Overall, the outer loading results indicate that all indicators meet the criteria for convergent validity.
Indicators with loadings above 0.
70 are considered highly valid, while those between 0.
60 and 0.
70, such as COMPX2 and MI2, are still acceptable as long as the construct's AVE meets the minimum threshold.
The results show that all variables are valid, with Average Variance Extracted (AVE) values above 0.
Thus, based on the convergent validity test using loading factors and AVE, all indicators and variables are deemed valid.
Table 5 Outer Loading & AVE
Variable Advantage Compatability Code
ADV1
ADV2
ADV3
COMP1
COMP2
COMPX1
COMPX2
Complexity COMPX3
Government Support
GOV1
GOV2
GOV3
MGT1
Top Management Support
MLFF
Implementation MGT2
MGT3
MI1
MI2
MI3
ORG1
Indicator MLFF technology increases toll operational efficiency.
MLFF provides a better user experience for toll customers.
Reduces waiting time in toll transactions.
The MLFF system is compatible with existing toll collection workflows.
Ease of integration with existing toll collection systems.
MLFF implementation does not require special training for employees.
BUJT (Toll Road Business Entit.
easily understands the MLFF transaction MLFF implementation requires easy and affordable technological The government has provided clear regulations for MLFF.
The government provides relevant technical support to support MLFF The government encourages road users to use MLFF technology .
Top management understands the vision and mission to be achieved in MLFF Management provides the necessary resources for MLFF implementation.
Management is proactive in supporting the success of MLFF.
BUJT has a strong intention to implement MLFF technology.
BUJT considers MLFF implementation the right choice to improve toll road operational efficiency.
BUJT has formed a positive attitude towards the MLFF Implementation plan.
BUJT already has the necessary resources for MLFF implementation.
Loading 0,773 0,839 0,813 0,905 0,888 0,894 0,691 AVE
0,654
0,804
0,607
0,738
0,739
0,921
0,683
0,808
0,769
0,761
0,957
0,903
0,696
0,607
0,578
0,743
0,878
0,735
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Variable Organizational Readiness Code
ORG2
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Indicator BUJT already has a workforce with adequate technical skills for MLFF BUJT's business processes support the implementation of the MLFF system.
The vendor provides reliable MLFF technology according to specifications.
The vendor provides responsive technical support during MLFF The vendor has strong experience and technical expertise in implementing similar technologies.
ORG3
VEND1
VEND2
Vendor Quality VEND3
Loading AVE
0,783
0,906
0,911
0,898
0,815
0,900
Discriminant validity testing ensures that each latent variable is truly distinct from the others.
The results show that all variables are valid, with HTMT values below the threshold of 0.
In the second method using the Fornelarcker criterion, the square root of AVE for each variable is greater than its correlations with other variables.
Therefore, based on both HTMT and Fornell-Larcker tests, all variables meet the minimum requirements and are considered valid.
Table 6 Heterotrait-Monotrait (HTMT) COMP
COMPX
GOV
MGT
ORG
VEND
ADV
0,542
0,545
0,549
0,707
0,534
0,396
0,524
COMP
COMPX
GOV
MGT
ORG
0,777
0,502
0,476
0,362
0,381
0,513
0,633
0,496
0,404
0,653
0,68
0,459
0,472
0,324
0,748
0,204
0,739
0,603
0,36
0,496
0,508
VEND
Table 7 Fornell-Lacker Criterion
ADV
COMP
COMPX
GOV
MGT
ORG
VEND
ADV
0,809
0,405
0,390
0,405
0,567
-0,368
0,327
0,429
COMP
COMPX
GOV
MGT
ORG
VEND
0,896
0,554
0,384
0,390
-0,250
0,302
0,422
0,779
0,441
0,343
-0,270
0,457
0,505
0,826
0,376
-0,355
0,263
0,623
0,834
-0,176
0,629
0,524
0,761
-0,241
-0,380
0,857
0,444
0,903
Reliability testing is used to measure the consistency or dependability of a research instrumentAispecifically, how consistently the indicators within a construct produce similar results under the same conditions.
In the context of PLS-SEM, construct reliability is evaluated using two key indicators: CronbachAos Alpha and Composite Reliability (CR).
The results show that all variables are considered reliable, with Composite Reliability values 7, indicating high consistency among indicators in measuring their respective constructs.
CronbachAos Alpha values are all above 0.
6, suggesting adequate internal consistency, although some constructs are close to the minimum threshold.
Table 8 CronbachAos Alpha and Composite Reliability Construct
Advantage Compatibility Complexity Government Support
Top Management Support
MLFF Implementation Organizational Readiness Vendor Quality Composite Reliability 0,850 0,891 0,821 0,865 0,871 0,801 0,892 0,930 CronbachAos Alpha
0,735
0,756
0,679
0,773
0,822
0,628
0,826
0,888
Structural Model Evaluation The R-Square value for the dependent variable "MLFF Implementation Readiness" is 0.
24, indicating a weak explanatory power.
This means that 24% of the variance is explained by variables such as Compatibility.
Advantage.
Complexity.
Organizational Readiness.
Top Management Support.
Government Support, and Vendor Quality, while the remaining 76% is influenced by other factors outside the model.
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Table 9 R-Squares Dependent Variable MLFF Implementation Readiness R-Squares Interpretation Weak Although model fit is not the primary focus in PLS-SEM, it can still be assessed.
The model's NFI value is 0.
indicating that the model fits approximately 59% of the actual data.
Table 10 Model Fit
SRMR
d_ULS Chi-square NFI
Saturated model
0,093
2,365
1,070
443,602
0,585
Estimated model
0,093
2,365
1,070
443,602
0,585
Hypothesis Testing Results Hypothesis testing in PLS-SEM was conducted using the bootstrapping method.
Based on the results, only H2 (Advantage Ie MLFF Implementation Readines.
was statistically significant, with a t-statistic of 2.
496 (>1.
and a p-value of 0.
013 (<0.
This confirms that perceived advantages of MLFF significantly influence BUJT readiness for implementation.
The remaining hypotheses (H1.
H3.
H4.
H5.
H6, and H.
were not supported, as their t-statistics were below 1.
96 and p-values above 0.
05, indicating no significant effect of compatibility, complexity, organizational readiness, top management support, government support, and vendor quality on MLFF implementation readiness.
Table 11 Hypothesis Results Hypothesis Advantage -> MLFF Implementation Compatablitiy -> MLFF Implementation Complexity -> MLFF Implementation Government Support -> MLFF Implementation Top Management Support -> MLFF Implementation Organizational Readiness -> MLFF Implementation Vendor Quality -> MLFF Implementation T statistics (|O/STDEV|) 2,496 0,404 0,342 0,881 1,295 1,058 1,202 P values 0,013 0,687 0,732 0,378 0,196 0,29 0,23 Result Accepted Rejected Rejected Rejected Rejected Rejected Rejected DISCUSSION The study findings indicate that among the seven tested hypotheses, only one variable.
Advantage showed a statistically significant effect on the implementation readiness of MLFF.
This suggests that most initially assumed factors did not strongly influence MLFF adoption based on the available data.
Advantage had a significant impact .
= 2.
p = 0.
, showing that the perceived benefits of MLFF .
operational efficiency, reduced toll gate congestion, cost savings, improved service qualit.
are key drivers of BUJT's readiness.
Therefore, implementation strategies should focus not only on technical infrastructure but also on increasing perceived value through pilot projects or case studies from other countries.
This aligns with Mahirah et al.
and Min & Kim .
, who found that perceived relative advantage significantly influences technology adoption.
Compatibility was not significant .
= 0.
p = 0.
Although theoretically important (Rogers, 2.
, it appears that in this context, alignment with existing systems is not a major concern.
This aligns with UTAUT findings where adoption in public sectors is often driven more by external pressure and perceived benefits than technical fit (Venkatesh et al.
, 2.
Nonetheless, compatibility remains crucial to avoid operational disruptions, and technical assessments .
, gap analysi.
should be conducted (Hermawan & Aruan, 2.
Complexity showed no significant impact .
= 0.
p = 0.
, though its small positive coefficient suggests it may still pose a potential barrier.
High system complexity could hinder adoption unless addressed through simplified design and process flows (Rogers, 2003.
McNerney et al.
, 2.
Implementation should prioritize minimizing technical complexity to facilitate user adaptation.
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Top management support was not significant .
= 1.
p = 0.
This may be due to the passive role of BUJT in Cluster 3 Transjawa, which is not part of the MLFF trial phase (Rahayu, n.
Without direct involvement or incentives, senior management lacks urgency for transformation.
Active engagement can be encouraged through regulatory mechanisms or incentives such as toll rate adjustments or SPM evaluations (Min & Kim, 2.
Organizational readiness had no significant effect .
= 1.
p = 0.
, possibly because BUJTs in Cluster 3 Transjawa are not yet involved in trials (Rahayu, n.
Despite this, internal readiness is essential for success and includes training, infrastructure upgrades, and promoting a culture of innovation (Min & Kim, 2.
Efforts such as annual innovation competitions reflect commitment to preparing all organizational levels.
Government support was not significant .
= 0.
p = 0.
, possibly due to insufficient policy integration or support perceived by BUJT.
Prior studies note coordination challenges in ETC implementation in Indonesia (Kamiliah & Wijaya, 2024.
Hermawan & Aruan, 2.
Similarly.
Naeem et al.
found that lack of government regulation limited mHealth adoption.
The government, through BPJT, should not only regulate but also actively support through incentives and technical guidance.
Vendor quality was also insignificant .
= 1.
p = 0.
This could be due to vendor selection (PT RITS) being government-appointed, limiting BUJT's perception of its importance.
This finding aligns with Gui et al.
, who showed that in vendor lock-in situations, vendor quality has little impact on adoption.
However, when organizations can choose vendors, quality becomes critical (Setiyani & Rostiani, 2.
Therefore, continuous performance monitoring through performance-based contracts is recommended.
VI.
CONCLUSION AND RECOMMENDATION
This study aims to analyze the factors influencing the readiness of Toll Road Operators (BUJT) to implement the Multi Lane Free Flow (MLFF) system based on the Technology-Organization-Environment (TOE) framework.
The results indicate that Advantage is the only factor significantly influencing the readiness for MLFF implementation, highlighting that the perceived benefits, such as increased efficiency, cost savings, legal certainty, and improved service quality, are the main drivers for organizations to adopt the system.
Other variables, including Compatibility.
Complexity.
Government Support.
Top Management Support.
Organizational Readiness, and Vendor Quality, did not show statistically significant effects.
However, these factors remain important in practical and managerial contexts.
The readiness of BUJT is not solely driven by technological aspects but also requires synergy between internal organizational support and external ecosystems, including government policies and technology partners.
Additionally, the complexity of the system can be a challenge that might impede implementation if not addressed with adequate human resources and infrastructure.
Based on these findings, several recommendations are provided.
The government should enhance the socialization of MLFF to BUJTs by emphasizing its benefits through workshops, pilot projects, field visits to countries with MLFF experience, and sharing data-driven information.
BUJTs should focus on internal organizational readiness, including continuous training, human resource development, and infrastructure updates, which can help in adapting to technological changes.
Although Complexity did not show a significant impact, efforts to simplify the system and make it easier to use are essential, such as by streamlining procedures, providing hands-on training, creating system prototypes for BUJTs, and developing standard operating procedures (SOP.
Furthermore.
Top Management Support needs to be reinforced through strategic communication, involvement in decision-making, and ensuring sufficient resources for the implementation of MLFF.
The government should also provide supportive policies and legal certainty, enhance vendor selection transparency, and establish task forces dedicated to MLFF implementation.
Finally, this study acknowledges several limitations.
The sample size was limited, and the research only focused on certain BUJT stakeholders, which may not fully represent the entire population of BUJTs in Indonesia.
Additionally, the study's variables did not account for all potential influencing factors, such as organizational culture, resistance, prior technology experience, or public enthusiasm, which could also play significant roles in MLFF adoption.
Data collection was conducted within a specific period, meaning it may not capture ongoing changes in traffic conditions or organizational readiness.
Furthermore, this research was entirely quantitative, and future studies using qualitative approaches, such as interviews, could offer deeper insights into the challenges and perceptions faced by BUJTs during the MLFF implementation process.
Hidayatullah.
& Aziz.
Jurnal Manajemen Indonesia (Vol 25.
, pp.
118 -130, 2.
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