Journal of Information Systems Engineering and Business Intelligence

Exposing Causative Factors on Software Discontinuity using an Elaborative Qualitative Method

Arfive Gandhi, Dana Sulistiyo Kusumo, Indra Lukmana Sardi — Tue, 22 Jul 2025 00:00:00 +0700

Background: Software discontinuity due to the inability to accommodate the needs of users is a the significant challenge facing the software development life cycle. This implied that the development team must be capable of producing software with extended lifespan, including the ability to detect outages early, to maintain continuity. Organizations need to determine the contributing and inhibiting factors responsible for discontinuity usage.

Objective: This research aimed to explore the factors that contribute and inhibit the discontinuation of software use in organizations as well as the prevention strategies.

Methods: The summative content analysis technique was used to capture, codify, and classify statements from respondents to discover usage pattern. Data were collected through interview and questionnaire techniques with 10 respondents from various Indonesian companies. The respondents had various sectoral backgrounds in software usage for more than a year. The data collected were compared, contrasted, and synthesized to deliver a holistic pattern among respondents.

Results: The result showed that 10 key factors contributed to software discontinuity, namely Loss of Perceived Usefulness (LUS), Loss of Perceived Ease of Use (LEU), Decreased Effort Expectancy (DEX), Decreased Performance Expectancy (DPX), Social Influence (SOI), Lack of Facilitating Conditions (LFC), Decreased Price Value (DPV), Lack of Habit (LHB), Hedonic Motivation (HDM), and Loss of Perceived Behavioral Control (LBC). The factors were further categorized into three big issues, including Software Usability (LUS, LEU, DEX, and DPX), External Triggers (DPV, SOI, and LBC), and Risk Management after Discontinuity (LFC, LHB, and SOI). Furthermore, the results indicated that nine factors contributed to software discontinuity except HDM with LEU and LUS having weak significance since most respondents stated partial agreement and disagreements.

Conclusion: This research employed a rigorous qualitative method to validate the factors in the proposed software discontinuity model with 10 causative factors. The acquired knowledge is expected to aid organizations or related development units to build software that accommodates user needs, including meeting long-term business targets.

Keywords: Software, Software Discontinuity, Influencing Factors, Qualitative Method

The Influence of Gamification Affordance on Customer Loyalty among E-Commerce in Indonesia

Luther Risman Luosaro Zega, Andi Reza Perdanakusuma, Uun Hariyanti — Tue, 22 Jul 2025 00:00:00 +0700

Background: The e-commerce industry in Indonesia is experiencing competition due to the rising number of users and price-sensitive consumers, making user loyalty a major challenge for companies. Although gamification, such as task/quest type, was recognized as a strategy to boost loyalty, previous studies showed inconsistent results regarding its impact on hedonic and utilitarian values.

Objective: This study aimed to explore the relationships among task/quest-type gamification affordance (GA), hedonic value (HV), utilitarian value (UV), satisfaction (SA), and loyalty (LOY) among Indonesian e-commerce users.

Methods: A total of 284 e-commerce app users who had engaged in task/quest-type gamification were selected as participants using a convenience sampling method. A quantitative method was adopted and survey data were examined by covariance-based structural equation modeling (CB-SEM) conducted in SmartPLS4.

Results: The analysis showed that gamification affordance significantly impacted users’ perceived hedonic and utilitarian values. An increase in these values significantly enhanced user satisfaction, and strongly correlated with loyalty. Gamification affordance also indirectly influenced loyalty through hedonic value, utilitarian value, and satisfaction.

Conclusion: Task/quest-type gamification affordance effectively enhanced user loyalty in Indonesian e-commerce by improving perceived hedonic and utilitarian values and satisfaction. These results suggested that gamification strategies focusing on task/quest-type elements could foster loyalty in a competitive e-commerce environment.

Keywords: Gamification Affordance, Hedonic Value, Utilitarian Value, Satisfaction, Loyalty

Assessing Information Security Awareness Among Indonesian Government Employees: A Case Study of the Meteorology, Climatology, and Geophysics Agency

Aji Prasetyo, Rizal Fathoni Aji, Wahyu Setiawan Wibowo — Tue, 22 Jul 2025 00:00:00 +0700

Background: Cybersecurity is important for government agencies and the usefulness shows the need for a thorough understanding of information security awareness (ISA) among employees in order to enhance protective measures and ensure compliance with regulations. The Meteorology, Climatology, and Geophysical Agency (BMKG) of Indonesia is very important in providing essential national data and this responsibility shows the need to assess and promote ISA among the employees. The efforts to ensure a robust ISA culture can allow BMKG to safeguard sensitive meteorological and geophysical data, strengthen operational resilience, maintain public trust, and mitigate potential cyber threats that are capable of compromising national security.

Objective: This study aimed to evaluate the level of organizational ISA among employees at BMKG and to improve measures considered important.

Methods: The Human Aspects of Information Security Questionnaire (HAIS-Q) was administered as the reference model to assess the knowledge, attitudes, and behaviors of employees regarding information security. A descriptive statistical analysis and Partial Least Squares Structural Equation Modelling (PLS-SEM) were further applied to analyze data from 459 BMKG employees across various security domains, including password management, email use, internet use, social media use, mobile device security, and incident reporting.

Results: The results showed that BMKG employees possessed a high overall level of ISA (88.06%) with the average knowledge, attitudes, and behaviors recorded to be 88.06%, 81.89%, and 80.74%, respectively. Meanwhile, specific areas such as email use (78.70%) and mobile device use (73.19%) had only moderate awareness. The structural model analysis also showed that behavior exerted the most significant influence on ISA (β = 0.423), followed by attitude (β = 0.289) and knowledge (β = 0.214).

Conclusion: The overall awareness level was positive but there was a need for targeted efforts in password management, email use, and mobile device security to improve ISA practices. Moreover, the implementation of comprehensive information security policies, regular training, and organizational support was suggested to be important for fostering a robust security culture within BMKG.

Keywords: Information Security Awareness, Cybersecurity, BMKG, PLS-SEM, Government Employees, Indonesia

Aligning Software Product Management with Software Engineering Concepts: A Systematic Literature Review

Chalani Oruthotaarachchi, Janaka Wijayanayake — Tue, 22 Jul 2025 00:00:00 +0700

Background: Software Product Management (SPM) plays a vital role in the success of many software projects by aligning customer needs with their business objectives and ensuring a seamless and effective software product lifecycle. SPM is established as a collection of tools, techniques, and practices that help an organization accomplish its objectives and enhance the predictability and profitability of software product development. However, despite its significance, SPM research has been fragmented into specific topics having limited SPM literature reviews. This research study addresses this gap and discusses the status of the SPM domain in a more holistic spectrum.

Objective: The study aims to review recent literature on SPM, focusing on the alignment of SPM with software engineering concepts, a product manager’s role, the existing framework, ontologies, and best practices that support ensuring the success of a product manager’s role.

Methods: A systematic literature review was conducted using SCOPUS, IEEE Xplore, ACM Digital Library, ScienceDirect, and ProQuest Central as databases. 71 articles were selected following a rigorous screening process as per the PRISMA 2000 statement.

Results: Integrating SPM and SE is crucial in delivering value-driven software solutions. Available theoretical models and frameworks can help with this integration; however, implementing these frameworks often has challenges. Even though product managers play a vital role in the software lifecycle, they lack sufficient organizational support to enrich their skills and knowledge. Other major challenges are the lack of knowledge to use emerging technologies such as AI for data-driven decision-making processes and the tendency to replace humans with such technologies.

Conclusion: Aligning strategic vision with agile flexibility is important to integrate SPM with SE practices. To improve decision-making and ensure better alignment of SPM with business objectives, organizations have to enhance product managers’ capabilities by leveraging emerging technologies. Research can focus on developing adaptable and user-friendly SPM frameworks that match both medium-scale and large-scale organizational expectations.

Keywords: Organizational Value, Product Manager Role, Software Engineering Integration, Software Product Management, SPM Challenges, SPM Frameworks

Enhancing the Comprehensiveness of Criteria-Level Explanation in Multi-Criteria Recommender System

Rita Rismala, Nur Ulfa Maulidevi, Kridanto Surendro — Tue, 22 Jul 2025 00:00:00 +0700

Background: The explainability of recommender systems (RSs) is currently attracting significant attention. Recent research mainly focus on item-level explanations, neglecting the need to provide comprehensive explanations for each criterion. In contrast, this research introduces a criteria-level explanation generated in a content-based pardigm by matching aspects between the user and item. However, generation may fall short when user aspects do not match perfectly with the item, despite possessing similar semantics.

Objective: This research aims to extend the aspect-matching method by leveraging semantic similarity. The extension provides more detail and comprehensive explanations for recommendations at the criteria level.

Methods: An extended version of the aspect matching (AM) method was used. This method identified identical aspects between users and items and obtained semantically similar aspects with closely related meanings.

Results: Experiment results from two real-world datasets showed that AM+ was superior to the AM method in coverage and relevance. However, the improvement varied depending on the dataset and criteria sparsity.

Conclusion: The proposed method improves the comprehensiveness and quality of the criteria-level explanation. Therefore, the adopted method has the potential to improve the explainability of multi-criteria RSs. The implication extends beyond the enhancement of explanation to facilitate better user engagement and satisfaction.

Keywords: Comprehensiveness, Content-Based Paradigm, Criteria-Level Explanation, Explainability, Multi-Criteria Recommender System

Incorporation of IndoBERT and Machine Learning Features to Improve the Performance of Indonesian Textual Entailment Recognition

Teuku Yusransyah Tandi, Taufik Fuadi Abidin, Hammam Riza — Tue, 22 Jul 2025 00:00:00 +0700

Background: Recognizing Textual Entailment (RTE) is a task in Natural Language Processing (NLP), used for question-answering, information retrieval, and fact-checking. The problem faced by Indonesian NLP is based on how to build an effective and computationally efficient RTE model. In line with the discussion, deep learning models such as IndoBERT-large-p1 can obtain high F1-score values but require large GPU memory and very long training times, making it difficult to apply in environments with limited computing resources. On the other hand, machine learning method requires less computing power and provide lower performance. The lack of good datasets in Indonesian is also a problem in RTE study.

Objective: This study aimed to develop Indonesian RTE model called Hybrid-IndoBERT-RTE, which can improve the F1-Score while significantly increasing computational efficiency.

Methods: This study used the Wiki Revisions Edits Textual Entailment (WRETE) dataset consisting of 450 data, 300 for training, 50 for validation, and 100 for testing, respectively. During the process, the output vector generated by IndoBERT-large-p1 was combined with feature-rich classifier that allowed the model to capture more important features to enrich the information obtained. The classification head consisted of 1 input, 3 hidden, and 1 output layer.

Results: Hybrid-IndoBERT-RTE had an F1-score of 85% and consumed 4.2 times less GPU VRAM. Its training time was up to 44.44 times more efficient than IndoBERT-large-p1, showing an increase in efficiency.

Conclusion: Hybrid-IndoBERT-RTE improved the F1-score and computational efficiency for Indonesian RTE task. These results showed that the proposed model had achieved the aims of the study. Future studies would be expected to focus on adding and increasing the variety of datasets.

Keywords: Textual Entailment, IndoBERT-large-p1, Feature-rich classifiers, Hybrid-IndoBERT-RTE, Deep learning, Model efficiency

User Experience as a Predictor of E-commerce Continuation Intention in Indonesia: Examining the Role of Shopping Orientation as a Moderator

Tue, 22 Jul 2025 00:00:00 +0700

Background: The integration of Stimulus-Organism-Response (SOR) framework and Technology Acceptance Model (TAM) is still in need of improvement, particularly in studies examining individual behavior in Indonesian e-commerce context. A common challenge in e-commerce adoption is individual willingness and intention to adopt, which is influenced by previous user experience. Consequently, there is a need for the establishment of standard to measure user experience in e-commerce.

Objective: This study aims to measure the post-adoption experience of e-commerce user, which will shape attitude and influence future continuance intention (CI).

Methods: This study integrated SOR and TAM frameworks, followed by the collection and analysis of data from 263 respondents using Structural Equation Modeling-Partial Least Squares (SEM-PLS). Among the four hypotheses proposed, two represented novel contributions to the existing literature.

Results: The results showed a positive and significant influence of Interaction Experience (IE), Sense Experience (SE), and Flow Experience (FE) on Attitude Toward Using (ATU). The data analysis also indicated a positive and significant effect of ATU on Continuance Intention (CI). However, the influence of ATU on CI became insignificant when moderated by Shopping Orientation (SO).

Conclusion: Based on the results, not all hypotheses proposed in this study are supported. However, the results provide both theoretical and practical contributions.

Keywords: SOR, TAM, User Experience, Continuance Intention, e-commerce

Exploring Enabling Factors of E-Recruitment Adoption in the Public Sector and Its Contribution to Public Value Creation

Tue, 22 Jul 2025 00:00:00 +0700

Background: E-recruitment systems are increasingly prevalent in the public sector to improve candidate outreach and enhance transparency. Despite their potential, users remain skeptical due to challenges such as recruitment fraud and limited system availability, especially in developing countries like Indonesia. Consequently, it remains unclear how much e-recruitment systems contribute to public value creation. This uncertainty is mainly because there is a lack of research that directly explores the relationship between these systems and public value creation in the public sector, especially in developing countries.

Objective: This research aims to examine the factors that influence the use of e-recruitment systems in the public sector and the impact into creation of public values.

Methods: This quantitative study collected data from 408 respondents via an online survey, all of whom had used Indonesian National Civil Service Agency's e-recruitment system. Data were analyzed using the Partial Least Square—Structural Equation Model (PLS-SEM) method.

Results: The study revealed that system, information, and service quality have a positive impact on perceived usefulness and perceived ease of use and have a positive impact on the use of the e-recruitment system. It also shows that the adoption of an e-recruitment system gives a positive impact on public value creation.

Conclusion: This research highlights the critical role of system information quality in fostering e-recruitment adoption and its positive impact on public value creation in the public sector. These findings enrich previous studies that have not yet explored the direct relationship between the use of e-recruitment systems and public value creation. Future research may investigate technological aspects, like artificial intelligence and virtual reality, that could enhance user experience and the adoption of e-recruitment systems in the public sector.

Keywords: E-recruitment, PLS-SEM, Information System Success Model, Technology Acceptance Model, Public Value Theory

Factors Influencing the Diffusion of Blockchain Technology in the Indonesian Goverment

Eltyasar Putrajati Noman, Anderson Kevin Gwenhure — Tue, 22 Jul 2025 00:00:00 +0700

Background: Blockchain can improve the security and efficiency of government information systems. However, the adoption of this technology in Indonesia is still limited, especially in the government sector. Previous studies have emphasized the importance of regulatory and legal aspects in blockchain implementation. This condition is a challenge and an opportunity to examine the factors that influence the diffusion of blockchain innovation in the Indonesian government.

Objective: This study aims to identify and analyze the factors that influence the diffusion of blockchain technology in the Indonesian government through hypothesis testing and conceptual model development, as well as to determine the current stage of blockchain technology diffusion in the Indonesian government.

Methods: This study uses data from a questionnaire survey of 24 government agencies in Indonesia, representing various levels of central, provincial, district, and city, and focusing on the technology sector. A total of 192 responses were successfully collected. The collected data were analyzed using SmartPLS software to test the validity and reliability of the instrument, research hypothesis, and proposed conceptual model, and the results of the hypothesis test were used to determine the current stage of blockchain technology diffusion in the Indonesian government.

Results: The study's results indicate that the research instruments used are valid and reliable and meet the requirements for use in this study. Of the eight hypotheses proposed, three were accepted, and five were rejected. The tested conceptual model showed good agreement with the empirical data.

Conclusion: This study concludes that relative advantage and stakeholder roles are key factors significantly influencing the Indonesian government's intention to adopt blockchain technology. In contrast, complexity, regulation, top management support, and competence do not significantly influence adoption intentions. The diffusion of blockchain technology in the Indonesian government is still in the knowledge stage, so the decision to adopt it has not been reached. The implication is that the government needs to prioritize blockchain advantages and actively involve stakeholders, such as experts and developers, in efforts to adopt this technology.

Keywords: Diffusion of Innovation, Blockchain, Information Systems, E-Government, Information Technology Management

Optimizing IndoBERT for Revised Bloom's Taxonomy Question Classification Using Neural Network Classifier

Lazuardy Syahrul Darfiansa, Fitriyani , Sza Sza Amulya Larasati — Tue, 22 Jul 2025 00:00:00 +0700

Background: A major challenge in Indonesian education system is the continued dominance of exam questions that primarily assess basic thinking skills, such as remembering and understanding. In order to effectively nurture students with critical, analytical, and creative thinking skills, the integration of higher-order thinking questions has become increasingly urgent. An effective conceptual framework that can be utilized in this regard is Revised Bloom's Taxonomy (BT). This framework classifies cognitive skills into 6 levels, namely remember, understand, apply, analyze, evaluate, and create. Furthermore, the framework is particularly important as it promotes the development of exam questions that transcend lower-level thinking skills, fostering a deeper and higher level of understanding among students. In this context, automated systems powered by deep learning (DL) have shown promising accuracy in classifying questions based on BT levels, thereby offering practical support for educators aiming to design more meaningful and intellectually stimulating assessments.

Objective: This research aims to develop a classification system that can effectively classify Indonesian exam questions based on BT using IndoBERT pretrained models. These models were combined with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) classifiers (referred to as IndoBERT-CNN and IndoBERT-LSTM) to determine the model with the highest performance.

Methods: The dataset utilized was self-collected and underwent several stages of preparation, including expert labeling and splitting. Furthermore, preprocessing was conducted to ensure the dataset was consistent and free from irrelevant features related to case folding, tokenization, stopword removal, and stemming. Hyperparameter fine-tuning was subsequently carried out on IndoBERT, IndoBERT-CNN, and IndoBERT-LSTM. Model performance was evaluated using Accuracy, F-Measure, Precision, and Recall.

Results: The fine-tuned IndoBERT model results showed that IndoBERT-LSTM outperformed IndoBERT-CNN. The optimal hyperparameter configuration, batch size of 64 and learning rate of 5e-5, showed the highest performance, achieving Accuracy of 88.75%, Precision of 85%, Recall of 88%, and F-Measure of 86%.

Conclusion: IndoBERT, IndoBERT-CNN, and IndoBERT-LSTM reflected promising results, although the performance of the models was significantly affected by respective architectures and hyperparameter settings. Among the three observed models, IndoBERT was found to perform best with smaller batch sizes and moderate learning rates. IndoBERT-CNN achieved stronger results with a higher learning rate and similar batch sizes. IndoBERT-LSTM recorded the highest accuracy with larger batch sizes for gradient stability. However, IndoBERT was constrained by its focus on Indonesian language, and the interpretability of the predictions made, specifically in relation to expert-labeled data, remained unclear.

Keywords: Bloom’s Taxonomy, CNN, Hyperparameter Fine-Tuning, IndoBERT, LSTM, Question Classification

A Systematic Literature Review of Topic Modeling Techniques in User Reviews

Ilham Zharif Mustaqim, Ryan Randy Suryono — Tue, 22 Jul 2025 00:00:00 +0700

Background: The escalating volume of user review data is necessitating automated methods for extracting valuable insights. Topic modeling was a vital method for understanding key discussions and user opinions. However, there was no comprehensive analysis of the scientific work specifically on topic modeling applied to user review datasets, including its main applications and a comparative analysis of the strengths and limitations of identified methods. This study addressed the gap by characterizing the scientific discussion, identifying potential directions, and exploring currently underutilized application areas within the context of user review analysis.

Objective: This study aimed to recognize the implementation trend of topic modeling in various areas and to comprehend the methodology that could be applied to the user review dataset.

Methods: A systematic literature review (SLR) was adopted by implementing Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines within six-year spans, narrowing 1746 to 28 selected primary studies.

Results: The underlying insight was that user reviews had been critical as the primary data for topic modeling in analyzing various applications. Digital banking and transportation applications were the sectors that received the greatest attention. In this context, Latent Dirichlet Allocation (LDA) was the most extensively used method, with a focus on overcoming its limitations by incorporating additional strategies into LDA-based models.

Conclusion: The bibliometric analysis and mapping study practically contributed as a reference when assessing the dominant topic in similar app categories and topic modeling algorithms. Furthermore, this study comprehensively analyzed various topic modeling algorithms, presenting both the strengths and weaknesses of informed selection in relevant applications. Considering the keywords cluster analysis, service quality could be adopted based on the output of the topic modeling.

Keywords: Topic modeling, User review, Systematic literature review, Bibliometric analysis

Boosting Multiverse Optimizer by Simulated Annealing for Dimensionality Reduction

Wamidh K. Mutlag, Wamidh Jalil Mazher, Hadeel Tariq Ibrahim, Osman Nuri Ucan — Tue, 22 Jul 2025 00:00:00 +0700

Because of its dynamic graph structure and exceptional global/local search abilities, the Multiverse Optimizer (MVO) is widely used in feature selection. The exponential growth of the search space makes finding the optimum feature subset for numerous dimensional datasets quite challenging. Despite that MVO is a promising algorithm, the sluggish convergence issue affects the multi-verse optimizer performance. This work focuses on hybridizing and boosting MVO with the powerful local search algorithm, Simulated Annealing algorithm (SAA), in order to get around MVO limitations and enhance feature selection efficiency in high dimensional datasets. Stated differently, a paradigm known as high-level relay hybrid (HRH) is put forth that sequentially implements self-contained optimization (i.e. MVO and SAA). As a result, the optimal regions are found by MVO and then supplied to SAA in the suggested MVOSA-FS model. Ten high-dimensional datasets obtained from the Arizona State University (ASU) repository were used to verify the effectiveness of the proposed method; the results are compared with other six state-of-the-art feature selection algorithms: Atom Search Optimization (ASO), Equilibrium Optimizer (EO), Emperor Penguin Optimizer (EPO), Monarch Butterfly Optimization (MBO), Satin Bowerbird Optimizer (SBO), and Sine Cosine Algorithm (SCA). The results validate that the proposed MVOSA-FS technique performed better than the other algorithms and showed an exceptional ability to select the most significant and optimal features. The lowest average error rates, classification standard deviation (STD) values, and feature selection (FS) rates are obtained by MVOSA-FS across all datasets.

Classification and Counting of Mycobacterium Tuberculosis using YOLOv5

Nia Saurina, Nur Chamidah, Riries Rulaningtyas, Aryati Aryati — Tue, 22 Jul 2025 00:00:00 +0700

Background: Indonesia is a nation with the third-highest number of tuberculosis (TB) cases worldwide, after China and India. TB detection has been facilitated using YOLOv5 deep learning framework despite previous studies not having incorporated assessment metrics recommended by International Union Against Tuberculosis and Lung Disease (IUATLD).

Objective: This study aims to present a method for classifying and enumerating Mycobacterium tuberculosis by using YOLOv5 architecture with IUATLD evaluation standards. Sputum samples served as the primary medium for identifying the presence of Mycobacterium tuberculosis. In addition, the method showed precise delineation of bacterial boundaries to minimize classification inaccuracies and improve edge clarity through YOLOv5.

Methods: Following the acquisition of microscopic images of TB, the data were resized from 1632x1442 to 640x480 pixels. Annotation was performed using YOLOv5 bounding boxes, and the model was subsequently trained as well as tested according to IUATLD guidelines.

Results: During the analysis, YOLOv5-based classification system produced optimal performance. The model achieved 84.74% accuracy, 87.31% precision, and Mean Average Precision (mAP) score of 84.98%. These metrics showed high reliability in identifying Mycobacterium tuberculosis in the image dataset.

Conclusion: The classification and quantification of Mycobacterium tuberculosis using YOLOv5 framework shows high precision, with mAP score of 84.98%, signifying strong model performance. Additionally, the counting process achieves a MAPE (Mean Absolute Percentage Error) of 0.15%, reflecting excellent prediction accuracy.

Keywords: IUATLD, Tuberculosis, YOLOv5.

IT Maturity Model Design and Evaluation for Sustainable Smart Cities Assessment

Ehab Juma Adwan — Tue, 22 Jul 2025 00:00:00 +0700

Background: The Economic Vision for sustainable smart cities (SSC) necessitates a continuous monitoring tool that assesses the long-term planning progress of ‎the Economic ‎maturity level (ML) which is dependent on the Maturity Models (MM) of the Enabling Technology/ICT capabilities as its analyzes, measures the maturity levels (ML) of Smart Cities (SCs), and assesses the Economic ML of the SSCs. Recent MM have several shortcomings such that they are: 1) undedicated and overlapping the SC domains, 2) missing details of SC cases, 3) applying indicators ‎from ambiguous databases, 4) unable to identify SC baseline, 5) lacking easiness, usefulness, decision support, comprehensiveness, ‎timeliness, and usage intention, and/or 6) not targeting the Economic dimension of SSC.

Objective: Aiming at monitoring the long-term planning progress of ‎the SSC’s Economic ‎maturity level (ML), this study ‎‎developed and evaluated an Enterprise Architectural (EA) MM tool (BSSC-ML) that is capable to continuously assess the SC’s transition from ‎‎AS-IS (SC) to TO-BE (SSC’s Economic MLs) by ‎analyzing the Enabling Technology/ICT capabilities, 2) measuring the MLs of Enabling Technology/ICT capabilities based on 20 formulated ‎indicators, and 3) ‎assessing the MLs of Economic SSC based on 30 formulated KPIs.

Methods: The Design Science ‎Research ‎methodology (DSRM) ‎orchestrated the development of BSSC-ML at which design, implementation, data collection & ‎analysis, ‎validation, ‎and evaluation were ‎‎performed by utilizing semi-structured ‎interviews were conducted ‎with 7 officials of the ‎Information & eGovernment Authority (iGA), while the ‎web content analysis and Delphi methods respectively were employed to ‎analyze the ‎official portals while preserving the validation quality and ‎‎to evaluate the model.

Results: The findings revealed 50.3% ML score w.r.t 116 Business services and ‎‎3 sets of 260 Technology/ICT capabilities, 3rd ML score w.r.t Economic ‎SSC, and ‎‎‎88.123%‎ w.r.t evaluation’s acceptance rate.

Conclusion: The study described the development process of BSSC-ML for SSC’ Economic MLs assessment at which the evaluation scores proved its effectiveness as a monitoring too for local and global SCs.

Keywords: Technology/ICT Maturity Model, Smart City, Enterprise Architecture‎, Design and Evaluation, Economic Sustainability

Exploring the Barriers to Public Transport App Adoption Using Innovation Resistance Theory

Tue, 22 Jul 2025 00:00:00 +0700

Background: The adoption of digital solutions in public transportation has transformed mobility services worldwide. However, resistance to innovation remains a significant challenge, preventing the successful implementation of transport applications. Despite advancements in mobile technology and smart transit solutions, many users remain hesitant to adopt new applications due to various barriers, including information quality concerns.

Objective: This study aims to investigate the relationship between information quality and innovation resistance in the adoption of public transport applications. Utilizing the Innovation Resistance Theory (IRT), this research examines how different resistance factors impact the intention to use transport apps.

Methods: A mixed-methods approach was applied, consisting of a quantitative survey with 443 respondents from an urbanized region and analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Additionally, qualitative insights were gathered through interviews with 30 individuals, analyzed using content analysis.

Results: Findings indicate that information quality significantly reduces innovation resistance, facilitating the adoption of transport applications. Moreover, usage barriers, value barriers, and tradition barriers negatively affect users’ intention to use transportation apps, while risk, image, and complexity barriers show no significant influence.

Conclusion: This study underscores the critical role of information quality in overcoming resistance to innovation in public transportation applications. The findings provide insights for app developers to enhance data accuracy and usability, as well as for policymakers to improve digital transportation services by addressing key resistance factors.

Keywords: Public Transport App, Innovation Resistance, M-Commerce, Intention to Use, Innovation Resistance Theory, Information Quality, PLS-SEM