Journal of Information Systems Engineering and Business Intelligence

Revolutionizing Payment Systems: The Integration of TRAM and Trust in QRIS Adoption for Micro, Small, and Medium Enterprises in Indonesia

Adisthy Shabrina Nurqamarani, Sarah Fadilla, Ari Juliana — Mon, 28 Oct 2024 00:00:00 +0700

Background: In an era where digital finance is growing rapidly, the Quick Response Code Indonesian Standard (QRIS) revolutionizes the payment system through a single unifying code. This study brings novelty in integrating TRAM and Trust in the adoption of QRIS in micro, small, and medium enterprises (MSMEs) in Indonesia, for which studies are still limited.

Objective: To observe determinants of QRIS adoption by integrating the Technology Readiness Acceptance Model (TRAM) and Trust in the emerging Indonesian market where QRIS is in a growing stage.

Methods: This study collects data through the survey of 210 MSME owners and staff who are familiar with and/or have used QRIS through convenience sampling. In analyzing the data, this study uses the Structural Equation Model-Partial Least Square (PLS-SEM) to examine the relationship between variables that explain influencing factors of QRIS adoption.

Results: The results show that 7 of 13 hypotheses were accepted; optimism and trust positively significantly affect perceived ease of use and perceived usefulness, while insecurity and innovation have no significant influence on perceived ease of use and perceived usefulness. Besides, this study shows unexpected positive results between discomfort, perceived ease of use, and perceived usefulness. Overall, the proposed TRAM and Trust model contributes 60.9 % in explaining QRIS adoption.

Conclusion: This study emphasizes the importance of optimism, discomfort, trust, perceived ease of use, and perceived usefulness in influencing QRIS adoption in micro and small businesses in Indonesia. It guides QRIS providers, policymakers, financial institutions, and MSMEs in having effective QRIS adoption in business operations.

Keywords: Micro Small and Medium Enterprises, QRIS, TRAM, Trust

Unveiling User Sentiment: Aspect-Based Analysis and Topic Modeling of Ride-Hailing and Google Play App Reviews

Mon, 28 Oct 2024 00:00:00 +0700

Background: Mobile app usage is increasing in the digital age, with Ride-Hailing app becoming the primary example of this trend. To obtain valuable understanding of how people perceive and interact with mobile app, user reviews on platforms such as Google Play are usually analyzed. This analysis can assist developers to identify areas for improvement in both Ride-hailing and Google Play App. A promising method that can be used to analyze user perception in this instance is Aspect-Based Sentiment Analysis (ABSA).

Objective: This research aimed to apply ABSA to user reviews using Bidirectional Encoder Representations from Transformers (BERT) models. In this context, aspect identification and topic modeling were performed by using Latent Dirichlet Allocation (LDA). The model extracted topics from the reviews and used Generative Artificial Intelligence (GenAI) to define the aspects of the topics to further enhance the analysis. For consistency and accuracy, the method included sentiment annotation by a human annotator.

Methods: A total of two datasets were used in this research, with the first collected by scraping user reviews of Ride-Hailing App while the second was obtained from Kaggle, and to identify relevant topics, modeling was performed using LDA. These topics were then categorized into aspects using GenAI, covering areas, such as customer experience, service, payment, app features, task management, and event management. Subsequently, sentiment labeling was conducted using human annotators to provide a reliable baseline. BERT model was then used to classify sentiment with aspect hints, and the evaluation included calculations of accuracy, precision, recall, and F1-score.

Results: The results showed that BERT model achieved the highest accuracy of 97% in sentiment analysis across all datasets.

Conclusion: This research provided valuable understanding of user experience and established a strong ABSA framework for analyzing user reviews using LDA, Aspect Annotation, GenAI, and BERT sentiment models. Future research could expand this method to other app categories and incorporate real-time ABSA for continuous monitoring and dynamic feedback.

Keywords: User Reviews, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Topic Modeling, Generative Artificial Intelligence (GenAI)

Factors Influencing Behavioral Intention to Apply Freemium Services in Islamic Lifestyle Digital Applications Using Unified Theory of Acceptance and Use of Technology (UTAUT)

Mon, 28 Oct 2024 00:00:00 +0700

Background: Islamic lifestyle digital applications (ILDA) are a sector in the rapidly increasing digital halal media and leisure industry. To ensure sustainable revenue for ILDA developers, freemium strategy needs to be implemented. However, there is a lack of research examining the factors that influence the adoption of freemium strategies in ILDA products.

Objective: This research aimed to explore the factors within Unified Theory of Acceptance and Use of Technology (UTAUT) framework that aid ILDA users to use freemium services. Specifically, the research focused on users trust (UT) and users satisfaction (US) as factors influencing the increase in behavioral intention (BI).

Methods: Quantitative method was adopted in this context and purposive sampling method was used to obtain 400 data from respondents. The data were then analyzed using Partial Least Square Structural Equation Model (PLS-SEM) method.

Results: The results showed that performance expectancy (PE) as well as effort expectancy (EE) positively and significantly influenced US. Similarly, facilitating conditions (FC) and social influence (SI) significantly affected UT. Both UT and US positively influenced users BI toward freemium services. Digital literacy (DL) had a positive moderating effect between PE and business expectations in US, but the effect was not statistically significant.

Conclusion: The research described that all UTAUT variables, along with UT and US, influenced the intention to adopt freemium services in ILDA. Moreover, DL did not have any moderating effect on the framework considered in this context. These results signified that users tended to be satisfied and trust the benefits enjoyed, rather than being influenced by DL level.

Keywords: Behavioral Intention, Digital Literacy, Islamic Lifestyle Digital Application, Satisfaction, Trust, UTAUT

Challenges and Technology Trends in Implementing a Human Resource Management System: A Systematic Literature Review

Mon, 28 Oct 2024 00:00:00 +0700

Background: Human Resource Management System (HRMS) is an important aspect of managing organizations. However, the successful integration of the system into respective roles is often associated with diverse technological challenges and trends. Some major obstacles identified in recent research include reluctance to change, lack of training, fragmented Human Resource (HR) data, rigid processes, and continuous changes in organizational needs. Exciting technology trends offer promise for next-generation HRMS solutions, including artificial intelligence (AI), machine learning, predictive analytics, and mobile accessibility. This shows the need for a systematic literature review to comprehensively map the challenges and technology trends shaping the implementation of HRMS.

Objective: This research aimed to conduct a comprehensive review of existing literature to identify the main challenges faced during HRMS implementation and the latest technology trends in the space.

Methods: A systematic literature review was adopted through the Kitchenham method with a focus on five databases including Scopus, Emerald, IEEE, Science Direct, and ProQuest.

Results: The result was in the form of a table mapping of the challenges faced by each stakeholder in HRMS, including resistance to change, lack of management support, and limited technology infrastructure. Meanwhile, the most common technology challenges found were system integration issues, data security, and lack of technical capabilities or skills. The potential opportunities from technology trends to address the issues included training and skills development, enhanced cybersecurity, and effective change management methods. These recommendations were designed to support organizations in further optimizing HRMS utilization and leveraging the latest technologies such as AI and blockchain.

Conclusion: The review used a structured method to develop a rich overview through tabular presentations summarizing problem identification and technology trend compilation of HRMS. The systematic exploration aimed to contribute valuable insights into the complexities of HRMS implementation and offer a comprehensive perspective on the emergence of relevant technology trends. The results were expected to contribute to future research directions in this important area at the nexus of Human Resource Management (HRM) and technological innovation.

Keywords: Human Resource Management System, Challenges, Technology Trends, Systematic Literature Review

Changes in Customer Behavior Towards Video Advertising Post-Pandemic

Surjandy Surjandy, Cadelina Cassandra — Mon, 28 Oct 2024 00:00:00 +0700

Background: Video Advertising (VI) is a powerful media tool used by several companies as a marketing strategy. During COVID-19 pandemic, there was a wide adoption of digital media, particularly VI, to promote company products. However, some changes occurred post-pandemic, which influenced customer behavior.

Objective: This research aimed to explore changes in customer behavior towards VI post-pandemic. The exploration focused on understanding changes in four major factors which included Sensory Appeal (SEN), Informativeness (INF), Entertainment (ENT), and Telepresence (PRE).

Methods: Data were collected using snowball sampling method, resulting in 744 responses. After deleting outliers and non-shopping customer, there were 584 analyzable data. Covariance-Based Structural Equation Model (CB SEM) method facilitated by Lisrel Application was used for data analysis.

Results: The result showed that significant changes have occurred in customer behavior to VI post-pandemic. Among the 13 tested hypotheses, 11 showed significant influences, while 2 did not, indicating shifts in customer behavior.

Conclusion: COVID-19 pandemic led to significant changes and imparted customer with a new understanding of VI, which became a major marketing tool. These changes were due to experiences during the pandemic, which affected SEN (72%), INF (77%), ENT (76%), and PRE (70%). Further analysis showed that ENT affected Customer Trust (CT) and Actual Purchase (APU) by 20% and 27%, while PRE caused 34% and 20% respectively, indicating a decrease in customer response from VI to CT and APU. Based on these results, further exploration should build on the identified factors and investigate additional variables that had not been considered.

Keywords: Post-Pandemic, COVID-19, Customer Behavior, Video Advertising

Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm

Elmo Juanara, Chi Yung Lam — Mon, 28 Oct 2024 00:00:00 +0700

Background: Tsunami caused by volcanic collapse are categorized as non-seismic uncommon events, unlike tsunamis caused by earthquakes, which are common events. The traditional tsunami early warning based on the seismic sensor (e.g. earthquake detectors) may not be applicable to volcanic tsunamis because they do not generate seismic waves. Consequently, these tsunamis cannot be detected in advance, and warnings cannot be issued. New methods should be explored to address these non-seismic tsunamis caused by volcanic collapse.

Objective: This study explored the potential of machine learning algorithms in supporting early warning level issuing for non-seismic tsunamis, specifically volcanic tsunamis. The Anak Krakatau volcano event in Indonesia was used as a case study.

Methods: This study generated a database of 160 collapse scenarios using numerical simulation as input sequences. A classification model was constructed by defining the worst tsunami elevation and its arrival time at the coast. The database was supervised by labeling the warning levels as targets. Subsequently, a decision tree algorithm was employed to classify the warning levels.

Results: The results demonstrated that the classification model performs very well for the Major Tsunami, Minor Tsunami, and Tsunami classes, achieving high precision, recall, and F1-Score with very high accuracy of 98%. However, the macro average indicates uneven performance across classes, as there are instances of ‘No Warning’ in some coastal gauges.

Conclusion: To improve the model performance in the ‘No Warning’ class, it is necessary to balance the dataset by including more ‘No Warning’ scenarios, which can be achieved by simulating additional scenarios involving very small-volume collapse. Additionally, exploring additional collapse parameters such as dip angle and outlier volume could contribute to developing a more robust classification model.

Keywords: Machine Learning, Classification, Volcanic Tsunamis, Early Warning, Decision Tree

Leveraging Social Media Data for Forest Fires Sentiment Classification: A Data-Driven Method

Warih Maharani, Hanita Daud, Noryanti Muhammad, Evizal Abdul Kadir — Mon, 28 Oct 2024 00:00:00 +0700

Background: The rise in forest fires over the last two years, which is due to rise in dry weather conditions and human activities, have greatly impacted an area of 1.6 million hectares, leading to significant ecological, economic, and health issues, hence the need to improve disaster response strategies. Previous research determined the lack of coverage regarding public response during forest fires with conventional methods such as satellite images and sensor data. However, social media platforms provide real-time information generated by users, along with location information of disaster events. Sentiment analysis helps in understanding the public reactions and responses to natural disasters, thereby increasing awareness about forest fires.

Objective: The purpose of this research is to assess the efficiency of Long Short-Term Memory (LSTM) method in classifying sentiment for social networks in regard to forest fires. This research aims to examine the effect of TF-IDF, unigram, and the FastText features on the effectiveness of the classification of sentiment.

Methods: The precision, recall, and F1 score of 2, 3, and 4 determined in the LSTM models with commonly available sentiment analysis tools, such as the Vader Sentiment Analysis and SentiWordNet was used to evaluate the performance of the model.

Results: With an improvement of roughly 10%, the four layers of the LSTM model generated the best performance for the evaluation of sentiments about forest fires. The LSTM model with FastText achieved F1, recall and precision scores of 0.649, 0.641, and 0.659, which exceeds lexicon-based method including SentiWordNet and Vader.

Conclusion: The experimental results showed that the LSTM model outperformed lexicon-based methods when used to analyse the tweets related to forest fire. Additional research is required to integrate rule-based models and LSTM models to develop a more robust model for dynamic data.

Keywords: Forest Fire, Disaster, Long Short-Term Memory, LSTM, Vader, SentiWordnet

20 Years of Scientific Study on Business Intelligence and Decision-Making Performance: A Bibliometric Analysis

Abdessamad Charkaoui, Siham Jabraoui — Mon, 28 Oct 2024 00:00:00 +0700

Background: Business intelligence (BI) is an area in which data analytics is applied to generate crucial information supporting business decision-making and has been a significant domain for over three decades. However, there is uncertainty regarding whether investments can effectively improve organizational outcomes.

Objective: This study aimed to provide a comprehensive overview of the knowledge generated and disseminated in previous investigations related to the intricate relationship between BI and decision-making performance (DMP) over the past 20 years.

Methods: An R-tool namely bibliometrix, which supports suggested workflow for conducting bibliometrics and includes descriptive as well as knowledge structure analysis was used on a dataset containing 1,484 English-language articles published between 2003 and 2023 and indexed in Web of Science databases.

Results: The results showed that field study has stabilized over the past three years, signaling a shift in the focus of scholars. However, only a few studies use decision theory and further investigations are required to fully understand how BI impacts DMP both inside and outside organizational boundaries.

Conclusion: Based on the results, BI studies tend to be more application-oriented and there is a need to change the emphasis from focusing only on tools to variables such as the role of effective use and competencies that might improve decision quality.

Keywords: Business Intelligence, Decision-Making Performance, Decision Quality, Bibliometrics, R-tool.