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ARTICLE INFO ABSTRACT

Article history:

Received XX Month 2024

Revised XX Month 2024

Accepted XX Month 2024

Online first

Published 1 September 2024

 The anticipated economic recession. All countries globally aim to attain economic stability, as it can boost a nation's GDP. Nations must carefully analyse their economic situations to develop strategic economic policies that promote growth and attract investment. The current dashboard focuses just on the inflation rate in the European zone and fails to include other important economic data such as overall economic production, current GDP rate, currency rates, and unemployment rate. The indicators were shown visually on a separate platform without a thorough explanation, which could result in misinterpretation. Investors may make a mistaken assessment, resulting in a financial loss. The main goal of this project is to develop a dashboard that accurately evaluates the economic stability of states in the Southeast Asian region. The main objectives of this study are to develop the console using Big Data approaches and to evaluate the effectiveness of this system. This study utilizes the Rapid Application Development (RAD) model, chosen for its ability to efficiently construct a system within limited time constraints. The mentioned objectives are successfully accomplished during the designated stages of RAD. The participants included employed Malaysians from different sectors and self-employed individuals. The system's average score of 4.76 indicates that the dashboard is a beneficial tool for users in aiding decision-making processes. Since this study involved prediction, it was anticipated that the predicted value would differ from the actual value. It is advisable to utilize machine learning as the predictive model for the project's future deployment.

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DOI:

10.24191/jcrinn.v9i2
# INTRODUCTION Nations must diligently evaluate their economic situations to establish strategic economics that promote growth and attract potential investors. The present dashboard lacks broader economic statistics such as the current GDP rate, exchange rates, and unemployment rate. ## Research Motivation The current dashboard solely emphasizes the inflation rate, which is insufficient for assessing a country's economic stability. The European Central Bank's current dashboard solely concentrates on the inflation rate in the Euro region. The inflation rate of the Euro region rose by 8% from August 2022 to September 2022. The housing, electricity, and gas components have the highest HICP value of 21.1, while communication has the lowest value of -0.8 among all the components examined by HICP. The inflation rate at the end of the period is measured by the International Monetary Fund using the consumer price index. The International Monetary Fund has created a dashboard showing the GDP growth of all countries worldwide. It includes a global map, a map timeline, and a stacked line chart. Trading Economics measures the currency exchange rate of all world currencies by exchanging them with the US Dollar. The price column displays the current price of the currency exchange, which fluctuates over time to reflect whether the currency is appreciating or depreciating, as indicated in the day column. Conversely, another graphic displays the unemployment rate of all countries on the International Monetary Fund website. The dashboard contains a line chart and a geographic map with an embedded map timeline. Currently, all indicators are shown on a separate platform without a detailed explanation, leading investors to potentially make a mistaken assessment that could have financial consequences. ## Problem Statement Every nation strives for a healthy economy to enhance its national income. Countries must evaluate their economic position to develop a strategic economic plan that will enhance economic growth and attract investors. Each ASEAN country should own an economic indicator dashboard to effectively monitor economic stability. Therefore, it is crucial for the country to own a dashboard capable of monitoring the inflation rate, GDP, currency exchange, and unemployment rate. The existing web dashboard provided by the European Central Bank is inadequate for global consumers as it solely concentrates on the European region and the inflation rate. The inflation rate does not encompass all economic production or consumption, the current GDP rate is inadequate for assessing a country's power, there is insufficient data on the impact of uncertainty on exchange rate fluctuations, and there is uncertainty regarding the limited information available on the unemployment rate. Hence, it is necessary to combine these indicators as they are not robust enough on their own. Accordingly, this study proposed an economic stability indicator dashboard to help users analyze the performance of inflation rate, GDP, currency exchange and unemployment rate and the relationship between these indicators with economic stability in order to conclude the economic stability of the ASEAN countries. This study can help investors make decisions regarding their investments by using the projections displayed in the dashboard. ## Research Objectives This study intends to assist ASEAN countries in visualizing the inflation rate, GDP, currency exchange and unemployment rate of their countries and the relationship between these variables and economic stability with the aid of a dashboard. In order to reach this goal, the following objectives have been identified: 1) To determine the techniques and requirements of an economic stability indicator dashboard for ASEAN countries. 2) To design and develop a data warehouse and economic stability indicator dashboard based on Big Data warehouse and visualization technique. 3) To evaluate the usefulness of the economic stability indicator dashboard for ASEAN countries through the Technology Acceptance Model (TAM). ## Novelty An economic stability indicator console to assist users in analysing the performance of inflation rate, GDP, currency exchange and unemployment rate. The association between these variables and economic stability might conclude the economic stability of the ASEAN countries and may assist the investors in decision-making with the offered projections. **2. LITERATURE REVIEW** The term "big data" encompasses a vast amount of information that possesses significant potential for problem-solving (Dash et al., 2019). To improve the quality of service provided by businesses, several industries in both the public and commercial sectors utilize big data for the purposes of generating, storing, and analysing information. According to Kar and Dwivedi (2020), various sources of big data can be identified, including social media platforms, emails, news outlets, online forums, internet of things (IoT)-connected devices, telecommunications devices, sensor-based applications in gadgets, and multiple sources in multi-modal studies. ## 2.1 The Role Big Data in Business Operations Big data is valuable in various industries, extending beyond the realm of technology. Numerous case studies have been conducted to examine the application of big data across several areas, including economics, banking, and marketing. The utilization of big data is employed to address challenges associated with the management of vast quantities of data. Therefore, numerous industries employ big data to address this issue (Dash et al., 2019). According to Kenton et al. (2022), an economy can be defined as a multifaceted system comprising interrelated manufacturing, consumption, and exchange activities, which collectively determine the allocation of resources among various stakeholders, including suppliers and consumers. An economy can be symbolized by a nation, a region, a sector, or a household. This mechanism is crucial for ensuring the efficient functioning of the economic process. To ascertain the robust stability of these ASEAN countries, it is imperative that they exhibit a low inflation rate (Smith, 2019), a high GDP rate (Smith & Boyle, 2021), a low local currency exchange rate, and a low unemployment rate. *2.1.1 Economy* According to Dubey et al. (2019), the utilization of big data analytics (BDA) plays a pivotal role in generating significant insights that inform decision-making processes. There is a growing interest in integrating Big Data Analytics (BDA) and the circular economy (CE) in this context (Gupta et al., 2019). The concept of Circular Economy (CE) pertains to the process of closing production and consumption loops while optimizing resource utilization (Murray et al., 2017). Academic attention has been directed towards investigating the connections between Big Data Analytics (BDA) and decision-making effectiveness in developing market businesses, given the importance of BDA in organizations (Shamim et al., 2020). The significance of Big Data Analytics (BDA) capabilities in the context of the Circular Economy (CE) has garnered considerable attention from both scholars and professionals (Gupta et al., 2019). *2.1.2 Banking Sector* During the past few decades of the information science revolution, researchers with competence in data mining (DM) have shown a strong interest in implementing banking systems (Hassani et al., 2018). The proliferation of real-time financial data is rapidly growing as a result of the advancement and widespread adoption of e-banking and mobile banking. The banking industry has a significant responsibility in acquiring proficiency in utilizing suitable big data analytics technologies due to the continuous advancements and rapid expansion of big data accessibility. *2.1.3 Marketing* Currently, big data is widespread, encompassing both unstructured data generated by emerging communication technologies and user editing platforms, such as text, photographs, and videos, as well as structured data generated by conventional databases utilized by businesses, such as customer relationship management (Amado et al., 2018). The influence of social media platforms such as Facebook and Twitter on consumer decision-making is substantial, leading firms and organizations to incorporate data derived from these platforms into their marketing strategies (Amado et al., 2018). Consequently, the scope of Big Data is growing. The utilization of Big Data-driven marketing analytics has the potential to assist organizations in addressing a range of challenges. These challenges include the identification of customers who are more inclined to positively respond to a telemarketing campaign (Amado et al., 2018), the development of interactive reports and dashboards for managers, and the detection of noteworthy trends derived from social media conversations about the brand (Amado et al., 2018). According to Amado et al. (2018), Big Data solutions can be regarded as the fundamental components of perceptive systems that effectively assist marketers, alleviating the burden of labor-intensive and human analysis. ## 2.2 Case Studies Several previous research have been conducted on the relevant topics of this study, including the implemented technology, the field of economics, and the field of big data. However, it is imperative to identify the deficiencies in each relevant study in order to enhance the utility of this research and address the problem through the utilization of diverse methodologies derived from the current body of literature. *2.2.1 Analysis and Implementation of Data Visualization Technology* According to Meng et al. (2022), the authors highlight the significance of technological improvements in data mining and data visualization as effective means of visualizing diverse data sets. In the current era characterized by an abundance of information and data, the qualities inherent in these resources are often overshadowed by the overwhelming volume of data. This scenario has prompted a research endeavour that uses the Extract, Transform, and Load (ETL) methodology to effectively handle the Gross Domestic Product (GDP) data of the most prominent global economies. This study used a JavaScript-based library as the data visualization tool, capable of generating a dynamic histogram graphic that ranks historical data. The dashboard of the Analysis and Example Implementation of Data Visualization Technology is depicted in Figure 1. ![A graph with colorful lines Description automatically generated with medium confidence](66559799df7af_media/media/image1.png) Fig. 1. Analysis and Example Implementation of Data Visualization Technology *2.2.2 Forecasting Unemployment in Brazil* Lila et al. (2022) outline two separate viable approaches employed for the analysis of unemployment data from the Brazilian labour force survey. The data is shown graphically using a line chart. In general, the robust methodologies provide promising predictive capabilities across many scenarios and when assessed using multiple assessment indicators. Hierarchical forecasting techniques leverage the hierarchical structure of data to generate outcomes that are generally more objective and exact compared to benchmark techniques, achieved through base forecast reconciliation. Nevertheless, it is worth noting that certain predictions have the potential to behave as outliers when used in regression-based reconciliation algorithms, thereby impacting the overall reconciliation process. The dashboard for Forecasting unemployment in Brazil is depicted in Figure 2. ![A graph on a graph Description automatically generated](66559799df7af_media/media/image2.png) Fig. 2. Forecasting unemployment in Brazil: A robust reconciliation approach using hierarchical data *2.2.3 A data visualization approach for predicting the income class of the population* In their research, Singh et al. (2021) applies machine learning techniques and data visualization methods to evaluate the patterns of poverty, unemployment, and economic growth in Uttarakhand. Additionally, they investigate the impact of the state's growing population on its natural resources. This study employs the coefficient of correlation and linear regression techniques to ascertain the association between the variables. The findings are visually shown using several graphical representations, including line charts, bar charts, scatter plots, and pie charts. This work used the Gradient Boosting Classifier Model, which demonstrates a superior accuracy of 85.78%, in contrast to prior research that relied on census data obtained from censusindia.gov.in/. The dashboard depicted in Figure 3 illustrates a data visualization methodology employed to forecast the income class of the population. ![A graph with different colored bars Description automatically generated](66559799df7af_media/media/image3.png) Fig. 3. A data visualization approach for predicting the income *2.2.4 Case Studies Analysis* The methodologies employed in the associated studies encompass ETL, Clustering Analysis, Principal Component Analysis, and predictive analysis, as indicated by the literature review. Data visualization is an essential component of data analysis, as highlighted by Meng et al. (2022). To facilitate data visualization, it is necessary to import the data into the data warehouse prior to conducting data analysis. Consequently, the Extract, Transform, Load (ETL) procedure was employed to manage the data within the data warehouse. The Linear Regression approach is the most often employed technique in predictive analysis. Linear regression is a statistical technique that facilitates the comprehension of the association between input and output variables. On the other hand, the correlation coefficient is employed to ascertain the nature of the link between the variables, specifically whether it is positive or negative (Singh et al., 2021). The utilization of Hierarchical Predictive yielded outputs that are generally objective and more accurate compared to benchmark methodologies. This was achieved by arranging the data in a hierarchical manner using base forecast reconciliation. The accuracy of predictions can be enhanced by integrating hierarchical predictive techniques with robust estimations (Lila et al., 2022). The line chart and bar chart are the most commonly utilized data visualization tools for developing an economy-related dashboard. The economic dashboard also includes a pie chart; however, it is less frequently utilized compared to the line chart and bar chart. Additionally, the scatter plot serves the purpose of visually representing the dispersion of the data or the correlation between variables inside the dashboard. **3. METHODOLOGY** The Rapid Application Development (RAD) model has been selected as the research approach due to its appropriateness in effectively constructing a system within restricted time limitations (Ridoh et al., 2020). The selection process was conducted with consideration of its ability to effectively create the dashboard within a limited time constraint. The process consists of four separate stages (analysis, prototype development, testing, and implementation) that involve a variety of interconnected tasks, with the user taking on different responsibilities, especially during the prototype phase. **3.1 Analysis** The analysis phase is the initial stage of the RAD model. The problem statement, objectives, scope, and importance of the study were established during the analysis phase subsequent to its proposal. During the process of studying definition, the necessary criteria for the dashboard were determined. Subsequently, the timeline was constructed utilizing Microsoft Excel in order to generate a Gantt Chart. To facilitate the initial stage, a comprehensive literature study was conducted by perusing news stories, scholarly publications, and papers pertaining to inflation, GDP, currency exchange, and unemployment. This review was conducted using reputable journal databases like IEEE, ScienceDirect, ACM, and Scopus. Subsequently, the baseline specifications for the dashboard were established. The initial objective is achieved subsequent to the establishment of the system requirements. **3.2 Prototype** The prototype phase is the second stage of the approach. This stage encompasses the iterative process of building, demonstrating, and testing, which entails the involvement of users in various roles. The individuals who participated in this phase include the professors at UiTM Perlis who possess specialized knowledge in the field of information technology. This stage encompasses three primary operations, namely construction, demonstration, and refinement. *3.2.1 Build* The wireframe of the dashboard was created during the construction process by utilizing Uizard to design the user interface design. Subsequently, the dashboard was constructed in accordance with the outlined wireframe. The procedure commenced by gathering data from Trading Economics. Subsequently, the ETL process was executed, encompassing the extraction, transformation, and loading of the data. After the data has been gathered, it is then converted into a suitable format for a data warehouse. A further step involved the loading and storage of the data into the data warehouse. The data warehousing tool employed in this investigation was HDFS. The datasets contained in the Hadoop Distributed File System (HDFS) encompass the inflation rate, GDP rate, currency exchange rate, and unemployment rate. After storing the data, it will be more convenient to get the data for subsequent analysis and visualization. *3.2.2 Demonstrate* Data analytics was employed to query the stored data in the data warehouse during the demonstration phase. The utilized tool was Apache Hive, with the query language employed being HiveQL, which is a statement similar to SQL. The data were queried throughout this phase to conduct an analysis of the economic stability of ASEAN countries. The concluding phase of the dashboard development process was the visualization of data. After the completion of data analysis, the visualization process would involve the utilization of a business intelligence application known as Microsoft Power BI. The data were then shown in graphical and graphical formats, including bar charts, line charts, and pie charts. In order to enhance the interactivity of the dashboard and facilitate the visualization of the economic position of ASEAN countries over time, a Pareto chart was incorporated (Figure 4). The achievement of the second objective was realized upon the establishment of the ASEAN Countries Economic Stability Indicator Dashboard. ![A screenshot of a graph Description automatically generated](66559799df7af_media/media/image4.png) Fig. 4. Data Visualization Homepage The dashboard displays four indicators, including the inflation rate, GDP, currency, and unemployment rate. The overview page, depicted in Figure 5, presents a visual representation of the analyzed data within the Hive warehouse. This webpage presents an analysis of the economic stability of ASEAN countries spanning the period from 2000 to 2022, encompassing all four specified indicators. In addition, the inflation page provides comprehensive information regarding the inflation table, encompassing data spanning from 1958 to 2023. The GDP page contains data spanning from 1975 to 2022. The currency page presents a comprehensive representation of the currency table, encompassing data spanning from 1983 to 2023. Finally, the unemployment website provides comprehensive information regarding the unemployment table spanning from 1977 to 2023. The metrics including the GDP rate, currency exchange rate, currencies, and unemployment rate on all four pages offer prognostications for the forthcoming decade in the ASEAN nations. ![A screenshot of a computer Description automatically generated](66559799df7af_media/media/image5.png) Fig. 5. Page Overview *3.2.3 Refine* In the event that any areas requiring enhancement were identified in relation to the generated dashboard, the subsequent step involved resuming the construction activity to create a wireframe for the absent components of the dashboard. The aforementioned methods were iterated throughout the cycle till the emergence of the ultimate dashboard for the ASEAN Countries Economic Stability Indicator Dashboard. **3.3 Testing** The testing phase is the third stage of the process. Prior to deploying the final dashboard to the end user, this phase is of utmost importance. In order to conduct functional testing, the functional requirements of the dashboard were compiled and presented in a tabular format. Subsequently, the developer verified the satisfaction of all the prerequisites to ascertain whether the dashboard has met all the system needs. **3.4 Implementation** The final phase of the approach is the implementation phase. This phase encompassed two primary actions, namely evaluation and documentation. During the evaluation process, the usability of the dashboard will be assessed using the Technology Acceptance Model (TAM) technique. The study's sample consisted of 53 individuals from Malaysia, encompassing both males and females aged between 24 and 59 years. The participants were provided with a questionnaire regarding the dashboard that was created as part of this study. The study will conclude once all 50 respondents have completed the surveys. Finally, all the specifics of the dashboard, from the first proposal to its implementation, were recorded during the documentation process. The attainment of the third objective occurs subsequent to the successful implementation of the dashboard's usability. **3.5 Summary** The methodology section of this study centers on the techniques employed in the development of the ASEAN Countries Economic Stability Indicator. The Rapid Application Development (RAD) approach was utilized in this study. The selection of this methodology was based on its capacity to efficiently complete the dashboard within a specified timeframe. This technique consists of four distinct phases, each involving a series of interconnected activities. During the prototype phase, the user plays a crucial role in these activities. The active participation of both the user and the developer during the prototype phase facilitates the developer's ability to enhance the dashboard based on user feedback. The further development of the dashboard is facilitated by the active input of both the user and the developer during the prototype phase. The aforementioned objectives are effectively achieved during the several phases of RAD. **4. RESULT AND DISCUSSION** The evaluation process employs the Technology Acceptance Model (TAM). The study sample comprised 53 Malaysian individuals who were employed and were within the age range of 24 to 59 years. The assessment comprises four attributes, namely Perceived Ease of Use, Perceived Usefulness, Attitude, and Intention to Use. These attributes are assessed using a 5-point Likert scale instrument. The mean score for the comprehensive dimensions is 4.76, indicating that the approach is considered advantageous. The mean score for each dimension is depicted in Figure 6. The use of the console is beneficial for investors as it allows them to obtain valuable information about the performance of certain ASEAN countries. ![A graph of positive attitude and negative attitude Description automatically generated with medium confidence](66559799df7af_media/media/image6.png) Fig. 6. Mean Score using TAM **4.1 Perceived Ease of Use** This dimension represents the initial aspect of the TAM evaluation model. This section will primarily concentrate on evaluating the extent to which the produced system offers a user-friendly experience. The results indicate that the average score for the initial dimension is 4.74. The findings indicate that all participants expressed high agreement or agreement with the ease with which adults can study the content in order to acquire economic knowledge through the utilization of the ASEAN Countries Economic Stability Indicator Dashboard. **4.2 Perceived Usefulness** The second dimension of the TAM evaluation model is being referred to. This dimension pertains to the assessment of whether the system has improved the performance of the users. The results indicate that the average score for the initial dimension is 4.75. Based on the findings, it was observed that 41 participants expressed a strong agreement with the notion that the ASEAN Countries Economic Stability Indicator Dashboard will enhance their comprehension of the economy. Ten individuals agreed on this matter, while the remaining two remained neutral. **4.3 Attitude** The third dimension of the TAM evaluation model is represented by this. This dimension pertains to the user's disposition towards the system. The questionnaire results indicate that the average score for this dimension was 4.81. According to the findings, a total of 44 participants expressed a significant inclination toward endorsing the utilization of the dashboard as a tool for decision-making. Seven individuals agreed with this inquiry, while the remaining individuals remained neutral. **4.4 Intention to Use** This dimension is the final aspect of the TAM evaluation model. This dimension pertains to the assessment of the user's desire to utilize the built system. The mean score for this dimension was determined to be 4.75 based on the findings. All respondents expressed a strong agreement or agreement with the notion that they intended to utilize the dashboard as a tool for decision-making, assuming they had access to the dashboard. **5. CONCLUSION** This study has presented a data visualization depicting the correlation between inflation, GDP, currency exchange, and unemployment rates, and their impact on economic stability among the ASEAN countries. Inflation has emerged as a worldwide and inevitable concern for all nations. Based on the findings of Carlsson-Szlezak et al. (2020), it is anticipated that this issue will be worsened in the forthcoming years. This study revealed that customers have come to recognize the significance of saving money during this crucial period. Additionally, it can assist suppliers in determining the optimal quantity of products to avoid excessive production. Investors are able to gain insights into the performance of each ASEAN country, enabling them to make informed decisions regarding their investment choices and maximize potential returns. This study holds significant value for the governments of ASEAN countries, particularly for the National Recovery Council since it aims to facilitate the economic recovery of these nations. Regarding the researchers and developers, this methodology can be employed in studies pertaining to time series forecasting, the creation of a standardized dashboard for different geographical areas, and other investigations that prioritize the observation of economic stability. The console functions as a valuable instrument for users in facilitating the processes of decision-making. The assessment of the performance of each ASEAN country will be made available to investors, allowing them to make informed investment decisions and optimize potential profits. It has the potential to be applied in predicting time series data and creating dashboards that can be used in multiple locations. # 6\. Acknowledgements/Funding (DOUBLE-BLIND reviewing. Leave the section as is. Only include Acknowledgements text in the final submission paper) # 7\. Conflict of interest statement (DOUBLE-BLIND reviewing. Leave the section as is. Only include CONFLICT OF INTERESTS text in the final submission paper) # # 8\. Authors’ contributions Each author contribution must be stated clearly reflecting each contribution to the body of the work and manuscript. Authors can refer to [CRediT](http://credit.niso.org/) (Contribution Roles Taxonomy) for the detailed information about individual contributions to the work. For example ***(Double Blind Review: Leave this section blank until final camera-ready submission)*:** # 9\. References [https://doi.org/10.1016/S0378-3820(03)00112-7](https://doi.org/10.1016/S0378-3820\(03\)00112-7)Vuthaluru, H. B. (2004). Thermal behaviour of coal/biomass blends during co-pyrolysis. *Fuel Processing Technology, 85*(2–3), 141–155. Ahmed, I. I., & Gupta, A. K. (2010). 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