Abstract: Renewable energy development is important for improving energy security and economic growth in Indonesia. This study identifies the best renewable energy potential in Southwest Sumba, East Nusa Tenggara Province, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method based on 5C criteria: Consolidated, Controllable, Continue, Clean, and Cheap. The research uses a multi-criteria decision-making approach, using primary data from expert interviews and secondary data from literature reviews. The TOPSIS analysis shows that solar energy has the highest preference value, followed by bioenergy and hydropower. Technical assessments show important implementation requirements for each renewable energy option. The study recommends prioritizing solar energy development, supporting bioenergy projects, improving micro-hydro facilities, and creating clear renewable energy policies. Success depends on cooperation between stakeholders and aligning renewable energy development with regional sustainability and community needs. These efforts can help Southwest Sumba develop its renewable energy sector and contribute to national energy security goals.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Socio-economic development in countries cannot be without the contribution of enterprises, including Vietnam. In particular, factors affecting the decision to access financial resources are a topic of interest in Vietnam and developing countries. The objective of the study is to clarify the factors affecting the decision to access financial resources. Through quantitative analysis, the research results show that enterprise management has a negative impact on the decision to access capital, similar results also show that corporate financial management and policy on financial access have a negative impact on the decision to access capital. The research results show that the business environment and policy on financial access have no impact on the decision to access financial resources at enterprises.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The study's objective is to verify the effects of strategic review on the financial performance and risk management of banks in PortSudan City- Sudan. The descriptive analytical approach was used to accomplish the study's goals. By designing and distributing 180 questionnaires, of which 170 were collected. They were analyzed using path analysis using the partial squares technique. The main results indicated a positive effect of a strategic review on the financial performance of Sudanese banks. It also showed the positive effects of a strategic review on the risk management of Sudanese banks. The value of these results is that improved financial performance will make financial reports more reliable and trustworthy; therefore, it may attract more funds from the public. Investors and other stakeholders are interested in the bank's financial position and expected future operating results. They will use this information to prepare risk reports or make important business decisions. Therefore, if external decision-makers provide a reliable positive return, improving risk management will also contribute positively to shareholder value.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Amid the ongoing pandemic, such as the Covid-19 outbreak, there exists a critical need to comprehend and forecast the dynamic trends of daily confirmed cases to effectively prevent and mitigate the impact of its consequences. This study aims to investigate the essential factors acting as predictors for forecasting daily new confirmed cases specifically within the Indonesian setting. Utilizing advanced Deep Learning (DL) methodologies, including Deep Feedforward Neural Networks (DFNN), Long Short-Term Memory (LSTM), one-dimensional convolutional neural networks (CONV1D), and Gated Recurrent Units (GRU), this research endeavors to predict daily confirmed Covid-19 cases in Indonesia. To achieve this, a comprehensive set of 80 variables (predictors), encompassing the effective reproduction number (Rt), was utilized as input parameters. Before model construction, rigorous variable selection procedures and statistical analyses were conducted to enhance data understanding. The effectiveness of the predictive model was assessed using various metrics, such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Scaled Error (MASE), which evaluates MAE relative to a baseline model. Results indicate that DL models incorporating two key predictors—daily confirmed case count and Rt—exhibited superior predictive performance, capable of forecasting daily confirmed cases up to 13 days in advance. The inclusion of additional variables was found to diminish the predictive accuracy of DL algorithms.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The study aims to identify the effect of environmental sustainability accounting (ESA) on competitive advantage (CA), and the effect of ESA on making decisions (funding, investment, and strategic) in Sudanese banking. A questionnaire was used to collect data from 135 accountants and managers of Sudanese banking. A descriptive method was used to confirm that the study's goals had been met. The questionnaire data is analyzed, and hypotheses are tested, using the Smart pls application. The study found a positive relationship between ESA and CA in the Sudanese banking sector. Additionally, ESA has a positive relationship with funding, investment, and strategic decisions in the Sudanese banking sector. Based on these findings, future research can help accountants comprehend the intricacies of ESA according to national and cultural conditions, especially in developing countries. Also, larger sample sizes may be used in future research on this topic, particularly if it is studied internationally.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: In the complex and dynamic stock market landscape, investors seek to optimize returns while minimizing risks associated with price volatility. Various innovative approaches have been proposed to achieve high profits by considering historical trends and social factors. Despite advancements, accurately predicting market dynamics remains a persistent challenge. This study introduces a novel deep reinforcement learning (DRL) architecture to forecast stock market returns effectively. Unlike traditional approaches requiring manual feature engineering, the proposed model leverages convolutional neural networks (CNNs) to directly process daily stock prices and financial indicators. The model addresses overfitting and data scarcity issues during training by replacing conventional Q-tables with convolutional layers. The optimization process minimizes the sum of squared errors, enhancing prediction accuracy. Experimental evaluations demonstrate the model's robustness, achieving a 67% improvement in directional accuracy over the buy-and-hold strategy across short-term and long-term horizons. These findings underscore the model’s adaptability and effectiveness in navigating complex market environments, offering a significant advancement in financial forecasting.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: A consideration of the integral variables of customer location, traffic flow, and road conditions to determine the best feasible delivery routes is a big challenge in Logistical operations. A poor routing strategy that delivers products places an ineffective gloss and eventually converts into high operating expenses, over-consumption of fuel, and shipment delays. The paper’s goal is to build a model for the logistics management of the company which aims for effective management of the truck allocation and vehicle routing using K-means clustering and TSP. K-means clustering is often used to classify the sites of delivery based on their closeness in space, hence simplifying the problem by reducing its dimensionality. The proposed algorithm considered customer location prioritization in deliveries, delivery task allocation, and truck allocation to enable timely delivery. Therefore, this paper presented a solution to enhance the logistics operations of beverage brand “KINZA” by optimizing its truck loading and delivery route. The model would ensure that each truck is able to travel optimally, with vehicle-routing algorithms applied in a way to avoid all unnecessary waste of time and distance. Finally, the main scope of this paper is to develop and design a dynamic logistics platform for the KINZA Company distribution network.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study fills the research gap on the existence of joint control in management control systems—the object-oriented control framework (MCS-OOC)—by focusing on the interaction between results and action control, especially in companies that employ prospector strategies that were not considered in previous studies. This study aims to investigate the functioning of joint control by introducing a novel construct known as effectuation control, which forms effectuation MCS. Effectuation control is the synergistic, complementary, and simultaneous effects of a special relationship between action control and result controls. This study will contribute to the understanding of the dynamics of MCS or the control tightness of MCS-OOC. The Effectuation MCS model modifies the MCS-OOC model to account for uncertainty factors, thereby leveraging its capabilities to ensure the long-term sustainability of the company. In terms of methodology, this research will employ two initial models and two modified models, one for each of the prospector and non-prospector manufacturing companies. By comparing these four models and investigating several hypotheses using SEM-PLS, the results demonstrate that result control is no more significant toward existing capabilities when effectuation control is included in the model. Effectuation control significantly influences existing capabilities, whereas result control significantly influences new capabilities. In times of uncertainty and unpredictability, prospectors who implement a pay-for-performance system (result control) in conjunction with the implementation of sound policies, rules, procedures, and bureaucracy (action control) can leverage the company's existing capabilities and explore new ones, thereby enhancing its performance both now and in the future. Action control, a component of effectuation control, serves as a buffer against complex and confusing situations arising from high uncertainty, as every employee responds and refers to the same guidance, policies, rules, and procedures. On the other hand, result control serves as a buffer as well as a driving force, leveraging its capabilities to discover new capabilities amidst uncertainty with the aim of achieving breakthroughs, leading the market, and maintaining sustainability. This result is relevant only to prospectors, as they possess the ability to quickly adapt to uncertainty and seize opportunities presented by these changes, a trait that non-prospectors lack.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The purpose of this research work is to evaluate the use of statistical tools, specifically Ordinary Least Squares (OLS) and Artificial Neural Networks (ANN) and with the help of these tools to be able to independently and effectively predict the costs. of production in the context of supply logistics in the Peruvian paper industry. Both models that turn out to be different in their analysis, however, turn out to be complementary for a more exact and precise result, highlighting the ANNs for their superior performance in the precision of the evaluated metrics, where they managed to achieve an RMSE of 0.0171 and a MAE of 0.0122 compared to the OLS statistical model that achieved an RMSE of 0.0181 and a MAE of 0.2070. Likewise, the analysis between the dimensions studied, purchasing management stands out with a negative coefficient of -0.4978, which shows that its optimization will generate a positive impact on production costs, contrary to the case with the other two dimensions, which are: storage management and inventory management, which resulted in positive coefficients (0.7457 and 0.4667), which shows that their optimization does not necessarily generate a positive impact on production costs, but quite the opposite, that their inadequate management On the contrary, it can harm production costs. These results highlight the inherent need that Peruvian paper companies must have in being able to implement updated logistics systems, capable of integrating advanced statistical tools such as the use of ANN and MCO, which can scientifically help better decision making, allowing thereby improving your supply processes and thus being able to reduce your production costs.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Research on measuring airport operational performance has predominantly focused on technical aspects. However, there is still a need for studies to be conducted on measuring sustainable logistics and operational performance at international airports. The objective of this study is to develop a comprehensive measurement system for the Airport Operations Division in Indonesia that incorporates both operational and sustainable logistics performance. This will be achieved by integrating the company's vision, mission, and strategy into various performance measures using the Balanced Scorecard concept. The research methodology employed quantitative research methods, including primary data collection through observation and questionnaires. These questionnaires were developed using a pairwise comparison matrix derived from the Analytical Hierarchy Process (AHP). Data was collected from five international airports in Indonesia. The findings of the study demonstrate that the application of the Balanced Scorecard, coupled with the Objective Matrix method for setting performance targets and enriched with the AHP approach, enables the identification of priorities and assessment of performance. The research emphasizes the significance of considering non-financial aspects when measuring airport performance. This is crucial for supporting strategic decision-making and promoting sustainable performance improvement.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: In today’s environment of escalating competition, companies are adapting their management and production strategies, and product diversity is rapidly increasing. Companies require cellular manufacturing systems to produce products with high diversity in a short amount of time, ensuring the desired quality and meeting customer expectations. Cellular manufacturing systems, which have a more flexible structure compared to traditional production systems, are a good and effective solution for managers. Cellular manufacturing is an approach that aims to produce products with varying diversity in the shortest possible time and at the lowest cost, targeting an increase in efficiency. In this study, a cell manufacturing system proposal is made and cell formation is carried out to increase efficiency and effectiveness in a company that manufactures industrial refrigeration cabinets. A productivity-based 0-1 integer mathematical programming model is prepared that facilitates the simultaneous grouping of part and machine families in cell formation. In addition to the intracellular and intercellular transportation costs found in productivity-based models in the literature, labor costs, maintenance costs, the depreciation costs of the machines used in the cells, and the waiting costs of the machines are also added to the prepared model. The model is solved with the help of the GAMS 23.5.1 software package, creating part families and machine groups. Group efficiency values are measured, and the current and proposed situations are compared.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study aimed at investigating the impact of marketing change capabilities on marketing strategic change collecting data using a questionnaire from a sample consisting of executives and managers in manufacturing firms. Performing data analysis by SmartPLS software, the results indicate that the three dimensions of marketing change capabilities (micro, meso, and macro change capabilities) exerted significant effects on marketing strategic change. Theoretically, this study fills a considerable gap in marketing literature concerning such effects and identifying 12 marketing-related change capabilities. Empirically, the study calls decision makers in manufacturing firms for start building marketing change capabilities to be ready to face marketing strategy failure due to the changing nature of market arrangements.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: In today's rapidly evolving business environment, driven by technological advancements and increasing stakeholder expectations, firms must strategically innovate to ensure long-term success and competitiveness. This study examines the interconnections between co-creation, resource-based management, environmental policy, organizational learning support, and organizational performance within the framework of Industry 4.0, grounding its analysis in the resource-based view theory. Focusing on the emerging market context of Vietnam, the research utilizes data collected by means of a survey of 321 managers across various industries, applying Partial Least Squares Structural Equation Modeling to explore these respondents’ perspectives on the relationships between the factors listed above. The findings provide actionable insights and strategic recommendations of relevance to companies which aim to thrive in the dynamic landscape of Industry 4.0, particularly in emerging markets. This study contributes to the existing literature by offering practical implications for the optimization of resource management and enhancement of organizational performance in the context of ongoing industrial transformation.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Trust is a key attribute of social cohesion that is a major phenomenon in social relationships. This research aims to trust levels in social relationships and understand how social relationships affect trust levels. This research uses the theory of social relationships as an understanding of the level of trust in modern organizations, the theory of trust based on three dimensions namely trust in information, motives, and competence. Statistical descriptive qualitative research method is used as an approach supported by Delphi analysis, Analytical Hierarchy Process (AHP), and TOPSIS (Technique for Others Preference by Similarity to Ideal Solution). In identifying factors in the social relationship between policy and community, nine social relationship factors were obtained, including Communication (A1); Trust (A2); Cultural (A3); Procedural Justice (A4); Problem-Solving (A5); Transparency (A6); Engagement (A7); Collaboration (A8); Empowerment (A9). On the one hand, in the context of relative importance, the weight value at the criteria level is trust in Information (C1) (19.8%); Trust in Motives (C2) (31.2%); Trust in Competence (C3) (49%). Based on the results of the 3D trust level-based mapping analysis on social relationships, of the nine alternatives there are no factors with complete level (level 5) and Ignorance (Level 1). Overall, there are two alternative social relationship factors with high trust level (level 4), namely Trust (A2) and Collaboration (A8). These findings suggest that social relationship factors, such as trust (A2) and Collaboration (A8), play an important role in increasing the trust value of institutions related to trust from the community.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study conducts a bibliometric analysis of data mining publications in the Scopus database, examining the evolution of the field from 2015 to 2024. The study examines the bibliometric structure of data mining in management. Analyzing 2,942 publications, the research identifies significant growth in data mining studies. It reveals gaps in integrating data mining with decision-making, artificial intelligence, forecasting, and sentiment analysis. Despite a large number of publications, interdisciplinary applications of data mining are limited. The scientific publication on data mining and its relationship with decision-making, artificial intelligence, forecasting, and sentiment analysis is found to be weak, showing significant research gaps in these areas. China and the USA are prominent contributors, indicating geographical concentration. The study highlights the need for broader interdisciplinary exploration in data mining beyond traditional areas, urging global researchers to diversify contributions. The analysis focuses solely on publications indexed in Scopus, potentially excluding relevant studies from other databases or sources. This study provides insights into the evolution of data mining research and identifies areas for further interdisciplinary exploration, contributing to the advancement of the field's boundaries.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The focus of this research is to analyze how supply chains’ management affects production costs in the cardboard and Packaging sector in Peru, specifically through the creation of artificial neural networks (ANN) to improve the logistical activities. Non-experimental quantitative design was applied, collected the data from the Year 2020 to the Year 2024 and sought to assess variables such as supplier capacities, stocks held, bottom line costs incurred and stock out ratios. The study revealed that there exists a proportionate inverse relationship between the logistical costs and production costs, proving that as the cost of acquiring goods needed for production as well as the cost of keeping and managing stock decreases, the overall production cost also decreases significantly. The ANN model was able to perform cost predictions with a high degree of accuracy which points out the relevance of sophisticated instruments in the shift of the supply chain. Also, it is important to note the core contribution of the research – effective logistics management is emphasized as a way of increasing competition in industries where supply chains are of critical importance. This research reinforces the effectiveness of designing ANN in minimizing costs, while adding knowledge to the reporting practice of the companies aimed at bettering their costs. The results are a good contribution in terms of technological change in logistics aimed at helping the organizations remain flexible in a changing economy.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This document addresses the Nurse Scheduling Problem (NSP) and presents a dynamic multi-criteria optimization model for its solution considering a predefined time horizon. The purpose is to maximize the level of "work well-being" of nurses formulated as the minimization of "aversion" which translates into costs or penalties for certain undesirable work shifts. For this, a series of criteria are defined to estimate the preference structure of nurses according to the hospital center specifications by assigning costs for undesirable shift assignments. The proposed methodology involves developing a heuristic to decompose the global problem into daily subproblems for which a dynamic algorithm is implemented that considers a cost accumulation process for all criteria and all nurses. Daily models are dynamically solved by modifying the coefficients of the well-being function to achieve equity throughout the planning period by updating and accumulating different averages. This methodology has shown satisfactory results for scheduling work shifts for doctors, paramedics, security guards, and drivers in numerous hospital centers in Colombia.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study aims to select accountants for a business in Vietnam. The study engaged in focused discussions with experts to establish the criteria for an accountant. Next, structured interviews were conducted with experts to collect data comparing each pair of criteria and expert scoring data for each candidate according to each criterion. Then, the Analytical Hierarchy Process (AHP) was applied to determine the weight Wj of each criterion of an accountant, and finally the TOPSIS method was applied to find the similarity coefficient with the ideal solution Ci* for each candidate selection option. The result was that candidate A1 was selected because he had the highest Ci* coefficient of 0.81479; at the same time, through the weighted results Wj of the criteria, it showed that experts highly appreciated the candidate for the following outstanding characteristics: communication skills (W1 = 0.4108), professional skills (W4 = 0.2527), and personal skills (W2 = 0.1613).

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The study explored the influence of the main dimensions of accounting information (AI) relevance and reliability on supply chain performance (supply chain exchange information, supply chain collaboration, supply chain integration) at Noon e-commerce companies in Saudi Arabia. The researcher followed the descriptive analytical approach to describe the study variables based on previous studies and explore the study gap. The study adopted a questionnaire, of which 170 were collected. The data was analyzed using partial least squares (PLS) through structural equation modeling (SEM). The results indicated a positive effect of the relevance and reliability of AI on the dimensions of SC performance (SC exchange information, SC collaboration, SC integration), there is a positive effect of the reliability of AI on the dimensions of supply chain performance (SC exchange information, SC integration) and a negative effect of the reliability of AI on the SC collaboration parties. These results clarified the value and benefit of accounting information in improving supply chain performance.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Using technology, such as human-machine interfaces, can enhance operational performance processes and increase the flexibility of the supply chain. Human-machine interfaces can produce operational control systems quickly and accurately. The research aims to explore the impact of human-machine interface on operational performance through collaborative decision making and supply chain agility. The sample criteria are the manufacturing companies with over 20 employees in Indonesia. The questionnaires were distributed offline (76 respondents) and online through Google Forms (427 respondents), so 503 questionnaires were valid—data processing using SmartPLS software version 4.0. The study results showed that the human-machine interface technology positively affects collaborative decision-making, supply chain flexibility, and operational performance with coefficients of 0,559, 0,490, and 0,340, respectively. Collaborative decision-making involving customer partners in planning decisions and communicating decisions with external partners influences supply chain flexibility by a coefficient of 0.375 and operational performance by 0.149. Moreover, supply chain flexibility with flexible planning and production processes and flexible labor placement influences operational performance by a coefficient of 0.381. The practical contribution of research enlightens company managers to build integrated systems and automation. It encourages top management and owners to think about investing in machines with high automation in the economy. Besides, these findings enrich the theoretical background in supply chain management and the resource-based view.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The shift in learning communication patterns in higher education emerged along with the end of the pandemic, accelerating the transformation of digital communication. This study aims to explain the digital communication variable of the CMC model, the digital technology variable of the technology and determinism model, the SIKA LMS variable of the technology acceptance model (TAM), the discipline variable of the Attitude and Behavior theory model, and the graduation variable of the Media Dependence model as part of the communication using the computer. Quantitative research method with SEM PLS analysis. Furthermore, the data collection technique was used with a proportionate stratified random sampling population of 3416 students and a sample of 302 active students working on the final project. The analysis in this study uses Structural Equation Modeling, with 5 variables, namely digital communication (X1), digital technology (X2), SIKA LMS (X3), student discipline (X4) and timely graduation (X5), conducting outer model tests, goodness of fit models, and model testing inner. Results show that the discipline variable obtained the highest average of 4.22. In contrast, the digital communication transformation obtained a very significant direct influence on the shift in learning patterns for timely graduation.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: In recent years, fintech has received much attention due to the introduction of new technologies in banking and electronic payment. For financial service providers to compete in the industries, they should apply the business model as a conceptual framework to improve performance. The current research is exploratory and tries to identify the factors influencing fintech design in electronic payment using the Osterwalder business model. This study aims to integrate three methods named DEMATEL, ISM, and ANP from MCDM techniques. To analyze the identified factors affecting the design of fintech in electronic payment, the indicators were examined in terms of influence and effectiveness by the DEMATEL method, then the levels of influence and effectiveness of the factors were investigated using the interpretive structural modeling method. Finally, the network analysis method was used to prioritize the factors. The findings showed that recognizing and identifying electronic payment customers is the most effective among the factors, and determining the type of relationship with customers is the most impressionable factor. In addition, after ranking the factors, the type of relationship with customers was the first rank, and the criteria of the company's cost structure and revenue streams were determined as the second and third, respectively.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)