The importance of green building concepts is emphasized in the current era due to the drastic decline in global climate conditions. However, their development is hindered as they are primarily applied to new buildings, while almost two-thirds of the world's buildings are already constructed. This study aims to improve the efficiency of Green Retrofitting, accelerating the growth of green buildings in Indonesia. It identifies the procurement and permitting processes for Green Retrofitting in high-rise office buildings in Jakarta, along with high-risk activities from these processes. Additionally, it develops a model of the relationship between these high-risk activities and the implementation efficiency of green retrofitting, using a Monte Carlo approach based on the Regulation of the Minister of Public Works and Housing No. 21 of 2021 and the Green Building Council Indonesia. The analysis uses data from 26 expert respondents on green retrofitting procurement and permitting, finding 83 activities with 214 risk indicators influencing green retrofitting efficiency, including 57 high risks. Identifying the most risky activities, the study develops a relationship model and conducts simulation and optimization to improve project time efficiency, ultimately accelerating the growth of green buildings in Indonesia.

To underscore the critical role of predictive capabilities in third-party logistics (3PL) companies for assortment planning, particularly within the rapidly evolving e-commerce sector and business to business (B2B) flows. This study employs a comprehensive literature review on the forecasting capabilities of 3PL firms, enriched by empirical research across nine logistics facilities. It leverages statistical tools and the ARIMA_PLUS algorithm to evaluate the precision and dependability of demand and supply forecasts generated by these companies. The research reveals that 3PLs possess the ability to generate accurate demand and supply forecasts utilizing advanced forecasting tools. The effectiveness of these forecasts is closely linked to the quality of data available, and the expertise of the personnel involved. Challenges arise in forecasting for smaller order volumes, which are more common in e-commerce flows. The study also highlights that technological advancements and investments in data analytics are pivotal in enhancing forecast accuracy. The investigation focuses on a select group of 3PL companies, potentially limiting the generalizability of the findings. Moreover, the study underscores the necessity for further exploration into how technological innovations impact forecasting capabilities. By emphasizing the significance of 3PL firms' predictive abilities, also for e-commerce assortment planning, this paper addresses a notable gap in existing research. Its insights are invaluable for businesses contemplating logistics outsourcing and for 3PL providers aiming to advance their forecasting proficiency. The findings stress the importance of integrating advanced forecasting models and analytics to stay competitive in the dynamic e-commerce landscape.

The purpose of this research is to investigate the influence of green recruitment and selection, green training, and green intellectual capital on employee performance in Yogyakarta, Indonesia. The data collection method used in this research is to look for observation, interviews, and the distribution of questionnaires. Structural Equation Modeling is used to be an analysis method with SmartPLS assistance. Within the SEM model, this research predominantly employs the Partial Least Squares (PLS) technique, a component-based or variance-based structural equation modeling approach. PLS enables the testing of theories or theory development for predictive purposes based on experimental results. Based on the results of this study, green recruitment and selection have a significant and positive impact on employee performance. However, green training does not have any statistically significant positive effect on employee performance in the same region. On the other hand, green intellectual capital shows a significant and positive influence on employee performance. These results contribute to understanding the relationship between green human resource practices and employee performance.

Greenhouse gases (GHGs) have caused extreme temperature changes. In January 2023, temperatures were 0.1°C higher than the normal 30-year monthly average. Construction, especially high-rise offices, which occupy 42% of Jakarta, contributes significantly through energy con-Sumption. To reduce carbon emissions, Indonesia has started to implement green retrofits as part of the Net Zero Emission 2050. Due to high costs and lack of public education on new and existing green buildings, the implementation of green retrofits is inhibited, and owners prefer conventional buildings. This research aims to analyze the feasibility and investment risk of implementing green retrofits in high-rise office buildings using the life cycle cost method and the Minister of Public Works and Public Housing Regulation No. 21 of 2021 to generate a feasible and safe in-vestment. It has been proven with cost savings in energy and water consumption of up to 15% compared to conventional office buildings. Profits have also been achieved by providing 9 benefits to the building owner, building manager and building occupants. Therefore, this research has the potential to accelerate the green revolution through feasible and safe green retrofit investments in Jakarta's office buildings.

This study aims to develop a new data-driven methodology for identifying suitable influencers for a brand using data from social media. The increasing presence of such figures in these communication channels makes it challenging to select consistent and influential influencers for a specific audience. This paper introduces an innovative approach to defining these figures based on the analysis of relationships within the brand's network. Specifically, this methodology will be applied to the case study of a brand named “Anemonia”. The approach relies on the sequential application of various steps, including the use of tools such as Social Network Analysis (SNA) centrality, Sentiment Analysis (SA), and Analytical Hierarchical Process (AHP). Through the application of this methodology, the brand has been able to identify influencers consistent with its aesthetics and vision.

In the dynamic landscape of digital enterprises, cybersecurity has emerged as a critical determinant of organizational effectiveness. This study delves into the intricate realm of cybersecurity investment within ASEAN organizations, exploring the key facets that drive decision-making in this domain. Using a quantitative approach through structural equation modeling (SEM), we conducted an in-depth analysis based on a sample of 419 enterprises meeting cybersecurity criteria. Our findings reveal that cybersecurity strategy, financial considerations, and institutional and regulatory conditions are the primary factors influencing cybersecurity investments in the ASEAN region. In particular, financial resources emerged as the most critical determinant, underscoring the importance of adequate funding to address evolving cyber threats. Furthermore, our study highlights the crucial role of institutional and regulatory frameworks in shaping investment behavior, indicating a heightened awareness among firms regarding compliance with legal requirements. By unpacking these dynamics, our research provides deep insights into the intricate interplay of factors shaping cybersecurity investments in ASEAN organizations. This study contributes to the discourse by emphasizing the imperative nature of understanding the impact of risk aversion, organizational structures, and long-term practices on cybersecurity resilience. The implications of our findings extend to policy making, innovation, and future research directions in the cybersecurity domain, offering valuable insights to improve cybersecurity preparedness and resilience against evolving cyber threats.

This research presents a short review of Multiple Criteria Decision-Making (MCDM) methods and research in various fields, including marketing and business management. The academic literature shows that MCDM methods in the area of marketing are used by academics to solve problems related to the positioning of products and services, market segmentation, brand management, promotion and advertising strategies, product development and market entry strategies, customer relationship marketing and channel distribution. With regard to business and management domains they are used to prioritize various decision-making aspects, like project assessments, resource allocation, strategic planning, risk management, performance evaluation, supplier and vendor selection, human resource management and strategic investment decisions. We can claim that in both domains, MCDM brings a systematic and transparent approach to decision-making, helping marketing managers to make more informed and objective choices. In summary, the continual refinement of these methods and the integration of cutting-edge technologies hold promise for further enhancing the effectiveness and efficiency of decision-making processes in the dynamic landscape of business and management. Further, the analysis highlights emerging trends and challenges for the future of MCDM research.

This research is aimed at analyzing perception and identifying determinants of the decision to convert to organic mango farming in Mekong Delta (MD) Vietnam. The research was conducted by using a direct survey data set from 109 household heads in this region collected by stratified random sampling method. The research method used was descriptive statistics and the binary Logit model. The research results revealed some interesting points. In the total observations gathered, only about half of households decided to convert, mainly due to local implementation and awareness of safety for consumers and environmental protection. Still, the most important reason for farmers to convert was to get a higher selling price. The binary Logit model analyzing the determinants found that the older the farmer, the more difficult it is to decide to convert. At the same time, training and enhancing awareness about organic farming will increase the probability of conversion decisions. Based on the research results, several relevant solutions on investment, production linkage, and propaganda to raise people's awareness were recommended, thereby increasing the probability of deciding to convert.

Annual increases in power use need improved energy management. Several universities and research organizations have focused on pursuing energy efficiency and renewable energy to satisfy this stipulation. This study provides a thorough and organized systematic review of over 2000 operational research studies conducted between 2019 and 2023. In summary, this study explores potential innovations to enhance existing literature utilizing mathematical tools in energy management. Our systematic literature review indicates that geometric planning is a novel mathematical technique in energy management. Based on the specific problems, this paper discusses geometric planning and proposes its integration with other mathematical techniques.

This study aims to develop an innovation network model to help MSMEs recover from the downturn due to the pandemic. The basic model used is an innovation network consisting of associations, suppliers, customers and government. The basic model was developed by adding capital and digitalization variables to suit the needs. Data processing uses a structural equation model and the sample is MSMEs in West Java province. This study is the first on the innovation network model for MSMEs affected by the pandemic and the largest number of MSME respondents. The results conclude that according to MSMEs in Indonesia, suppliers, customers, government, associations, capital, and digitalization have an effect on the innovation network. This study concludes that associations, suppliers, customers, government, capital and digitalization all have a positive effect on the innovation network. This finding is the first in the innovation network model for MSMEs that accommodates the needs of the industry to recover from the pandemic situation and adds new literature on industrial innovation networks. While previous studies have a lot of similar literature, all focus on certain types of businesses and on normal economic conditions. This study is different from similar studies because it was conducted on MSMEs in all business sectors and economic conditions in crisis due to the pandemic. These results can be used as a reference in decision making to increase the growth of MSMEs with limited resources.