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This study explores the key determinants of Supply Chain 4.0 (SC4.0) maturity in the context of a developing country by examining the relationships between supply chain ambidexterity, supply chain agility, and the maturity of SC4.0. The study was carried out using the survey method involving 154 managers from Indonesian manufacturing companies. The SC4.0 maturity model was developed and tested using structural equation modeling. From our analysis, it was found that supply chain ambidexterity emphasizing on innovation positively influences the companies' agility and SC4.0 maturity levels, and supply chain agility partially mediates supply chain ambidexterity. This paper contributes to the operationalization of SC4.0 maturity determinants that incorporate innovation and technological aspects and extends the extant literature by empirically elaborating the determinants and antecedents of SC4.0 maturity that may expedite the achievement of SC4.0 maturity. This implies that companies and supply chain professionals aiming at achieving their SC4.0 maturity should do so by being outward-looking and, at the same time, foster supply chain collaboration with external networks. This paper is pioneering the empirical study on SC4.0 and offers a means to achieving SC4.0 maturity through SC ambidexterity and SC agility, particularly in the context of a developing country. [ABSTRACT FROM AUTHOR]

COVID-19 severely impacted small industries in Indonesia and many collapsed. Sustainable education is essential to help small industries recover post-pandemic through an open innovation design program to improve small industries' economic, social, and environmental performance. This paper examined the impacts of the sustainable education program implemented and explored open innovations suitable for the sustainability of small industries in developing countries, such as Indonesia, after the pandemic. This research employed a Participatory Action Research (PAR) approach involving academics, government, the community, and four small industries in West Java, Indonesia. Our findings reveal that the sustainable education program through learning activities and direct practice provides valuable knowledge and experience for small industries in designing economic, social, and environmental innovations. Open innovations post-pandemic suitable for small industries in Indonesia include simple business digitalization for company economic growth, the recruitment of people losing their jobs due to the pandemic to achieve social benefits, and innovations in using environmentally friendly packaging and charcoal briquette fuel as an environmental awareness effort. This study recommends that the open innovation designed for small industries be right on target and done using a participatory method to achieve sustainability after the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The objective of this study is to elaborate on the development of micro and small enterprises (MSEs) at the bottom of the economy, where most of them began as unbanked micro-ventures and may continue to be micro-enterprises even after being elevated to higher levels. This study contributes to the field of entrepreneurial finance by identifying the factors that influence the drivers and constraints of MSE upgrading. This paper employed models to show how unbanked MSEs transition to bankable ones, as well as to reveal the dynamics of MSEs' transition to bankability during the COVID-19 pandemic. This study's unit of analysis is MSEs, which initially have little or no access to bank loans. This study employs system dynamics (SD) modeling to investigate and manage the multifaceted response system of MSEs' growth toward bankability in the face of a coronavirus pandemic in Indonesia. This study also simulates a coronavirus pandemic outbreak to fine-tune MSEs' progress toward bankability. The dynamic models in this study reveal a number of variables that are critical in accelerating the bankability status of MSE from un-bankable to bankable, namely entrepreneurial MSE time to bankability, channel business, and MSE NPL. Policymakers can create high leverage policies for MSE bankability progression by changing these values. [ABSTRACT FROM AUTHOR]

As a rapidly developing country, Indonesia faces a challenge in improving air quality that is made more difficult by frequent forest fires linked to deforestation and volcanic eruptions. This paper analyses the link between development and air pollution in 30 Indonesian cities from 2002 to 2019. Air quality is measured using two novel regional air quality indicators (AQIs) for PM 2.5 and PM 10 estimated from weather observations and NASA satellite data on Aerosol Optical Depth (AOD). Results from an Environmental Kuznets Curve (EKC) analysis, based on a dynamic panel data model, show that forest fires and volcanic activity have significantly worsened regional air quality in Indonesia. The model also shows that the EKC hypothesis is rejected for Indonesia as cities have followed a monotonically increasing trend in air pollution with respect to economic growth. An additional 1% of economic growth increased long run pollution as measured by AQI PM 2.5 and PM 10 by 0.35% and 0.46%, and forest fire events by 0.11% and 0.06%. A partial Benefit-Cost Analysis (BCA) of forest fires shows that particulate matter pollution is excessive. Decreasing the frequency of forest fires by 1% would generate a net benefit of between US$17 million to 145 million. The lower estimate of the health benefits exceeds the agricultural benefits of forest fire without accounting for other non-market costs due to forest fires. Our results imply that, as Indonesia grows economically, air quality is likely to continue deteriorating. Further, regions affected by regular volcanic activity and wildfires should apply tighter emission standards to controllable point source emissions from industry and non-point source emissions such as agricultural forest fires. [ABSTRACT FROM AUTHOR]

Several studies have focused on generating seismic vulnerability maps for earthquake-prone areas, particularly in Indonesia. Building typologies are a key factor in determining vulnerability to earthquakes. However, conducting large-scale field surveys to determine the spatial distribution of building typologies in a city is uneconomical. This paper explores the use of a convolutional neural network (CNN) to automatically detect building typologies from diverse regions in Indonesia, utilizing both conventional and automated building image acquisition processes. In this study, datasets from three distinct image acquisition methods are trained with four unique CNN architectures to identify the best-performing model to classify building typologies. The sample size effect on CNN performance is also investigated. The results showed that randomly sampled Google Street View (GSV) images are the most effective dataset for the CNN model, achieving an f1-score of 84.33%. Among the network architectures tested, MobileNet demonstrated superior performance on the majority of evaluated datasets. As the sample size increases by about 350% in the dataset, there is a positive correlation with up to 2.3% f1-score improvement. Using the best-performing CNN model, two building vulnerability models were employed to assess the spatial distribution of building damage in the urban area of Bandung, considering a hypothetical scenario of an M7 earthquake. Incorporating local construction data, one of the generated maps estimated that approximately 55% of buildings in Bandung would experience moderate to severe structural damage. This study showcases the potential of CNN models in automating regional seismic assessments and providing valuable insights for comprehensive seismic mitigation strategies. • A decision tree to label building typology objectively in Indonesia was developed. • CNN model trained with randomly sampled GSV dataset outperforms other models. • The CNN and probability model generated a city-scale building vulnerability map. [ABSTRACT FROM AUTHOR]

Chloride attack is the main cause of corrosion of reinforced concrete structures subject to marine environment. This paper deals with the assessment of a reinforced concrete jetty structure in Indonesia after 25 years of service life and simultaneously investigate the effect of applying newest Indonesian earthquake code on jetty structural element safety. The work comprises detailed inspection of the structure, on-site and laboratory tests of the structure, and analyses of existing and future structural strength based on the tests data. Current and future strength prediction is performed by assuming average and worst-case scenarios for corrosion. Based on the average-case scenario, the concrete jetty structural elements have no strength reduction up to 2028. By contrast, using the worst-case scenario, the concrete jetty structural elements start experiencing strength reduction in 2003. Applying the 2019 Indonesian earthquake code to analyze the jetty, shows significant reduction of safety factor of steel pipe pile element of the jetty. [ABSTRACT FROM AUTHOR]

Pyrethroid residues in the Citarum River, Indonesia, was first investigated based on their occurrences, water assimilative capacity, and risk assessment. In this paper, first, a relatively simple and efficient method was built and validated for analysis of seven pyrethroids in a river water matrix: bifenthrin, fenpropathrin, permethrin, β-cyfluthrin, cypermethrin, fenvalerate, and deltamethrin. Next, the validated method was used to analyze pyrethroids in the Citarum River. Three pyrethroids, β-cyfluthrin, cypermethrin, and deltamethrin, were detected in some sampling points with concentration up to 0.01 mg/L. Water assimilative capacity evaluation shows that β-cyfluthrin and deltamethrin pollution exceed the Citarum river water capacity. However, due to hydrophobicity properties of pyrethroids, removal through binding to sediments are expected. Ecotoxicity risk assessment shows that β-cyfluthrin, cypermethrin and deltamethrin pose risks to the aquatic organisms in the Citarum River and its tributaries through bioaccumulation in food chain. Based on bioconcentration factors of the detected pyrethroids, β-cyfluthrin poses the highest adverse effect to humans while cypermethrin is the safest. Human risk assessment based on hazard index suggests that acute non-carcinogenic risk associated to consuming fish from the study location polluted with β-cyfluthrin, cypermethrin and deltamethrin is unlikely. However, hazard quotient shows that chronic non-carcinogenic risk associated to consuming fish from the study location polluted with β-cyfluthrin is likely. However, since the risk assessment was performed separately for each pyrethroid, further assessment on the impact of mixture pyrethroid to aquatic organisms and humans should be performed to explore the real impact of pyrethroids to the river system. [Display omitted] • Pyrethroids occurrence and risk assessment in the Citarum River was first reported. • A simple and efficient method was built and validated for pyrethroids analysis. • β-cyfluthrin, cypermethrin, and deltamethrin, were detected in the Citarum River. • β-cyfluthrin and deltamethrin have exceeded the Citarum river water capacity. • Chronic non-carcinogenic risk associated to β-cyfluthrin is likely. [ABSTRACT FROM AUTHOR]

The growing demand for private vehicles associated with the rise in the number of middle-income consumers in Indonesia has led to an enormous energy demand and high-level CO 2 emissions. In order to increase the sustainability in the transportation sector, the Indonesian government has given special attention to electric vehicles as key future solutions to achieve clean and green transport and to improve energy supply security. This paper aims to investigate public perception in relation to the adoption of electric motorcycles (EMs) in Indonesia. We conduct a survey involving 514 participants and find that only 36% of participants agree to adopt EMs as their next motorcycle, and 40% are still in doubt (i.e. neutral). To identify factors influencing EMs adoption, we expand the Theory of Planned Behaviour (TPB) model and analyse the data collected using Partial-Least Squares Structural Equation Modelling (PLS-SEM). The findings show that an adequate perception of EMs, environmental benefits, economic benefits, and incentive policies positively affect the attitude toward the adoption of EMs, which reinforce the results of the previous studies. However, perceived cost and perceived risks appear to be insignificantly influential in our study. Furthermore, factors related to high-behavioural control and support from relatives and closest people are shown to significantly affect the adoption of EMs. This paper also presents the acceptable price range of EMs and the optimal price point (OPP) for segments differentiated by monthly income. All these findings provide valuable insights for the different stakeholders interested in enhancing the adoption of EMs in Indonesia. [ABSTRACT FROM AUTHOR]

The growing demand for private vehicles associated with the rise in the number of middle-income consumers in Indonesia has led to an enormous energy demand and high-level CO 2 emissions. In order to increase the sustainability in the transportation sector, the Indonesian government has given special attention to electric vehicles as key future solutions to achieve clean and green transport and to improve energy supply security. This paper aims to investigate public perception in relation to the adoption of electric motorcycles (EMs) in Indonesia. We conduct a survey involving 514 participants and find that only 36% of participants agree to adopt EMs as their next motorcycle, and 40% are still in doubt (i.e. neutral). To identify factors influencing EMs adoption, we expand the Theory of Planned Behaviour (TPB) model and analyse the data collected using Partial-Least Squares Structural Equation Modelling (PLS-SEM). The findings show that an adequate perception of EMs, environmental benefits, economic benefits, and incentive policies positively affect the attitude toward the adoption of EMs, which reinforce the results of the previous studies. However, perceived cost and perceived risks appear to be insignificantly influential in our study. Furthermore, factors related to high-behavioural control and support from relatives and closest people are shown to significantly affect the adoption of EMs. This paper also presents the acceptable price range of EMs and the optimal price point (OPP) for segments differentiated by monthly income. All these findings provide valuable insights for the different stakeholders interested in enhancing the adoption of EMs in Indonesia. [ABSTRACT FROM AUTHOR]

The Sediment Transport and Erosion Prediction (STEP) model is methodically described in this paper. The STEP model was implemented at a 3.47 km × 3.47 km grid resolution in the Kalisapi sub-watershed, Central Java, Indonesia. It was calibrated and validated in the period of 2003–2008 and 2010–2014 respectively. The result shows that the STEP model functions satisfactorily at monthly and annual time window in the calibration and validation periods. In both periods, the STEP model is able to produce fruitful annual sediment transport rates with R2 values of 0.71 and 0.9 in the calibration and validation period respectively. Moreover, the STEP model exhibits more equitable spatial variability than that of the widely known USLE model. Correspondingly, it can be surmised that the STEP model is capable of simulating spatial and temporal variability of sediment transport from a watershed at monthly and annual time scales. • STEP model is developed to simulate soil erosion, deposition and sediment transport processes in time and space. • STEP model has been successfully implemented, calibrated and validated in the Kalisapi sub-watershed, Indonesia. • Calibrating STEP model to fit the observation is straightforward as it only needs to modify two model parameters. • STEP model source code and input data for the study area are freely available in the Bitbucket repository service. [ABSTRACT FROM AUTHOR]

It is important for companies to be able to make their business models dynamic. This enables them to adapt to changing circumstances and remain viable. The aim of this paper is to combine insights from the literature on business models and business model dynamics into a comprehensive dynamic business model framework. The framework that is developed in this paper takes into account various origins of changes in business models (internal or external to the company) and various types of changes in business models (primary or secondary changes and forced changes or strategic choices) and also includes the issue of business model consistency. In order to combine different origins and different types of business model change into one dynamic business model framework, some simplifications of reality were needed. The framework is described in text and shown in a comprehensive picture. The application of the framework to two cases of renewable energy companies in Indonesia shows that the framework is able to capture business model dynamics in a simplified and comprehensive way and that it allows for case study comparison. In a thorough discussion, it is shown how the framework can be adapted to make it better able to represent more complex dynamics. [ABSTRACT FROM AUTHOR]

• We discuss Flying Sidekick Traveling Salesman Problem (FSTSP) with multiple drops. • The drone can execute a short delivery trip to serve multiple customers. • We present a new mathematical formulation for this multi-drop FSTSP. • A new heuristic based on Adaptive Large Neighborhood Search (ALNS) is developed. Drones are the latest trend in commercial logistics research, especially in the context of last-mile delivery. Combining a drone and a truck offers numerous distinctive capabilities that introduce new opportunities to enhance the performance of the last-mile delivery system even further. To deal with the challenges of routing optimization for the combined system, the present paper proposes a new mathematical formulation and a new heuristic approach based on Adaptive Large Neighborhood Search (ALNS) for the Flying Sidekick Traveling Salesman Problem (FSTSP) with multiple drops (multi-drop FSTSP). The effectiveness of the proposed approach was demonstrated in several test instances, some of which are based on a real case delivery problem in Indonesia. It appears that the proposed ALNS approach performs better than the state-of-the-art method adapted from the previous literature. [ABSTRACT FROM AUTHOR]

Biomass residue is one of the focus areas for green energy in Indonesia, elaborated in Government Regulation no 79 the year 2014 on national energy development. The assessment of residual resources is an important way to show the availability distribution to realize the target of biofuel is more than 5% of the energy supply in 2025. Thus, this paper will present a method to assess the energy production from biomass residue as a primary consideration in its development. Statistical data and field observations have been used to estimate the total availability of residue. Furthermore, to calculate the energy potential of biomass residue, the minimum and maximum value parameters, such as residue to product ratio, moisture content, and heating value, were obtained from works of literature. The power potential was also analyzed through three scenarios of the conversion factor efficiency, i.e., low, medium, and high, to provide comprehensive results. The proposed methods allow the annual Indonesian biomass residue production from agriculture and forest residue to be nearly 155,271 and 2554 tonnes, respectively. It can be generated an energy potential around 1261 PJ, covering 22.12% of national energy consumption. Given the high potential for sustainability in biomass residue supply and utilization, strategic factor in the areas of the government regulation and policy, greenhouse gasses emission, land-use change, and conversion technology were identified to support residue valorization in the context of planning, organizing and operating. The challenges and barriers that can be minimized by integrated collaboration and synergy among policymakers, entrepreneurs, academics, and community to assist the sustainability of green energy production in the future, and evaluation of environmental, economic, and social analysis as a framework to improve the quality and quantity of the production processes. [Display omitted] • A mapping analysis is a model used to estimate the energy potential of the biomass residue. • Biomass residue reached 1097.77 PJ per year, covering 33.5% of national energy demand. • The spatial distribution represents the location spread of the availability of energy potential. • The analysis may be a helpful tool for policymakers to develop bio-energy sustainability. • It is necessary for the integrated collaboration of implementing bio-energy sustainability. [ABSTRACT FROM AUTHOR]

Contact tracing is a pivotal component for containing an outbreak to stop the spread of COVID-19. As the 4th most populous country in the world, contact tracing in Indonesia is resource intensive. There was an insufficient human resources to undertake the reporting task with surveillance staff already overburdened with sample and data collection. Participation of various stakeholders is critical in providing an effective capacity surge in tracing. This paper describes how multi-sectoral collaboration training can contribute to accelerate contact tracing capacity in Indonesia. A series of contact tracing training program in Indonesia was held through the various stakeholder collaboration, including the Ministry of Health, COVID-19 National Task Force, Ministry of Education and Culture, Universities, and international partners. Not only primary healthcare officers, the training engaged community volunteers, young professional, and university students to enable them performing COVID-19 contact tracing and data management across Indonesia. Descriptive statistics and paired t-test analysis were taken to measure the effectiveness of the pre- and post-test training, and evaluation questionnaires to improve participants' knowledge. A total of 2,513 community volunteers and young professional from 10 provinces and 2,005 university students from 268 universities across Indonesia have been trained from January - July 2021. 4 batches of trainings have produced 64 data managers in all 34 provinces. At the beginning of the training, only 33% of participants reached satisfactory score. Post-training scores increased significantly to 66% participants reached satisfactory score with p-value = 0.000 (CI: 95%). 82% participants rated the effectiveness of training as good, and 90% stated that the training responded to their needs. The training has successfully increased the number of tracers in Indonesia as and improved the national tracing data collection. Surveillance data showed that from January to July 2021, the national tracing data has increased from 261,482 to 785.711 COVID-19 close contact traced. Multi-sectoral participation and collaborative contact tracing training has helped the Government of Indonesia to increase human resource numbers and capacity in providing an effective capacity surge of tracing for the country. This will enable to support COVID-19 mitigation and intervention program and policy. [ABSTRACT FROM AUTHOR]

• An integration model for biomass to methanol supply chain is built. • Using an agent-based simulation–optimization is recommended in analyzing and reporting results. • Two different technological pathways of producing methanol from biomass are benchmarked. • Major decision making is included in the agent-based simulation–optimization model. • Methanol from biogas is more favorable than gasification technology. Methanol is a key product in the chemical industry, not only for producing other higher economic value chemicals, but also serves as a source of renewable energy that can substantially reduce carbon emissions. However, limited data are available on methanol total supply chain costs that consists of production, biomass cost, investment cost and the efficiency of the conversion processes. This paper recommends using an agent-based simulation–optimization in analyzing and reporting results by system elements (techno-economic assessment) or unit processes (life cycle assessment) as well as by the overall product system, of methanol supply chain. Major decision making are included in the agent-based simulation–optimization model to evaluate methanol synthesis pathways. This evaluation includes biomass and methanol ordering policy, capital budgeting during the lifetime of the methanol supply chain, carbon emissions per metric ton of methanol with specific focus on contribution to Indonesia Gross Domestic Product. The principal results obtained from the agent-based simulation show a better conversion technology pathway that leads to lower carbon emissions, higher internal rate of return, shorter payback period, and less price sensitivity of the methanol supply chain. The article proposes methanol from biogas as the chosen conversion technology pathway by considering its capability of sequestering carbon dioxide from bio-digester and lower capital investment, as well as its economic attractiveness to methanol production investors. [ABSTRACT FROM AUTHOR]

Recently, the two decacorn Startups in Indonesia decided to collaborate with a merger scheme and accumulate over 100 million monthly active users. The Merger triggers a new perception for customers and has an impact on the level of Brand Loyalty. However, no research examines how customer perception to mergers between the startup with decacorn status. Therefore, this study intends to fill this gap. Using the mix method approach, this study investigates how customer perception of startups' mergers decacorn status and examines its effect on customer behavior. The findings revealed that customer self-congruency successfully mediates the research gap between customer perceptions on merger scheme (sig. 0.000 < 0.05, β = 0.575) and purchase intention (sig. 0.000 < 0.005, β = 0.390) by combining impulse buying strategies (sig. 0.003 < 0.05, β = 0.329). The paper contributes theoretically to the body of knowledge in the customer's perception of the merging company. The study also gives new insight that customers' perception of the Merger of two decacorn companies cannot guarantee that customers will be loyal to the company without connecting the customer self-congruency in each partner. It is recommended that the manager gain stimulus in building conformity of company image with customer perceptions that create self-congruency. [ABSTRACT FROM AUTHOR]