Indonesia has been striving to become the world’s largest ornamental fish exporter for the past two decades, but success has been limited. This study analyzes Indonesia’s export competitiveness and identifies the barriers encountered. A trade analysis using the UN-Comtrade database for 2012–2021 was conducted using the Revealed Comparative Advantage (RCA), Constant Market Shares (CMS), and Competitiveness Matrix formulas to evaluate the competitiveness. Qualitative data from interviews with forty-three industry stakeholders were analyzed using NVivo to evaluate export barriers. The findings revealed that Indonesia had not been optimally exporting to some of the world’s largest markets and faced various barriers, such as product quality and continuity, regulations, marketing, shipment, and other internal and external barriers. The government’s role in overcoming those barriers is crucial while maintaining environmental sustainability. We also propose a matrix model for export barrier levels consisting of export readiness, export market accessibility, export operational challenges, and dynamic export environment.

In a start-up, the level of technological innovation is crucial to the start-up’s competitiveness, especially in the digital age; as a result, high-tech start-ups stand a better chance of being more profitable than middle-tech and low-tech start-ups. The aim of this study is to identify and examine research papers regarding the role of technological innovation in advancing Sustainable Development Goal 8 (SDG) in the current context. This study intends to fill research gaps by performing a systematic literature review and meta-analysis following the PRISMA guidelines on the subject. To investigate advancements in the use of start-up technologies, scientific publications were obtained from the Scopus database, yielding a total of 384 entries for the preferred reporting items for systematic reviews and the meta-analyses identification stage. The findings indicate that high technology encompasses artificial intelligence (AI), blockchain, the Internet of Things (IoT), and collaborative robots; medium technology comprises mobile applications, big data, and cloud computing; and low technology consists of software and connectivity. Each of the technological innovations plays a significant role in advancing SDG 8, encompassing aspects such as economic growth, employment, productivity, creativity, innovation, entrepreneurship, development policies, and business growth.

The study aimed to visualize and understand how circular economy practices had been adopted within emerging apparel industry by employing the Soft Systems Methodology (SSM) through Rich Picture analysis. Qualitative data were collected from interviews, focus group discussions, and policy document reviews. The study explored interactions among key stakeholders within the apparel industry ecosystem, which consisted of government institutions, apparel producers ranging from large brands to Small and Medium Enterprises (SMEs), associations, recyclers, and consumers. The analysis applied SSM through Rich Picture to visualize technological readiness, entrepreneurial innovation, stakeholder relationships, and socio-political dynamics that shape the adoption process. The finding revealed that the adoption of circular practices was still at an early and fragmented stage. Limited technological capacity, weak policy coordination, and unequal power relations between dominant brands and smaller producers created significant barriers to progress. Several emerging initiatives led by ethical brands and community-based recyclers demonstrated a growing entrepreneurial creativity and moral commitment toward sustainable production. The Rich Picture captured these dynamics visually, connecting technological constraints, regulatory gaps, and market perception that influenced the system’s current state. This study concluded that accelerating circular adoption required systemic collaboration among entrepreneurs, policymakers, and technology actors. By extending SSM to the context of sustainability-driven innovation, this research provided practical insights for policymakers and industry practitioners to strengthen technological capacity, financial incentives, and partnership mechanism in building a more circular and inclusive apparel ecosystem.

Coffee tree replanting is needed in Indonesia, yet a financing scheme is not available at the moment due to its economic feasibility and farmers’ reluctance to reinvest. This research aims to design contract farming for coffee tree replanting in Indonesia to support coffee farmer welfare. The method used in this study is Soft System Methodology (SSM), including interviews with several coffee cultivation respondents, with case studies in the Lampung region. The initial study indicates that the replanting program must integrate coffee farmers with banks or investors and coffee processing companies. The study of one farmer community successful in profiting from a new coffee production method enables the possibility of a new model of replanting through contract farming. Aside from banks, institutions that will have an essential role in the development of successful replanting are land owners and exporters or processing companies as offtakers. The implementation of the European Union Deforestation Regulation on traceability will impact Indonesia’s replanting effort, as exporters or importers will be accountable for the compliance of their supply chain. The five main factors that need to be evaluated consist of (1) financing as an initial investment, (2) farmer competencies as the farmer’s skill to implement the coffee tree replanting, (3) technology to ensure the successful implementation of coffee tree replanting, (4) production to maintain the income flow, and (5) synergy between actors to ensure the role in contract farming development.

Income is often viewed as the main determinant of life insurance demand. However, in the last two decades, the world’s life insurance penetration has continued to decrease even as income grows. This study investigates the relationship between income and life insurance demand using panel data from forty-one countries from 2013 to 2022, along with education and life expectancy as control variables. The study finds a non-monotonic relationship between income and life insurance penetration and between education and life insurance penetration, while life expectancy shows a monotonic relationship with life insurance penetration. This study provides significant policy implications for insurers to predict life insurance demand and suggests that non-high-income countries emphasize the improvement of their life insurance sector development.

The retail industry in Indonesia is still in the transition phase from multichannel to omnichannel. Hence, the behavior of shoppers is divided into two broad categories, namely multichannel and omnichannel shoppers. The research aimed to know the socio-demographic characteristics of multichannel and omnichannel shoppers in Indonesia. It also tested the difference between both in showing cross-channel behavior and loyalty to the retailers. The data were obtained by questionnaires from 341 respondents with purposive sampling. The descriptive method with cross-tabulation analysis was applied to capture the socio-demographic characteristics of multichannel and omnichannel shoppers. Meanwhile, the comparative method with an independent sample t-test tested the difference between both types of shoppers in showing cross-channel behavior and loyalty to the retailers. The results show that millennial and female shoppers are the most potential segment at this time to be targeted in the context of omnichannel retailing. Both multichannel and omnichannel shoppers are dominated by those whose income is less than Rp4,5 million to Rp10 million with 3 to 5 persons in their household. Moreover, there are significant differences between multichannel and omnichannel shoppers in the context of cross-channel behavior and loyalty. Omnichannel shoppers show cross-channel behavior and loyalty more than multichannel shoppers.