Limited Diversification Research Articles

Economic Development and Diet Composition: Cross-Continental Insights into Bennett's Law

The study assesses Bennett's Law, which posits that higher incomes lead to reduced consumption of starchy staples in favor of more diverse, nutrient-dense diets, and its relevance across various global regions. Using regression models, the research examines the relation between GDP per capita and the share of starchy staple foods in caloric intake across continents. The findings indicate significant regional variations in adherence to Bennett's Law. For instance, while South America aligns with Bennett's predictions, Europe deviates, showing increased starchy staple consumption with rising incomes, potentially due to cultural and eco-conscious dietary trends. Africa and parts of Asia display limited dietary diversification, often due to structural barriers and economic constraints. Contrarily, Oceania and North America exhibit a mixed relationship, influenced by income inequality and health trends. These results suggest that Bennett's Law's applicability is region-specific and influenced by socioeconomic, cultural, and policy factors, underscoring the complexity of dietary transitions and cautioning against one-size-fits-all assumptions about the impacts of economic development on food consumption.

Cointegration Analysis of the Indonesian Stock Exchange with the Stock Exchanges of the United States, China, and Southeast Asia

This research investigates the long-term cointegration and short-run causality among seven key equity indices—Dow Jones Industrial Average (United States), Shanghai Composite (China), Hang Seng (Hong Kong, China), IDX Composite (Indonesia), FTSE Straits Times (Singapore), FTSE Malaysia KLCI (Malaysia), and SET Index (Thailand)—to assess the effectiveness of geographical portfolio diversification amid increasing globalization and liberalization of financial markets. Utilizing daily closing price data from January 2007 to February 2025, the study applies the Johansen and Engle-Granger cointegration tests, as well as the Vector Error Correction Model (VECM), across three distinct periods: 2007–2009, 2020–2022, and 2007–2025. The findings reveal that, over the 2007–2025 period, there is significant cointegration between the Hang Seng (Hong Kong) and IDX Composite (Indonesia), suggesting limited diversification benefits between these markets in the long run. During 2020–2022, cointegration is also observed between the FTSE Straits Times (Singapore) and IDX Composite (Indonesia), as well as between the Hang Seng and IDX Composite. Additionally, VECM analysis for 2007–2009 uncovers short-run causality from the Hang Seng to the Shanghai Composite, indicating dynamic interdependence during that period. Overall, the results highlight that while some Asian equity markets exhibit integration, others remain segmented, underscoring the importance of monitoring cointegration patterns when making international investment decisions.

Assessing Disaster Resilience in Climate-Vulnerable Communities: A Financial Resilience-Based Grading Framework for Bangladesh

Bangladesh, one of the world's most climate-vulnerable nations, faces increasing challenges due to high population density and frequent extreme weather events. Coastal communities, in particular, suffer substantial economic losses from climate-related hazards. This study aimed to develop a conceptual framework for identifying disaster resilience indicators, focusing on financial resilience, and creating a grading system for community disaster resilience. Drawing from resilience assessment frameworks like the Climate Disaster Resilience Index (CDRI) and the 5 Capitals Model, the research crafted a tailored framework for the local context. Using the Grade Point Index (GPI) method, scores were calculated for each parameter and dimension. The study focused on the climate-vulnerable, low-income communities of Koyra, Shyamnagar, and Monirampur, identified through consultations and vulnerability assessments, with support from Islamic Relief Bangladesh. Findings revealed critical gaps in financial capacity, limited income diversification, and low household savings, weakening overall resilience. Access to social safety nets was also limited, particularly in the most vulnerable areas. The newly developed resilience grading tool showed nearly 90% of indicators in critical categories, underscoring significant shortcomings in community resilience. This tool allows for quick assessments, ongoing monitoring, and comparative analysis, offering valuable insights for planning, management, and policy development. The study recommends policymakers and development agencies adopt this grading method to prioritize interventions and support to make resilient of any vulnerable community.

Uncovering dynamic relationships across sustainable-ethical financial assets: A new outlook from Indonesia

Against the rapid developments in cross-border investment that shed light on portfolio diversification opportunities, this study investigates the relationship between Indonesia’s sustainable ethical stocks and global sustainable financial assets. Amid market uncertainty, the need for safe havens and diversification of investment portfolios is imperative. Given the remarkable performance of Indonesia’s Islamic stocks, which are considered ethical stock, and the importance of sustainable stocks, this study examines how global financial assets such as Green Bonds, Artificial Intelligence (AI) stocks, and clean cryptocurrencies are interconnected. Employing a Dynamic Conditional Correlation – Generalized Autoregressive Heteroskedasticity (DCC-GARCH) model within the Structural Vector Autoregression (SVAR) framework, this study examined daily data spanning January 2, 2020, to August 6, 2023. This study finds strong evidence of dynamic relationships across assets, implying limited diversification benefits in the market. The results show that Cardano, as a clean cryptocurrency, can serve as a short-term safe haven, while the Green Bond potential is a long-term safe haven against Indonesia’s Islamic stocks. However, green bonds, Cardano, and AI stocks are suggested as potential diversifiers for sustainable stocks. Understanding these dynamics offers valuable insights into asset selection and diversification strategies, particularly for investors focusing on sustainable ethical assets.

ANALYSING COGNITIVE BIASES, FEAR OF LOSSES, AND OVERCONFIDENCE ON INVESTMENT CHOICES AMONG INVESTORS

This study examines how psychological factors like cognitive biases, fear of losses, and overconfidence affect investment decisions among individual investors. Using data collected from 317 participants, the research explores the impact of these behavioural patterns on portfolio diversification, risk-taking, and decision-making. The findings reveal that fear of losses significantly influences investment behaviour, often leading to overly cautious choices and limited diversification. The study also shows that financial literacy helps reduce the influence of biases, enabling more rational investment decisions. However, overconfidence was not found to have a strong effect on risky trading behaviour. Additionally, there were no major differences in cognitive biases between male and female investors. Overall, the research emphasizes the importance of financial education and self-awareness in improving investor decision-making. Keywords: Cognitive Biases, Loss of Aversion, Overconfidence, Investment Decisions, Behavioural Finance.

Assessment of Vulnerabilities in the Real Sector of European Economies

The assessment of economic vulnerability is a key tool for analyzing the stability and resilience of the real sector of national economies, particularly in the context of global economic transformations and external shocks. This article proposes an approach to evaluating the economic vulnerability of the real sector in European countries, taking into account factors such as industrial development, macroeconomic indicators, and employment levels. A comprehensive methodology is introduced, combining the Entropy Weight Method (EWM) to determine indicator significance with a sensitivity and adaptability framework for measuring economic vulnerability. The study analyzes major macroeconomic indicators, including GDP growth rates, inflation, unemployment, and savings levels. Structural vulnerability of the real sector is assessed using statistical data from 2000 to 2023, and a Real Sector Vulnerability Index is developed based on data from the World Bank’s economic development indicators. The findings reveal significant regional disparities: countries in Northern and Western Europe exhibit lower levels of vulnerability due to balanced industrial structures and effective economic policies, while countries in Eastern and Southern Europe display greater sensitivity to economic shocks, reflecting structural imbalances. The methodology is based on econometric modeling tools and utilizes Python-based data analysis packages to explore dynamic relationships between macroeconomic variables over time. The results indicate that countries with diversified economic bases tend to demonstrate higher resilience, whereas economies with limited industrial diversification remain more susceptible to external risks. The proposed approach enhances the methodological foundation for evaluating economic vulnerability and provides a basis for informed policy decisions aimed at strengthening the resilience of the real sector under conditions of economic instability. The insights gained are of critical importance for policymakers in identifying and mitigating risk factors, thereby contributing to sustainable economic development across European regions.

Global Market Analysis of India's Silk Industry: Trade Dynamics and Competitiveness Assessment

India's silk industry holds a significant position in the global market, ranking as the second largest producer after China. An analysis of trade dynamics and competitiveness reveals notable trends in both exports and imports. Bangladesh leads India's export destinations with a robust Compound Annual Growth Rate (CAGR) of 21.63 per cent, followed by China at 8.01 per cent, indicating expanding market presence. On the import front, Vietnam and Brazil emerge as key suppliers, with high CAGRs of 42.03 per cent and 33.36 per cent, respectively, suggesting growing dependence on these countries for raw silk or silk yarn. Revealed Comparative Advantage (RCA) analysis from 2011 to 2021 shows that Uzbekistan (25.04) and Romania (10.02) have shown comparative advantage and strong positions in silk exports, whereas two countries show a comparative disadvantage. Market concentration, the global silk export market has a high concentration with an HHI of 0.305, as per the thresholds defined by the UK CMA and US FTC. Meanwhile, global silk imports have an HHI of 0.194, indicating moderate concentration. India’s silk exports show an HHI of 0.188 moderate by UK standards but high by US standards suggesting limited export market diversification and dependency on select importing countries.

Financing Sustainability: The Role of Green Bonds in India's Low-Carbon Transition

Green bonds have emerged as a pivotal mechanism in global climate finance, enabling the mobilisation of large-scale investments for environmentally aligned projects. This paper investigates the development, structure, pricing dynamics, regulatory ecosystem, and international comparisons relevant to India’s green bond market from 2020 to 2025. It evaluates the presence of “greenium” in Indian corporate and sovereign bonds, compares the performance of ten major green and conventional bonds, and analyses SEBI’s evolving green debt regulations. Drawing insights from interviews with key stakeholders—including regulators, issuers, and institutional investors—the paper highlights persistent structural barriers such as verification costs, absence of a national green taxonomy, and limited thematic diversification. Comparative analysis with Brazil, China, and Indonesia reveals both opportunities and gaps in India’s current approach. Concluding with actionable recommendations, the study underscores the need for coordinated institutional reform, investor confidence-building measures, and expanded green financial innovation to strengthen India’s climate finance ecosystem and enable a resilient low-carbon transition.

SWOT Matrix Based Business Strategy Analysis: Case Study of PT Berkah Utama Transsindo East Lampung

PT Berkah Utama Transsindo (Butrans) has experienced a decrease in market share, declining from 74% in 2022 to 62% in 2023, while its competitor, PT Adhitama Surya Abadi Express (ASAE), expanded its share from 26% to 38%. This study examines Butrans’s internal strengths and weaknesses as well as its external opportunities and threats by utilizing the Internal Factor Evaluation (IFE) and External Factor Evaluation (EFE) matrices, alongside SWOT analysis and the Quantitative Strategic Planning Matrix (QSPM) Total Attractiveness Score. The aim is to identify key areas for improvement and prioritize strategic initiatives. The results position Butrans in quadrant IV of the Internal-External (IE) Matrix, indicating a relatively strong strategic stance where internal strengths moderately outweigh external challenges. The analysis highlights major strengths such as an extensive fleet and an integrated vehicle monitoring system, while identifying key weaknesses including high operating costs and limited cargo diversification, which reduce adaptability. Opportunities driven by economic growth and infrastructure expansion are counterbalanced by threats from natural disasters and intensifying competition. To strengthen its market position, the study recommends cost reduction, diversification of services, technological enhancements for greater efficiency, stronger client engagement, and the development of contingency strategies to mitigate external risks. By implementing these recommended strategies, based on insights from the IFE and EFE analyses, Butrans is expected to recover market share and maintain its leadership in the logistics industry of East Lampung.

WAYS TO IMPROVE AND PROSPECTS FOR THE DEVELOPMENT OF COLLECTIVE INVESTMENT SYSTEMS IN THE REPUBLIC OF KAZAKHSTAN

Kazakhstan's collective investment sector is expanding, yet it faces regulatory inefficiencies, limited diversification, and weak risk management frameworks. This study analyzes investment fund performance, identifies key risk factors, and proposes strategies to optimize risk management and investor confidence. A mixed-methods approach combines quantitative statistical analysis of risk-adjusted returns (Sharpe and Sortino ratios) and qualitative case studies of Kazakhstan's leading investment funds. Comparative analysis with global benchmarks highlights structural weaknesses in investment strategies and regulatory oversight. Findings reveal that Kazakhstan's investment funds underperform relative to global counterparts due to concentrated investments, liquidity constraints, and regulatory gaps. Diversified portfolios show 20% lower volatility, emphasizing the need for alternative assets and international exposure. Funds implementing hedging strategies, liquidity buffers, and stricter compliance measures demonstrate resilience. However, delays in regulatory approvals, transparency issues, and slow adoption of AI-based risk assessment tools hinder sector growth. The study proposes dynamic asset allocation, AI-driven risk analytics, blockchain for transparency, and regulatory reforms aligned with OECD standards to address these challenges. Strengthening investor education and governance mechanisms will further enhance Kazakhstan's investment ecosystem, ensuring sustainable growth and financial stability.

High-Throughput Sequencing Reveals Apple Virome Diversity and Novel Viruses in the Czech Republic

Apple viruses pose significant threat to global apple production. In this study, HTS technology was used to investigate the apple virome in the Czech Republic. Previously reported viruses, including ACLSV, ASPV, ASGV, ApMV, AGCaV, and CCGaV, were confirmed, and near-complete genomes were assembled. Additionally, two novel viruses, ARWV1 and ARWV2 were identified for the first time in the Czech Republic. Phylogenetic analyses showed low genetic variability among ARWV2 isolates, suggesting a possible recent introduction or limited diversification. In contrast, ARWV1 isolates displayed distinct clustering in the coat protein coding region, separating symptomatic and asymptomatic samples, indicating a potential involvement of genetic determinants in symptom expression. Mixed infections were prevalent, with multiple molecular variants of ACLSV, ASPV, and AGCaV detected within individual samples, along with co-infections involving viruses from different families. Recombination analysis identified frequent recombination events in ACLSV and ASPV, often involving non-apple parental sequences, suggesting their potential for cross-host infections. Additionally, an interspecific recombination event was detected in an almond ApMV isolate, with PNRSV as a minor parent. These findings highlight the impact of agricultural practices on viral evolution and host adaptation. This study demonstrates the utility of HTS as a powerful tool for uncovering viral diversity, recombination events, and evolutionary dynamics.

Digitalizing Pindang Industry with Business Model Canvas for Sustainable Blue Economy

This study aims to design a tailored Business Model Canvas (BMC) for the pindang business in Babat Village, Tangerang, to support the blue economy framework. A qualitative approach was employed, including field surveys, in-depth interviews with stakeholders, and SWOT analysis. The findings highlight strengths such as a loyal local customer base and traditional expertise, as well as opportunities to expand into modern retail and export markets. However, weaknesses like limited product diversification and threats such as fluctuating fish supplies were also identified. The study provides strategic recommendations, including sustainable product innovation, digital technology adoption, and strategic partnership development, to enhance the competitiveness of pindang products while promoting environmental sustainability. Additionally, the integration of information technology is emphasized to optimize supply chain management, improve digital marketing strategies, and enhance customer engagement through e-commerce platforms. This research contributes to the academic discourse on blue economy implementation, business model innovation in traditional industries, and the role of information technology in modernizing small-scale fisheries. It distinguishes itself from other BMC applications in traditional industries by specifically addressing the unique challenges and opportunities in pindang production, particularly within the context of sustainability and digital transformation.

Stock Market Integration, Risk and Spillovers Across BRICS Nations: An Assessment During COVID-19

The present study aims to examine the downside risk, volatility persistence, market connectedness and hedging strategies in Brazil, Russia, India, China and South Africa (BRICS) markets during the COVID-19 period, comparing these factors with the Asian Financial Crisis and Global Financial Crisis. Using value-at-risk, dynamic conditional correlation generalized autoregressive conditional heteroskedasticity and the Diebold–Yilmaz Spillover Index, the study addresses key research objectives. It is observed that volatility created persists longer, and market connectivity increases during crises. Notably, Russia and Brazil are observed to be the net spreaders of volatility, while India, China and South Africa demonstrate opposite trends. Moreover, very limited diversification benefits exist in the bloc.

Integrated farming systems leveraging rainwater harvesting for enhanced productivity, profitability, and sustainability in hyper-arid regions of India

Rainfed farming in hyper-arid regions faces significant challenges, including low productivity, poor economic returns and inefficient resource utilization due to erratic rainfall and limited diversification. The study was conducted during July 2019 to June 2023 at ICAR-Central Arid Zone Research Institute, Regional Research Station, Bikaner, Rajasthan to evaluate the productivity, profitability, and resource use efficiency of rainwater harvesting based Integrated Farming System (IFS) model with conventional cropping system of moth bean [Vigna acontifolia (Jacq.) Marechal] and cluster bean [Cyamopsis tetragonoloba (L.) Taub] under rainfed condition. Study assessed a 3.0 ha IFS model integrating arable crops, horticulture, fodder, dairy, vermicomposting, and mushroom cultivation, supported by rainwater harvesting. Results showed that compared to conventional sole cluster bean and moth bean cropping systems, IFS exhibited superior productivity with a mean Cluster Bean Equivalent Yield (CEY) of 7608 kg/ha, significantly surpassing the CEY of 274 kg/ha and 191 kg/ha under conventional rainfed cropping. The IFS achieved annual net returns of ₹1,79,899 with a B:C ratio of 2.40, representing 5.6 and 6.2 times higher net returns than sole cluster bean and moth bean, respectively. In terms of income share, dairy contributed 45.8% of net returns, while horticulture and fodder accounted for 17.8% and 16.5%, respectively. The system demonstrated high water use efficiency (2.498 kg/m³) and a reduced water footprint (400 L/kg). With a Sustainable Value Index (SVI) of 0.560, the IFS model proves to be a resilient, sustainable, and economically viable approach for hyper-arid regions.

THE EFFECTIVENESS OF AI IN PREDICTING STOCK MARKET TRENDS: A COMPARATIVE STUDY OF THE LAST FEW YEARS OF INDIAN MARKETS

The integration of Artificial Intelligence (AI) into financial forecasting has transformed traditional stock market prediction methods. This research paper explores the effectiveness of AI techniques in forecasting stock trends within the Indian stock market over the last few years. We examine AI methodologies, including machine learning (ML), deep learning (DL), and hybrid models applied to the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). By comparing historical data with AI-predicted trends, this study evaluates prediction accuracy and market relevance (Patel et al., 2015). Furthermore, the research outlines existing study gaps and proposes a future scope of integrating AI with behavioral finance and real-time analytics (Chen et al., 2022). The prediction of stock market trends remains a significant challenge due to the stochastic and non-linear nature of financial time series data (Zhang & Zhou, 2020). With the proliferation of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) techniques, there has been a paradigm shift in the modeling and forecasting of stock price movements (Fischer & Krauss, 2018). This paper presents a comprehensive study on the effectiveness of AI in predicting stock market trends within the Indian financial ecosystem, focusing on a comparative analysis of AI models implemented over the last five years (2018–2023) on major indices and stocks listed on the NSE and BSE. This research evaluates the performance of multiple AI algorithms—including Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and hybrid ensemble models—in forecasting short- and medium-term price trends using historical stock data (Krauss et al., 2017; Chen & He, 2021). Standard evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and classification accuracy are employed to assess model efficacy. The results indicate that while traditional machine learning algorithms offer moderate predictive power, advanced deep learning and hybrid models significantly outperform them, particularly during periods of high market volatility, such as the COVID-19 pandemic and post-lockdown recovery phases (Weng et al., 2018; Jain & Jain, 2021). The study identifies key research gaps, including limited sectoral diversification in datasets, inadequate integration of sentiment and behavioral data, overfitting issues in complex models, and the lack of real-time prediction systems in the Indian context (Gupta & Pathak, 2022). Furthermore, current models often ignore nonquantitative factors such as investor sentiment, macroeconomic indicators, and global events, which can critically impact prediction accuracy (Nassirtoussi et al., 2014). These limitations suggest the need for a more holistic and interdisciplinary approach to AI-driven financial forecasting. The scope for future research includes the development of real-time, adaptive AI systems using high-frequency trading data, the incorporation of behavioral finance through social media and news analytics, and the exploration of quantum computing-based AI models (Liu et al., 2023). From a practical standpoint, the findings of this study offer valuable insights for institutional investors, financial analysts, regulatory bodies, and developers of AIpowered trading platforms. The study concludes that while AI is not a definitive solution for market prediction, it provides a powerful augmentative tool that, when designed with robustness, transparency, and adaptability, can significantly enhance decision-making in India’s fastevolving financial markets.

Cross-Sectoral Portfolio Optimization in Emerging Markets: Value at Risk Assessment of Indonesian Consumer and Financial Stocks

This study examines the comparative risk profiles of single-asset investments versus portfolio strategies using two prominent Indonesian companies: PT. Mayora Indah and PT. Sinar Mas Multiartha. Employing a quantitative approach with Monte Carlo simulation and Value at Risk (VaR) methodology, the research analyzed daily stock returns over a one-year period (January-December 2023). Results reveal that despite similar historical volatility levels between the individual stocks (standard deviations of 2.65% and 2.88%), their correlation coefficient was notably low (0.13), creating significant diversification opportunities. Monte Carlo simulations generated 1,000 potential return scenarios for robust risk assessment, finding that at the 95% confidence level, maximum expected losses on a Rp 100 million investment were Rp 4.78 million for PT. Mayora Indah and Rp 4.58 million for PT. Sinar Mas Multiartha individually. However, a portfolio combining both stocks (60% PT. Mayora Indah, 40% PT. Sinar Mas Multiartha) reduced this potential loss to Rp 2.90 million—representing approximately 37% risk reduction compared to either single-asset investment. This substantial risk mitigation was consistent across all confidence levels (99%, 95%, and 90%). The portfolio also demonstrated improved return characteristics in simulation (0.39% expected return) compared to historical data (0.09%), while maintaining similar risk levels. These findings provide empirical support for the practical value of diversification strategies in the Indonesian equity market, highlighting how even limited diversification across two stocks from different economic sectors can yield substantial improvements in risk-adjusted investment outcomes.

Incidencia de la crisis energética en los costos de producción en empresas manufactureras

The study analyzes the impact of the energy crisis on the production costs of manufacturing companies in Ecuador, a country with a high dependence on oil and limited diversification of its energy matrix. Using a mixed approach, financial projections were made based on statistical data from INEGI, the Central Bank of Ecuador and corporate income statements for the periods 2023-2024, complemented by a qualitative analysis that included case studies, reports and internal communications. The results show that energy instability has increased operating costs, affecting productivity due to interruptions, wasted raw materials, and damaged machinery. Although strategies such as the adoption of efficient technologies, diversification towards renewable energies and reorganization of production schedules have partially mitigated these effects, economic losses persist due to the lack of structural planning and effective public policies. The discussion highlighted the need to modernize energy infrastructure and establish incentives for sustainable technologies, as well as comprehensive policies that promote a diversified and efficient energy matrix. It is concluded that a comprehensive approach combining energy efficiency, advanced cost management and government support is key to reducing the vulnerability of the manufacturing sector and ensuring its long-term competitiveness.

Risk spillovers among Islamic and conventional banks in Saudi Arabia: frequency TVP-VAR approach

While extensive research has explored interconnectedness, volatility spillovers, and risk transmission across financial systems, the comparative dynamics between Islamic and conventional banks during crises, particularly in specific regions such as Saudi Arabia, are underexplored. This study investigates risk transmissions and contagion among banks operating in Islamic and conventional modes in the Kingdom of Saudi Arabia. Daily banking stock data spanning November 2018 to November 2023, encompassing two major crises—COVID-19 and the Russian-Ukraine war—were analyzed. Using the frequency TVP-VAR approach, the study reveals that average total connectedness for both banking groups exceeds 50%, with short-run risk transmission dominating over long-term effects. Graphical visualizations highlight time-varying connectedness, driven predominantly by short-run spillovers, with similar patterns observed in both Islamic and conventional banking networks. The main contribution of this paper is the insight that long-term investment strategies are crucial for mitigating potential risks in the Saudi banking system, given its limited diversification opportunities.

STRATEGIC BRAND IDENTITY DESIGN FRAMEWORK FOR MICRO AND SMALL-SIZED DESIGN COMPANIES

Micro and small design companies often face resource constraints, leading to limited diversification in skillsets. This forces team members to take on multiple roles, such as combining consulting, strategy, and creative direction. The research aims to support these companies by developing an easy-to-use strategy kit to assist in brand identity creation projects. The study introduces a new framework, serving as the foundation for a toolkit, and focuses on examining brand identity design theories while integrating them with design thinking methods. The goal is to help micro-enterprises build strong brand identities that align with their client's vision and customer needs. The framework is based on David Aaker’s Brand Identity Planning Model, emphasizing the Brand Identity System, including core and extended identity, value proposition, and brand-customer relationships. Aaker’s Strategic Brand Analysis, which covers customer, competitor, and self-analysis, is also briefly discussed as a foundation for understanding the client and the initial stages of branding. The study employed a qualitative analytical approach, using case studies and interviews to gather data. The findings led to the development of a new framework for brand identity creation, with conclusions drawn from the study’s evaluation and findings.

Ketahanan Masyarakat Pesisir terhadap Dampak Perubahan Iklim

This research investigates the resilience of coastal communities in Desa Sukamulya, Kecamatan Paku Haji, in responding to the impacts of climate change. Using a quantitative approach with a survey method, data were collected from 40 respondents, including fishermen and coastal business actors. Findings reveal significant challenges faced by these communities, such as extreme weather, shifting fishing seasons, and declining fish catches, which negatively affect their economic sustainability. While awareness of climate change exists, gaps remain between understanding and actual adaptation efforts due to limited resources and inadequate infrastructure. The high dependency on fishing as a primary income source increases vulnerability, compounded by low savings, limited job diversification, and weak participation in social and cooperative networks. This study recommends enhancing livelihood diversification, improving coastal infrastructure, strengthening community organizations, providing climate education, and rehabilitating coastal ecosystems like mangroves and coral reefs. These strategies aim to bolster the resilience of coastal communities, ensuring better adaptation to climate change and the sustainability of their livelihoods.