This study explores the role of taxation and customs strategies in promoting sustainable design and environmental responsibility in supply chain management. The research examines their direct effects on sustainable and environmental responsibility in Jordan, as well as the moderating influence of socioeconomic factors. It also investigates the mediating effect of sustainable practices adoption. Quantitative analysis using Smart PLS4 software was conducted. The results highlight the positive influence of taxation strategies on sustainable and environmental responsibility. Effective taxation strategies contribute to a stronger commitment to sustainable practices and environmental concerns within supply chains. Similarly, customs strategies positively influence sustainable and environmental responsibility, demonstrating that efficient customs management aligns with sustainable practices. In addition, socioeconomic factors were identified as significant moderators. The relationship between taxation strategies and sustainable responsibility, as well as environmental responsibility, varied based on the socioeconomic context. This suggests that the effectiveness of these strategies depends on the specific circumstances in which organizations operate. Furthermore, sustainable practices adoption was found to moderate the relationship between taxation and environmental strategies, and sustainable responsibility. This indicates that the successful implementation of sustainable practices enhances the impact of taxation and environmental strategies in promoting sustainability within the supply chain. This paper emphasizes the importance of developing and implementing effective taxation and customs policies that align with sustainable practices. Organizations should consider the influence of socioeconomic factors and prioritize the sustainable practices adoption to maximize the impact of these strategies. The findings provide valuable insights for strategic decision-making and encourage businesses to integrate sustainability into their supply chain management, contributing to environmental conservation and long-term success.

This study aimed to explore the approaches for transforming Small and Medium Enterprises (SMEs) into high-value markets, focusing on constructing a structural equation model of guidelines for the development of small and medium enterprises into high-value markets. The conceptual research framework consisted of one exogenous latent variable, the Value Discovery element, combined with the four endogenous latent variables, Value Development, Value Communication, Value Distribution, and Value Experiences. The study utilized mixed-methodology research, including qualitative research through in-depth interviews with nine experts, a focus group discussion with 11 successful entrepreneurs, and quantitative research through a survey of 500 SMEs in Thailand. The results revealed that SMEs operating in high-value markets are divided into tangible and intangible products, representing 250 respondents from each group. The five elements of the guidelines for the development of small and medium enterprises into high-value markets were found to be of high importance both overall and in each element. The developed SEM model was consistent with the empirical data of five elements with 27 observed variable factors. Furthermore, the hypothesis test results for analyzing the causal influence between latent variables in the SEM model revealed that all six hypotheses were supported at the 0.001 level of statistical significance. The value of the study is beneficial for SMEs who target to do business in high-value markets and can improve their competitiveness, develop unique value propositions, and provide better customer experiences.

This study aims to provide implications for the application of the halal logistics traceability model to food commodities in Indonesia and to be able to recommend alternative policy scenarios for the government. The system dynamic approach is used to model the traceability of halal logistics for food commodities in Indonesia. The urgency of this research is that it will contribute to improving the quality of halal logistics which has implications for the halal industry in Indonesia. This research also has implications for food quality and safety which helps food security in Indonesia, the relationship between Halal Certification and Traceability on SMEs Performance, analyzing the relationship between Halal food supply chain management and Halal Certification and Traceability. This research method is a quantitative survey, research data obtained by distributing online questionnaires to 390 food SME owners who have implemented the Halal Assurance Management System. Data analysis used a structural equation model (SEM) with SmartPLS 3.0 software. The stages of data analysis are validity, reliability and significance tests. The sampling technique used is non-probability sampling. The questionnaire used in this study uses a Google form which will be distributed to respondents. This questionnaire measurement method uses a Likert scale of 5, namely Strongly Disagree (STS), (2) Disagree Answers (TS), (3) Neutral Answers (N), (4) Agree Answers (S), Strongly Agree (SS). The independent variable used in this research is halal supply chain management. The dependent variables used in this study are halal certification and traceability and SMEs Performance. The results of this study indicate that Halal food supply chain management has a positive and significant effect on the performance of SMEs, Halal Certification and Traceability have a positive and significant effect on SMEs Performance, Halal Certification and Traceability have a positive and significant effect on Halal food supply chain management.

The study examines whether audit committee (AC) characteristics influence firm performance, and whether this relationship is moderated by board of director’s (BOD) ownership. The sample is listed manufacturing firms in Jordan. AC characteristics, as an indicator of corporate governance mechanism, include its size, meeting frequency, independence, and experience. Firm performance is proxied by return on assets (ROA). Thirty firms are included in the sample. Data are collected from 2015 to 2021 for a total of 210 observations. The first model indicates that AC meetings and independence positively and significantly influence firm performance. On the other hand, AC size is not a significant predictor of firm performance. The second model shows that the interaction effects (AC size, AC independence, and AC experience) are significant and positive on firm performance. The results provide insights on how to improve AC effectiveness so as to improve the performance of listed manufacturing firms in Jordan. They also suggest the significance of BOD ownership in enhancing internal corporate governance mechanisms, particularly the AC. Jordanian policy makers must therefore ensure the effectiveness of these mechanisms, especially AC, through relevant regulations and recommendations.

The research objective is to investigate the effect of brand equity on Thai cosmetic purchasing decisions. The study involved 500 individuals aged 20-59 who had purchased and used Thai cosmetic products within the past year. A multistage sampling method was employed to select the participants. Data collection was carried out using questionnaires, and both descriptive and multivariate statistical analyses were conducted, explicitly utilizing Second-order Confirmatory Factor Analysis and Structural Equation Modeling. The research results showed four factors influencing the purchasing decisions of Thai brand cosmetic products: Brand Equity, Buying Behavior, Consumer Satisfaction, and Perceived Value. The Second-order Confirmatory Factor Analysis and Structural Equation Model demonstrated that these factors met the assessment criteria when applied to the empirical data. Consequently, if a brand can establish brand equity that surpasses its competitors in the eyes of consumers, it will contribute to long-term profitability and sustained growth.

This research examines the synergistic influence of inventory competition, artificial intelligence (AI) adoption, and digital marketing intensity on quality improvement decisions within contemporary supply chains. With a focus on enhancing product and service quality, we investigate the intricate relationships among these variables. A quantitative approach involving 380 supply chain professionals reveals that heightened inventory competition, increased AI adoption, and robust digital marketing significantly contribute to quality enhancement initiatives. The study builds upon prior research by empirically validating these connections and offers practical insights for supply chain practitioners. The findings underscore the strategic imperative of organizations to strategically balance these factors to optimize their quality management strategies, fostering customer satisfaction and competitiveness. While offering valuable contributions, the study acknowledges limitations in terms of self-reported data and a specific respondent group. Future research could extend this investigation to diverse industries and geographical contexts. In the end, this study sheds light on the complex interplay that exists between inventory competition, the use of AI, digital marketing, and judgments about quality improvement. As a result, a road map has been provided for efficient quality management of supply chains in the digital age.

In order to identify the determinants that influence the credit risk of Peruvian municipal savings banks, this quantitative research uses a nonexperimental design and a longitudinal sample to analyze monthly data corresponding to macroeconomic variables and microfinance institutions’ internal variables from 2011 to 2020. Using multiple linear regression, the results show that the interest rate, unemployment rate, and liquidity ratio positively influence the credit risk of Peruvian municipal savings banks; the study also shows that gross domestic product, efficiency of administrative expenses, solvency, and coverage of provisions exert a negative influence on credit risk. It is concluded that seven of the eight independent variables studied influence the credit risk of Peruvian municipal savings banks; only the inflation variable does not significantly influence credit risk.

In Indonesia, natural disasters cases have significantly increased from time to time and have the largest impact on economic losses. To avoid losses in the future due to natural disasters, the insurance company needs to estimate the risk and determine the rate of premium that would be charged to the policyholder. In conjunction with the present issue, this study seeks to determine the premium rate and estimate the size claim of insurance by considering the mitigation fund reserve decisions using The Collective Risk Model (CRM). The data was analyzed using the Poisson process with Weibull distribution to determine the natural disaster frequency and losses. The distribution of losses is estimated using Maximum Likelihood Estimation (MLE), and the magnitude of losses was estimated using the CRM. Also, the mean and variance estimators of the aggregate risk were used to estimate the premium charged. The results indicated that expectation and variance of the frequency of incident claims have the same value, i.e., 2562. Also, the loss claims follow the Weibull distribution with the expected value and variance of 5.81309×10¹⁰ and 2.5301×10²², respectively. The mean and variance of the aggregate (collective) claims are 148,931,365,800,000 and 7.35×10²⁵, respectively. In conclusion, this study has successfully determined the efficient pure premium model through the Standard Deviation Principle (SDP). SDP provides a much cheaper premium than the Expected Value Principle with the same loading factor. In addition, SDP considers the standard deviation of the collective risk of natural disasters. The implications of the results of the premium determination are expected to be the basis for decision-making for insurance companies and the government in determining insurance policies for natural disaster mitigation.

Vulnerability is one of the prominent features of rural areas due to their distinctive characteristics, such as remoteness, geographical conditions, and socio-economic dependence on primary sectors. Addressing the vulnerability of rural areas in terms of the rural development paradigm is both urgent and relevant. This study aims to address this issue using the current state-of-the-art machine learning method, using the socio-ecological framework and integrated vulnerability index of villages in Lampung Province in Indonesia. The study attempts to predict and classify villages' vulnerability to be applied for better planning and rural development. Based on random forest classification and decision tree algorithm, the results show that the village governance system represented by rural water management and the level of education of village leaders are suitable prediction variables related to the low vulnerability index. This study can draw lessons learned to improve rural development in developing countries.

In deciding to invest in stocks traded in the capital market, investors need to predict which stocks provide the prospect of return and the risks to be faced. This paper aims to predict the return and risk of stock asymmetry using a time series model approach. Predicting stock returns and risk is based on the Autoregressive Integrated Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GJR-GARCH) model. In contrast, the largest risk potential measurement is performed using the Value-at-Risk (VaR) model. The data analyzed are the best ten stocks according to the criteria that apply on the IDX, the period between 17 December 2018 to 14 December 2021, which includes the names of stock BBCA, BBNI, BBRI, BMRI, ASII, ICBP, PGAS, PTBA, TLKM, and UNVR. The analysis results show that of the best ten stocks, based on the ratio between the predicted values of the average return and Value-at-Risk, those with relatively better performance are PTBA, TLKM, UNVR and BBCA stocks. Based on the results of this analysis, it can be used as a reference in making investment decisions for investors, specifically investing in the ten stocks analyzed.