The agricultural sector is directly affected by climate variables. The presence of climate variability causes a considerable risk to agricultural productivities. Thus, risk management is an alternative to reduce risks, including optimizing the allocation of farmland and choosing crop insurance for a specific planting date. The purpose of this study is to investigate the agricultural risk management through risk measure of climate variability using the Conditional Value-at-Risk (CVaR) in rice production. This paper investigated several possible considerations of agricultural insurance premiums based on losses climate index. We concluded that the climate index insurance policy is the best choice that farmers can choose for each planting date, the higher the significance value considered, the more the value of Value-at-Risk and Conditional Value-at-Risk.

This study aimed to identify the effect of Total Quality Management (TQM) practices on their dimensions on employee performance who worked for Qatar Ministry of Interior, with the moderating share of knowledge. The components of TQM include customer focus, employee participation, continuous improvement, leadership & good vision and operations manage-ment. The study uses a quantitative research, where 280 questionnaires are collected from respondents to obtain the necessary information for testing the hypotheses of the study. Multiple regression and gradient regression are used to analyze the research data. The results indicate that TQM practices with its dimensions had effects on employee performance through knowledge sharing. The results of this study contribute to develop and implement different strategic directions that enhance the performance of employees through the adoption of TQM and the dissemination of a culture of knowledge sharing.

This paper presents a Mori-Tanaka-based statistical methodology to predict the effective Young modulus of carbon nanotubes (CNTs)-reinforced composites considering three variables: weight content, reinforcement dispersion and orientation. Last two variables are quantified by two parameters, namely, free-path distance between nano-reinforcements and orientation angle regarding the loading direction. To validate the present methodology, samples of multi-walled CNTs (MWCNTs)-reinforced polyvinyl alcohol (PVA)-matrix composite were manufactured by mixing solution. The MWCNT/PVA Young modulus was measured by nano-indentation, while the MWCNTs Young modulus was quantified by micro-Raman spectroscopy. Both stretched and unstretched composite specimens were fabricated. Transmission electron microscopy (TEM) and in-plane image analysis were used to obtain fitting coefficients of log-normal frequency distribution functions for the free-path distance and orientation angle. It was evidenced that numerical results fit well to measured values of effective Young modulus of MWCNTs and MWCNT/PVA, with exception of some particular cases where significant differences were found. Microstructural heterogeneities, cluster formation, polymer chains alignment, errors associated with the dispersion, orientation and mechanical characterization procedures, as well as idealization and statistical errors, were identified as possible causes of these differences. Finally, using the proposed methodology and the dispersion and orientation distribution functions experimentally obtained, the effective Young modulus is estimated for three kinds of thermoplastic matrices (polyvinyl alcohol, polyethylene ketone, and ultra-high molecular weight polyethylene) with different kinds of nanotubes (single wall, double wall, and multi-walled), at different weight contents, finding the superior mechanical performance for double-walled CNTs-reinforced composites and the lower one for multi-walled CNTs-reinforced ones.

The ranking of decision-making units (DMUs) is one of the main issues in data envelopment analysis (DEA). Hence, many different ranking models have been proposed. However, each of these ranking models may produce different ranking results for similar problems. Therefore, it is wise to try different ranking models and aggregate the results of each ranking model that provides more reliable results in solving the ranking problems. In this paper, a novel ranking method (Aggregating the results of aggressive and benevolent models) based on the CRITIC method is proposed. To prove the applicability of the proposed ranking method, it is examined in three numerical examples, six nursing homes, fourteen international passenger airlines and seven biomass materials for processing into fuel briquettes. First, benevolent and aggressive models were used to calculate the efficiency rating for each DMU. As a result, the decision matrix was generated. In the decision matrix, the results of benevolent and aggressive models were viewed as criteria and DMUs were viewed as alternatives. Then, the weights of each criterion were generated by the CRITIC method. Finally, each DMU was ranked. In a comparative analysis, the proposed method can lead to achieving a more reliable decision than the method which is based on a stand-alone method.

Operating in today’s turbulent and competitive world marketplaces, manufacturers must find the best production scheme and delivery policy to meet timely client’s multiproduct requirements and minimize the total manufacturing-shipment expenses. This study proposes a two-stage delayed differentiation model for a multiproduct manufacturer-retailer coordinated supply chain featuring the adjustable-rate for making common parts and a multi-shipment policy for transporting finished goods. The aim is to help present-day manufacturers achieve their operational goals mentioned above. The mathematical techniques help us build a specific model to explicitly represent the problem and derive its overall operating expense. Then, the convexity of the total expense is verified by Hessian matrix equations. The differential calculus helps derive the cost-minimized fabrication-shipment decision. This study offers an example to demonstrate the applicability and capabilities of our proposed model numerically. The following crucial information has been made available to the managers to facilitate their operating decision makings: (1) the problem’s best fabrication-shipment policy; (2) the collective influence of various common part’s completion rates and values on the problem’s total expenses and optimal fabrication-shipment policy; (3) the impact of various adjustable-rates in stage one on utilization and stage one’s uptime; (4) the details of cost contributors to the problem; and (5) the collective impacts of critical features on the problem’s performance.

This research strives to offer valuable insights into business intelligence and analytics utilization. Besides, its expansion encompasses essential success factors for business intelligence and strategic management in the hospitality industry. The study's sample comprises 387 hotel managers from the Jordanian hospitality industry. Data collected to support the research goal was examined using multiple regression analysis through SPSS, with the assistance of PROCESS Macro v3.5. The results of this study reveal that business intelligence and analytics can serve as a bridge to connect crucial success factors for business intelligence and strategic management. These findings have implications for existing literature and stakeholders. Hotels that have integrated business intelligence and analytics acknowledge the advantages of enhancing their strategic management capabilities and decision-making processes.

Deteriorating inventory models are used as decision support tools for managers primarily, although not exclusively, in the retail trade. The mathematical modeling of deteriorating items allows managers to analyze their inventory management systems to identify areas that can be improved and to measure the corresponding potential benefits. This study develops an enhanced deteriorating inventory model for optimizing the inventory control strategy of companies operating in sectors with deteriorating products. In contrast with previous studies, our model holistically accounts for the overall financial effect of a company’s policies on product price discounting and on inventory shortages while considering the time value of money (TVM). We aim to find the optimal replenishment strategy and the optimal price reductions that maximize the discounted profit function of this analytical model over a fixed planning horizon. To this end, we use an economic order quantity model to study the effects of the TVM and inflation. The model accounts for pre- and post-deterioration discounts on the selling price for non-instantaneous deteriorating products with the demand rate being a function of time, price-discounts and stock-keeping units. Shortages are allowed and partially backordered, depending on the waiting time until the next replenishment. Additionally, we consider the effect of discounts on the selling price when items have either an instant deterioration or a fixed lifetime. We propose five implementable solutions for obtaining the optimal values, and examine their performance. We present some numerical examples to illustrate the applicability of the models, and carry out a sensitivity analysis. The study reveals that accounting for TVM and inventory shortages is complex and time-consuming; nevertheless, we find that accounting for TVM and shortages can be valuable in terms of increasing the yields of companies. Finally, we provide some important managerial implications to support decision-making processes.

This study aims to investigate the impact of educational background on perceived opportunities, perceived capacities, and entrepreneurial intention among Vietnamese students. By collecting data from 617 students at universities, colleges and institutes in Vietnam, the study employs some quantitative approaches including descriptive statistics, explorative factor anal-ysis (EFA), correlation coefficient analysis, ANOVA tests and multiple linear regression to analyze the relationship between educational background, perceived opportunities perceived capacities and entrepreneurial intention. In addition, Chi-square and Cramer’s V tests are implemented to show the difference of educational background in Vietnamese students’ entrepreneurial intention. The research results show that there was a negative relationship between field of studies and perceived opportunities, perceived capacities, and entrepreneurial intention while educational level had positive effects on perceived opportunities, perceived capacities and entrepreneurial intention. Moreover, Chi-square and Cramer’s V tests report that there was a strong evidence of educational level difference in entrepreneurial intention but no difference of field of study in entrepreneurial intention.

Supply chain management is an activity that effectively integrates suppliers, companies, retailers where goods are produced and distributed at the right quality, location, and time with minimum cost levels to provide the highest quality services for consumers. Supply chain agility, supply chain adaptability, supply chain alignment, which is known as the Triple-A strategy, are elements to form supply chain performance. In this study, we tried to apply it to SMEs in developing countries, such as Indonesia. The purpose of this study is to show whether it is true that the supply chain cannot be applied to SMEs, while for a disruption as it is today, competition is getting tougher not only among SMEs but also against large companies, and SMEs need to develop several strategies that were previously unimaginable. This study uses quantitative techniques to determine the effect of supply chain agility, supply chain adaptability, supply chain alignment on supply chain performance either partially or simultaneously. The results showed that all hypotheses were accepted. This shows that supply chain management can be a strategy to create better SMEs performance and can even be used to achieve competitive advantage.

This research investigates the mediating effect of Innovative Behavior on the relationship between Emotional Intelligence, Intellectual Intelligence, and supply chain performance. Data were collected from 198 employees of water supply companies across five major cities in Central Java, using purposive sampling and analyzed through structural equation modeling. The findings indicate that both Emotional Intelligence and Intellectual Intelligence positively and significantly impact supply chain performance. Additionally, Innovative Behavior not only positively influences supply chain performance but also mediates the relationship between Emotional and Intellectual Intelligence with supply chain performance. The study suggests that enhancing Emotional and Intellectual Intelligence through Innovative Behavior can significantly improve supply chain performance in water supply companies. Organizations are recommended to incorporate Emotional Intelligence into selection and training programs, develop initiatives to boost Emotional and Intellectual Intelligence through training in areas such as emotional management, interpersonal communication, problem-solving, critical thinking, and creativity, and create a supportive work environment that encourages innovative behavior. Implementing these strategies can lead to more efficient operations, better resource management, and increased customer satisfaction, thereby enhancing overall supply chain performance.