The objective of this paper is to investigate the influence of supply chain integration (SCI) on customer satisfaction in the potash supply chain in Jordan. 320 samples are collected after completing the sample checking process. The research study uses partial least squares for processing of data. Study outcomes show a connection among the integration of supply chain and customer satisfaction. In addition, the study shows that enterprise resource planning (ERP) system, business strategy, and suppliers have impacts on the integration of supply chain and customer satisfaction.

The study applies a postponement strategy to a multi-item fabrication-shipment decision making in a vendor-buyer coordinated environment with multi-delivery, quality reassurance, and overtime. To cope with the recent client demand trend asking for rapid response, quality, and diversified goods, today’s manufacturers require a multi-item production-shipping scheme to satisfy customers’ needs in cost-saving, quality, and timely matter. In our model, we first produce all needed mutual components and postpone manufacturing of finished goods in the second phase. To expedite mutual parts’ fabrication time, overtime is used. Product quality is reassured through screening the defeats and reworking repairable defectives in both fabrication phases. To decide the optimal fabrication-shipment policy, we build a math model and apply the cost minimization technique to the problem. Upon deriving the optimal policy, we utilize an example to demonstrate how our model works and its capability in exposing various previously inaccessible information to the problem. These detailed results can facilitate managerial decision-making and boost the performance of such a specific multi-item postponement fabrication-shipment system in cost-saving, product quality, and timely response.

A value chain plays a crucial role in increasing production efficiency due to business uncertainty and high competition between business entities. Of these, the business must carry out the activities effectively and efficiently to achieve maximum profit. However, the study that focuses on the moderating role of the value chain in its relationship to firm performance is still limited. Thus, the present study aims to examine the effect of intellectual capital and environmental uncertainty toward firm performance and the mediating role of the value chain in the relationship between intellectual capital and environmental uncertainty to firm performance. This study was designed using a quantitative approach through an online survey. A total of 207 staff from non-financial state-owned enterprises consists of 10 clusters and 76 companies. The data were analyzed using the Structural Equation Modeling with Partial Least Square method (SEM-PLS) and assisted by statistical software, namely SmartPLS 3.3.3. The result indicated that intellectual capital and environmental uncertainty have a significant effect on firm performance. Also, this study found that a value chain moderates the relationship of intellectual capital and environmental uncertainty toward firm performance. In conclusion, this study has successfully examined the effect of intellectual capital and environmental uncertainty on firm performance—also, the role of a value chain in the relationship of studied variables. In addition, the findings of this study showed that a value chain is an important tool for companies to improve their business performance.

Disruptive impact as the Covid-19 pandemic reduces the motivation of managers working in the supply chain function. A motivation as organizational commitment is crucial for organizations to foster supply chain resilience through enhancement of internal and external supply chain integration. This study draws upon the budgeting literature to explore the role of participative budgeting on the supply chain resilience amid Covid-19 pandemic. Data were collected from 191 managers working in supply chain functions of organizations operating in industrial zones in Vietnam. The technique of partial least square structural equation modelling was used to assess data. The results suggest that Covid-19's disruptive impact increases participative budgeting, which results in organizational commitment. This commitment leads to the enhancement of internal and external supply chain integration, which in turn leads to supply chain resilience. This study is the first study to explore how and why budgeting practices lead to the enhancement of supply chain resilience amid Covid-19 pandemic.

Achieving and maintaining good business performance is a core concern of every business entity. This quantitative study investigates the impact of sourcing strategies and logistics capabilities on the performance of Jordanian pharmaceutical enterprises using partial least square structural equation modeling (PLS-SEM). The views and perceptions of 951 managers and assistant manager respondents working in Jordanian pharmaceutical companies were gathered anonymously via an electronic online questionnaire using a convenience sampling technique. The findings revealed that sourcing strategies and logistical capabilities have a significant positive impact on organizational performance in pharmaceutical companies. Insourcing, near-sourcing, few / many suppliers, joint ventures, and virtual enterprises were perceived to be effective sourcing strategies in improving organizational performance. In contrast, outsourcing, and vertical integration were perceived to have a negligible impact on the performance in the context of pharmaceuticals. Furthermore, the findings confirmed that individual logistic capabilities (safety and compliance, storage, delivery, and imports and exports) of pharmaceutical firms were perceived as impacting positively on firm performance. This research provides useful insight for decision makers in pharmaceutical companies in Jordan when reviewing their supply chain, particularly during challenging and turbulent times such as the COVID-19 pandemic.

Student’s digital entrepreneurial intention is a key to develop nascent digital entrepreneurs from university graduates in an uncertain era. The aim of this study is to analyze the effect of digital entrepreneurial education, risk propensity and environment support on entrepreneurial self-efficacy and digital entrepreneurial intention of university students in Semarang. The research population is students from several universities in Semarang City who have participated in digital entrepreneurship learning. 90 students are elected to be tested as research respondents. The collected data are then analyzed using descriptive analysis of the structural equation model. Risk propensity, entrepreneurial education, and environmental support positively support entrepreneurial self-efficacy. Risk propensity and entrepreneurial education also found a significant effect on digital entrepreneurial intention, but environment support found insignificant effect on digital entrepreneurial intention. University, government and the private sector are highly expected to increase students' digital entrepreneurial intention through training and learning programs both in the classroom or outside the classroom.

Loan Self Control (LSC) can be defined as the ability of self-control related to behavioral control, cognitive control, and decisional control in managing loans or debts for financial welfare. The study aims to determine the factors of loan self-control in Kredit Usaha Rakyat (KUR) banking customers of Micro Small Medium Enterprise (MSMEs) in Indonesia so that they can obtain three-time financing from banks. This study used 87 samples collected by purposive sampling technique. Primary data was collected using an online survey. Factor analysis formed 3 loan self-control factors, namely Behavioral control, Cognitive control and decisional control from 14 indicators tested.

The new fully substituted (1H-1,2,3-triazol-4-yl)acetic acids were synthesized from available precursors (1H-1,2,3-triazol-4-yl)ethanones and 1Н-1,2,3-triazole-4-carboxylic acids, which are easily prepared by the Dimroth reaction from azides and 1,3-dicarbonyl compounds. The practical methods of homologization (the Arndt–Eistert reaction and homologization as a result of nucleophilic substitution by cyanide anion) and the Willgerodt-Kindler reaction were compared. The Willgerodt-Kindler method starting from (5-methyl-1H-1,2,3-triazol-4-yl)ethanones was selected as the most convenient method for the synthesis of 2-(1-aryl-5-methyl-1H-1,2,3-triazol-4-yl)acetic acids, which are promising building blocks for drug discovery. Additionally, (1H-1,2,3-triazol-4-yl)ethanones were studied for the synthesis of alcohols, amines and in the Johnson–Corey–Chaykovsky reaction.

In this study, we attempted to conduct a comprehensive review of the existing and pertinent literature on the topic of factor investment. We performed Scientometric analysis of studies published in reputable finance journals, i.e., The Journal of Portfolio Management, The Financial Analysts Journal, The Journal of Asset Management and others, during the years 2014 to 2023. To obtain the research data for our study, we gathered and examined a collection of 76 bibliographic records sourced from the Web of Science database. This database provided a comprehensive and reliable source of scholarly publications in the field of finance. To analyze the data, we employed Scientometric networks as part of our analytical approach. Scientometric networks allowed us to explore the relationships and connections between different publications, authors, and keywords within the domain of factor investment. To visualize and present the research findings, we utilized the Bibliometrix package for R, a powerful tool specifically designed for bibliometric analysis. This package enabled us to generate insightful visualizations that showcased the key patterns, trends, and interconnections within the literature on factor investment. By employing Scientometric analysis and leveraging the capabilities of the Bibliometrix package, we aimed to provide a comprehensive overview of the existing scholarly research in this field and contribute to the understanding of factor investment.

In today's financial landscape, individuals face challenges when it comes to determining the most effective investment strategies. Cryptocurrencies have emerged as a recent and enticing option for investment. This paper focuses on forecasting the price of Ethereum using two distinct methods: artificial intelligence (AI)-based methods like Genetic Algorithms (GA), and econometric models such as regression analysis and time series models. The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. Additionally, we employ the Autoregressive Moving Average (ARMA) model to assess the relationships between variables (dependent and independent variables). To evaluate the performance of our chosen methods, we utilize daily historical data encompassing economic and global indices from the beginning of 2019 until the end of 2021. The results demonstrate the superiority of AI-based approaches over econometric methods in terms of predictability, as evidenced by lower loss functions and increased accuracy. Moreover, our findings suggest that the AI approach enhances computational speed while maintaining accuracy and minimizing errors.