Purpose—In the business sector, reliable and timely data are crucial for business management to formulate a company’s strategy and enhance supply chain efficiency. The main goal of this study is to examine how strong brand strength affects shareholder value with a new Supplier Relationship Management System (SRMS) and to find the specific system qualities that are linked to SRMS adoption. This leads to higher brand strength and stronger shareholder value. Design/Methodology/Approach—This study employed a cross-sectional design with an explanatory survey as a deductive technique to form hypotheses. The primary method of data collection used a drop-off questionnaire that was self-administered to the UAE-based healthcare suppliers. Of the 787 questionnaires sent to the healthcare suppliers, 602 were usable, yielding a response rate of 76.5%. To analyze the data gathered, the study used Partial Least Squares Structural Equation modelling (PLS-SEM) and artificial neural network (ANN) techniques. Findings—The study’s data proved that SRMS adoption and brand strength positively affected and improved healthcare suppliers’ shareholder value. Additionally, it demonstrates that user satisfaction is the most significant predictor of SRMS adoption, while the results show that the mediating role of brand strength is the most significant predictor of shareholder value. The results demonstrated that internally derived constructs were better explained by the ANN technique than by the PLS-SEM approach. Originality/Value—This study demonstrates its practical value by offering decision-makers in the healthcare supplier industry a reference on what to avoid and what elements to take into account when creating plans and implementing strategies and policies.

The cultivation of vegetables serves as a vital pillar in horticulture, offering an alternative avenue towards achieving economic sustainability. Unfortunately, farmers often lack adequate knowledge on optimizing resource utilization, which subsequently results in low productivity. Furthermore, there has been insufficient research conducted on the comparative profitability and efficient use of resources for pea cultivation. So, the present study was conducted to examine the profitability and resource use efficiency of conventional and organic pea production in Northwestern Himalayan state. Using the technique of purposive sampling, the districts and villages were selected based on the highest area. By using simple random sampling, a sample of 100 farmers was selected, out of which 50 were organic growers and 50 were inorganic growers, who were further categorized as marginal and small. The cost incurred was higher for the cultivation of inorganic vegetable crops, whereas returns and output-input ratio was higher in organic cultivation. The cultivation of peas revealed that the majority of inputs were being underutilized, and there was a need for proper reallocation of the resources, which would result in enhanced production. Further, major problems in the cultivation of vegetable crops were a high wage rate, a lack of organic certification, a shortage of skilled labour and a lack of technical knowledge.

This paper mainly uses the idea of pedigree clustering analysis, gray prediction and principal component analysis. The clustering analysis model, GM (1,1) model and principal component analysis model were established by using SPSS software to analyze the correlation matrices and principal component analysis. MATLAB software was used to calculate the correlation matrices. In January, The difference in price changes of major food prices in cities is calculated, and had forecasted the various food prices in June 2016. For the first issue, the main food is classified and the data are processed. After that, the SPSS software is used to classify the 27 kinds of food into four categories by using the pedigree cluster analysis model and the system clustering. The four categories are made by EXCEL. The price of food changes over time with a line chart that analyzes the characteristics of food price volatility. For the second issue, the gray prediction model is established based on the food classification of each kind of food price. First, the original data is cumulated, test and processed, so that the data have a strong regularity, and then establish a gray differential equation, and then use MATLAB software to solve the model. And then the residual test and post-check test, have C <0.35, the prediction accuracy is better. Finally, predict the price trend in June 2016 through the function. For the third issue, we analyzed the main components of 27 kinds of food types by celery, octopus, chicken (white striped chicken), duck and Chinese cabbage by using the data of principal given and analyzed by principal component analysis. It can be detected by measuring a small amount of food, this predict CPI value relatively accurate. Through the study of the characteristics of the region, select Shanghai and Shenyang, by looking for the relevant CPI and food price data, using spss software, principal component analysis, the impact of the CPI on several types of food, and then calculated by matlab algorithm weight, and then the data obtained by the analysis and comparison, different regions should be selected for different types of food for testing.

This paper presents an assessment approach to fostering socioeconomic re-development and resilience in Iraqi regions emerging from the destruction and instability, in the aftermath of the war conflict in Iraq. Focusing on the intricate interplay of logistics infrastructure and economic recovery, the present study proposes a novel framework that integrates general resilience insights, data analytics, infrastructure systems, and decision support from Data Envelopment Analysis (DEA). We draw inspiration also from historical cases on “creative destruction” or “Blessing in Disguise” (BiD) phenomena, like the post-WWII reconstruction of Rotterdam, so as to develop the notion of stepwise or cascadic prosilience, analyzing how innovative logistics systems may in various stages contribute to economic rejuvenation. Our approach recognizes the multifaceted nature of regional resilience capacity, encompassing both static (conserving resources, rerouting, etc.) and dynamic (accelerating recovery through innovative strategies) dimensions. The logistics aspect spans both the supply side (new infrastructure, ICT facilities) and the demand side (changing transportation flows and product demands), culminating in an integrated perspective for sustainable growth of Iraqi regions. In our study, we explore several forward-looking strategic future options (scenarios) for recovery and reconstruction policy factors in the context of regional development in Iraq, regarding them as crucial strategic elements for effective post-conflict rebuilding and regeneration. Given that such assets and infrastructures typically extend beyond a single city or area, their geographic scope is broader, calling for a multi-region approach. By leveraging the extended DEA approach by an incorporation of a super-efficiency (SE) DEA approach so as to better discriminate among efficient Decision-Making Units (DMUs)—in this case, regions in Iraq—our research aims to present actionable and effective insights for infrastructure investment strategies at regional-governorate scale in Iraq, that optimize efficiency, sustainability and resilience. This approach may ultimately foster prosperous and stable post-conflict regional economies that display—by means of a cascadic change—a new balanced prosilient future.

This paper aims to explore the relationship between corporate overinvestment and management incentives, focusing particularly on the influence of different ownership structures. Utilizing agency theory and ownership structure theory, this study constructs a theoretical framework and posits hypotheses on how management incentives might influence corporate overinvestment behaviors under different ownership structures. Listed companies from 2010 to 2020 were selected as the research sample, and the hypotheses were empirically tested using descriptive statistics, correlation analysis, and regression analysis. The findings suggest that a relatively concentrated ownership structure may encourage management to adopt more cautious investment strategies, thus reducing overinvestment behaviors; while under a dispersed ownership structure, the relationship between management incentives and overinvestment is more complex. This study provides new evidence on how management incentive mechanisms influence corporate decision-making in different ownership environments, offering significant theoretical and practical implications for improving internal control and incentive mechanisms.

This research analyzes the relationship between political stability, renewable energy utilization, economic progress, and tourism in Indonesia from 1990 to 2020. We employ advanced econometric techniques, including the Fourier Bootstrap Autoregressive Distributed Lag (ARDL) approach and Fourier Toda-Yamamoto causality testing, to ensure the robustness of our results while accounting for smooth structural changes in the data. The analysis uncovers a long-term equilibrium relationship between tourism and its fundamental determinants. Our research reveals significant positive impacts of political stability and renewable energy consumption on tourism in Indonesia. A stable political environment creates a favorable climate for tourism development, instilling confidence in both domestic and international tourists. Promoting renewable energy usage aligns with sustainable tourism practices, attracting environmentally conscious travelers. Furthermore, our findings demonstrate a bi-directional causal relationship between these variables over time. Changes in political stability, renewable energy consumption, and economic growth profoundly influence the tourism sector, while the growth of tourism itself can also stimulate economic development and foster political stability. Our findings underscore the need for environmentally sustainable and politically stable tourism policies. Indonesia’s tourism sector can grow sustainably with renewable energy and stability. Policymakers can develop strategies with tourism, political stability, renewable energy, and economic prosperity in mind.