Demand For Information Research Articles

A distributionally robust optimization approach for the potassium fertilizer product transportation considering transshipment through crossdocks

Potassium fertilizer is an essential input for agricultural productivity, and plays a critical role in various plant processes, influencing water uptake, enzyme activation, and photosynthesis. The efficient delivery of potassium fertilizer products to the end-users, typically farmers and agricultural enterprises, is of utmost importance. Due to the long traveling distance between the supplier and customers, decision makers of supplier normally set up multiple crossdocks to aid in transition and storage of potassium fertilizer products. In this situation, making an optimal transportation plan as well as the inventory level of crossdocks will directly enhance the efficiency and effectiveness of long-distance transportation. In this study, we model this problem as a dynamic transportation problem with transshipment considering the uncertain time-varying customers’ demand. To characterize the demand information, we first construct an ambiguity set of customers’ demand based in limited historical data and then develop a distributionally robust optimization-based (DRO) framework to optimize the transportation plan and related inventory level of crossdocks simultaneously. We also propose a general approach to overcome the computational challenges of DRO by transforming the original DRO into a second-order cone programming based on duality theory. Additionally, we introduce linear decision rules to adjust the optimization strategy based on the new observed demand, thus lead the model to handle the dynamic information flow in real time. In case study, all involved data are collected or derived from a real potassium fertilizer company located at Western China. The results show our model reduces the cost by 18.07% compared to stochastic model (sample average approximation), indicating a significant effectiveness of our model on the improvement of delivery efficiency and cost saving in real dynamic logistic systems. Also, we conclude the managerial insight that decision-makers should develop a comprehensive strategy, including improving communication to ensure order status updates, planning rationally to evenly distribute orders, and proactively allocating resources to meet operational demands.

The Nature and Norms of Vigilance

Abstract Many people have long-term commitments that require coordination and cooperation with others. To achieve this, we construct plans to settle when, how, and for how long to pursue certain goals rather than others. This raises an interesting cognitive problem, namely that individuals can, at any given moment, manage significantly less information than they will need to accomplish their goals. Call this the Problem of Scarce Information. The solution requires a special self-regulatory system that strategically manages the varying informational demands of one's activity. This paper argues that vigilance has structural and functional characteristics that make it apt to play this role. This paper illuminates both crucial aspects of temporally extended agency, but also the unique practical norms that structure how we act over time.

Human papilloma virus (HPV)-related information acquisition and seeking behavior among infected women: A single center cross-sectional survey in Shanghai, China.

Lacking of adequate knowledge is an obstacle to effective prevention of cervical cancer, yet factors that affect the information acquisition and seeking behavior as well as the information communication process are not well studied. We assessed information acquisition and seeking behavior, as well as perceived barriers of doctor-patient communication regarding human papilloma virus (HPV)-related information of infected women. Among 437 participants, 405 (93%) expressed demands for HPV-related information, while only a small proportion (100/437, 22.9%) actively sought information and felt obstacles comprehending. Web-based channels were most frequently utilized and medical personnel were the most trusted information source. Patients' satisfaction was significantly correlated with doctor's patience (r = 0.581, p < 0.001) and emotional caring (r = 0.555, p < 0.001). Compared to patients not actively seeking information, those actively seeking information were more likely to be single (p = 0.005), had higher education (p = 0.009) and monthly individual-level income (p = 0.023), and was more likely to undergo regular cervical cancer screening (p = 0.003), and were already or willing to be vaccinated (p = 0.008). The actively seeking information group also achieved higher scores in HPV knowledge test (p = 0.007). Public health interventions targeting HPV-infected women using specifically designed educational materials may influence information seeking behavior, increase HPV literacy and knowledge, which could potentially increase HPV vaccine uptake and cervical cancer screening rate.

Spillover effects in transit networks: A parameterized weight matrix spatial lagged approach

Public Transit (PT) systems aim to provide social, economic, and environmental benefits to modern cities offering reliable transportation service to users and ultimately reducing the problems related to traffic externalities due to car dependency. With the growing trend of urbanization and the associated phenomenon of cities’ sprawl being increasingly evident, significant attention should be given to understanding PT systems’ performance and then improving their efficiency. Considering the spatial characteristics of the PT system's performance promotes the environmentally friendly transport “character” that every city endeavors. This paper aims to incorporate the spatial spillover effects of a realistic PT system by augmenting information about the service network along with socio-economic variables, in a spatial demand-supply econometric framework. In detail, geographically separated demand and supply information on bus stops and lines was analyzed by a spatial econometric model, namely, the Spatial Lagged X (SLX) model which may be formatted so that can soundly handle, spatial and –in particular- network data like those coming from the General Transit Feed Specification (GTFS) protocol widely used in PT systems. The novelty and the importance of the proposed model rely on the ability to introduce transit network structure within the framework of spatial econometric regression, fostering the explanatory statistical analysis over networked information. The developed modeling approach was applied to the urban and rural PT system of Nicosia (Cyprus), incorporating the spatial spillover effects of the system over more than 1,500 bus stops, 40 lines, and 252 different but spatially connected postcode areas. The results of the SLX model were compared with other demand models of this form typically used, namely the Ordinary Least Square model and standard Spatial Autoregressive models, providing solid evidence of the benefits of incorporating the network structure in spatial demand modeling, giving valuable input for further planning purposes.

(011) PREVALENCE OF SEXUAL DYSFUNCTIONS AND HEALTHCARE NEEDS OF TRANSGENDER AND GENDER-DIVERSE PEOPLE: RESULTS FROM A REPRESENTATIVE CROSS-SECTIONAL SURVEY IN GERMANY

Abstract Introduction Sexual dysfunctions (SD) are a common issue for transgender-identified individuals and may persist despite access to gender-affirming care. Healthcare disparities experienced by transgender and gender-diverse people (TGDP) are often overlooked. Data on the prevalence of SD in TGDP from representative samples are rare, and little is known about the help-seeking behavior and healthcare needs regarding sexual problems in this subgroup compared to the needs of cisgender women (CW) and men (CM). Patient and Public Involvement can improve the quality and impact of research on under-researched topics. Objective This study aims to investigate the prevalence of SD in TGDP in the German population and to compare sexual health and healthcare needs between TGDP and the groups of CW and CM. Methods This cross-sectional study used data from an online survey with a sample representative of the German population in terms of gender, age, and federal state. ICD-11 criteria and FSDS-DAO were used to assess sexual health outcomes. Questionnaire items on gender, risk factors for SD, and healthcare needs were developed in collaboration with a culturally and gender-diverse group of eight external co-researchers and advisors following the patient and public involvement methodology. Outcomes were examined by gender group. Participants who reported identification with a gender different from their gender assigned at birth, a non-binary gender, a diagnosis related to gender incongruence, and/or variations of sex development were assigned to the TGDP group. Results Data from N=4,023 participants (CW: n=1,985, CM: n=1,815, TGDP: n=223) were collected with a mean age of 49.0 years (SD = 16.5). The 6-month prevalence of sexual dysfunction (SD) according to ICD-11 criteria was considerably higher among TGDP (34.4%) compared to CW (16.3%) and CM (16.6%), as reflected in the FSDS-DAO scores of the TGDP subgroup (M = 21.1, SD = 15.3), CW (M = 11.1, SD = 12.4), and CM (M = 12.3, SD = 12.7). The demand for information regarding sexual problems among those experiencing SD was more frequently reported by TGDP (67.5%) than by CW (44.0%) and CM (47.3%), with an odds ratio of 4.3 for TGDP vs. cisgender. Across all gender groups, the most preferred source for information was online research, followed by peer networks (29.0%) and friends (26.5%) in the TGDP group, in contrast to healthcare providers and partners for CW and CM. The most desired dialogue partners among healthcare providers (HCP) were urologists and general practitioners in TGDP (26.4% and 23.9%) and CM (37.3% and 25.4%), and gynecologists (36.0%) and psychotherapists (11.5%) for CW. Among TGDP with SD, 49.9% had undergone therapy for sexual dysfunctions (in CW 10.2%, in CM 15.2%), with psychotherapy (36.0%) being the most frequently reported treatment. The TGDP group showed higher needs for information and help, identifying the lack of services considering individual needs related to culture, gender identity, physical illness, and mental health conditions as the primary barriers to care. Diversity sensitivity training for HCPs, drug developments, improved resources, and 'blended care' solutions that encompass both digital and physical procedures were identified as the most urgent developments to improve care for sexual dysfunctions. Conclusions The results indicate the alarming extent of the burden of SD for TGDP. Furthermore, the findings emphasize the importance of tailored interventions that acknowledge and address the diverse needs of TGDP. Disclosure No.

Customer Geographic Clustering and Revenue Recognition: Evidence from China

ABSTRACT Although theoretical studies suggest that customers may have an impact on a supplier firm’s accounting practices, empirical evidence remains limited and inconclusive. We posit that geographic clustering increases the likelihood of communication between customers and that managers who perceive this likelihood will make more disciplined revenue reporting decisions. Using major customer data from China, we find that firms with more geographically clustered customers report less absolute discretionary revenue. This result is more pronounced when the supplier’s customers are more likely to communicate with each other, when their demand for information is stronger, when supplier-customer information asymmetry is more severe, and when the supplier has less bargaining power. The main results survive after addressing endogeneity concerns and a serial of other robustness tests. Our findings offer insight into how the relationship between customers relates to earnings quality and we extend the literature on corporate geographic characteristics.

Elementary Catastrophe’s Chaos in One-Dimensional Discrete Systems Based on Nonlinear Connections and Deviation Curvature Statistics

This study shows, by means of numerical analysis, that the characteristics of discrete dynamical systems, in which chaos and catastrophe coexist, are closely related to the geometric statistics in Finsler geometry. The two geometric statistics introduced are nonlinear connections information, denoted as [Formula: see text], and the mean deviation curvature, denoted as [Formula: see text]. The quantity [Formula: see text] can be used to determine the occurrence of chaos in terms of nonequilibrium stability. The resulting chaos is characterized by [Formula: see text] in terms of the trajectory’s robustness, which is related to the localization or globalization of chaos. The characteristics of catastrophe-induced chaos are clearly visualized through the contour topography of [Formula: see text], in which an abrupt change is represented by cliff topography (i.e. a line of critical points); initial dependence is reflected in the reversibility of topographic patterns. On overlaying the contour topography with the singularity pattern, it is evident that chaos does not arise around the singular point. Furthermore, the extensive development of cusp and butterfly chaos demands information on the nonlinear connections within the singularity pattern. The asymmetry in swallowtail chaos is less distinguishable in an equilibrated state, but becomes more evident when the system is in a state of nonequilibrium. In many analyses, chaos and catastrophe are examined separately. However, these results demonstrate that when both are present, the two have a complex relationship constrained by the singularity.

Perception of Librarians in Agricultural Research Institutes Towards the Use of Big Data for Library Operations and Services in Kwara State, Nigeria

This study examined the perception of use associated with big data in agricultural research institutes' libraries. A descriptive design was employed, and thematic analysis was done on the information obtained through key informant interviews. The population of this study consists of two librarians from each of the agricultural research institutes' libraries. In the study, four research questions were addressed. The results showed librarians believe big data assists them in providing clear and straightforward information that will quickly aid the demands of the researchers in the institutes. The study also revealed how big data helps librarians better understand how they can meet the information demands of researchers. The difficulties associated with the use of big data in agricultural research libraries in Kwara State, Nigeria, stem from the presence of incompetent personnel, insufficient funds, irregular electricity supply, sluggish internet connectivity, and the expense of training and retraining on big data. In conclusion, the perception of the use of big data in agricultural research libraries allows librarians to streamline operations and services to exactly what the researcher wants. Based on the findings, the study recommends ensuring the effectiveness of big data, agricultural research libraries should recruit more data analyst experts to manage big data in their libraries and ensure alternative power generation such as solar systems, inverters or standby generating machines are made available.

Illuminating the murk: The effect of business complexity on voluntary disclosure

Analyzing the complex financial landscape of multi-segment conglomerates requires a more nuanced approach than that required for single-segment firms. This paper reveals that conglomerates strategically enhance their transparency by voluntarily disclosing more information to compensate for their business complexity. This finding is particularly pronounced when there is an increased demand for information from stakeholders and analysts or when the executive pay-performance sensitivity is higher. By strategically embracing transparency, conglomerates transform the complexities inherent in their financial reporting into a catalyst for higher valuation and lower capital costs. Overall, our study demonstrates that multi-segment firms tactically deploy voluntary disclosure to navigate their intricate business environment effectively.

Uniform or demand-driven allocation? Optimal management of social donations distribution in response to sudden outbreaks

PurposeThis study aims to address challenges in the distribution of social donations during epidemic emergencies, focusing on issues such as uneven distribution and material stacking. The goal is to propose optimized strategies that enhance equity and efficiency in the allocation of donated resources.Design/methodology/approachFirstly, the satisfaction function is constructed from two perspectives of the designated hospital and the Red Cross. On this basis, the fairness perception level of the two is portrayed. Then, we set the time weights, and construct a multi-objective programming model by combining the resource constraints in the social donation distribution process. The combined algorithm of NSGA-II and TOPSIS is also designed for model solving. Finally, an example of social donation distribution of the Red Cross Society of China Wuhan Branch is conducted for numerical analysis.FindingsNumerical analysis reveals that timely transmission of demand information favors a demand-oriented distribution strategy for optimal efficiency. However, in scenarios with poor demand information transmission, an equal distribution of social donations proves to be a more effective strategy. Equal distribution ensures rapid allocation while minimizing perceived unfairness at designated hospitals, ultimately improving overall satisfaction levels and emergency response effectiveness.Practical implicationsThe findings provide practical insights for emergency response planners. These include translating the developed methods into guiding principles, establishing real-time monitoring systems, enhancing training for relevant departments, and implementing evaluation mechanisms. Practitioners can utilize this knowledge to optimize the efficiency of social donation distribution during sudden outbreaks.Social implicationsThe equitable distribution of social donations ensures efficient resource allocation and minimizes perceived unfairness, contributing to improved social satisfaction levels. This has broader implications for community resilience and support during emergencies.Originality/valueThis research contributes to the field by proposing a comprehensive model for optimizing social donation distribution in emergencies. The integration of fairness perception, time weights, and a multi-objective planning approach, along with the application of the combined algorithm of NSGA-II and TOPSIS, adds novelty and practical value to the existing literature. The study serves as a decision-making reference for enhancing emergency response theories in sudden event.

Information Sharing Between Competitors With Endogenous Production Timing

We study two competing firms’ incentives for demand information sharing and their production timing strategies. One firm adopts routine timing, where her production time is fixed according to her previous product models’ manufacturing time. The other firm uses strategic timing, where his production time can be strategically chosen to occur before, concurrently with, or after that of the routine-timing firm. The firms decide whether to disclose their private demand information and make quantity decisions based on the available demand information, either simultaneously or sequentially. We analyze the optimal production timing decisions for the strategic firm under different information sharing scenarios and find that a pre-emptive move is generally not optimal. We demonstrate that endogenous production timing can create incentives for information sharing and characterize the conditions under which both firms share information, one firm shares information, or neither firm shares information. Additionally, we uncover several interesting implications of information sharing under endogenous production timing: firms are more likely to share information in intensified competition, a firm may benefit from its rival’s superior information capability, and the option of information sharing enhances social welfare, which may also benefit from more intense competition.

Location and Distribution Decision of Emergency Logistics Network under Uncertain Demand Distributions

To ensure the supply of materials after urgent events, including recent public health crisis, it is crucial to formulate a reasonable preventive strategy for the location and distribution of emergency materials within the logistics network. In this paper, a class of multi-category emergency material storage distribution models under scenarios with uncertain post-event demand distribution is studied. Based on historical small-sample post-event demand information, the model constructs a two-stage material storage and scheduling distributionally robust optimization model. By applying a dual method, the non-linear distributionally robust optimization is transformed into linear optimization, and an improved Lagrangian L-shaped algorithm is designed to solve the two-stage model. Finally, the robustness of the model and algorithm is verified by implementing the method to a numerical example, and the sensitivity of the location and distribution decisions to material shortage under different levels of urgent events is analyzed.

Distributionally robust newsvendor problem with partial demand information under carbon emission regulations

Carbon emission schemes are widely adopted and implemented in practice to control greenhouse gas emissions. However, a firm restricted by carbon emission schemes with only partial demand information may face greater challenges. In this paper, we investigate an ordering and pricing newsvendor problem with partial demand information, which considers carbon emissions generated from the processes of ordering and disposing of the excess inventory. We propose three distributionally robust models to maximize the worst-case profit under different carbon emission regulations: carbon tax, cap-and-trade and carbon cap, respectively. We derive the implicit solution under the carbon tax scheme and design heuristic algorithms based on Lagrangian relaxation under the cap-and-trade and carbon cap regulations. Numerical analysis indicates: (i) all three regulations are effective in reducing carbon emissions; (ii) the implementation of carbon schemes leads to higher optimal pricing and lower optimal ordering quantity, both carbon tax and carbon cap regulations will always lead to lower profit, while the retailer may benefit from the cap-and-trade regulation by selling out the carbon credit; (iii) the proposed approach performs well under different distribution specific scenarios.

Quantifying effects of climate change and farmers' information demand on wheat yield in India: a deep learning approach with regional clustering

IntroductionWith increasing demand for food and changing environmental conditions, a better understanding of the factors impacting wheat yield is essential for ensuring food security and sustainable agriculture. By analyzing the effect of multiple factors on wheat yield, the presented research provides novel insights into the potential impacts of climate change on wheat production in India. In the present study, datasets consisting of countrywide environmental and agronomic factors and wheat yield were collected. In addition, the study also analyzes the effect of information demand of farmers on production.MethodologyThe study employs a regional analysis approach by dividing the country into five zonal clusters: Northern Hills, Central India, Indo-Gangetic Plains, North-Eastern India, and Peninsular India. Correlation and Principal Component Analysis (PCA) were performed to uncover the month-wise key factors affecting wheat yield in each zone. Furthermore, four Machine Learning/Deep Learning-based models, including XGBoost, Multi-layer Perceptron (MLP), Gated Recurrent Unit (GRU), and 1-D Convolutional Neural Network (CNN), were developed to estimate wheat yield. This study estimated partial derivatives for all factors using Newton's Quotient Technique, a numerical method-based approach.ResultsThe analysis focused on applying this technique to the best-performing wheat yield estimation model, which was the GRU-based model (with RMSE and MAE of 0.60 t/ha and 0.46 t/ha, respectively).DiscussionIn the later sections of the article, multiple policy recommendations are communicated based on the extracted insights. The results of the presented research help inform decision-making regarding the development of strategies and policies to mitigate the impacts of climate change on wheat production in India.

Solution approaches to reduce problems with unbalanced supply and demand in transportation and harvest planning

ABSTRACT This study addresses forestry planning challenges arising from supply-demand imbalances. In forest planning, supply often exceeds demand because supplies are known in advance, while demands are known more short term when ordered. This leads to so-called “creaming,” where forest planners select nearby areas first. With static supply and incremental demand information, average transportation distance increases over the planning horizon. To mitigate this, we propose an approach to artificially balance supply and demand. This can be achieved by including additional time periods with additional demand making up the factual difference. We evaluate three planning approaches to model the extended demand, varying the number of time periods and extension duration. Through simulations, we compare these approaches to traditional methods and theoretical solutions. Our proposed approach aims to better keep the average distance balanced throughout the overall planning periods. It ensures that average transportation distances are not excessively favorable in the initial periods, nor unreasonably high in the later periods, resulting in a favorable equilibrium in the average transportation distance over time. It makes sure that we do not need the additional truck capacity at certain times. We assess our proposed approaches using a case study from a Swedish forestry company, demonstrating their superiority over current practices.

Effective Adaptive Exploration of Prices and Promotions in Choice-Based Demand Models

We consider the problem of setting the optimal prices and promotions for a large number of products when the firm lacks demand information using adaptive experimentation.

Battery recycling models selection and contractual incentives: From an information sharing perspective

As the battery recycling market in China is still in an embroynic stage, the mechanisms, channels, pricing, and efficiency of battery recycling are shrouded in opacity. Battery recycling in China is currently dominated by electric vehicle manufacturer, supplemented by other market players such as battery producers and electric vehicle retailers. Due to the lack of information, battery recycling lacks cooperation and communication, and the whole market is dealing with "Information Island." Therefore, based on information sharing and channel competition, this paper designs a Stackelberg game model with an electric vehicle manufacturer, an electric vehicle retailer, and a third-party recycler as the primary participants. This study found that effective information sharing can reduce intensified market competition and promote the transformation of market players into “collaboration state”. To promote partnership in the battery recycling market, this paper proposes a revenue-sharing contract incentive for the electric vehicle retailer to share demand information about battery recycling. It finds a revenue-sharing contract based on information sharing can help companies and competitors make appropriate behavioral choices in the battery recycling market. This study demonstrates that information sharing benefits the electric vehicle manufacturer and the third-party recycler while offering no advantages to the electric vehicle retailer. The decreasing accuracy of demand signals results in the diminishing value of information sharing for the electric vehicle manufacturer and the third-party recycler. The revenue-sharing contract can incentivize the electric vehicle retailer to share information.

Application of 3D printing surgical training models in the preoperative assessment of robot-assisted partial nephrectomy

BackgroundTo explore the application effect of 3D printing surgical training models in the preoperative assessment of robot-assisted partial nephrectomy.MethodsEighty patients who underwent robot-assisted partial nephrectomy surgery between January 2022 and December 2023 were selected and divided into two groups according to the chronological order. The control group (n = 40) received preoperative assessment with verbal and video education from January 2022 to December 2022, while the observation group (n = 40) received preoperative assessment with 3D printing surgical training models combined with verbal and video education from January 2023 to December 2023. The preoperative anxiety, information demand score, and surgical awareness were compared between the two groups. The physiological stress indicators, including interleukin-6 (IL-6), angiotensin II (AT II), adrenocorticotropic hormone (ACTH), cortisol (Cor), mean arterial pressure (MAP), and heart rate (HR), were also measured at different time points before and after surgery.They were 6:00 am on the day before surgery (T0), 6:00 am on the day of the operation (T1), 6:00 am on the first day after the operation (T2), and 6:00 am on the third day after the operation (T3).The preparation rate before surgery was compared between the two groups.ResultsThe anxiety and surgical information demand scores were lower in the observation group than in the control group before anesthesia induction, and the difference was statistically significant (P < 0.001). Both groups had lower scores before anesthesia induction than before preoperative assessment, and the difference was statistically significant (P < 0.05). The physiological stress indicators at T1 time points were lower in the observation group than in the control group, and the difference was statistically significant (P < 0.05). The overall means of the physiological stress indicators differed significantly between the two groups (P < 0.001). Compared with the T0 time point, the T1, T2, and T3 time points in both groups were significantly lower, and the difference was statistically significant (P < 0.05). The surgical awareness and preparation rate before surgery were higher in the observation group than in the control group, and the difference was statistically significant (P < 0.05).ConclusionThe preoperative assessment mode using 3D printing surgical training models combined with verbal and video education can effectively reduce the psychological and physiological stress responses of surgical patients, improve their surgical awareness, and enhance the preparation rate before surgery.

How e-commerce can boost China’s high-quality agricultural exports

IntroductionExport quality improvement is a crucial path for a country or region to climb up the advanced division of labor in the global value chain. For developing countries that have long been at the lower end of the global value chain division of labor, the dominance of agricultural trade is often controlled by international retailers, leading to higher costs of agricultural trade and hindering the upgrading of the quality of agricultural exports. As a traditional agricultural exporting country, China still needs a competitive advantage despite the large scale of its agricultural exports. Currently, e-commerce can provide buyers and sellers with comprehensive transaction information and technical support, allowing them to realize rational allocation of resources and export upgrading. Therefore, studying how e-commerce can promote the quality upgrade of agricultural exports is crucial for China to build a trade power.MethodsThis paper clarifies the mechanism of e-commerce’s theoretical impact on the quality of agricultural exports and then empirically tests it using a high-dimensional fixed-effects model with Chinese customs data from 2000 to 2020 as a sample.ResultsThe results show that e-commerce significantly promotes the quality of agricultural exports in all regions of China, and this conclusion still holds after various robustness tests; the heterogeneity test shows that e-commerce fosters the quality of agricultural exports in eastern China, densely populated regions, and economically underdeveloped regions, especially in the case of heterogeneous products; the mechanism analysis confirms that e-commerce promotes the quality of agricultural exports mainly through the optimization of the agricultural industry chain and supply chain, and the enhancement of the level of servicing. Mechanism analysis confirms that e-commerce improves agricultural export quality mainly through optimizing the agricultural industry chain and supply chain, improving the level of servitization, and docking the demand information of the consumer market, etc. Expanded analysis shows that constructing cross-border e-commerce platforms also significantly improves the quality of micro enterprises’ agricultural exports. This paper enriches the research on e-commerce and agricultural trade.DiscussionThis paper provides an essential reference for constructing a robust agricultural trade country and developing high-quality agriculture. Despite encompassing data up to 2020, in the context of today’s global economic volatility directly affecting agricultural trade, future investigations will broaden the scope to delve deeper into how e-commerce can help countries cope with global economic uncertainty.

Study of the Impact of Fairness Concern on Low Carbon Dual Channel Supply Chain

A supplier-dominated secondary low-carbon dual channel supply chain is used as the object of study, and the expected demand function is constructed when the seller has the advantage of market information in the information asymmetry situation, in order to explore the impact of fairness concern on supply chain decision making. The utility functions in the three scenarios were analyzed and compared, and mathematical simulations were conducted according to market demand information available to the seller and the effort input of decision-making subject. Studies have shown that: Whichever decision-making subject has the fairness concern behavior does not affect the supplier as a higher profit maker; information on market demand also needs to be taken into account when considering the impact of the level of fairness concern on profits of the decision maker. When suppliers misjudge market demand, wholesale price increases are slowed if sellers have equity concerns, and sellers may engage in retaliatory behavior if equity demands are not met. In addition, low carbon demand is profitable for suppliers in supplier-led supply chains regardless of market demand, and low carbon sales efforts are not necessarily profitable for retailers.