Collaborative logistics network: a new business mode in the platform economy

Xiang, Xu; He, Yangyang; Ji, Qiang

doi:10.1080/13675567.2021.1926948

Cited by 9 publications

(9 citation statements)

References 36 publications

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“…Many scholars began to use machine learning methods to study sales forecasting, which improved the fitting ability of the forecasting model, and the combined forecasting model can further improve the forecasting effect. Many scholars have shown that there are factors that can be used for sales forecasting in addition to sales volume itself [ 16 ]. In addition to considering the change in historical sales, this topic also uses other marketing information affecting sales as independent variables for sales prediction, especially unstructured data such as pictures, which have been proven by many scholars' studies to be the most important marketing information online and have a huge impact on users' purchasing decision behavior.…”

Section: Analysis Of the Factors Influencing Knowledge Sharing In Vir...mentioning

confidence: 99%

A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing

Tian

Wang

2022

Computational Intelligence and Neuroscience

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In this paper, a neural network algorithm is used to conduct in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. Both the expected returns and the social network structure are analyzed, and both have positive effects on knowledge sharing in the actual development process, but the degree of them also possesses certain variability. A model of the factors influencing the quality of knowledge shared by users is constructed to explore the relationship between the dimensions in social capital and how they affect the community users’ perceptions of knowledge quality. Exploring the strong influencing factors of product repurchase rate has key implications for promoting product sales and sales forecasting. The scale of this paper has undergone several minor revisions, and the content validity is very good. Criterion validity is generally reflected by Person correlation coefficient, and construct validity includes exploratory factor analysis and confirmatory factor analysis. First, the values were clustered, and the optimal variables were selected using stepwise regression and fitted with Poisson regression models to explore the relationship between the repurchase rate of different products and the factors that strongly influence the repurchase rate in the case. To predict the sales of goods, the advantages of the BP neural network, LSTM neural network, and Verhulst gray model are combined: BP neural network can predict sales by combining the current data corresponding to independent variables, LSTM neural network can explore the influence of historical data on sales, and Verhulst model can predict sales based on the growth trend of variables. A BP-LSTM-Verhulst nested neural network based on the AP algorithm is constructed to predict sales volume, and the accuracy of this method is proved by example. Finally, it is found that the proposed sales prediction method has higher accuracy than exponential regression and shallow neural networks. The deep learning prediction method combining unstructured data such as images proposed in the paper not only provides a more accurate sales prediction method for short life cycle products in e-commerce but also provides an effective deep learning method for management practices. The KMO value obtained after the test is less than 0.5, which is not suitable for factor analysis. Using SPSS22.0 to expand the KMO value and Bartlett’s sphericity test, the KMO value is significantly greater than the minimum requirement of 0.5, the KMO value of self-efficacy is 0.826, and the KMO value of expected return is 0.870.

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Section: Analysis Of the Factors Influencing Knowledge Sharing In Vir...mentioning

confidence: 99%

A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing

Tian

Wang

2022

Computational Intelligence and Neuroscience

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“…Collaborating with the logistics department helps minimisation of CO 2 emissions through redesigning of collaborative logistics networks (Aloui et al, 2021). Route optimisation minimises energy consumption, reduces carbon footprints and reduces overall logistics costs (Vargas et al, 2020;Werning & Spinler, 2020;Xu et al, 2021). Thus, we propose the following hypotheses.…”

Section: Research Hypotheses and Modelmentioning

confidence: 99%

“…Environmental collaboration with logistics service provider Vargas et al (2020), Xu et al (2021) We collaborate with logistics service providers to ensure minimum trip based on consignment requirement.…”

Section: Questionnaire Item Referencesmentioning

confidence: 99%

“…Collaborating with the logistics department helps minimisation of

{CO}_{2}

emissions through redesigning of collaborative logistics networks (Aloui et al, 2021). Route optimisation minimises energy consumption, reduces carbon footprints and reduces overall logistics costs (Vargas et al, 2020; Werning & Spinler, 2020; Xu et al, 2021). Thus, we propose the following hypotheses.H1a Environmental collaboration with suppliers positively influences business performance of the firm.H1b Environmental collaboration with customers positively influences business performance of the firm.H1c Environmental collaboration with the marketing department positively influences business performance of the firm.H1d Environmental collaboration with the design department positively influences business performance of the firm.H1e Environmental collaboration with logistics providers positively influences business performance of the firm.Given the multitude of environmental and social problems and their complexity, interorganisational and intra‐organisational collaboration in several cases can be the most significant way to attain SDGs, including RCP (Escher & Brzustewicz, 2020).…”

Section: Research Hypotheses and Modelmentioning

confidence: 99%

“…An emerging body of literature suggests the significance of interorganisational and intraorganisational collaborations in achieving environmental and economic performance targets (Ahmed et al, 2020; Grekova et al, 2016; León‐Bravo et al, 2017; Xu et al, 2021). Notwithstanding the rich literature on organisations' collaboration with other types of organisations (Arora et al, 2020; Grekova et al, 2016a; Yu et al, 2020), fewer studies deal with the issue of interorganisational and intraorganisational collaboration concerning RCP practices (Priyadarshini & Abhilash, 2020; Ruiz‐Molina et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Exploring the relationship between environmental collaboration and business performance with mediating effect of responsible consumption and production

2022

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This study aims to examine the relationship between inter-organisational and intraorganisational environmental collaboration practices and business performance. It also investigates the mediating role of responsible consumption and production practices on various environmental collaboration and business performance relationships.Drawing from the relational view and resource-based view, a model to analyse environmental collaboration, responsible consumption and production practices and business performance relationships is theorised and examined using a sample of 249 from manufacturing firms. Direct and mediating relationships between constructs have been assessed through structural equation modelling technique. Environmental collaboration with suppliers, customers and logistics providers directly influences firms' business performance. However, mediation analysis suggests the direct relationship between environmental collaboration practices with suppliers and business performance weakened in the presence of responsible consumption and production practices, indicating partial mediation between environmental collaboration with suppliers and business performance relationship. Also, direct relationships between environmental collaboration with customers and logistics providers and business performance become nonsignificant in the presence of responsible consumption and production practices, indicating the full mediating effect of responsible consumption and production practices. This study extends supply chain collaboration and sustainable development goal literature by offering valuable insights into environmental collaboration, responsible consumption and production and business performance relationships. The findings can assist practitioners in improving business performance through environmental collaboration and responsible consumption and production practices.

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Optimization of Collaborative Transport and Distribution Strategies: Trends and Research Opportunities

Chabba

Oualidi²,

Ahlaqqach³

2022

Communications in Computer and Information Science

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Collaborative logistics network: a new business mode in the platform economy

Cited by 9 publications

References 36 publications

A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing

A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing

Exploring the relationship between environmental collaboration and business performance with mediating effect of responsible consumption and production

Optimization of Collaborative Transport and Distribution Strategies: Trends and Research Opportunities

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