In China’s modern economic system, e-commerce has become a crucial driver for the development of businesses and supply chains. To clarify the impact and mechanisms of e-commerce development on the competitiveness of corporate supply chains, this study conducts an empirical analysis using data from A-share listed companies between 2010 and 2022. The results indicate that: (1) The development of e-commerce significantly enhances the competitiveness of corporate supply chains, with results remaining robust after endogenous and robustness tests; (2) The level of e-commerce development can further promote the improvement of supply chain competitiveness by improving resource integration, alleviating financing constraints, and increasing R&D investment; (3) The effects of e-commerce development exhibit heterogeneity: in state-owned enterprises and firms with lower competition, the influence of e-commerce on supply chains is more significant; in regions with high-speed rail (HSR) connectivity, e-commerce not only stabilizes customer relationships but also promotes corporate innovation, whereas in areas without HSR, e-commerce development may significantly mitigate issues related to capital occupation. The core innovation of this paper lies in the construction of a “e-commerce—resource integration/financing constraints/innovation investment—supply chain competitiveness” three-dimensional driving framework, establishing a multi-indicator evaluation system, and conducting heterogeneity and mechanism analyses from multiple perspectives. This provides systematic empirical evidence for the role of e-commerce in enhancing supply chain resilience and competitive advantage, offering policy recommendations for businesses to effectively improve their competitiveness.

This study investigates the dynamics of literacy, trust, awareness, loyalty, and corporate sustainability among Social Security Management Agency (BPJS) of Employment. Social Security Management Agency (BPJS) of Employment in West Java, Banten, and Lampung Province, Indonesia. Employing structural equation modeling, the study reveals significant relationships and mediating effects. Literacy emerges as a key factor, positively impacting loyalty and corporate sustainability. Trust is found to directly influence corporate sustainability but not loyalty. Awareness significantly affects both loyalty and corporate sustainability. Notably, loyalty plays a mediating role in the relationships between literacy, awareness, and corporate sustainability. These findings contribute to literacy and engagement models, sustainability frameworks, and mediating models in organizational literature. Practical implications include the recommendation for literacy enhancement programs, multifaceted trust-building strategies, proactive communication campaigns, and loyalty-building initiatives. Future research recommendations encompass longitudinal studies, diverse organizational settings, mixed-methods approaches, exploration of moderating variables, and intervention studies. The research contributes to a nuanced understanding of participant engagement and sustainability perceptions, offering actionable insights for organizations, particularly those in the public sector like Social Security Management Agency (BPJS) of Employment.

Firms' activities to govern their supply chains are defined as supply chain management practices. This research explores a model that describes the association between organizational performance and supply chain management practices. Based on existing research, a theoretical framework for the investigation was established. A standardized questionnaire was developed to gather data for the research, which was completed by 396 supply chain professionals from Saudi Arabian food companies. Partial least square (PLS)–structured equation modeling was used to examine the model and hypotheses. Four independent variables were chosen to scrutinize the impact of supply chain management strategies on organizational performance: customer relationship, strategic supplier partnership, level of information sharing, and postponement. According to the research, customer relationships, strategic supplier partnerships, and information sharing all have a major impact on organizational performance. On the other hand, the postponement has no significant impact on organizational performance.

Today, the ability to identify the profitable customers, creating a long-term loyalty in them and expanding the existing relationships are considered as the key and competitive factors for a customer-oriented organization. The prerequisite for having such competitive factors is the presence of a very powerful customer relationship management (CRM). The accurate evaluation of customers’ profitability is considered as one of the fundamental reasons that lead to a successful customer relationship management. RFM is a method that scrutinizes three properties, namely recency, frequency and monetary for each customer and scores customers based on these properties. In this paper, a method is introduced that obtains the behavioral traits of customers using the extended RFM approach and having the information related to the customers of an organization; it then classifies the customers using the K-means algorithm and finally scores the customers in terms of their loyalty in each cluster. In the suggested approach, first the customers’ records will be clustered and then the RFM model items will be specified through selecting the effective properties on the customers’ loyalty rate using the multipurpose genetic algorithm. Next, they will be scored in each cluster based on the effect that they have on the loyalty rate. The influence rate each property has on loyalty is calculated using the Spearman’s correlation coefficient.

Customers are believed to be the main part of any organization’s assets and customer retention as well as customer churn management are important responsibilities of organizations. In today’s competitive environment, organization must do their best to retain their existing customers since attracting new customers cost significantly more than taking care of existing ones. In this paper, we present a hybrid method based on neural network and Cox regression analysis where neural network is used for outlier data and Cox regression method is implemented for prediction of future events. The proposed model of this paper has been implemented on some data and the results are compared based on five criteria including prediction accuracy, errors’ type I and II, root mean square error and mean absolute deviation. The preliminary results indicate that the proposed model of this paper performs better than alternative methods.

During the past few years, Iranian auto industries sell their products directly to customers and the increase competition among domestic auto makers has increased motivation to pay especial attention to their customers. This paper investigates relationship between marketing and customer’s loyalty. The study designs a questionnaire consists of three parts. The first part is associated with customer information, the second part considers relationship marketing factors and the third part measures components of customer’s loyalty. Cronbach alpha in relationship marketing, loyalty and overall alpha were measured 0.878, 0.891 and 0.712, respectively. Obtained data were analyzed using LISREL software. Ranking the components of the internal and external latent variable are discussed by using the Friedman test and our investigation indicates that some components are not meaningful when the level of significance is five percent. Based on the results of Freedman test, common sense is the most important factor with relative weight of 4.08, followed by relationship (4.01), trust (3.90), commitment(3.86), trouble shooting (2.88) and for the variable of loyalty, quality(3.89), reputation(3.57), decoration(3.4), responsibility(2.96), clients seeking variety (1.75) have the highest average respectively. Note that the results of Friedman test are consistent with the results of structural equation modeling. Chi-Square values have been calculated as 597.701 and 108.917 for marketing relationship and loyalty, respectively.