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In response to the intense competition in the platform economy, e-commerce platforms are actively introducing value-added services to maintain their competitiveness. However, how effective these value-added services are in fulfilling this purpose remains unclear. This paper explores how value-added services can enhance e-commerce platform competitiveness, measured by both user scale and reputation, considering the effect of network externalities.

A bilateral e-commerce platform with potential high-quality sellers and low-quality sellers on one side and potential buyers on the other side was chosen as research setting. Game theory models are constructed to simultaneously consider the behaviors of all actors (including sellers, buyers and the platform).

On the one hand, to increase the seller scale, basic services play a substituting role in determining the effect of value-added services. On the other hand, to increase the buyer scale and improve platform reputation, basic services play a fundamental role in determining the effect of value-added services. Furthermore, the higher the loss rate of the product value, the bigger the room for providing value-added services. With increasing loss rate of the product value, participating buyers who are attracted by value-added services are the fastest growing indicators; this indicates that the most significant effect of value-added services is its increase in the buyer scale.

Basic services determine the lower limit of platform competitiveness, while value-added services set the upper limit. The results of this paper can instruct different types of platforms to enhance their competitiveness in different ways.

(1) While previous studies on how to enhance platform competitiveness only considered scale or reputation separately, this paper applies a new perspective of platform competitiveness, namely the improvement of both the seller scale/buyer scale and platform reputation. (2) According to the characteristics of bilateral platforms, game theory models are constructed to explore how value-added services can enhance platform competitiveness considering both positive and negative network externalities. (3) The existing literature studies basic services and value-added services in a fragmented state; this paper contributes to research on value-added services by considering the mutual effect between basic and value-added services.

We explore the driving forces behind the channel choices of the manufacturer and the platform by considering asymmetric selling cost and demand information.

This paper develops game-theoretical models to study different channel strategies for an E-commerce supply chain, in which a manufacturer distributes products through a platform that may operate in either the marketplace channel or the reseller channel.

Three primary models are built and analyzed. The comparison results show that the platform would share demand information in the reseller channel only if the service cost performance is relatively high. Besides, with an increasing selling cost, the equilibrium channel might shift from the marketplace to the reseller. With increasing information accuracy, the manufacturer tends to select the marketplace channel, while the platform tends to select the reseller channel if the service cost performance is low and tends to select the marketplace channel otherwise.

All these results have been numerically verified in the experiments. At last, we also resort to numerical study and find that as the service cost performance increases, the equilibrium channel may shift from the reseller channel to the marketplace channel. These results provide managerial guidance to online platforms and manufacturers regarding strategic decisions on channel management.

Although prior research has paid extensive attention to the driving forces behind the online channel choice between marketplace and reseller, there is at present few study considering the case where a manufacturer selling through an online platform faces a demand information disadvantage in the reseller channel and sales inefficiency in the marketplace channel. To fill this research gap, our work illustrates the interaction between demand information asymmetry and selling cost asymmetry to identify the equilibrium channel strategy and provides useful managerial guidelines for both online platforms and manufacturers.

Motivated by the real-world practice of the thriving e-commerce, manufacturers are transcending traditional boundaries of merely producing and selling directly by implementing encroachment. Concurrently, supply chain (SC) members who overlook the corporate social responsibility (CSR) sharing will be left behind, which is closely linked to their profits. This paper aims to investigate a better way to share CSR under the scenarios of manufacturer encroachment and no-encroachment.

This paper constructs game-theoretic models in a SC consisting of a manufacturer (M) and a retailer (R), where the manufacturer can sell products by retailing, and may sell directly by implicating encroachment. The manufacturer and retailer jointly consider whether to share CSR and the proportion of it by taking consumer surplus into account. Furthermore, equilibriums for each model are derived using backward induction. Then, the authors analyse the impact of CSR sharing proportion and compare the equilibrium outcomes under different scenarios. Finally, the numerical analyses are presented to verify the results.

Several interesting results are found in this paper. First, the retailer shares more CSR can benefit SC members and social welfare when the manufacturer does not implement encroachment. However, the results may change which is decided by the unit cost of encroachment when the manufacturer does so. Second, the proportion of CSR shared by manufacturer and the unit cost of encroachment has an interactive impact on equilibrium outcomes. Finally, both manufacturer encroachment and SC members share CSR may be the best for the perspective of SC members and consumers.

Based on the analytical results, this paper provides novel managerial implications to assist manufacturer and retailer in determining the optimal strategies for CSR sharing and encroachment. Furthermore, the appropriate proportion of CSR shared by manufacturer and the unit cost of encroachment may let manufacturer, retailer and consumer surplus achieve a win-win-win situation.

To the best of the authors’ knowledge, this paper is the first attempt to explore the strategy of CSR sharing under the scenarios of manufacturer encroachment and no-encroachment.

This study aims to develop analytical models that consider product quality and production volume as essential drivers for profitability in the marketplace. It also considers product demand and price dynamics to understand related nuances backed by empirical validation.

The pricing mechanism is influenced by production quality, while product demand is influenced by both price and quality. The study considers cost elements, including production cost and quality loss cost which in turn are influenced by production volume and product quality. It establishes analytical conditions for optimal product quality and applies them to numerical analyses considering four distinct industry settings.

The study reveals that unique solutions exist for optimal product quality at each production level in four industry scenarios. The optimal production volume depends on product quality, and empirical research validates these findings from analytical models and numerical analysis.

This study represents a pioneering effort to investigate operational strategies in both analytical and empirical contexts, thus contributing to the existing body of knowledge in this area.

Collaborative emission reduction among supply chain members has emerged as a new trend to achieve climate neutrality goals and meet consumers’ low-carbon preferences. However, carbon information asymmetry and consumer mistrust represent significant obstacles. This paper investigates the value of blockchain technology (BCT) in solving the above issues.

A low-carbon supply chain consisting of one supplier and one manufacturer is examined. This study discusses three scenarios: non-adoption BCT, adoption BCT without sharing the supplier’s carbon emission reduction (CER) information and adoption BCT with sharing the supplier’s CER information. We analyze the optimal decisions of the supplier and the manufacturer through the Stackelberg game, identify the conditions in which the supplier and manufacturer adopt BCT and share information from the perspectives of economic and environmental performance.

The results show that adopting BCT benefits supply chain members, even if they do not share CER information through BCT. Furthermore, when the supplier’s CER efficiency is low, the manufacturer prefers that the supplier share this information. Counterintuitively, the supplier will only share CER information through BCT when the CER efficiencies of both the supplier and manufacturer are comparable. This diverges from the findings of existing studies, as the CER investments of the supplier and the manufacturer in this study are interdependent. In addition, despite the high energy consumption associated with BCT, the supplier and manufacturer embrace its adoption and share CER information for the sake of environmental benefits.

The firms in low-carbon supply chains can adopt BCT to improve consumers’ trust. Furthermore, if the CER efficiencies of the firms are low, they should share CER information through BCT. Nonetheless, a lower unit usage cost of BCT is the precondition.

This paper makes the first move to discuss BCT adoption and BCT-supported information sharing for collaborative emission reduction in supply chains while considering the transparency and high consumption of BCT.

In autonomous driving, the inherent sparsity of point clouds often limits the performance of object detection, while existing multimodal architectures struggle to meet the real-time requirements for 3D object detection. Therefore, the main purpose of this paper is to significantly enhance the detection performance of objects, especially the recognition capability for small-sized objects and to address the issue of slow inference speed. This will improve the safety of autonomous driving systems and provide feasibility for devices with limited computing power to achieve autonomous driving.

BRTPillar first adopts an element-based method to fuse image and point cloud features. Secondly, a local-global feature interaction method based on an efficient additive attention mechanism was designed to extract multi-scale contextual information. Finally, an enhanced multi-scale feature fusion method was proposed by introducing adaptive spatial and channel interaction attention mechanisms, thereby improving the learning of fine-grained features.

Extensive experiments were conducted on the KITTI dataset. The results showed that compared with the benchmark model, the accuracy of cars, pedestrians and cyclists on the 3D object box improved by 3.05, 9.01 and 22.65%, respectively; the accuracy in the bird’s-eye view has increased by 2.98, 10.77 and 21.14%, respectively. Meanwhile, the running speed of BRTPillar can reach 40.27 Hz, meeting the real-time detection needs of autonomous driving.

This paper proposes a boosting multimodal real-time 3D object detection method called BRTPillar, which achieves accurate location in many scenarios, especially for complex scenes with many small objects, while also achieving real-time inference speed.

This work aims to analyze the factors that influence the performance and efficiency of Portuguese companies, namely the influence of social and environmental features.

To achieve our aim, we have used the Portuguese benchmark index, the Portuguese stock index – PSI, during the period from 2016 to 2020. To test the hypothesis panel data methodology was used, specifically, the GMM system originally proposed by Arellano and Bond (1991) and the Value-Based DEA developed by Gouveia et al. (2008).

The results of the GMM model show that social performance has a negative relationship with the company’s performance, from the perspective of different stakeholders, reinforcing that the cost-benefit trade-off of social spending is not yet understood as advantageous for the company’s performance. On the other hand, environmental performance, for external stakeholders, positively influences the company’s performance, perhaps due to pressure from society and the tradition of disclosing environmental matters. The value-based DEA results reinforce that from the perspective of the external stakeholder, non-efficient companies must increase their environmental performance to become efficient, highlighting the role of environmental performance in explaining efficiency. It is unanimous that social performance is still not seen as a lever of efficiency.

This is the first work to use a hybrid methodology to understand the performance determinants of a small banking-oriented country, emphasizing environmental and social aspects.

This paper investigates the impact of AI penetration rate on the degree of corporate greenwashing and aims to assess the potential of AI in enhancing firms' environmental performance and reducing false disclosures.

This study employs a year and firm fixed-effects model to analyze data from Chinese listed firms from 2012 to 2022. We use the low-carbon city pilot as a quasi-natural experiment to address endogeneity concerns and conduct a series of robustness tests, including adding control variables and transforming the model.

The results of this paper show that the application of AI can inhibit firms' greenwashing behavior, with green innovation activities further enhancing this inhibitory effect. In state-owned firms and those with Party Organizations, the inhibitory effect of AI on corporate greenwashing is more significant. This reduction in greenwashing is more likely to be observed in firms that are heavily influenced by Confucian culture, receive higher public attention regarding their environmental impact, face less market competition, suffer from more serious pollution and face less financial constraints.

We propose a new research perspective that offers novel insights into promoting the green development of firms by revealing the potential of AI in reducing their greenwashing behavior. Corporate boards can explore specific strategies for applying AI to monitor, prevent and correct greenwashing, thereby enhancing corporate environmental performance and social responsibility.

By studying the green development efficiency (GDE) of 33 cities in the provinces of Jiangsu, Zhejiang, and Fujian in China, this study strives to conduct an analysis of the sustainable practices implemented in these developed regions, and derive valuable insights that can foster the promotion of green transformation.

First, the urban green development system (GDS) was decomposed into the economic benefit subsystem (EBS), social benefit subsystem (SBS), and pollution control subsystem (PCS). Then, a mixed network SBM model was proposed to evaluate the GDE during 20152020, with Moran’s I and Bootstrap truncated regression model subsequently applied to measure the spatial characteristics and driving factors of efficiency.

Subsystem efficiency presents a distribution trend of PCS > EBS > SBS. There is a particular spatial aggregation effect in EBS efficiency, whereas SBS and PCS efficiencies have no significant spatial autocorrelation. Furthermore, urbanization level contributes significantly to the efficiency of all subsystems; industrial structure, energy consumption, and technological innovation play a crucial role in EBS and SBS; external openness is a pivotal factor in SBS; and environmental regulation has a significant effect on PCS.

This study further decomposes the black box of GDS into subsystems including the economy, society, and environment. Additionally, by employing a mixed network SBM model and Bootstrap truncated regression model to investigate efficiency and its driving factors from the subsystem perspective, it endeavors to derive more detailed research conclusions and policy implications.

In today’s global and competitive e-commerce market spaces, social media influencers (SMIs) exert substantial influence on consumer behavior. This study aims to examine how electronic word of mouth (e-WOM), Instagram usage and the credibility of SMIs shape the dynamics of consumer purchase behavior (PB).

Information was gathered from 498 users in Ghana through judgmental sampling using SmartPLS 4.

The findings revealed that influencers’ credibility has a substantial impact on their followers’ parasocial interactions. As a promotional tool, Instagram plays a significant role in how followers perceive the credibility of influencers by modifying the associations between parasocial connections, e-WOM and consumer PB.

The findings offer valuable information for marketing professionals looking to improve their advertising efforts by collaborating with influencers, along with unique perspectives on influencer dynamics in a diverse socioeconomic context, extending beyond conventional boundaries.

Through an examination of the complex mechanisms underlying social media influencer advertisements on an e-commerce platform, namely, Instagram, this research uncovered the essence of customer behavior in the digital era, including the human need for connection, authenticity and trust, thus contributing to the literature empirical data from Africa, a region often overlooked in academic studies.

在当今全球化且竞争激烈的电子商务市场中, 社交媒体影响者对消费者行为有着重要的影响。本研究旨在探讨网络口碑、Instagram使用以及社交媒体影响者的可信度如何塑造消费者购买行为的动态变化。

使用SmartPLS 4, 通过判断抽样收集了来自加纳的 498 位用户信息。

研究表明, 影响者的可信度对其粉丝的准社会互动有着显著影响。作为一种推广工具, Instagram通过影响准社会联系、网络口碑和消费者购买行为之间的关系, 在粉丝感知影响者可信度方面发挥着重要作用。

本研究通过深入剖析社交媒体影响者在Instagram等电子商务平台上进行广告活动的复杂机制, 揭示了数字时代消费者行为的本质, 其中包括人们对联系、真实性和信任的需求。此外, 研究还提供了经常被学术界忽视的非洲地区的实证数据。

研究结果为希望通过与影响者合作来提升广告效果的营销专业人士提供了宝贵的信息。本文超越传统界限, 从不同的社会经济背景下提供了影响者动态的独特视角。

En un mercado tan global y competitivo como el del comercio electrónico actual, los influencers en redes sociales ejercen una influencia sustancial en el comportamiento del consumidor. Este estudio examina cómo el boca a boca electrónico, el uso de Instagram y la credibilidad de los influencers en redes sociales dan forma a la dinámica del comportamiento de compra del consumidor.

Se recogió información de 498 usuarios en Ghana mediante muestreo de conveniencia y se testó el modelo utilizando SmartPLS 4.

Los resultados revelaron que la credibilidad de los influencers tiene un impacto sustancial en las interacciones parasociales con sus seguidores. Como herramienta promocional, Instagram juega un papel significativo en la percepción de los seguidores sobre la credibilidad de los influencers, modificando las asociaciones entre las conexiones parasociales, el boca a boca electrónico y, finalmente, el comportamiento de compra del consumidor.

A través del estudio de los complejos mecanismos que subjacen en los anuncios y publicaciones de los influencers en Instagram, esta investigación desveló la esencia del comportamiento del cliente en la era digital, incluyendo la necesidad de conexión humana la conexión, la autenticidad y la confianza, contribuyendo así a la literatura con datos empíricos de África, una región a menudo pasada por alto en estudios académicos.

Los resultados ofrecen información valiosa para los profesionales del marketing que buscan mejorar sus esfuerzos publicitarios colaborando con influencers, junto con perspectivas únicas sobre la dinámica de éstos en un contexto socioeconómico diverso, más allá de los límites convencionales.