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Reward-based crowdfunding, an emerging financing channel for SMEs, has attracted significant attention from scholars and practitioners. Scholars have mostly explored investors’ herding behavior in platforms to better understand investors’ decision-making mechanisms and management of funding projects. However, current evidence is inconsistent regarding herding behavior during the funding process. This study proposes prior funding performances have a nonlinear effect on subsequent funding performance and that this nonlinear relationship is conditional on competition intensity and information disclosure.

Based on objective data collected from a dominant reward-based crowdfunding platform in China, this study follows a panel ordinary least squares (OLS) model to estimate the effects of prior funding performance on the subsequent funding performance and the moderating role of environmental factors (i.e. competition intensity and information disclosure) in a given platform.

The results show prior funding performance had an inverted U-shaped effect on subsequent performance; this inverted U-shaped relationship was attenuated when the number of interactive messages was larger and competition was more intense, and it was strengthened when information updates were more frequent.

The effects of prior funding performance on subsequent performance at different stages of the fundraising process and under different platform environments remains unclear. The authors revisit the varying viewpoints in existing research and propose that the enhancement and substitution effects of prior funding performance are dominant at different funding stages. Overall, the results of this study highlight that the crowdfunding platform environment may become a boundary condition for investors' herding behavior.

Drawing on dynamic capability theory, this study investigates how online–offline channel integration (OOCI) affects a firm's supply chain resilience and how such an effect is moderated by market turbulence and regulatory uncertainty.

A sample of 273 Chinese firms that conduct online and offline business and hierarchical regression analysis were used to examine the research model.

The results suggest that the effect of OOCI on supply chain resilience differs in terms of its dimensions (i.e. information integration, transaction integration and service integration). While information integration and service integration were positively associated with supply chain resilience, transaction integration had a non-significant relationship with supply chain resilience. Moreover, market turbulence negatively moderated the effect of transaction integration and positively moderated the effect of service integration. Regulatory uncertainty positively moderated the effect of transaction integration and negatively moderated the effect of service integration. Implications and suggestions for future research are discussed.

This study examines the effect of OOCI on supply chain resilience. It further explores the influence of market turbulence and regulatory uncertainty on the relationship between OOCI and supply chain resilience.

This paper investigates the dynamic pricing strategy of a firm for the successive-generation products under the conditions of the limited trade-in duration and strategic customers. Further, it explores the effect of a limited trade-in duration on the choice of the myopic and strategic customers, besides the optimal dynamic pricing and trade-in strategy of the firm.

Based on the choice behavior of the myopic and strategic customers, the authors have developed a two-period game-theoretic analytical model to decide the optimal retail prices of the successive-generation products and the optimal trade-in rebate when the firm adopts a dynamic pricing strategy and then investigate three extensions of the basic model to discuss the change in the results owing to the relaxation of certain conditions.

The authors find from the results that, in terms of profit maximization, it is better to extend the limited trade-in duration, and hence, the firm should implement a dynamic pricing strategy. However, in the situation of using a static pricing strategy, the firm should extend the limited trade-in duration only if the incremental value of the new generation products is below a certain threshold. Moreover, the firm should use a dual rollover strategy instead of a single rollover one. If all customers in the market are myopic, then the firm should also extend the limited trade-in duration.

This study mainly discusses the impact of limited trade-in duration on the firm's dynamic pricing strategy when facing strategic customers, which provides several directions for future research. First, if the government offers subsidies to consumers, how will strategic consumers make purchase decisions? How would the enterprise make its pricing decision? Second, when asymmetric information exists between consumers and firms, how will it affect consumers' choice behavior and firms' pricing decisions? All these issues are worth exploring in the future.

These results offer certain managerial insights for the firm in the decision making on pricing within the trade-in program.

This is the first work to study the dynamic pricing strategy of the firm for the successive-generation products under the conditions of the limited trade-in duration and strategic customers. Further, this work discusses the changes in results owing to the relaxation of certain conditions.

With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection.

This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure.

This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs.

The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate.

The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security.

This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.