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The advanced technology enables retailers to develop customer profile analysis (CPA) to implement personalized pricing. However, considering the efficiency of developing CPA, the benefit to different retailers of implementing more precise personalized pricing remains unclear. Thus, this essay aimed to investigate the impact of efficiency on participants’ strategies and profits in the supply chain.

A two-stage game model was introduced in the presence of a manufacturer who sets his wholesale price and a retailer that decides her CPA strategy. The equilibrium results were generated by backward induction.

Most retailers are willing to develop the highest CPA to implement perfect personalized pricing, but those inefficient retailers with high production costs would like to determine a middle CPA to implement bounded personalized pricing. The retailers’ profits may decrease with the efficiency of developing CPA when the efficiency is middle. In this case, as the efficiency improves, the manufacturer increases the wholesale price, resulting in lower demand and thus lower profits. Moreover, define a Pareto Improvement (PI) strategy as one that benefits both manufacturers and retailers. Therefore, uniform pricing is a PI when the unit cost is high and the efficiency is low; personalized pricing is a PI when the unit cost is low and the efficiency is low or high; otherwise, there is no PI.

This study is the first that investigates how the retailer develops CPA to implement personalized pricing on a comprehensive spectrum, which can provide practical insights for retailers with different efficiencies.

The authors investigate the manufacturer's choice of discount schemes in a supply chain with competing retailers.

Using a game-theoretic model, the authors build two discount frameworks and compare and analyze the effects of different discount schemes on the performance of supply chain members.

The authors find that the retail price (market demand) in the quantity discount scheme is always higher (lower) than that in the market share discount scheme. The authors also find that the retailers' preference for discount schemes is antithetical to the manufacturer's preference in most cases. However, under certain conditions, there will be a win-win situation where Pareto-optimization occurs between the manufacturer and retailers when they choose the same discount scheme.

On the one hand, the authors assume that the two retailers are symmetrical in market size and operation efficiency. It would be interesting to study the effect of different discount schemes on retailers when the retailers have different market sizes or operating efficiency. On the other hand, the authors study the manufacturer's choice of discount schemes in a supply chain with one common manufacturer and two competing retailers. However, in practice, there exist other supply chain structures. Future research can examine the problem of choices of discount schemes in other different supply chain structures.

This paper help retailers and manufacturers to choose the best discount schemes.

This paper suggests that a high discount scale is not always beneficial (detrimental) to retailers (the manufacture).

The authors build two discount schemes (the quantity and the market share) in a supply chain consisting of one manufacturer and two retailers, and the authors focus on the effects of different discount schemes on the competition between two retailers. By comparing the two discount schemes, the authors study which discount scheme is the better choice for the manufacturer when facing competing retailers.

The purpose of this paper is to demonstrate the main advantages of integrating bottleneck theory, action learning and transformation capabilities to phenomenon and process analysis systems.

This paper selects three typical cases, using grounded theory standardized coding procedures, and selects exploratory case study approach.

Inward small and medium manufacturing enterprises use the bottleneck breakthrough program and provide a correct knowledge input for action learning. Action learning provides a strong guarantee that for the implementation of bottleneck breakthrough program, programming and action learning are required to continually solve problems and achieve goals in the process.

The authors used inward manufacturing small- and medium-sized enterprises as research subjects The authors did not analysis the role of knowledge services; the future studies could explore how to improve the performance through the transformation value co-creation.

Because of the lack of resources and capacity, small- and medium-sized enterprise adopt appropriate micro-innovation and continuous micro-transformation to break the bottleneck stage and achieve small victories.

Learning and development enterprises are not only through multinational clients which restructuring enhance the learning capacity of the international M & A path. It does not conduct thorough and comprehensive change, and also not related to the structural of readjustment organization. In fact, the radical change and transformation strategy is different than other strategies.

This paper aims to explore the effect of teacher–student collaboration on academic innovation in universities in different stages of collaboration.

Based on collaboration life cycle, this paper divided teacher–student collaboration into initial, growth and mature stages to explore how teacher–student collaboration affects academic innovation.

Collecting data from National Science Foundation of China, the empirical analysis found that collaboration increases the publication of local (Chinese) papers at all stages. However, teacher–student collaboration did not significantly improve the publication of international (English) papers in the initial stage. In the growth stage, teacher–student collaboration has a U-shaped effect on publishing English papers, while its relationship is positive in the mature stage.

The results offer suggestions for teachers and students to choose suitable partners and also provide some implications for improving academic innovation.

This paper constructed a model in which the effect of teacher–student collaboration on academic innovation in universities was established.

Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

In the search for alternative income-generating assets, the paper addresses the following question, one that the literature has yet to answer: what is a reasonable allocation, if any, to asset-backed securities within a 60–40% stock-bond balanced portfolio of mutual funds?

The authors apply the Black–Litterman model of Modern Portfolio Theory to test the efficacy of adding asset-backed securities to the classic 60–40% stock-bond portfolio of mutual funds. The authors use out-of-sample tests of one, three, five, and ten years to determine a reasonable asset allocation. The data are monthly and range from January 2000 through September 2021.

The statistical evidence indicates a modest reward-risk added value from the addition of asset-backed securities, as measured by the Sharpe “reward-to-variability” ratio, in holding periods of three, five, and ten years. Based on the findings, the authors conclude that a reasonable asset allocation for income-seeking, risk-averse investors who follow the classic 60%–40% stock-bond allocation is 8%–10%.

The findings apply to a stock-bond balanced portfolio of mutual funds. Other fund combinations could produce different results.

Investors and money managers can use the findings to improve portfolio performance.

For investors seeking higher income-generating securities in the current record-low interest rate environment, the authors determine a reasonable asset allocation range on asset-backed securities. This study is the first to provide such direction to these investors.

The aim of this study is to depict the spatial pattern of the development level of China's state-owned forest farms, thereby providing theoretical reference and empirical evidence for the improvement of the corresponding development policies.

A development evaluation index system was established in this paper to comprehensively measure the development level of China's state-owned forest farms based on the Pressure-State-Response (PSR) model analysis framework and the actual situation of state-owned forest farms by using the entropy weight - technique for order preference by similarity to an ideal solution (entropy weight TOPSIS) evaluation method and exploratory spatial analysis method.

Studies show that the state-owned forest farms in China are generally not well developed. The pressure system that represents the input level displays an apparent restrictive effect on provinces whose comprehensive score <0.15. The response system, which represents development dynamism, has an apparent restrictive function on the provinces whose comprehensive score is 0.35. In terms of the specific spatial characteristics, the V-shape displayed by southwest–northwest and southeast–northwest has an inward trend of gradual reduction, with high-low agglomeration and low-low agglomeration correlation effects as well as apparent basin characteristics.

In this paper, the development level and spatial pattern of state-owned forest farms in China were accurately depicted, and the development path support and decision-making basis were provided for improving the overall development level of state-owned forest farms in China.

The purpose of this paper is to find the relationship between alliance partner selection and innovation performance of technological standard alliance in China.

The authors designed the selection factors of alliance partners in terms of reputation, compatibility and technological standardization ability and, based on the empirical research data, analyzed the correlation between partner selection and innovation performance using the method of cross validation statistically.

It was found that the selection of alliance partners would directly affect the operation and management of technological standard alliance in China. Appropriate partners among technological standard alliance will play a significant role in improving partners' independent innovation capability. The empirical results showed that potential cooperative partners' reputation, compatibility and standardization ability had an obvious effect on innovation performance.

The paper finds and verifies the key index of alliance partner selection. This result can provide some important references for managers when selecting appropriate cooperative partners. Technological standard alliance managers can evaluate potential cooperative partners according to the index of reputation, technological standardization ability and compatibility designed in this paper. High‐tech firms can cultivate their cooperative innovation ability to adapt to the demands of technological standard alliance according to the key index.

Axleboxes are an important structure that connects the wheelset with the bogie frame. Inside axlebox bogies have lower wheelset yaw angle stiffness and better curve-passing performance. The purpose of this paper to study the differences in the wear evolution law and the influencing factors of the two types of metro vehicles.

This paper established the dynamic model and wear model of both outside axlebox and inside axlebox metro vehicles to research the wheel wear evolution law of the two types of vehicles. The curve passing performance of two vehicles is analyzed. The effect of key parameters on wheel wear is studied, including the lateral distance of the axlebox, the longitudinal stiffness of the rotary arm node, the lateral stiffness of the rotary arm node and the wheel profiles.

The results showed that the model of inside axlebox metro vehicles improved vehicle safety and curve-passing performance. At the same time, inside axlebox metro vehicles reduce wheel wear of the wheel tread area and wheel flange area. When the S1002 wheel tread profile matched with the vehicle parameters, the wheel wear is minimized.

This paper established a dynamic model for inside axleboxes metro vehicles, then used a wheel wear model to analyze the evolution of wheel wear and the key influencing factors of the inside axleboxes metro vehicles.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0256/

With the increased speed and mileage of high-speed lines, the problem of rail wear is increasing. In actual operation, a large number of abnormal wear phenomena occur on both vehicles and rails during fixed line operation; therefore, the purpose of the study is to explored the rail wear for a variety of vehicles running in mixed operation.

This paper used the universal mechanism multibody dynamics software to establish the CRH2 high speed train (HST) and the CRH3 HST vehicle dynamic models, respectively. The mixed running of HSTs on the effect of rail wear evolution law was analyzed. The rail wear of the two vehicles with different curve radii, different wheel diameters and different under-rail stiffness was compared and analyzed.

The result showed that the rail wear of CRH3 HST is greater than that of CRH2 HST. The rail wear in the tangent track under mixed operation conditions is 25.4% less than when CRH3 HST operated independently. When there is a 1-mm wheel diameter difference, the maximum rail wear of CRH2 HST and CRH3 HST increases by 263% and 44%, respectively. The amount of rail wear is proportional to the under-rail stiffness, and the position of the maximum wear is almost unchanged.

Most studies on the evolution law of rail wear are conducted for a single vehicle type and a single line. This study explored the mixed running of HSTs on the effect of rail wear evolution law.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0276/