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Cycle time reduction is important for order fulling process but often subject to resource constraints. This study considers an unrelated parallel machine environment where orders with random demands arrive dynamically. Processing speeds are controlled by resource allocation and subject to diminishing marginal returns. The objective is to minimize long-run expected order cycle time via order schedule and resource allocation decisions.

A stochastic optimization algorithm named CAP is proposed based on particle swarm optimization framework. It takes advantage of derived bound information to improve local search efficiency. Parameter impacts including demand variance, product type number, machine speed and resource coefficient are also analyzed through theoretic studies. The algorithm is evaluated and benchmarked with four well-known algorithms via extensive numerical experiments.

First, cycle time can be significantly improved when demand randomness is reduced via better forecasting. Second, achieving processing balance should be of top priority when considering resource allocation. Third, given marginal returns on resource consumption, it is advisable to allocate more resources to resource-sensitive machines.

A novel PSO-based optimization algorithm is proposed to jointly optimize order schedule and resource allocation decisions in a dynamic environment with random demands and stochastic arrivals. A general quadratic resource consumption function is adopted to better capture diminishing marginal returns.

The increasing popularity of ERP solutions has provided dietary supplement manufacturing companies with modules to manage pricing and inventory. However, the decisions made by these modules are often independent and rely on deterministic forecasts. This paper studies a multi-product dietary supplement manufacturing system under stochastic demands. The purpose is to maximize the long-run expected profit by jointly considering pricing and inventory strategies.

The authors investigate both the general cases and three special cases including stable demand, negligible backlog and instantaneous replenishment. A two-stage algorithm named PAS is proposed. In the strategy construction stage, the constructed objective bounds are combined to provide estimates which then help to derive the optimal product prices. In the system operation stage, replenishment decisions are further made based on the prices generated from the previous stage.

It is proved that base-stock policy is optimal for the studied system, and the optimal based-stock level is provided. The global optimal strategies are obtained for three important special cases. For the general case, theoretical objective bounds are established. These bounds provide quick and reliable performance estimates for practical applications.

Very few studies have jointly considered pricing and inventory strategies with uncertainty demands in the dietary supplement industry. The PAS algorithm developed integrates these decisions and consistently generates high-quality solutions even under highly varying demands. Such algorithm could be a valuable add-on to the pricing and inventory management modules in ERP systems.

The purpose of the paper is to examine the impact of investor sentiment on managers’ decisions to provide CSR disclosures. The core issue focuses on whether, why and how managers adjust their approach to CSR disclosure to cater to the investor sentiment.

On the basis of 13,488 observations of A-share listed companies, the authors examine the impacts of investor sentiment on CSR disclosure, which is measured separately by the propensity to issue a standalone CSR report and the quality of CSR reports. Furthermore, the authors examine the moderating role of institutional factors in China.

The authors find that during low-sentiment periods, managers are more likely to issue a standalone CSR report and the quality of CSR reports is higher, and vice versa. Additionally, the authors find that the negative correlations between CSR disclosure and investor sentiment are stronger in state-owned enterprises.

First, the measurement of investor sentiment reflects only a part of characteristics of investor sentiment. Second, the authors pay less attention to the specific items of a CSR report.

The study contributes to the literature on CSR disclosure and investor sentiment by combining the two fields together. Furthermore, the study deepens the understanding of the institutional context in China and contributes to research on the predictors of CSR disclosure.

This research aims to improve the performance of rail fastener defect inspection method for multi railways, to effectively ensure the safety of railway operation.

Firstly, a fastener region location method based on online learning strategy was proposed, which can locate fastener regions according to the prior knowledge of track image and template matching method. Online learning strategy is used to update the template library dynamically, so that the method not only can locate fastener regions in the track images of multi railways, but also can automatically collect and annotate fastener samples. Secondly, a fastener defect recognition method based on deep convolutional neural network was proposed. The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region. The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.

Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways. Specifically, fastener location module has achieved an average detection rate of 99.36%, and fastener defect recognition module has achieved an average precision of 96.82%.

The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways, which has high reliability and strong adaptability to multi railways.

The purpose of this paper is twofold: to improve the traditional conditional autoregressive Wishart (CAW) and heterogeneous autoregressive (HAR)-CAW model to account for heterogeneous leverage effect and to adjust the high-frequency volatility. The other is to confirm whether CAW-type models that have statistical advantages have economic advantages.

Based on the high-frequency data, this study proposed a new model to describe the volatility process according to the heterogeneous market hypothesis. Thus, the authors acquire needed and credible high-frequency data.

By designing two mean-variance frameworks and considering several economic performance measures, the authors find that compared with five other models based on daily data, CAW-type models, especially LHAR-CAW and HAR-CAW, indeed generate the substantial economic values, and matrix adjustment method significantly improves the three CAW-type performances.

The findings in this study suggest that from the aspect of economics, LHAR-CAW model can more accurately built the dynamic process of return rates and covariance matrix, respectively, and the matrix adjustment can reduce bias of realized volatility as covariance matrix estimator of return rates, and greatly improves the performance of unadjusted CAW-type models.

Compared with traditional low-frequency models, investors should allocate assets according to the LHAR-CAW model so as to get more economic values.

This study proposes LHAR-CAW model with the matrix adjustment, to account for heterogeneous leverage effect and empirically show their economic advantage. The new model and the new bias adjustment approach are pioneering and promote the evolution of financial econometrics based on high-frequency data.

The purpose of this paper is to study the rent-seeking behavior of stakeholders in construction projects and to provide a reference for further studies on collaboration in China, which could thus improve the collaborative management in construction and reduce the waste of resources.

In this paper, the authors discuss and analyze the current situation of rent-seeking behavior in infrastructure construction projects, integrated project delivery (IPD), and the application of a building information model (BIM) in China. The authors analyze the collusive behavior between the supervision department and the contractor via the rent-seeking model on the basis of the game theory and present that BIM and IPD have a positive impact on rent-seeking activities of construction projects. The key factors influencing the rent-seeking activities from the perspective of the owner are studied via a questionnaire survey.

The research status of IPD in China includes the application of IPD, IPD collaborative management and the combination of lean construction (LC), IPD and BIM. The enthusiasm of the adoption of BIM and IPD is higher for design institute, construction units, research institutions and universities. The findings indicate that the owner appears to have a significant influence on stakeholders in construction projects, such as supervising efficiency, rewards and punishment. Therefore, the results also demonstrate that the construction project based on BIM and IPD can effectively avoid the rent-seeking activities of the participants.

The findings are primarily based on questionnaire data originated from Central China; hence there are some limitations that are worth noting.

First, it provides compelling data evidence for the adoption of BIM and IPD in China. Second, it paves a solid foundation for the behavior of stakeholders in construction projects based on BIM and IPD.

In this paper, a game model of contractor, supervisor and owner is established, and a preliminary attempt is made to introduce BIM and IPD into the model for the behavioral research of participants.

Companies are increasingly designing pro-environmental games to motivate users to implement pro-environmental behaviors (PEBs). However, how different types of virtual interactions affect PEBs in pro-environmental games is not clear. Thus, the authors propose that two types of virtual interaction, interactions with game objects and interactions with other users, can induce platform intimacy and love for nature and that platform intimacy has a direct effect on love for nature. Simultaneously, the authors examine the moderating effect of network externality on the relationship between the two types of virtual interaction and platform intimacy.

The authors, respectively, employed data from 92 students and 574 Chinese mobile users to empirically investigate the research framework.

The findings indicate that participants in interactions with game objects and interactions with other users reported stronger feelings regarding platform intimacy and love for nature, which, in turn, positively influences PEBs. Consumers with stronger perceptions of network externalities were more likely to be affected by the initiation effect of the interaction with game objects.

The authors introduce the notion of love for nature to the pro-environmental behaviors field and discuss the priming effect of two types of interactions on platform intimacy and love for nature. In addition, the authors focus on the important effect of network externality on users' emotions.

The purpose of this paper is to examine sharing economy online marketplaces with the aim of understanding how trust perceptions form and get communicated through sharing economy platforms.

The authors build on online user comments and reviews as aggregated by independent third-party websites, and apply a qualitative analysis.

The findings show that the quantity of information and communication are important drivers towards building trust perceptions, while an overall lack of interaction between users and the marketplace provider intensifies perceived risks.

The authors validated the importance of trust and the authors have illustrated that the critical conditions that hinder trust formation are information asymmetry as well as the lack of interaction. What is also an interesting implication is that the impact of both of these can be exacerbated when there is a perceived lack of support among users and between them and the marketplace operator.

Although e-wallet adoption has grown in recent years, there are some countries like Peru with low penetration rates. Contrary to prior studies that focused on customers' adoption of e-wallets, this study focuses on merchants' adoption to fully understand the use of these services. Additionally, considering that e-wallets are two-sided markets with the co-existence of two distinct economic agents (customer and merchants) interacting through these platforms, this study is the first to assess the dynamics of inter-side benefits from the merchants' perspective. Finally, interoperability was also included to determine its role in countries where the interaction between different e-wallets is limited.

Based on two-sided markets and social cognitive theory, a model was proposed including merchants' perceived benefits associated with the use of e-wallets, for them and for their customers (inter-side). Additionally, technical issues such as interoperability were measured. Data were collected from nanostore owners in Lima in 2022. A structural equation modeling technique was used to determine the impact of both types of benefits and technical features on merchants' adoption of e-wallets. Finally, a polynomial regression with response surface methodology was used to assess the interaction of the benefits for both sides of the platform from the merchants' perspective.

The two-sided-market features of e-wallets were validated. From merchants' view, the use of these platforms is the result of balancing the benefits for them and for their customers, and the interaction between these two types of benefits varies according to the socio-economic level in which the nanostore operates. Additionally, interoperability was found to be important for merchants, so future policies should commit to achieve an ecosystem that facilitates the interoperability not only among e-wallets but also between e-wallets and third-party services. Finally, since service availability is also important for merchants, e-wallet providers should invest in improving their infrastructure's scalability.

Prior studies have mainly focused on the customer side of e-wallets, with little research about the adoption of digital payment methods by the merchant side. In addition, no study has focused on the effect of one of the sides of the platform on the other side (inter-side benefits) when adopting these services. Finally, the effect of interoperability across platforms has not been addressed in detail yet. This study aims to fill these gaps by proposing a framework to understand the adoption of these services by merchants in terms of inter-side benefits and technical issues.