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This paper aims to optimize the charging schedule for battery electric buses (BEBs) to minimize the charging cost considering the time-of-use electricity price.

The BEBs charging schedule optimization problem is formulated as a mixed-integer linear programming model. The objective is to minimize the total charging cost of the BEB fleet. The charge decision of each BEB at the end of each trip is to be determined. Two types of constraints are adopted to ensure that the charging schedule meets the operational requirements of the BEB fleet and that the number of charging piles can meet the demand of the charging schedule.

This paper conducts numerical cases to validate the effect of the proposed model based on the actual timetable and charging data of a bus line. The results show that the total charge cost with the optimized charging schedule is 15.56% lower than the actual total charge cost under given conditions. The results also suggest that increasing the number of charging piles can reduce the charging cost to some extent, which can provide a reference for planning the number of charging piles.

Considering time-of-use electricity price in the BEBs charging schedule will not only reduce the operation cost of electric transit but also make the best use of electricity resources.

Firms have already begun integrating artificial intelligence (AI) as a replacement for conventional performance management systems owing to its technological superiority. This transition has sparked a growing interest in determining how employees perceive and respond to performance feedback provided by AI as opposed to human supervisors.

A 2 x 2 between-subject experimental design was employed that was manipulated into four experimental conditions: AI algorithms, AI data, highly experienced human supervisors and low-experience human supervisor conditions. A one-way ANOVA and Welch t-test were used to analyze data.

Our findings revealed that with a predefined fixed formula employed for performance feedback, employees exhibited higher levels of trust in AI algorithms, had greater performance expectations and showed stronger intentions to seek performance feedback from AI algorithms than highly experienced human supervisors. Conversely, when performance feedback was provided by human supervisors, even those with less experience, in a discretionary manner, employees' perceptions were higher compared to similar feedback provided by AI data. Moreover, additional analysis findings indicated that combined AI-human performance feedback led to higher levels of employees' perceptions compared to performance feedback solely by AI or humans.

The findings of our study advocate the incorporation of AI in performance management systems and the implementation of AI-human combined feedback approaches as a potential strategy to alleviate the negative perception of employees, thereby increasing firms' return on AI investment.

Our study represents one of the initial endeavors exploring the integration of AI in performance management systems and AI-human collaboration in providing performance feedback to employees.

This study aims to analyze the influence of farmers’ degree of participation in collective action on their adoption decisions and waiting time regarding soil and water conservation (SWC) measures.

The Probit model and Generalized Propensity Score Match method are used to assess the effect of the degree of participation in collective action on farmers’ adoption decisions and waiting time for implementing SWC measures.

The findings reveal that farmers’ engagement in collective action positively influences the decision-making process regarding terrace construction, water-saving irrigation and afforestation measures. However, it does not significantly impact the decision-making process for plastic film and ridge-furrow tillage practices. Notably, collective action has the strongest influence on farmers’ adoption decisions regarding water-saving irrigation technology, with a relatively smaller influence on the adoption of afforestation and terrace measures. Moreover, the results suggest that participating in collective action effectively reduces the waiting time for terrace construction and expedites the adoption of afforestation and water-saving irrigation technology. Specifically, collective action has a significantly negative effect on the waiting time for terrace construction, followed by water-saving irrigation technology and afforestation measures.

The results of this study underscore the significance of fostering mutual assistance and cooperation mechanisms among farmers, as they can pave the way for raising funds and labor, cultivating elite farmers, attracting skilled labor to rural areas, enhancing the adoption rate and expediting the implementation of terraces, water-saving irrigation technology and afforestation measures.

Drawing on an evaluation of farmers’ degree of participation in collective action, this paper investigates the effect of participation on their SWC adoption decisions and waiting times, thereby offering theoretical and practical insights into soil erosion control in the Loess Plateau.

The paper investigates the effect of corruption on corporate investment efficiency around the world.

The sample includes 218,350 observations from 30,074 firms across 42 countries. The authors measure corruption based on the Corruption Perception Index (CPI) from Transparency International, Corruption Control Index (CCI) from the World Bank and Corruption Index from the International Country Risk Guide.

The authors find that corruption is negatively related to investment efficiency. The robustness checks with different measures of corporate investment and alternative regression approaches show consistent findings. Moreover, the authors also find that the effect of corruption is stronger (weaker) in strong (weak) shareholder protection countries.

The paper has two important contributions to the literature. First, it shows that corruption environment is also a determinant of corporate investment efficiency. Second, legal protection of shareholders can mitigate the negative effect of corruption on corporate investment efficiency.

本研究擬探討世界各地貪污腐敗對企業投資效率的影響。

研究樣本涵蓋42個國家，30,074間公司，218,350個觀察。測量貪污腐敗的方法乃基於國際透明組織的腐敗感知指數、世界銀行的腐敗控制指數和國際國家風險指南的貪污指數。

研究結果顯示、貪污與投資效率成負相關。以企業投資的各種測量方法、以及用其他的回歸分析方法來進行的強度檢驗，均顯示一致的結果。而且，我們亦發現，在對股東的保障較大的國家，貪污所帶來的影響也會較大；同樣地、對股東的保障較小的國家，貪污的影響也相應會較輕微。

本研究對文獻有兩個重要的貢獻。首先，研究證明了貪污腐敗的環境亦是企業投資效率的決定因素；其次，研究亦證明給股東的法律保護會減低貪污對企業投資效率所帶來的負面影響。

This study aims to deeply explore the factors influencing mobile game players' willingness to make in-game purchases, providing references for game developers and marketers to formulate effective strategies.

This research integrates the coolness factors and hedonic motivation system acceptance model to construct a comprehensive theoretical model analyzing mobile game players' willingness to make in-game purchases. The framework includes multidimensional variables such as joy, coolness, immersion, and game experience. Using data from 392 surveys collected from mobile game forums and social networks, the study employs structural equation modeling to analyze the factors and mechanisms influencing players' willingness to make in-game purchases and to verify the related research hypotheses.

The findings reveal that coolness factors have a significant positive impact on game experience and immersion, which in turn affect players' willingness to make in-game purchases. Game experience has a significant positive impact on both immersion and purchase willingness. A good game experience not only increases players' immersion but also directly enhances their willingness to make in-game purchases. Immersion plays a mediating role in the influence of coolness factors and joy on purchase willingness.

By integrating coolness theory with the hedonic motivation system acceptance model, this study constructs a comprehensive model to explore mobile game players' willingness to make in-game purchases. The combination of variables, including personal psychological and social psychological factors, provides a thorough analysis of the factors influencing mobile game purchase willingness, enriching existing research.

This techno-centric and too much busy day-to-day living style of citizens pressurizes the implementation of E-ticketing service to adapt with change. Thus, this study aims to examine the factors influencing railway passengers’ E-ticketing service acceptance and usage intention in Bangladesh and to extend the widely used Technology Acceptance Model through inserting two new constructs.

This paper employs structural equation modeling to test model’s paths developed through theoretical research framework. Moreover, a structured questionnaire was administered at different railway stations in northern and western parts of Bangladesh to collect data. Total of 302 responses were considered for statistical analysis to test hypotheses after considering anomalies and outliers in raw data.

The study results show that technology trust (TT) has the strongest impact on passengers’ E-ticketing usage intention rather than perceived ease of use and perceived usefulness (PU). Meanwhile, the easiness of using technology to reserve tickets does matter to female passengers rather than male passengers wherein PU and TT do not do that.

The findings of this study might be helpful for the railway authorities to improve the ticket reservation service quality online by developing the advanced booking application and minimizing the pressure on other transportation. Therefore, this empirical study will contribute to this domain for further study that ensures full satisfaction of passengers and uplift the railway passengers’ usage intention for E-ticketing which then helps the government to implement the digitization slogan with efficiency and effectiveness.

This paper aims to systematically expound the theory and development background of supply chain finance and blockchain, design a railway freight supply chain financial platform based on blockchain, determine the risk management system and business support system of supply chain finance business and analyze the value generated by the combination of supply chain finance business and blockchain.

Investigation and research method; Prototype method; Model method; Value analysis.

The business model integrating supply chain finance and blockchain technology will bring great changes to freight industry. The development of supply chain finance is beneficial to the healthy development of the core participants of railway freight transport business and its upstream and downstream ecosystems. It links commerce, logistics, warehousing and financial services together and builds an industry-integrated ecological service platform through information technology platform and supporting system, taking data as the basis and combining information technology such as blockchain as innovative means.

This paper will provide important reference value for related research. This paper innovatively designs the supply chain financial platform of freight transportation industry-integrating blockchain technology and analyzes its business model, technical system, risk management and control system and value system in detail, which will provide technical support for the innovative reform of freight information technology and realize the stable and high-speed development of freight logistics informationization.

The missing travel time data for roads is a common problem encountered by traffic management departments. Tensor decomposition, as one of the most widely used method for completing missing traffic data, plays a significant role in the intelligent transportation system (ITS). However, existing methods of tensor decomposition focus on the global data structure, resulting in relatively low accuracy in fibrosis missing scenarios. Therefore, this paper aims to propose a novel tensor decomposition model which further considers the local spatiotemporal similarity for fibrosis missing to improve travel time completion accuracy.

The proposed model can aggregate road sections with similar physical attributes by spatial clustering, and then it calculates the temporal association of road sections by the dynamic longest common subsequence. A similarity relationship matrix in the temporal dimension is constructed and incorporated into the tensor completion model, which can enhance the local spatiotemporal relationship of the missing parts of the fibrosis type.

The experiment shows that this method is superior and robust. Compared with other baseline models, this method has the smallest error and maintains good completion results despite high missing rates.

This model has higher accuracy for the fibrosis missing and performs good convergence effects in the case of the high missing rate.

This paper aims to address the longitudinal control problem for person-following robots (PFRs) for the implementation of this technology.

Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR control.

A lab PFR with the bar-laser-perception device is developed and tested in the field, and the results indicate that the proposed models perform well in normal person-following scenarios.

This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research.

The research on corporate social responsibility (CSR) and firm performance (FP) has seen a surge over the years. However, the role of corporate reputation (CR), advertising strategy and market competition is still unclear. The purpose of this study is to consider this gap and test an integrative model of CSR-FP, in the context of India.

The data for CSR expenditure were collected from the annual reports of the selected companies. CR was captured using the ranks of Fortune India 500, Business Standard 1,000 and Economic Times 500. The financial data were collected from CMIE (Prowess) database.

Results of structural equation modeling (SEM) revealed a significant relationship between CSR expenditure of the firm and its reputation; but no relationship between CR and performance. When CR increases, the performance of a firm may not improve. Competitive intensity (CI) had no statistically significant role in the CR-FP relationship for performance. Results suggest that reputed firms perform well despite high competition within an industry. High reputation is effective in improving performance irrespective of competition. CI has a positive impact in the reputation–performance linkage. Advertising intensity (AI) played a significant moderating role in the CSR intensity and CR relationship.

This research represents an added value for the literature on CSR by highlighting the importance of CR, advertising strategy and market competition in the relationship between CSR and FP. The findings have several implications for theory and practice, which have been discussed in the study.