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This paper discusses a case of truck sharing as an application of the sharing economy. This case study examines a real mixed feed company with multiple factories. In this company’s operation, bulk trucks located in a factory had not previously been shared for delivery with other factories to their pre-assigned customers of stock farms. Therefore, this paper suggests a new delivery system that facilitates truck-sharing and analyzes its effects on the transport cost and trucks’ CO₂ emissions. To this end, this paper develops vehicle routing models to represent the current delivery practice and the new truck-shared delivery (TSD). In addition, models are developed for a carbon control policy of an emission trading scheme (ETS) and the effects of the ETS on truck-sharing are investigated. Numerical analysis is conducted to identify the effects of the TSD and the carbon control policy and draw practical implications.

The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.

The paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.

The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.

The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.

This study intends to explore the connection between Facebook-based social media marketing (FSMM) and Facebook-based online purchase order (FOPO) for 20 popular online fashion retail brands across three South Asian countries: India, Pakistan and Bangladesh. FSMM was further divided into four components: Perceived trust (PT), Perceived informativeness (PInf), Perceived interactivity (PInt) and Perceived benefit (PB). 

The authors selected 20 popular Facebook-based online fashion brands involved in clothing and fashion accessories businesses in those three countries. Later, the authors purposively selected 114 region-based Facebook page administrators (admins) responsible for operating those brands' Facebook pages and taking Facebook-based online orders. The authors collected primary data from those admins as respondents through a structured survey instrument. The authors applied SPSS 25 for descriptive analysis and a covariance-based structural equation modeling (CB-SEM) (through AMOS 25) for testing the hypothesized relations.

Based on the valid responses and application of proper statistical measures, it was revealed that three FSMM components: PT, PInf and PB have significant positive relationships with FOPO, while PInt has an insignificant relationship with FOPO. 

South Asia is a growing business hub and the largest consumer market in terms of population. This study was conducted to identify the relationship between FSMM and FOPO in the three most prominent South Asian countries. As the first study was undertaken ever on customer perceptions of FSMM in a multi-country South Asian context, this paper is expected to be helpful for academics in conducting further empirical investigations on Facebook-based marketing as well as practitioners and policymakers in formulating and implementing Facebook-based marketing strategies.

Although the tasks of managing carbon peaks and achieving carbon neutrality in China are arduous, they are also of great significance, which highlights China’s determination and courage in dealing with climate change. The power industry is not only a major source of carbon emissions but also an important area for carbon emission reduction. Thus, against the backdrop of carbon neutrality, understanding the development status of China’s power industry guided by the carbon neutrality background is important because it largely determines the completeness of China’s carbon reduction promises to the world. This study aims to review China’s achievements in carbon reduction in the electric industry, its causes and future policy highlights.

The methods used in this study include descriptive analyses based on official statistics, government documents and reports.

The research results show that, after years of development, the power industry has achieved positive results in low-carbon provisions and in the electrification of consumption, and carbon emission intensity has continued to decline. Policy initiatives play a key role in this process, including, but not limited to, technology innovations, low-carbon power replacement and supported policies for low-carbon transformation toward low-carbon economies.

This study provides a full picture of China’s power industry against the backdrop of low-carbon development, which could be used as a benchmark for other countries engaging in the same processes. Moreover, a careful review of China’s development status may offer profound implications for policymaking both for China and for other governments across the globe.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

This study aims to prove the complexity of the relationship between CSR and financial performance (FP) and to decompose the complexity of the relationship using neo-institutional theory.

This research employs a meta-analysis that integrates 55 various contexts studied between 1998 and 2017 using correlation coefficient as the effect size.

This study proves that the nature of the relationship between CSR and FP is complex and suggests that the analysis of the relationship between the two variables includes institutional factors to produce generalizable conclusions. Country characteristics, forms and dimensions of CSR, CSR measurements and FP measurements explain the complexity of the relationship between CSR and FP.

Future research is expected to include industry characteristics and the corporate governance model in the analysis of the relationship between CSR and FP. Differences in industry characteristics affect the selection of CSR forms and dimensions, bringing it the potential to influence the relationship between CSR and FP. The corporate governance model adopted by developing countries and developed countries also has the potential to be an institutional factor to influence the relationship between CSR and FP.

This research proves that the complexity of the relationship between CSR and FP is nature given. This research explores the factors causing the complexity of the relationship using neo-institutional theory, which, to the author's knowledge, has not been done by other researchers.

This paper aims to review the research on accounting professionalisation in China to develop insights into how the research is developing, offer a critique of the research to date and outline future research directions and opportunities.

This paper adopts a methodological approach of systematic literature review, as suggested by Tranfield et al. (2003) and Denyer and Tranfield (2009), to identify, select and analyse the extant literature on the Chinese public accounting profession. In total, 68 academic works were included in the review process.

This paper finds that the extant literature has produced fruitful insights into the processes and underlying motivation of accounting professionalisation in China, demonstrating that the Chinese experience has differed, to a large extent, from the hitherto mainly Anglo-American-dominated understandings of accounting professionalisation. However, due to the lack of common theoretical vernacular and an agreed upon focus, the extant literature illustrates a fragmented and contradictory picture, making attempts to accumulate prior knowledge in the field increasingly difficult.

This paper focusses only on research published in English. Consequently, the scope of review has been limited as some works published in languages other than English may be excluded.

This paper provides one of the pioneering exercises to systematically review the research on accounting professionalisation in China. It explores significant issues arising from the analysis and provides several suggestions for furthering the research effort in this field.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

Owing to increased energy demands, China has become the world’s top CO2 emitter, with electricity generation accounting for the majority of emissions. Therefore, the Chinese Government aspires to achieve a low-carbon transformation of the electric industry by enhancing its green innovation capacity. However, little attention has been paid to the green development of electric technology. Thus, this paper aims to uncover the spatiotemporal evolution of electric technology in the context of China’s low-carbon transformation through patent analysis.

Using granted green invention patent data for China’s electric industry between 2000 and 2021, this paper conducted an exploratory, spatial autocorrelation and time-varying difference-in-differences (DID) analysis to reveal the landscape of electric technology.

Exploratory analysis shows that the average growth rate of electric technology is 8.1%, with spatial heterogeneity, as there is slower growth in the north and west and faster growth in the south and east. In addition, electric technology shows spatial clustering in local areas. Finally, the time-varying DID analysis provides positive evidence that low-carbon policies improve the green innovation capacity of electric technology.

The different effects of the low-carbon pilot policy (LCPC) on R&D subjects and the LCPC’s effectiveness in enhancing the value of patented technology were not revealed.

This paper reveals the spatiotemporal evolutionary characteristics of electric technology in mainland China. The results can help the Chinese Government clarify how to carry out innovative development in the electric industry as part of the low-carbon transformation and provide a theoretical basis and research direction for newcomers in this field.

Mobile Facebook (m-Facebook) creates many business opportunities for brands and firms while increasingly drawing interest in scientific literature. However, research is scarce on the immersive experiences prompted by m-Facebook, and how these experiences facilitate users’ engagement, their positive attitude towards Facebook and their continued use of it. The paper aims to discuss these issues.

This paper theoretically and empirically analyses m-Facebook users’ immersive experiences, along with their affective and behavioural effects.

The results reveal the important role of imagery, presence and flow in the context of m-Facebook; the interplay between these three immersive phenomena; and the influence the user’s optimum stimulation level has on them.

The investigation offers a foundation for understanding users’ immersive experiences on m-Facebook, and informs practitioners who aim to enhance users’ engagement with, attitude towards, and continued use of m-Facebook content.