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This paper provides a systematic review of literature pertaining to the welfare impact of financial inclusion. We identify the 50 most influential publications in the field that have evolved into three distinct categories, each of which we critically review to identify the main contributions of this research area.

By conducting a state-of-the-art literature review, this paper identifies the most influential papers in the research fields on the welfare impact of financial inclusion. One caveat is that as newer publications generally have fewer citations, reviewing prior work can result in a misleading account of emerging trends and research directions. Manual assessment of publications after 2018 facilitates a discussion of important emerging research trends and their directions.

The three key research streams are identified as financial services and financial accessibility, financial capability, and financial literacy and household welfare. By assessing publications from 2018 to 2023, we also document four key emerging research trends: Fintech and digital financial inclusion, sustainability and climate change, growth, poverty, income inequality, financial stability, and Entrepreneurship. Drawing on these emerging trends, we highlight the opportunities for future research.

Keyword searches have limitations as some papers might be overlooked if they do not match the specific search criteria, despite their relation and significance to the overall topic of the welfare impact of financial inclusion. To address this issue, we have expanded this review by incorporating more literature from other databases, such as the Scopus database which may alleviate this issue.

The three key research streams contribute to a comprehensive understanding of the welfare impact of financial inclusion. The emerging trends integrate existing knowledge and leave the chance for innovative research to expand the research frontier.

This paper fulfils the systematic literature review streams in the welfare impact of financial inclusion and provides fruitful opportunities for future research.

This study sheds new light on the personality trait and provides evidence regarding the relation between narcissism and desirable accounting practices, specifically the impact of CEO narcissism on accounting conservatism.

The authors test the relation between CEO narcissism and accounting conservatism for a sample of 907 US companies and their corresponding CEOs for the period between 2010 and 2018. The authors apply three established models of accounting conservatism and measure executives' narcissism using a non-intrusive approach ubiquitous in the literature.

The authors find that CEO narcissism is associated with speculative accounting practices in the form of timely recognition of positive news and more prudent financial reporting of anticipated negative news. The authors provide the first empirical evidence that, despite its well-known negative effects on corporate financial reporting, executive narcissism can also produce positive outcomes.

While managerial overconfidence has received much attention, the effects of executives' narcissism are still widely unexplored (Chatterjee and Hambrick, 2007). The authors thus contribute to the literature by investigating the relationship between CEOs' narcissism and accounting conservatism. The authors conjecture CEO narcissism should have a twofold effect on prudent financial reporting. On the one hand, CEOs' narcissism should be associated with low levels of unconditional conservatism due to excessively fast good news recognition. On the other hand, narcissistic executives should be associated with early recognition of negative news and hence with higher levels of conditional conservatism.

The significance of smart mobility practices in shaping cities from a smart perspective has grown in recent years, influencing policies and the choices made by inhabitants. This transformation has led to the emergence of novel services and strategies, creating a new, vibrant and highly personalised urban environment that caters to the needs and preferences of both local residents and visitors. The purpose of this study is to evaluate the influence of smart mobility practices on tourism flows in cities, considering the moderating effect of airport activities on this relationship.

Using a generalised method of moments estimation and focusing on 20 Italian cities over an eight-year period, the authors highlight the current relationship between smart mobility practices and tourism flows. Moreover, the authors demonstrate that the yearly advancement of airports positively moderates this relationship.

The findings indicate a significant relationship between smart mobility practices in modern cities and tourism inflows because they influence the development of tourism services and emerging trends such as smart tourism and smart destinations. Furthermore, airport activities as a proxy for city openness play a crucial role in this link. The study shows that airports have an incremental impact on tourism and on the relationship between tourism and sustainable practices.

The limitations of this quantitative approach include the focus on a single country, the challenge of measuring the development of smart mobility practices due to a lack of standardised variables and the need for future research to expand the sample to different countries in relation to tourism inflows.

This study has practical implications for policymakers and governance in their task of effectively coordinating internal smart mobility practices and managing incoming tourism flows.

This study has social implications, highlighting the need for policymakers and governance to address the societal impacts of smart mobility practices and tourism inflows, ensuring inclusive and sustainable outcomes for local communities.

This study contributes to the existing literature as one of the first attempts to examine the interplay between smart mobility practices in smart cities and tourism flows. Furthermore, it emphasises the role of airports in this relationship, highlighting how the interaction between these variables benefits both stakeholders.

Improving productivity and efficiency has always been crucial for industrial companies to remain competitive. In recent years, the topic of environmental impact has become increasingly important. Published research indicates that environmental and economic goals can enforce or rival each other. However, few papers have been published that address the interaction and integration of these two goals.

In this paper, we identify both, synergies and trade-offs based on a systematic review incorporating 66 publications issued between 1992 and 2021. We analyze, quantify and cluster examples of conjunctions of ecological and economic measures and thereby develop a framework for the combined improvement of performance and environmental compatibility.

Our findings indicate an increased significance of a combined consideration of these two dimensions of sustainability. We found that cases where enforcing synergies between economic and ecological effects were identified are by far more frequent than reports on trade-offs. For the individual categories, cost savings are uniformly considered as the most important economic aspect while, energy savings appear to be marginally more relevant than waste reduction in terms of environmental aspects.

No previous literature review provides a comparable graphical treatment of synergies and trade-offs between cost savings and ecological effects. For the first time, identified measures were classified in a 3 × 3 table considering type and principle.

In the context of fixed-wing aircraft wing assembly, there is a need for a rapid and precise measurement technique to determine the center distance between two double-hole components. This paper aims to propose an optical-based spatial point distance measurement technique using the spatial triangulation method. The purpose of this paper is to design a specialized measurement system, specifically a spherically mounted retroreflector nest (SMR nest), equipped with two laser displacement sensors and a rotary encoder as the core to achieve accurate distance measurements between the double holes.

To develop an efficient and accurate measurement system, the paper uses a combination of laser displacement sensors and a rotary encoder within the SMR nest. The system is designed, implemented and tested to meet the requirements of precise distance measurement. Software and hardware components have been developed and integrated for validation.

The optical-based distance measurement system achieves high precision at 0.04 mm and repeatability at 0.02 mm within a range of 412.084 mm to 1,590.591 mm. These results validate its suitability for efficient assembly processes, eliminating repetitive errors in aircraft wing assembly.

This paper proposes an optical-based spatial point distance measurement technique, as well as a unique design of a SMR nest and the introduction of two novel calibration techniques, all of which are validated by the developed software and hardware platform.

This paper investigates the influence of political connections on sustainability disclosure in the context of China's Regulation 18.

The study employs a quasi-experimental approach, utilizing difference-in-difference (DiD) analysis, dynamic DiD and propensity score matching to analyze the effects of politically connected independent directors on sustainability disclosure following the implementation of Regulation 18.

Companies with politically connected independent directors show an improvement in sustainability disclosures after Regulation 18. This effect is stronger for firms facing high political pressure or lacking alternative political power. Additionally, the increase in value from sustainability disclosures compensates for the loss of politically connected independent directors, indicating a positive value impact of sustainability disclosures.

This study provides novel insights into the corporate disclosure policy in China by investigating the impact of politically connected directors on sustainability disclosure. Additionally, it sheds light on the limitations of political power and its substitution effects within companies.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

This study explores the influence of cultural heritage tourism perception on China's tourism image. It analyzes the role of the spiritual bond established between overseas Chinese youth and the motherland during their visit to the cultural heritage sites in China. This study constructs a theoretical model with 350 overseas Chinese youth as samples based on the identity theory, Stimulus-Organism-Response (S-O-R) theory, and Howard-Sheth model. The results show that cultural heritage tourism perception directly and positively promotes cultural identity among overseas Chinese youth. It is also indirectly and positively associated with their cultural identity through enhancing the tourism image. Cultural intelligence plays a positive moderating role between cultural heritage tourism perception and cultural identity. The results provide significant implications for developing cultural heritage tourism and cultural communication.

Infrastructure financialization plays a critical role in infrastructure development and urban growth around the world. However, on the one hand, the existing research on the infrastructure financialization focuses on qualitative and lacks quantitative country-specific studies. On the other hand, the spatial heterogeneity and influencing factors of infrastructure financialization are ignored. This study takes China as a typical case to identify and analyze the spatial characteristics, development process and impact factors of infrastructure financialization.

To assess the development and characteristics of infrastructure financialization in China, this study constructs an evaluation index of infrastructure financialization based on the infrastructure financialization ratio (IFR). This study then analyzes the evolution process and spatial pattern of China's infrastructure financialization through the spatial analysis method. Furthermore, this study identifies and quantitatively analyzes the influencing factors of infrastructure financialization based on the spatial Dubin model. Finally, this study offers a policy suggestion as a governance response.

The results demonstrate that infrastructure financialization effectively promotes the development of infrastructure in China. Second, there are significant spatial differences in China’s infrastructure financialization. Third, many factors affect infrastructure financialization, with government participation having the greatest impact. In addition, over-financialization of infrastructure has the potential to lead to government debt risks, which is a critical challenge the Chinese Government must address. Finally, this study suggests that infrastructure financialization requires more detailed, tailored,and place-specific policy interventions by the government.

This study not only contributes to enriching the knowledge body of global financialization theory but also helps optimize infrastructure investment and financing policies in China and provides peer reference for other developing countries.