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Feature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluation criterion. In order to address this problem, the standard deviation is employed to adjust the importance between relevancy and redundancy. Based on this idea, a novel feature selection method named as Feature Selection Based on Weighted Conditional Mutual Information (WCFR) is introduced. Experimental results on ten datasets show that our proposed method has higher classification accuracy.

This study aims to investigate whether temperature affects the product quality of exporters and whether the effect is non-linear. More specifically, whether the impact of high temperatures differs from the impact of low temperatures, and whether different types of companies or industries are affected differently.

The paper uses detailed data covering all Chinese exporters from 2000 to 2016 to estimate the effects of temperature on the product quality of export firms. To clarify the relationship between them, the authors use a semi-parametric regression method, trying to test whether there is a non-linear relationship between temperature and the export quality of firms.

The increase in the number of high temperature days significantly reduces the quality of exported products, and this negative effect increases as the temperature rises. High temperature has the most significant negative impact on export quality for firms with low technical complexity, private firms and firms with no intermediate imports and located in historical hot cities. Product quality of both labor-intensive and capital-intensive firms will be affected by heat. High temperatures have the greatest negative impact on the export quality of newly entering products, followed by exiting products, with the least negative impact on persisting product.

To the best of the authors’ knowledge, this paper is the first to examine the impact of temperature on the quality of economic development. The findings of this paper again show that the potential economic impacts of global warming are huge. In addition to some potentially devastating impacts in the future, global warming is already causing imperceptible impacts in the present. Public and economic agents need to fully understand the possible adverse impacts of climate change and take corresponding adaptation measures to cope with global warming.

This paper aims to present a tool-supported approach for visualising personas as social goal models, which can subsequently be used to identify security tensions.

The authors devised an approach to partially automate the construction of social goal models from personas. The authors provide two examples of how this approach can identify previously hidden implicit vulnerabilities and validate ethical hazards faced by penetration testers and their safeguards.

Visualising personas as goal models makes it easier for stakeholders to see implications of their goals being satisfied or denied and designers to incorporate the creation and analysis of such models into the broader requirements engineering (RE) tool-chain.

The approach can be used with minimal changes to existing user experience and goal modelling approaches and security RE tools.

Due to product diversity, traditional quality signals in the hotel industry such as star ratings and brand affiliation do not work well in the accommodation booking process on the sharing economy platform. From a suppliers’ perspective, this study aims to apply the signaling theory to the booking of Airbnb listings and explore the influence of quality signals on the odds of an Airbnb listing being booked.

A binomial logistic model is used to describe the influences of different attributes on the market demand. Because of the large sample size, sequential Bayesian updating method is utilized in hospitality and tourism field for the first attempt.

Results show that, in addition to host-specific information such as “Superhost” and identity verification, attributes including price, extra charges, region competitiveness and house rules are all effective signals in Airbnb. The signaling impact is more effective for the listings without any review comments.

This study contributes to the literature by incorporating the signaling theory in the analysis of booking probability of Airbnb accommodation. The research findings are valuable to hosts in improving their booking rates and revenue. In addition, government and industrial management organizations can have more efficient strategy and policy planning.

This study aims to explore how digital platforms (DPs) contribute to value co-creation in municipal solid waste (MSW) management systems.

The present paper conducts an explorative analysis using single case study methodology. The case in question involves a DPs operating in Italy.

Empirical analysis shows that DPs help engage citizens in MSW and reduce the fragmentation in waste management systems by fulfilling a brokerage role that connects citizens, municipalities and waste management companies. The development of bidirectional knowledge and resource flow among actors contributes to better waste recycling processes, as well as fosters economic, environmental and social value co-creation in a complex public service.

This research is limited to a single case study within the Italian context, which may influence the generalizability of the findings. Future research could expand the scope to include multiple case studies across different geographical regions.

For practitioners and policymakers, this paper underscores the strategic benefits of adopting DPs in MSW management systems and thereby improving public service delivery.

The case analysis highlights that DPs can assist public actors in achieving numerous sustainable development goals by enhancing recycling rates and activating learning mechanisms among citizens.

This study contributes to literature by connecting different fields of research (i.e. waste management and public management) and using network theory to show how DPs can contribute to the economic, environmental and social sustainability of MSW while generating relevant benefits for the actors involved.

Using a sample of 9,898 firm-year observations from 1,821 unique Chinese listed firms over the period from 2004 to 2019, this study aims to investigate whether the market rewards meeting or beating analyst earnings expectations (MBE).

The authors use an event study methodology to capture market reactions to MBE.

The authors document a stock return premium for beating analyst forecasts by a wide margin. However, there is no stock return premium for firms that meet or just beat analyst forecasts, suggesting that the market is skeptical of earnings management by these firms. This market underreaction is more pronounced for firms with weak external monitoring. Further analysis shows that meeting or just beating analyst forecasts is indicative of superior future financial performance. The authors do not find firms using earnings management to meet or just beat analyst forecasts.

The authors provide evidence of market underreaction to meeting or just beating analyst forecasts, with the market's over-skepticism of earnings management being a plausible mechanism for this phenomenon.

The findings of this study are informative to researchers, market participants and regulators concerned about the impact of analysts and earnings management and interested in detecting and constraining managers' earnings management.

The authors provide new insights into how the market reacts to MBE by showing that the market appears to focus on using meeting or just beating analyst forecasts as an indicator of earnings management, while it does not detect managed MBE. Meeting or just beating analyst forecasts is commonly used as a proxy for earnings management in the literature. However, the findings suggest that it is a noisy proxy for earnings management.

The purpose of this paper is to investigate the value relevance of Research and development (R&D) and free cash flow (FCF) in an efficient investment setup. Most importantly, this paper examines whether the value relevance of R&D and FCF is associated with life cycle stages. Furthermore, this paper reports whether the market response to R&D and FCF is different in competitive market as compared to the concentrated market.

The analysis is based on the Ohlson (1995) model for the determination of value relevance of earnings and book value. Capitalized R&D and FCF data comprising of the Chinese A-listed firms from the year 2008 to 2016 are selected for this study. Following Anthony and Ramesh (1992), the authors divided the firm life cycle into different stages. HHI index is used to measure the product market competition.

The main result shows that R&D and FCF are value relevant in Chinese A-listed firms. The impact of R&D and FCF on the value relevance of earnings and book value is also positive and significant. The findings of the effect of R&D and FCF on the value relevance of accounting information signify that the information content (R²=0.46) of the mature stage is higher than that of the growth and stagnant stage. The explanatory power measured by R² value for competitive industries (0.47) is much higher than the concentrated industries (0.33).

Despite taking into account all the possible available variables, there are few limitations of the study. This study only studies the effect of EPS, BPS, R&D and FCF on the value relevance of accounting information. Other determinant such as size, growth, leverage and firm age is ignored. Since the R&D expenditure is discretionary, therefore the findings cannot be generalized to all the sectors. A sector wise comparative study can be done in future, to understand the differences in the information contents of R&D and FCF. Also, the tax effect of R&D is ignored in this study. For future call, the value relevance of tax effect on R&D can be explored.

The investors can now determine the present value of all the future cash flows of investing activities. The results of the study are significant for the Chinese investors who should incorporate the R&D and FCF along with investment efficiency. The investors should keep in mind the life cycle stage while investing in a certain stock. The competitive markets have more information content than the concentrated markets. The corporate managers can benefit from this study while issuing new shares. The market responds positively to the stock having investment efficient R&D and FCF investment. For the policy implication perspective, the security market regulator should devise the effective pro-effective product market regulations.

The contribution of this study is manifold. First, according to the authors’ knowledge, this is the first study that incorporates investment efficiency with R&D and FCF and explores its effect on the value relevance of accounting information. Second, the impact of R&D on the value relevance is studied by numerous researchers (Lev and Sougiannis, 1996; Han and Manry, 2004). Similarly, FCF-agency cost effect has also been investigated by (Rahman and Mohd-Saleh, 2008; Chen et al., 2012) but the value relevance of R&D and FCF during different life cycle stages still needs to be answered. Finally, this study also tries to answers the question if the market response to R&D and FCF is different in a competitive market as compared to the concentrated market.

Motivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure to Chinese economic policy uncertainty at the individual stock level in large Asian markets.

We estimate the monthly uncertainty exposure (beta) for each stock and then employ the portfolio-level sorting analysis to investigate the relationship between the China’s uncertainty exposure and the future returns of major Asian markets over multiple trading horizons. The raw returns of the high-minus-low portfolios are then adjusted using conventional asset pricing models to investigate whether the relationship is explained by common risk factors. Finally, we check the robustness of the portfolio-level results through firm-level Fama and MacBeth (1973) regressions.

Applying portfolio-level sorting analysis, we reveal that exposure to Chinese uncertainty is negatively related to the future returns of large stocks over multiple trading horizons in Japan, Hong Kong and India. We discover this is unexplained by common risk factors, including market, size, value, profitability, investment and momentum, and is robust to the specification of stock-level Fama and MacBeth (1973) regressions.

Our analysis demonstrates the spillover effects of Chinese economic policy uncertainty across the region, provides evidence of China's emerging economic leadership, and offers trading strategies for managing uncertainty risks.

The findings of the study significantly improve our understanding of stock return predictability in Asian markets. Unlike previous studies, our results challenge the leading role of the US by providing a new intra-regional return predictor, namely, China’s uncertainty exposure. These results also evidence the continuing integration of the Asian economy and financial markets. However, contrary findings for some Asian markets point toward certain market-specific features. Compared with market-level research, our analysis provides deeper insights into the performance of individual stocks and is of particular importance to investors and other market participants.