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The purpose of this study is to identify antecedents of adoption and post-adoption switching of online pharmacy applications (OPA) in Indian society. A push-pull-mooring (PPM) model was formulated to evaluate the impact of various constructs upon “consumers’ switching intention” (CSI).

An online questionnaire was sent to 252 users of OPA in India. Hypotheses were generated to examine the push, pull and mooring effects of constructs developed. The relationships between dependent and independent variables were evaluated using structured equation modeling (SEM).

The study explicated the effect of PPM constructs on CSI in the context of OPA adoption. “Perceived usefulness,” “perceived ease of use” and “alternative attractiveness” had a significant “pull” effect on CSI. “Switching cost” had a “mooring” effect on CSI, whereas the degree of “customer involvement in decision-making” was found to have a “push” effect upon CSI.

This study theoretically established that the constructs of “perceived usefulness,” “perceived ease of use” and “alternative attractiveness” had significant “pull” effect on “consumers’ switching intention.” The construct of “switching cost” had a “mooring” effect on CSI, whereas the degree of “customer involvement in decision-making” was found to have a “push” effect upon CSI.

The study provided valuable insights regarding consumer behavior regarding OPAs. These findings could be applied by managers in framing effective strategies to grow and retain the customer base of OPAs.

To the best of the authors’ knowledge, this was one of the first empirical investigative studies to assess precursors of adoption and post-adoption characteristics of consumer behavior through the PPM model, in the context of Indian OPAs.

Despite the fact that hygiene and sanitation are becoming more critical for improving the present situation in developing nations, the factors that affect them are not well covered in the present research. This paper investigates the quality of the hygiene and sanitization factors and identifies the interrelations between the identified factors.

A graph theory-based approach is proposed to assess the factors influencing the practice, and a critical service index (CSI) is used to quantify the same.

Two Indian villages are used to illustrate the implementation of the suggested approach. This represents the validation of the suggested method, as well as assisting in the development of essential suggestions for increasing the quality of hygiene and sanitization in the Indian context. In spite of the increasing importance of hygiene and sanitation for improving the current situation in developing countries, the factors that influence them are not well-researched.

This study contributes in two ways. First, it provides an organized methodology for quantifying hygiene and sanitation factors and a critical service index that incorporates the findings. The suggested approach may also be used to evaluate and classify other sectors. Second, it shows how the methodology was used to create key recommendations for two Indian villages, which may be considered the first effort in India’s hygiene and sanitation initiatives.

This research discussed improvements in sanitation and hygiene habits among Indian households, which have not been achieved as expected under the Swachh Bharat Mission.

With the rapid development of the financial market, stock index futures have been the one of important financial instruments. Predicting stock index futures accurately can bring considerable benefits for investors. However, traditional models do not perform well in stock index futures forecasting. This study put forward a novel hybrid model to improve the predictive accuracy of stock index futures.

This study put forward a multivariate deep learning framework based on extreme gradient boosting (XGBoost) for stock index futures price forecasting. First, the original sequences were decomposed into several sub-sequences by variational mode decomposition (VMD), and these sub-sequences were reconstructed by sample entropy (SE). Second, the gradient boosting decision tree (GBDT) was used to rank the feature importance of influential factors, and the top influential factors were chosen for further prediction. Next, reconstructed sequence and the multiple factors screened were input into the bidirectional gate recurring unit (BiGRU) for modeling. Finally, XGBoost was used to integrate the modeling results.

For the sake of examining the robustness of the proposed model, CSI 500 stock index futures, NASDAQ 100 index futures, FTSE 100 index futures and CAC 40 index futures are selected as sample data. The empirical consequences demonstrate that the proposed model can serve as an effective tool for stock index futures prediction. In other words, the proposed model can improve the accuracy of stock index futures.

In this paper, an innovative hybrid model is proposed to enhance the predictive accuracy of stock index futures. Meanwhile, this method can be applied in other financial products prediction to achieve better forecasting results.

Using intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).

The authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.

The empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.

The study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

This study aims to investigate the perspective of corporate philanthropy during the coronavirus disease 2019 (COVID-19) in China for firms with various levels of corporate social responsibility (CSR). Specifically, the study appraises the impact of the COVID-19 pandemic on the stock returns and sustainable development of Chinese-listed companies and determines the likelihood of paying donations vis-à-vis firm reputation.

The study used data from 117 Chinese-listed firms engaged in philanthropy during the COVID-19 pandemic. The authors also utilized the stock returns and cash donation data, and owing to the cross-sectional data and continuous nature of dependent variables, they employed the ordinary least squares regression to test the research hypotheses.

The results show that irresponsible actions have a positive relationship with donations. The study particularly reveals that irresponsible firms have significant negative abnormal returns during the first wave of the COVID-19 pandemic.

To the best of our knowledge, this is the first empirical study to explore the perspective of corporate philanthropy during the COVID-19 pandemic for companies with different CSR levels. This study contributes to the empirical research on CSR and provides insights for managerial-cum-financial decisions to encourage managers of irresponsible firms to pursue philanthropic behaviors after crisis events.

The rapid growth of Fintech presents a growing challenge for banking institutions, particularly those with more traditional, service backgrounds. This paper aims to examine the relationship between Fintech innovation and bank performance by exploiting novel Chinese market data.

Guided by the work of Dietrich and Wanzenried (2011, 2014) and Phan et al. (2019), the authors construct a regression model to investigate the effect of Fintech innovation on the profitability of Chinese listed banks. The authors include their measures of Fintech innovation in each of their selected structures.

Results indicate that Fintech innovation is negatively associated with bank performance and that state-owned banks, joint-stock commercial banks and long-established banks are more negatively impacted by Fintech innovation relative to city and rural commercial banks and younger banks.

Risk tolerance levels, internal structure and efficiency and recent debt repayment performance channels are each shown to be significant, robust explanatory factors underpinning such results.

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 aims to assess how real income, oil prices and gold prices affect housing prices in China from 2010 to 2021.

This study uses a novel bootstrap autoregressive distributed lag (ARDL) testing to empirically analyze the short and long links among the tested variables.

The ARDL estimations demonstrate a positive impact of oil price shocks and real income on housing market prices in both the phrases of the short and long run. Furthermore, the results reveal that gold price shocks negatively affect housing prices both in the short and long run. The result can be attributed to China’s housing market and advanced infrastructure, resulting in a drop in housing prices as gold prices increase. Additionally, the prediction of housing market prices will provide a base and direction for housing market investors to forecast housing prices and avoid losses.

To the best of the authors’ knowledge, this is the first attempt to analyze the effect of gold price shocks on housing market prices in China.

Today's business world has been tarnished with numerous corporate irresponsible behaviors. It is thus of great importance for firms to carry out crisis management on the condition of corporate social irresponsibility (CSI). Taking a contingent social media crisis management perspective, the authors aim to investigate the relation between CSI and firm value while also examining the moderating effects of being known in social media and generalized favorability in social media on this relation.

The empirical analysis conducted in the authors' research is based on a sample of 203 CSI events that occurred within publicly listed firms in China between 2011 and 2015. During the process of the authors' data collection, the authors initially verified the occurrence of CSI events in publicly listed firms by reviewing reports from reputable sources such as the 21st Century Business Herald and China Securities Journal. Subsequently, the authors collected the information pertaining to media coverage of these CSI events from the China Core Newspapers Full-text Database (CCND). Additionally, the authors obtained the remaining data from reliable sources such as Guba, the China Stock Market and Accounting Research Database (CSMAR) and the Chinese News Analytics Database (CNAD). To test the authors' hypotheses, the event study and multiple-regression analysis methods are adopted.

The authors find CSI generates a negative impact on firm value. Moreover, while being known in social media strengthens the negative relation between CSI and firm value, generalized favorability in social media weakens such relation.

There are two streams of limitations that present promising avenues for future studies. Theoretically, the authors explore the mechanisms of CSI affecting firm value from a contingent social media crisis management perspective. Consequently, the authors' study does not encompass other potential mechanisms that may exist in the CSI–firm value linkage. In terms of empirical analysis, three issues arise that provide opportunities for further investigation. First, the authors have not accounted for all potential factors that could influence the link between CSI and firm value. Second, the authors' data are subject to limitation since it comes from manual collection. At last, because the authors confirm the sample based on the actual CSI events of publicly listed firms in China, the authors' sample size is small.

The authors' findings contribute to a more comprehensive understanding of the relation between CSI and firm value, as well as effective strategies for responding to CSI through the utilization of social media. Consequently, the authors' results have the potential to stimulate further research on the implications of CSI and the management of corporate crises through social media platforms.

The aim of this article is to highlight the major part played by executives in the escalation of corporate social irresponsibility (CSI). Based on the upper echelons theory, the authors developed a model which shows the essential role of CEOs in explaining CSI. The authors proposed that the key personality traits of CEOs—narcissism—, as well as their power, could explain the degree of CSI.

Due to the significant methodological challenges when investigating CSI, the authors explored a novel method for measuring CSI in order to assess the degree of irresponsible behaviors. The authors build a CSI scale based on the perceptions of key informants, i.e. experts with diverse professional backgrounds. The authors apply CSI scale in a sample of 84 Spanish companies that were involved in CSI.

The results of the authors’ empirical study show the positive and significant influence of CEO narcissism and CEO power on the degree of CSI.

On the one hand, corporate irresponsibility scandals have relevant social consequences and practical implications. On the other hand, narcissism is a natural feature of managers in top positions that is increasing in societies.

The authors’ findings may help CEOs, TMTs and corporate boards to acknowledge potential sources of CSI decreasing its likelihood through counterbalancing CEO's power and considering the dark side of narcissism.

On the one hand, corporate scandals have relevant social and practical implications. On the other hand, narcissism is a natural feature of managers in top positions that is increasing in societies.

In this paper, the authors highlight the role of CEOs characteristics and their firms as the key actors for explaining and understanding the degree of CSI.

本研究擬強調行政主管在加劇企業無社會責任感上所扮演的主要角色。我們根據高層梯隊理論, 開發了一個模型, 來顯示執行長如何在解釋企業無社會責任感上所起的關鍵作用。我們提出一個見解, 就是: 執行長的主要人格特質-自戀和其權力-或許能解釋企業無社會責任感的程度。

由於面對在關於企業無社會責任感的研究法上的重大挑戰, 我們探索了一個估量企業無社會責任感的新穎方法, 以能對企業無社會責任感行為的程度進行評估。我們根據主要的資訊提供者的觀念和看法, 制定了一個企業無社會責任感的等級 (這些主要的資訊提供者均為具有不同專業背景的專家), 我們在84間西班牙公司的樣本裡, 使用了這企業無社會責任感等級, 而這些公司均涉及企業無社會責任感的。

我們這實證研究的結果顯示, 執行長的自戀和其權力, 對企業無社會責任感的程度, 起著正面和顯著的影響。

在本學術論文裡, 我們強調了執行長的特徵和他們的公司, 是瞭解和解釋企業無社會責任感的程度的關鍵行為者。

一方面, 企業無社會責任感的醜聞會帶來相關的社會後果和實務方面的影響;而另一方面, 自戀是高層主管的自然特徵, 而這些特徵, 在我們的社會裡不斷增強。本研究的結果, 或許可幫助執行長、高層管理團隊和公司董事會去承認企業無社會責任感的潛在原因, 並以透過平衡執行長的權力和考慮自戀的陰暗面而減低企業無社會責任感產生的可能。