Empirical Results Research Articles

Corporate social responsibility and the likelihood of external financing

<p style="text-align:justify"><span style="font-size:16px"><span style="font-family:Times New Roman,Times,serif"><span dir="ltr" lang="EN-US"><span style="color:black">This paper empirically investigates the relation between firm performance in corporate social responsibility (CSR) and the need and likelihood of external financing to test the predictions of agency and stakeholder theories. Empirical results from Logit, Linear Probability Model, OLS and Firm fixed effects regressions indicate that CSR is negatively related to the likelihood and level of external financing. Further analysis indicates that CSR has a negative and significant effect on both net equity issued (NEI) and net debt issued (NDI), the two components of external financing. Overall, the empirical results support the predictions of agency theory.</span></span></span></span></p>

Ynamic time-domain and frequency-domain spillovers and portfolio strategies between climate change attention and energy-relevant markets

We combine the Diebold and Yilmaz (DY) method, Baruník and Křehlík (BK) method and TVP-VAR model to explore the time-domain and frequency-domain spillover effects among climate change attention, oil, new energy, clean energy, solar energy, wind energy, technology, and finance. Empirical results demonstrate that the spillover effect of climate change attention and energy-relevant market is significant. At different time frequencies, risk transmission occurs more frequently in the short-term. On average, oil, solar energy, wind energy, and finance are net receivers. Finally, based on the DCC-GARCH t-copula model, we achieve optimal hedging ratios, optimal portfolio weights, and hedging effectiveness between climate change attention and energy-relevant markets. Empirical results suggest that investors incorporate climate change attention into their investment portfolios.

Hybrid rater to quantify and measure the severity of infection and spread of infection in muskmelon

Disease severity index (DIS) is a way of calculating the percentage of infection spread across the field. The percentage of infection in each leaf has been considered at a time stamp is being calculated and based on that disease, severity of disease spread is analyzed. With the advancement in machine learning and deep learning algorithms in the field of computer vision, identification and classification of diseases is effortless. Percentage of infection in a particular leaf, disease index (DI) is calculated using image processing techniques like Otsu threshold method. With this DI and scales, grading the severity of the infection across the field can be achieved. In this paper various scales used for grading severity of infection namely Horsfall-Barratt (H-B scale) quantitative ordinal scale, Amended 20% ordinal scale, and nearest percent estimates (NPEs) in muskmelon is explored, and based on the empirical results Amended 20% ordinal scale is most efficient method of estimating the DIS is to use the midpoint of the severity scope for each class with twenty percent adjusted to ordinal scale. The results show that the density of leaves is directly proportional to spread of diseases in muskmelon plant.

Impact of new maize variety adoption on yield and fertilizer input in China: Implications for sustainable food and agriculture

CONTEXTCrop variety adoption is considered as one of the main strategies for farmers to enhance productivity under stress brought by the climate change, with less environment impacts. It holds an upstream position in the whole life cycle of agricultural clean production, compared to the field management and agricultural non-point source pollution treatment. OBJECTIVEThis paper aims to (i) assess the impact of new maize variety adoption on maize yield and fertilizer input; (ii) analyze the main determinants of farmers' new maize variety choice in the context of climate change, and (iii) identify the heterogeneity of small-scale and large-scale farmers in maize variety choice and its impacts. METHODSBased on plot-level data collected from three major grain producing provinces in China, we employ a treatment effects model to correct the potential bias resulting from the farmers' self-selection and estimate the direct impacts of farmers' new maize variety adoption and the main determinants of such adoption. RESULTS AND CONCLUSIONSThe empirical results indicate that the adoption of new maize varieties significantly increases maize yield by 9.4% but fertilizer use by 16.6 kg/mu (1 mu = 1/15 ha) as well, and this trade-off between economic impacts and environmental impacts is more pronounced among small-scale farmers. However, in the face of natural disasters, adopting new maize varieties results in a significant reduction in yield losses by 1.4% and a decrease in fertilizer input for natural disaster management by 2.45 kg/mu, as compared to the adoption of old varieties. What's more, our findings suggest that a higher frequency of pest and disease disasters significantly increases the probability of new maize variety adoption by farmers, while a higher frequency of climate disasters significantly decreases the probability. SIGNIFICANCEThe estimated impacts of new maize varieties point out the need of synergistically optimized resistance and nutrient use efficiency for crop breeding, which have important policy implications for sustainable food and agriculture. Also, the significant factor analysis contributes to the proposal of feasible ways to promote the farmers' adoption behavior.

Enhancing energy efficiency in tyre pressure and temperature monitoring systems

This study addresses the pivotal challenge of enhancing power efficiency in tyre pressure and temperature monitoring systems (TPMS) for heavy vehicles and trailers. Employing field-programmable gate arrays (FPGA) and adaptive channel frequency hopping in bluetooth low energy (BLE) communication, the research focuses on mitigating power consumption issues specific to heavy vehicles with multiple tyres. The proposed solution incorporates strategic BLE channel blocking and adaptive frequency hopping on the FPGA platform to alleviate channel congestion and interference, ultimately reducing TPMS power consumption. The FPGA's adaptability tailors frequency hopping strategies to automotive TPMS nuances, optimizing channel selection and minimizing energy-intensive processes. Empirical results showcase a significant reduction in power consumption, with the TPMS operating at 100 MHz during active mode consuming 66 mW, dropping to 11 mW in sleep mode, and reaching 0 mW in hibernate mode for the majority of operational time. This research establishes a practical FPGA-based approach for power optimization in commercial TPMS, promising heightened reliability, safety improvements, and environmental impact reduction in the automotive sector.

Direct interaction in digital interactive media and stock performance: Evidence from Panorama.

Media information plays an essential role in the stock market. Recent financial research has verified that media information could shock stock price by influencing investors' expectation. Now, a new type of interactive media, called Digital Interactive Media (DIM), is popular in Chinese stock market and becomes the main channel for investors to understand listed companies. Unlike general news media or investor forums, DIM enables direct interaction between listed companies and investors. In the modern society where digital economy is booming, media information would largely affect investors' decisions. Therefore, it is urgent to use natural language processing (NLP) technology to deconstruct the massive questions and answers (Q&A) interactive information in DIM and extract valuable factors that affect stock prices and stock performances to explore the influence mechanism of digital interactive information on stock performances. This paper firstly uses web crawling technology to obtain approximately 110000 Q&A text information from the digital interactive platform ('Panoramic Network') from 2015 to 2021. Then we use big data text analysis technology and emotional quantification technology to extract valuable influencing factors from the massive text. A Multiple Linear Regression (MLR) model was created to explore specific influence mechanism of digital interactive information on stock price performance. The empirical results show that the emotions implicit in investors' questions do not significantly impact stock performance. However, the emotions and attitudes of the answers by listed companies can significantly affect corresponding stock prices, which indirectly confirms the Proximate Cause Effect of behavioral finance. This effect is particularly evident in the stock prices on the current trading day and the next trading day. In the Robustness Test, this paper replaces dependent variable and adds relevant control variables, and the conclusion remains valid. In the Endogeneity Test, this paper selects sample data before the launch of Panorama Network in 2014 as a comparison, and uses a Difference-in-Difference (DID) model to prove the significant impact of the launch of Panorama Network on Chinese stock market. In the Heterogeneity Test, the paper classifies the market value, region, and industry of listed companies and regressed the sub samples, once again confirming the reliability of the empirical conclusions. The results of Robustness Test, Endogeneity Test, and Heterogeneity Test conducted in this paper all support empirical conclusions.

Can Piketty’s Interest Rate – Growth Rate Framework Explain Increasing Income Inequality in Pakistan?

Inequality in income distribution has important ramifications for economic growth, development, and social justice (see Atkinson (2000), Piketty (2013), Milanovic (2016), etc.). Piketty (2013) explains the phenomena of growing income inequality in high-income countries by comparing the interest rates (r) and growth rates (g) over time. The crux of the argument is that since the upper income groups generate a significantly greater proportion of their income from investments as compared to lower income groups (due to higher savings rate compared to lower income group), interest rates performing better than the national income growth rates in the high-income countries in the post-cold war era (r-g) explain growing income inequality in these countries. This paper examines the use of this methodology to explain income inequality in Pakistan. It compares the evolution of income of the top 20 percent of Pakistan’s population with the income of the bottom 20 percent, using Piketty’s lens of interest rate growth rate dynamics. Empirical results show that r-g methodology does not capture the income distribution dynamics of Pakistan. Upper income group appears to be negatively affected by increases in interest rates as compared to the lower income group. Prices, international trade, taxes, and financial development tend to be the leading reasons for reducing the impact of r and g framework to explain the income distribution. This paper makes novel contributions to the methodology and implications of Piketty’s framework for studying income inequality in small, open, less developed countries like Pakistan.

ClinicalGAN: powering patient monitoring in clinical trials with patient digital twins

Conducting clinical trials is becoming increasingly challenging lately due to spiraling costs, increased time to market, and high failure rates. Patient recruitment and retention is one of the key challenges that impact 90% of the trials directly. While a lot of attention has been given to optimizing patient recruitment, limited progress has been made towards developing comprehensive clinical trial monitoring systems to determine patients at risk and potentially improve patient retention through the right intervention at the right time. Earlier research in patient retention primarily focused on using deterministic frameworks to model the inherently stochastic patient journey process. Existing generative approaches to model temporal data such as TimeGAN or CRBM , face challenges and fail to address key requirements such as personalized generation, variable patient journey, and multi-variate time-series needed to model patient digital twin. In response to these challenges, current research proposes ClinicalGAN to enable patient level generation, effectively creating a patient’s digital twin. ClinicalGAN provides capabilities for: (a) patient-level personalized generation by utilizing patient meta-data for conditional generation; (b) dynamic termination prediction to enable pro-active patient monitoring for improved patient retention; (c) multi-variate time-series training to incorporate relationship and dependencies among different tests measures captured during patient journey. The proposed solution is validated on two Alzheimer’s clinical trial datasets and the results are benchmarked across multiple dimensions of generation quality. Empirical results demonstrate that the proposed ClinicalGAN outperforms the SOTA approach by 3–4×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} on average across all the generation quality metrics. Furthermore, the proposed architecture is shown to outperform predictive methods at the task of drop-off prediction significantly (5–10% MAPE scores).

Exports, FDI Inflows, Renewable Energy and Economic Growth: An Empirical Evidence from Singapore

The present study captures the impact of exports; FDI inflows and renewable energy on economic growth of Singapore economy by covering time period 1989-2022 in the context of Cobb-Douglas production function. This study applies ARDL approach for obtaining empirical results. The estimated results will further be tested by taking most of the diagnostic tests to make check whether the results are robust or not. The findings of this study reveal that exports; FDI inflows and renewable energy have a positive and statistically significant impact on the economic growth. However, short run impact of renewable energy is witnessed as insignificant while exports and FDI inflows contribute significantly in boosting economic growth. Moreover, labor and capital also play a positive role in enhancing economic growth. Based on these findings; this study suggests that exports; FDI inflows, and renewable energy may be given more importance by expanding their size if domestic production in Singapore is required to be increased.

Can financial development affect environmental quality in the presence of economic uncertainty and informal activities? Exploring the linkages in the middle-income countries

PurposeThe significance of this research lies in providing an understanding of how economic conditions, including financial development, informal economic activities and economic uncertainty, influence carbon emissions and tries to offer valuable insights for policymakers to promote sustainable development.Design/methodology/approachThe Panel-ARDL method is employed for a group of 30 developing countries from 1990 to 2018. This study analyzes the data obtained from the World bank, International Monetary Fund and World Uncertainty databases.FindingsBased on the empirical results of the extended model, an increase in GDP and energy intensity is associated with an 83 and 14% increase in carbon emissions, respectively. Conversely, a 1% increase in financial development and economic uncertainty is linked to significant decrease in carbon emissions (about 47 and 23%, respectively). Finally, an increase in the informal economy can lead to a negligible yet significant decrease in carbon emissions. These results reveal that financial development plays an effective role in reducing CO2 emissions. Moreover, while economic uncertainty and informal economy are among unfavorable economic conditions, they contribute in CO2 reduction.Practical implicationsTherefore, fostering financial development and addressing economic uncertainty are crucial for mitigating carbon emissions, while the impact of informal economy on emissions, though present, is relatively negligible. Accordingly, policies to control uncertainty and reduce the informal economy should be accompanied by environmental policies to avoid increase in emissions.Originality/valueThe originality of this paper lies in its focus on fundamental changes in the economic environment such as financial development, economic uncertainty, and informal activities as determinants of carbon emissions. This perspective opens up new avenues for understanding the intricate relationship between carbon emissions and economic factors, offering unique insights previously unexplored in the literature.

Does managerial myopia exacerbate firms excessive financialization? Evidence from Malaysia

Purpose This study aims to examine the influence of managerial myopia on the excessive financialization behavior of listed firms on Bursa Malaysia. Design/methodology/approach Through a sample of 313 firms from 2015 to 2021, the author examine whether managerial myopia promotes or inhibits corporate financialization. The author uses ordinary least squares and Logit as the baseline models and addresses potential endogeneity through the dynamic-panel generalized method of moments. The results are also robust to alternative measures of financialization and managerial myopia. Findings The results show a significant positive effect of managerial myopia on the excessive financialization of enterprises. Furthermore, the findings indicate that the impact of managerial myopia on the over-financialization of enterprises is more prominent in periods of low economic policy uncertainty. However, the relationship between excessive financialization and managerial myopia is weakened in the presence of female chief executive officers. Practical implications The empirical results have useful policy implications. First, firms should establish scientific managerial assessment and supervision systems to avoid excessive financial investment behavior by myopic managers caused by assessments that place too much emphasis on short-term performance. Second, regulators and policymakers should encourage firms to appoint women to top management positions, which may inhibit short-sighted financialization behavior. Finally, the regulatory authorities should undertake the necessary measures driving companies to disclose the investment direction of the funds so that shareholders and investors can understand the use direction of the funds in a timely manner, which can effectively prevent the economy “from the real to the virtual” and promote the development of the real economy. Originality/value This paper expands the existing research on corporate financialization behavior and provides a new theoretical basis for the underlying factors of excessive financialization. It studies the influence of corporate financialization from the perspective of short-run managerial actions and deepens the understanding of managerial myopia and companies’ financialization levels.

FinTech developments and their heterogeneous effect on digital finance for SMEs and entrepreneurship: evidence from 47 African countries

Purpose Lack of access to finance is a major constraint to the growth of small and medium-sized enterprises (SMEs) and entrepreneurship in developing countries. The recent proliferation of mobile phone services, access to the internet and emerging technologies has led to a surge in the use of FinTech in Africa and is transforming the financial sector. This paper aims to examine whether FinTech developments heterogeneously contribute to the growth of digital finance for SMEs and entrepreneurship in 47 African countries from 2013 to 2020. Design/methodology/approach The paper uses a novel method of moments quantile regression, which deals with heterogeneity and endogeneity in diverse conditions for asymmetric and nonlinear models. Findings The empirical results reveal that the rise of FinTech companies offering services in Africa heterogeneously increases digital finance for SMEs and entrepreneurship in their different stages of growth. FinTech developments have a strong and positive impact in countries with higher levels of digital finance than those with lower levels. FinTech developments and digital finance positively and significantly influence entrepreneurship in Africa, particularly in the nascent and transitional development stages of entrepreneurship. Institutional quality has a considerable positive moderating effect when used as a control rather than an interaction variable. Practical implications The results suggest the need to promote FinTech developments in Africa: to provide a wide range of alternative digital finance schemes to SMEs and to promote entrepreneurship, especially in countries where entrepreneurship is in the nascent and transitional development stages. The results also underscore the need to promote FinTech development through supportive regulations and institutional quality to reduce risks related to FinTech and digital financing schemes. Originality/value To the best of the authors’ knowledge, this paper is one of the first attempts to account for the often overlooked heterogeneity effects and show that the influence of FinTech developments is not homogenous across the varying development stages of digital finance and entrepreneurship.

Incremental versus Radical Innovation and Sustainable Competitive Advantage: A Moderated Mediation Model

Innovation is critical for organizations seeking to build and maintain a sustainable advantage in the competitive market. This study aims to construct a moderated mediation model to examine the effects of incremental and radical innovations on competitive advantage, which considers the mediating role of innovation speed and the moderating role of a supportive culture. Data from 201 Chinese firms were collected through questionnaires and the research hypotheses were tested using multiple regression analysis and bootstrapping techniques. The empirical results show that incremental and radical innovations have a significant positive effect on competitive advantage. Radical innovation has a greater impact on competitive advantage compared to incremental innovation. Innovation speed mediates the relationship between incremental and radical innovations and competitive advantage. Supportive culture positively moderates the relationship between incremental and radical innovations and innovation speed. Moreover, supportive culture positively moderates the conditional indirect effect of incremental and radical innovation on competitive advantage through innovation speed. Theoretical and practical implications are further discussed.

A Study on the Impact of Cruise Tourism Experience on Satisfaction, Revisit Behavior, and Consumer Spending Behavior

Abstract This study focuses on exploring the impact of cruise tourism experiences on travelers' satisfaction, revisit intentions, and consumer spending behavior. By distributing questionnaires to cruise travelers and collecting 372 valid samples, the study conducted a comprehensive analysis using structural equation modeling (SEM). The empirical results show that cruise tourism experiences play a crucial role in enhancing travelers' intrinsic and extrinsic satisfaction. These satisfaction factors further positively influence travelers' revisit behavior and directly increase their consumer spending, highlighting the critical role of cruise service quality in promoting tourism sustainability and enhancing economic benefits. Additionally, the study found that improvements in the quality of services provided by cruise operators, such as accommodation, dining, and entertainment, can significantly enhance travelers' overall satisfaction. This not only fosters customer loyalty but also positively influences customers' revisit behavior and consumer spending behavior. These findings provide empirical support for cruise operators in improving services and formulating market strategies. JEL classification numbers: Z30, Z32. Keywords: Cruise tourism experience, Satisfaction, Revisit behavior, Consumer spending behavior.

Informativeness of Announcement Returns in Cross-border Acquisition Completion: A Perspective from Emerging Economies

This study examines the informative role of announcement returns in the deal-closing decision for cross-border acquisitions from emerging economies. Using a sample of 903 acquisitions from five emerging economies: Brazil, Russia, India, China, and South Africa (BRICS), we find that the likelihood of deal completion increases if the market reacts favorably at the time of announcement. Further, we investigate if the relationship between announcement returns and deal completion becomes more significant under increased information asymmetry. Empirical results attest that the relationship between the market reaction and deal completion becomes more pronounced in unrelated deals and deals without advisors. JEL Codes: G14, G34

Decoding the future: Proposing an interpretable machine learning model for hotel occupancy forecasting using principal component analysis

Accurate hotel occupancy forecasting is vital for optimizing hotel revenue, yet interpretable machine learning tools lack extensive research. This paper presents a two-step approach utilizing historical and advanced booking data. Principal Components Analysis (PCA) groups similar patterns in booking curves, followed by a pickup forecasting model to predict occupancy. Evaluating the approach using real booking data from three European hotels (2018–2022), it outperformed two benchmarks: classical additive pickup and clustering-based pickup methods. Empirical results demonstrate the superiority of PCA-based methods across all hotels and forecasting horizons. Additionally, incorporating Average Daily Rates into PCA enhances daily hotel demand forecasts, offering potential for enhanced predictions with business operational information in a low-dimensional space.

Hometown religiosity and financial reporting quality: evidence from chairpersons

Purpose This study aims to investigate whether there is a relationship between the religiosity levels of chairpersons’ hometowns and the financial reporting quality of companies. Design/methodology/approach Using a unique hand-collected data set obtained from various sources, the authors use ordinary least squares and logistic regressions to test the hypotheses and further implement various methods to address potential issues such as omitted variables, reverse causality and selection bias problems. In addition, the authors control for the religiosity level of chief executive officers’ (CEOs) hometowns. Finally, the authors divide the sample into two subsamples – companies with strong corporate governance and companies with weak corporate governance – to investigate the effect of chairpersons’ hometown religiosity on financial reporting quality under strong or weak corporate governance. Findings The findings demonstrate that companies with chairpersons from religious hometowns produce high-quality financial reports. Additional tests, such as the Heckman selection model and instrument variable regression, confirm the robustness of the main results. Controlling for the religiosity level of the CEO’s hometown yields consistent findings with the main results. Finally, additional results indicate that the religiosity levels of chairpersons’ hometowns play a significant role in enhancing financial reporting quality in companies with weak corporate governance. Practical implications Companies should consider appointing board members or chairpersons from more religious hometowns, as the empirical results of this study support the positive effects of chairpersons’ hometown religiosity on financial reporting quality. Originality/value To the best of the authors’ knowledge, the current study is among the first to demonstrate the relationship between the religiosity level of the chairpersons’ hometown and the financial reporting quality of companies. The study introduces unique hometown religiosity proxies and controls for various variables related to corporate governance, chairperson attributes, company characteristics, and audit firm characteristics.

Impact of the COVID-19 vaccinations on economic outcomes and mental health services in the U.S

The development of vaccines has long been viewed as a critical public health tool to raise immunity against the COVID-19 virus and to reopen economic activities globally. Against this background, this study examines the impact of COVID-19 vaccinations on economic outcomes and mental health services in the U.S. We employed the Household Pulse Survey covering 549, 950 and 57, 892 vaccinated and unvaccinated individuals, respectively. For the causal analysis, we control the differences between the vaccinated and unvaccinated groups using propensity score matching (PSM) and augmented inverse probability weighting (AIPW) methods. Economic outcomes include whether individuals work on-site, telework or work from home, in-store shopping, job loss, or food sufficient. We also consider whether individuals experience mental health distress and receive mental health services as a potential outcome. The empirical results show that the probability of working on-site, in-store shopping, food sufficiency, and using mental health services are significantly higher among the recipients of the COVID-19 vaccines than non-recipients. We also find that the likelihood of teleworking and job loss decreased significantly among the recipients of the COVID-19 vaccine compared to non-recipients. The magnitude of the estimated impact across race and ethnic groups is mixed, which shows that vaccination effects are heterogeneous across the races/ethnic groups in the sample.

A note on the fractional momentum strategy

ABSTRACT The momentum strategy has been existent for a long time and under numerous versions all of which is based on some form of the past return. It was noted recently that this metric of taking the difference of two prices might be a too stringent transformation to achieve stationary. For this matter, a momentum strategy that is not based on simple past return was proposed, called fractional momentum strategy, that retains some memory in the price series. Empirical results have demonstrated significant returns improvement of this strategy over the traditional one albeit under weekly rebalancing. This article investigates the applicability of the strategy under more realistic rebalancing frequencies and finds that the strategy performs inferior to the traditional momentum strategy as rebalancing frequency is decreased.

The U-shaped relationship between corruption and international tourism demand: A gravity model approach

The tourism industry significantly contributes to a country’s economic growth and creates a positive image of the destination. This study assesses the impact of corruption on international tourism demand. It employs two-dimensional analyses using the gravity model and a pooled ordinary least square estimator to provide a unique recognition of international tourism demand. The study utilized a conditional quantile regression technique to analyze a dataset of 200 destination countries from 1995 to 2022. The empirical results demonstrate a mixed effect of corruption on international tourism demand. The analysis reveals a non-linear relationship between corruption and international tourism demand, with the inverted-U relationship being statistically significant only at the 50th–75th quantiles and not holding at the upper and lower quantiles. The research confirms that income has a positive impact on tourism across quantiles, but the impact is disproportionate. The sub-period of 2006–2022 experienced a significant decline in gross domestic product (GDP) due to the global financial crisis and its aftershocks, which severely impacted the attractiveness of destination countries for tourists. These valuable insights can inform national tourism policies and businesses.