Significant Explanatory Power Research Articles

Heterogeneity in the Integration of ESG Measures in Executive Compensation: Determinants, Contracting Details and Outcomes

Corporate social responsibility (CSR) contracting incorporates environmental, social, and governance (ESG) related measures in executive compensation plans. Current research on this practice is limited to a US setting, despite global adoption. We investigate heterogeneity in CSR contracting using data from 59 countries between 2002 and 2019. We find that besides firm-level past ESG performance and the industry-level adoption rate, country-level ESG regulations have significant explanatory power in firms’ tendencies to adopt CSR contracting. Hand-collected data reveal significant cross-country differences in CSR contracting details. Finally, CSR contracting is positively associated with subsequent financial performance only in countries with more stringent ESG regulations and stronger legal enforcement. In contrast, CSR contracting is associated with subsequent ESG performance regardless of country-level factors.

The Evolutionary Transition of the RNA World to Obcells to Cellular-Based Life.

The obcell hypothesis is a proposed route for the RNA world to develop into a primitive cellular one. It posits that this transition began with the emergence of the proto-ribosome which enabled RNA to colonise the external surface of lipids by the synthesis of amphipathic peptidyl-RNAs. The obcell hypothesis also posits that the emergence of a predation-based ecosystem provided a selection mechanism for continued sophistication amongst early life forms. Here, I argue for this hypothesis owing to its significant explanatory power; it offers a rationale why a ribosome which initially was capable only of producing short non-coded peptides was advantageous and it forgoes issues related to maintaining a replicating RNA inside a lipid enclosure. I develop this model by proposing that the evolutionary selection for improved membrane anchors resulted in the emergence of primitive membrane pores which enabled obcells to gradually evolve into a cellular morphology. Moreover, I introduce a model of obcell production which advances that tRNAs developed from primers of the RNA world.

Liquidity Risk and Currency Premia

The currency market is the world’s largest financial market by trading volume. We show that even in this highly liquid market, exposure to liquidity risk commands an economically significant risk premium of up to 3.6% per year. Liquidity risk is not subsumed by existing currency risk factors and successfully prices the cross section of currency excess returns. Moreover, we find that liquidity risk and carry trade premia are correlated, although this correlation is limited to static rather than dynamic carry trades. Building on this result, we propose a liquidity-based explanation for the carry trade, which adds significant explanatory power to existing theories. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01031 .

Optimizing investment portfolios with a sequential ensemble of decision tree-based models and the FBI algorithm for efficient financial analysis

This research presents a comprehensive, sequential ensemble framework meticulously crafted for optimizing investment portfolios, focusing on the construction industry. It employs decision tree-based and metaheuristic optimization algorithms to create an efficient stock-selection framework grounded in financial analysis. This approach offers a profitable investment strategy integrated with portfolio optimization while systematically lowering the portfolio investment threshold. In this framework, a web crawler was deployed to gather daily closing prices of stocks from the Taiwan Stock Exchange, along with monthly revenue data and financial statements. Subsequently, decision tree-based models were utilized to pinpoint the fundamental financial indicators with significant explanatory power over revenues. The stock selection conditions aligned with these indicators were optimized through a newly developed metaheuristic algorithm named the forensic-based investigation (FBI) optimizer. The optimal conditions were subsequently integrated with the equal-weighting scheme, mean-variance method, and hierarchical risk parity to identify the most effective investment portfolio strategies. Backtesting results showed that the proposed stock-portfolio investment strategy, optimized through machine learning and a metaheuristic algorithm, is well-suited for construction and all-stock categories. This study equips professional investment advisors or securities investment institutions with a decision-aid expert system for initial stock selection.

Feel, move, or walk? Which has a greater contribution to functioning in total knee arthroplasty? A comparative study between two instrumentations based on a classification and regression tree

Background: This study aimed to know which variables most contribute to the functioning acquired in the third month using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) and a multivariate analysis through classification and regression tree (CRT), comparing the conventional instrumentation (CI), and patient-specific instrumentation (PSI). Methods: This is an observational and retrospective study. The sample consisted of 252 patients, 68 receiving CI (27.0%) and 184 receiving PSI (73.0%). The functional variables of the study were: knee pain, passive flexion and extension, gait distance and the domains of the WOMAC index. Results: The CRT method identified that the only explanatory variable that contributed to the highest functioning in the CI group (13.2 in the WOMAC) was pain in the third month with a value ≤2.5 in the visual analog scale (VAS). In the PSI group, the variable that best explained functioning was pain in the first postoperative month (VAS ≤4.5), with the best functional result (2.8 in WOMAC) referring to the patients who walked >320.5 m in the 6-minute walk test in the first month and who had flexion of >112.5 in the third month. Conclusions: Feeling pain is the variable with the most significant explanatory power for the results achieved in functioning at the third month, regardless of the arthroplasty instrumentation employed. Moving the knee in higher flexion ranges and obtaining higher mean values of gait speed also positively influences functioning in patients subjected to PSI.

Impacts of Climate Change and Human Activity on Lakes around the Depression of Great Lakes in Mongolia

The western region of Mongolia is characterized by an arid climate and a fragile ecological environment. It is a sensitive zone in response to global climate change and one of the major sources of dust globally. This region is home to numerous lakes, and their dynamic changes not only reflect global climate variations but also have implications for the global ecological environment quality. In this study, Landsat images were used as the data source, and Google Earth Engine (GEE) was employed to extract lakes with an area larger than 1 km2 from 1992 to 2021. The spatiotemporal characteristics of lake water area (LWA) changes were analyzed, and a structural equation model was applied to attribute the lake changes. The results indicate an overall trend of increasing lake area followed by a decrease in the study area. Specifically, lakes in the provinces of Khovd and Gobi-Altai exhibited a decreasing trend followed by an increasing trend, while lakes in the provinces of Uvs and Zavkhan showed an increasing trend followed by a decreasing trend. Three typical types of lakes, namely, alpine lakes, throughflow lakes, and terminal lakes, all exhibited a trend of increasing area followed by a decrease. The analysis of driving forces behind lake area changes reveals that climate change and human activities primarily exert indirect influences on the lake area changes in each province. Specifically, climate change and human activities lead to changes in soil moisture, which have a significant explanatory power for lake area changes. Regarding the typical types of lakes, climate change serves as the primary driving force for alpine lakes, while human activities are the main driving forces for throughflow lakes and terminal lakes.

Spatial differentiation of soil nutrients and their ecological chemometrics based on geographic detector in National Agricultural Park of Tangchang, Southwest China.

In order to analyze the spatial variability of soil nutrients and their ecological chemometrics in Tangchang Town, National Agricultural Park, 20 influencing factors were selected: soil pH, Cd, Hg, As, Cu, Pb, Cr, Zn, Ni, Se, elevation, slope, aspect, land use type, distance from industrial land, distance from commercial land, distance from railway, distance from residential area, distance from highway and distance from river. The effects of various influencing factors on the spatial variability of total organic carbon (TOC), total nitrogen (N), total phosphorus (P), total potassium (K) and ecological stoichiometry were analyzed by means of geographic detector. The results showed that average contents of soil TOC, N, P and K in the study area are 10.24 g kg-1, 1.33 g kg-1, 1.14 g kg-1 and 23.60 g kg-1, respectively, and there were significant differences in the spatial distribution of soil nutrients and their eco-stoichiometry in the study area, and TOC, N, P, K, C/N, C/P, C/K, N/P, N/K and P/K has a significant correlation with each other and most correlation coefficients are above 0.5 or below -0.5. Factor detection showed that soil properties, distance from railway and distance from residential area had the most significant explanatory power to the spatial heterogeneity of soil nutrients and eco-stoichiometry. Interaction detection showed that the interaction between soil properties with other factors was the most important factor affecting the spatial differentiation of soil nutrients and their ecological chemometrics, and elevation, distance from railway and distance from residential area were also important factors. Risk detection showed that the differences of soil nutrients and their ecological stoichiometry were most significant in the subregions of soil properties (pH, Cd, Hg, As, Cu, Pb, Cr, Zn, Ni and Se).

Assessing the relationship between nutrition literacy and eating behaviors among nursing students: a cross-sectional study

BackgroundEating behavior is an essential aspect of life that can have long-term effects on health outcomes. Nutrition literacy is crucial for better health and well-being. It empowers individuals to make informed decisions about their nutrition and take control of their eating habits.ObjectivesThis study aimed to assess the relationship between nutritional literacy and eating behavior among nursing students at the nursing faculties of Ardabil University of medical sciences.MethodsA cross-sectional correlational study was conducted in Ardabil province, northwest Iran. The study collected data through simple random sampling at nursing schools in Ardabil province, with 224 nursing students participating. The study collected data from a demographic information form, the nutritional literacy self-assessment questionnaire for students (NL-SF12), and the adult eating behavior questionnaire (AEBQ). The data were analyzed using SPSS version 14.0 software.ResultsBased on the results, nutritional literacy explains 44% of the variance in eating behavior and shows significant explanatory power in two sub-scales of eating behavior. The adjusted R2 values for food approach and food avoidance scales were 0.33 and 0.27, respectively.ConclusionGiven the significant relationship between nutritional literacy and eating behaviors among nursing students, nursing faculty managers and health policymakers should develop new public health strategies to increase nutritional literacy among nursing students.

The Influence of Product Innovation, Consumer Awareness, and Environmental Policy on Sustainable Marketing in the Tourism Industry in Bali

This study explores the complex dynamics of sustainable marketing within Bali's tourism sector, emphasizing the influence of consumer awareness, product innovation, and environmental policy. The study uses Structural Equation Modeling with Partial Least Squares (SEM-PLS) to quantitatively analyze data from 245 varied participants in order to investigate the links between the major constructs. Psychometric qualities, demographic information, and descriptive statistics all support the measuring model's validity and dependability. The structural model coefficients show statistically significant positive correlations between Consumer Awareness, Environmental Policy, Product Innovation, and Sustainable Marketing. Discriminant validity analysis validates the distinctiveness of the components. The model has a significant explanatory power, as indicated by the R-Square values. In order to promote sustainability in Bali's tourism sector, firms, politicians, and researchers can benefit from the results' strategic insights.

BYOA and Security: Examining Perspective-Taking and Self-Determination

ABSTRACT Bring your own applications (BYOA) in organizational networks introduces security risks. We examine whether individuals who BYOA at work are motivated to use multi-factor authentication (MFA) to protect the organization’s security, a practice we describe as bring your own security (BYOS). The behaviors are examined through dual theoretical lenses (a) intrinsic motivation, capturing the personal satisfaction related to the use, and (b) perspective-taking, capturing the user’s ability to consider information security from the organization’s perspective. We analyzed survey data of 154 samples using partial least squares structural equation modeling (PLS-SEM). Our results demonstrate that while the intrinsic motivation path is instrumental in influencing MFA use, perspective-taking offers significant explanatory power to information security behaviors. This article extends the current knowledge of the impact of perspective-taking on voluntary security behavior, which makes a novel and significant contribution to cybersecurity research and practice.

Investor sentiment and the NFT hype index: to buy or not to buy?

PurposeThe primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.Design/methodology/approachThe research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.FindingsThe built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.Practical implicationsThe NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.Originality/valueThe authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.

Impact Analysis of the Use of IoT Technology and Environmental Monitoring Systems on Waste Reduction in the Food and Beverage Industry in Indonesia

The present research endeavors to examine the effects of IoT technology and environmental monitoring systems on waste reduction within Indonesia's food and beverage sector. Using a sample of 250 participants, a thorough study was carried out while accounting for the size, location, and kind of operation of the organization. Discriminant validity was verified after the measurement model underwent a thorough evaluation for validity and reliability. Tests of statistical significance demonstrated how important IoT technology and environmental monitoring systems are in affecting waste reduction. The model's significant explanatory and predictive power is demonstrated by the R-Square and Q2 values, while the model fit index highlights the estimated model's suitability. The results offer significant perspectives for industry participants and policy officials who seek to enhance sustainability measures in Indonesia's food and beverage industry.

Founder-CEO Extraversion and Sustainability Orientation in Initial Coin Offerings

This study contributes to sustainable entrepreneurship by examining the influence of entrepreneur personality on sustainability orientation in a digital finance market context. Extraverted entrepreneurs exhibit strong social connectivity. Consistent with the upper echelons theory, we show that extraversion has significant explanatory power for commitment to a sustainability agenda in initial coin offerings (ICOs). Drawing on institutional difference theory, we find that regulatory change and social pressure moderate the relationship between the extraversion of ICO entrepreneurs and sustainability orientation. Importantly, extraversion plays a significant role in an ICO’s success. Our study highlights the impact of entrepreneur personality traits on driving sustainability efforts and fundraising success.

The Influence of Urban Form on Land Surface Temperature: A Comprehensive Investigation from 2D Urban Land Use and 3D Buildings

Urban form plays a critical role in shaping the spatial differentiation of land surface temperature (LST). However, limited research has investigated the underlying driving forces and interactions of multidimensional urban form, specifically considering two-dimensional (2D) urban land use and three-dimensional (3D) buildings, on LST. Furthermore, their multi-scale outcomes remain unclear. Taking the main urban area of Wuhan City as an example, a total of nine indicators—the proportion of administration land (PA), the proportion of commercial land (PB), the proportion of industrial land (PM), the proportion of residential land (PR), the proportion of water area (PE), the building density (BD), the building height (BH), the floor area ratio (FAR), and the sky view factor (SVF)—were selected; this paper used the geographic detector model to investigate the driving force of LST spatial differentiation in winter and summer, as well as the interaction of various influencing factors from a multi-scale perspective. The results showed that (1) the average LST in industrial land was higher than that in commercial land, both in summer and winter. The LST in administration land was higher than that in residential land, while in winter, it is the opposite. (2) The spatial differentiation of summer LST was mainly dominated by 3D buildings, while the spatial differentiation of winter LST was mainly dominated by 2D land use. (3) BD was the leading driving force of LST spatial differentiation in summer, and the interaction between BD and any other indicator showed the most significant explanatory power, which is the same for PM in winter. (4) As scale increased, the explanatory power of 2D urban land use for LST spatial differentiation gradually increased both in winter and summer, while the explanatory power of PE on LST spatial differentiation decreased. The explanatory power of BD, FAR, and SVF on LST spatial differentiation remains basically unchanged. The explanatory power of BH on summer LST spatial differentiation decreases with increasing scale, while the explanatory power of BH on winter LST spatial differentiation remains in a stable state. (5) The interaction among all urban form factors primarily increases as the scale increases, except for the interaction between BH and 2D urban land use in summer, and the interaction between PE and PR in winter. The research can provide scientific decision-making support for the collaborative optimization of multiscale urban forms to improve the urban thermal environment.

All-cause Excess Mortality Cross-sectional Correlation Analysis at Global Scale during COVID-19, 2020-22

The excess mortality during COVID-19 is significantly higher than the reported death due to COVID-19 infections. This study conducts a cross-sectional correlation analysis between excess mortality and reported COVID-19 death, aging rate, GHS (Global Health Security) index, and government response stringency index in the timeframe from January 2020 to September 2022. This study aims to explore the main contributors to excess mortality, hence to provide guidance on future diagnosis and control of the pandemic. A cross-sectional log-linear model is fitted with 79 observations to estimate the explanatory power of reported COVID-19 death, aging rate, GHS index and stringency index on excess mortality at a global scale. The correlation between excess mortality and reported COVID-19 death is also examined on a nation level by calculating the Spearman correlation coefficient on the time series of the two. The cross-sectional log-linear model has adjusted . The estimate for log of reported COVID death is ; aging rate is ; GHS index is ; stringency index is not significant. On a nation level, Brazil and United States have high correlations between reported and excess death (), the reported death takes up 85% and 86% respectively, of excess death. India and Japan, similarly, have high correlation coefficient (), but with COVID-19 death accounting 9% and 47% of excess death. Germany and China have relatively small correlations between reported and excess death (), but Germany has reported death taking up 89% of excess death, whereas in China the ratio is 4%. At a global scale, the reported COVID-19 death has the most explanatory power in excess mortality, which means that most of excess mortality is attributed to death from COVID-19 infections. Aging rate has a negative correlation between excess mortality, this is partly because of the preference of aged population to live in countries with better health care capacities. There is no significant explanatory power of stringency index on excess mortality, suggesting that the impact of government responses on COVID-19 vary by each nation. On a country level, the correlation between COVID-19 death and excess mortality can also vary, mostly due to the different levels of health care disruptions, efficiency and impact of government measures and other COVID-19 unrelated events such as wars and heat waves.

Audit quality, value relevance, integrated reporting and the moderating role of business ethics: evidence from European ESG firms

PurposeThis study aims to investigate the relationship between audit quality and value relevance and subsequently ascertain the moderating effect of business ethics on integrated reporting (IR)–value relevance.Design/methodology/approachThis study applied linear regressions with panel data using the Thomson Reuters ASSET4 database from European countries to analyse data of 510 companies belonging to the environmental, social and governance (ESG) index between 2010 and 2022.FindingsA significant positive relationship was found between audit quality and value relevance. The results also suggest that IR has significant explanatory power on value relevance, and that business ethics moderate the relationship between IR and value relevance in European ESG firms.Practical implicationsManagers will see IR, business ethics and audit as a business strategy with incremental market value. In this regard, this study tried to provide insights and managerial solutions for managers of international companies to improve their strategy by drawing on the social, moral and business ethics approach. This finding will improve the informational relevance for investment opportunities, thus resulting in improved business performance.Originality/valueTo the best of the author’s knowledge, this is the first study to investigate the moderating role of business ethics in the relationship between IR and value relevance. This paper fulfils a recognised need to study the influence of audit quality on investor decisions. Furthermore, the contribution of this study could be observed in the fact that the market value analysis differs between the contractual and the business ethics approaches. Also, including a moderating variable in the explanation and determination of value relevance remains somewhat underexplored.

Spatial distribution analysis and driving factors of traditional villages in Henan province: a comprehensive approach via geospatial techniques and statistical models

Traditional villages are repositories for preserving human artifacts and cultural heritage. An investigation of the spatial distribution characteristics and factors influencing traditional villages in provincial administrative regions can provide new insights regarding the protection of traditional villages and rural development. This study focused on 275 traditional villages in Henan Province. Using ArcGIS and GeoDa software, we analysed the spatial autocorrelation and heterogeneity of the nearest neighbour index, Gini coefficient, Moran’s I, and kernel density of the villages. Additionally, in conjunction with the Python sklearn library and GeoDetector, 15 indicators were selected to construct a decision tree model, spatial lag regression model, and geographic detector. Then the influence and interaction mechanisms of each indicator were analysed. The results revealed that (1) the spatial distribution of traditional villages in Henan Province was clustered and uneven, with a spatial layout comprising “3 high-density areas + 1 medium-density belt”; (2) overall, the number of traditional villages was negatively correlated with altitude, slope, rainfall, population density, proportion of the minority population, and historical-cultural intensity; and (3) the decision tree model results demonstrated that the selected 15 indicators had good predictive ability and that population density was particularly important. The spatial lag regression model results showed that the spatial distribution of traditional villages was positively correlated with distance from rivers, urbanization rate, and tourism resources, and negatively correlated with population density, per capita GRP, historical-cultural intensity, and NDVI. (4) The GeoDetector results indicated that historical-cultural intensity and population density were the two factors with the most significant explanatory power for the spatial differentiation of traditional villages in Henan Province. In terms of interactive factors, population density cap population was the strongest interactive driving force, followed by population cap historical-cultural intensity.

Evolution pattern and driving mechanism of eco-environmental quality in arid oasis belt — A case study of oasis core area in Kashgar Delta

Kashgar Oasis is an ecologically fragile area in the southern margin of the Tarim Basin, which is affected by both natural conditions and human activities. Exploring the spatiotemporal dynamics and driving factors of ecological environment quality in this area is significant for maintaining regional ecological security and ecological protection. This study aims to examine the multi-year RSEI, calculated based on the GEE platform from 1990 to 2020 in the study area, located in the core area of the Oasis. The trend analysis method analyzed the spatiotemporal pattern of RSEI and dynamic evolution. Additionally, the regularity of migration directions was explored through gravity transfer and the linear directional mean. Finally, the driving forces factors were quantified by geographic detectors. The results showed that: (1) The ecological environment quality of the study area showed an increasing trend from 1990 to 2020, with the mean value of RSEI increasing from 0.542 to 0.577, an overall increase of 6.84%. (2) The improved and stable areas account for 60.901% and 5.428%, respectively, and the improved areas were mainly distributed in a large area, while the degraded area was mainly mildly degraded and had a patchy structure. (3) The change direction of different grades showed an east–west extension trend, with areas of significant improvement and slight degradation being more aligned with river flow. (4) The results showed that land use and land cover (LULC), representing human activities, had the most significant explanatory power, and the q-value of single factor detection and interaction detection of LUC-GDP were 0.506 and 0.550, respectively. This indicated that the fluctuations of eco-environmental quality in this area are closely related to the local land use patterns. This study can provide a reference for implementing local ecological environment protection and environmental restoration projects.

Time-Varying Hazard of Patient Falls in Hospital: A Retrospective Case-Control Study.

This study aims to evaluate the association between patient falls and relevant factors and to quantify their effect on fall risk. This is a retrospective case-control study using the secondary data collected from a tertiary general hospital. Study subjects were 450 patients who were admitted to the hospital between January 2016 and December 2020. The prevalence of falls was associated with the fall risk level by the Morse Fall Scale (MFS) and individual status at admission including history of admission, dizziness, sleep disorder, bowel dysfunction, and urinary incontinence. The odds ratios of patient falls were higher in the low-risk group by the MFS score (odds ratio (OR) = 2.61, p < 0.001) and the high-risk group (OR = 5.51, p < 0.001) compared to the no-risk group. The hazard ratio of patient falls was higher in the high-risk group by the MFS score (hazard ratio (HR) = 3.85, p < 0.001). The MFS had a significant explanatory power to predict fall risk. Sleep disorder and urinary incontinence were the significant factors influencing patient falls.

The explanatory power of the SKYTRAX’s airport rating system: Implications for airport management

This article presents a study of the explanatory power of SKYTRAX's airport rating system, using data from 423 airports for statistical analysis. The study utilizes ordinal regression and Maximum Likelihood Structural Equation Modeling (ML SEM) to propose and test several models. The study results show that only three service quality components based on SKYTRAX have significant explanatory power, i.e., terminal comfort, wayfinding signage, and airports' food and beverage outlets. The study findings have significant implications for airport management and key stakeholders. By prioritizing these service quality components, airport managers can improve operational efficiency and increase service quality, which can attract new investments. The study highlights the importance of addressing the vital elements of airport service quality, which can lead to time and cost savings in problem-solving processes and procedures. The findings also have implications for academia, providing a basis for further research on the importance of airport rating systems for customer satisfaction and service quality.