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This study examines the association between chief executive officer (CEO) gender and the readability of annual reports by considering some demographic attributes of female CEOs.

Ordinary least squares (OLS) regression is used to test the research hypotheses on a sample of S&P 500 firms between 2004 and 2016.

The results show that female CEOs are significantly positively associated with the readability of 10-K reports – in line with ethical-sensitivity theory. Further results show that this association is variable depending on the demographic attributes of female CEOs – in line with upper echelon theory. Specifically, older female CEOs and those with financial expertise are significantly associated with more readable 10-K reports. In contrast, female CEOs hired from within the firm are negatively associated with the readability of 10-K.

This study provides evidence on the effect of female CEOs and their demographic attributes on annual report readability, which was not addressed in prior research.

The findings show that the appointment of female CEOs seems like a helpful avenue to reduce concerns among the regulators about the textual complexity of annual reports. However, the most important policy implication of the study is that the decision to appoint female CEOs should be based more on their demographic attributes than on gender equality recommendations and full trust in women's behavioral consequences.

This study contributes to the academic literature on readability and gender. Prior research has not clarified which attributes and skills of female CEOs drive their abilities to improve shareholder value and make more ethical decisions. This study suggests that female CEOs are not better “per se” to improve corporate governance practices, and the impacts of female CEOs are not the same and differ according to their demographic attributes.

This study aims to explore the heterogeneity of the tourist market for people with a physical disability (PwPD) based on travel barriers, to serve them better, from a tourism marketing perspective.

A market segmentation analysis was conducted on a sample of 480 PwPD in Sichuan Province, China, based on their perceived travel barriers. Data were obtained through three on-site and four online surveys. A four-step factor-item mixed segmentation, including factor analysis, cluster analysis, discriminant analysis and chi-square tests, was applied to examine the differences among PwPD tourist market segments in terms of various demographic characteristics, disability conditions (e.g. duration of disabilities and causes of impairment) and travel features (e.g. travel frequency and tourist destinations).

This study revealed that the PwPD tourist market is heterogeneous due to individual perceived travel barriers. Three market segments were identified, namely, the Explorer Moderates group, the Explorer Minimals group and the Explorer Intensives group. Additionally, the three market segments were found to have significant differences in terms of travel barriers, demographic characteristics, travel features and disability conditions.

This research provides suggestions for authorities and private entities to optimize the layout of accessible facilities in public areas for the benefit of all. It also offers crucial implications for tourism marketers to determine the key facets of marketing, for travel organizers to evolve the organization of travel groups for PwPD, and for practitioners to provide personalized tourism services.

To the best of the authors’ knowledge, this study is the first to apply perceived travel barriers as a market segmentation criterion in understanding PwPD as a heterogeneous travel market. The findings of this study initially expand the scope of application of the travel barrier model and deepen understanding of the Chinese PwPD tourist market from a marketing perspective. The study results elucidated the heterogeneity and characteristics of this market through a four-step factor-item mixed segmentation approach, offering new insights into the behaviors and experiences of travelers with disabilities.

本研究旨在探索肢体残障人士旅游市场的异质性, 以便从旅游营销的角度更好地为他们服务。

基于对中国四川480名肢残人士出游障碍感知的问卷调查, 探索了肢残人士的旅游市场细分。数据是通过七次现场和在线调查获得; 采用四步因子-项目混合细分法, 根据残障状况、人口统计特征和旅游特征, 识别出肢残群体旅游细分市场之间的差异。

研究发现, 基于个体感知的出游障碍, 肢残群体旅游市场是异质的, 研究确定了三个细分市场, 即低度、中度和高度受限群体。三个细分市场在出行障碍、人口特征、出游特征和残障状况方面存在显著差异。

这项研究有助于政府管理部门优化公共区域无障碍设施布局; 旅游营销者确定营销的重点, 并为旅游组织者设计肢残旅游团体成员构成, 以及从业者提供个性化旅游服务提供重要的启示。

论文首次将感知出游障碍作为市场细分标准, 用以理解肢残群体作为异质游客市场。本研究的发现拓展了出游障碍模型的应用范围, 并从市场营销的角度加深了对中国肢残游客市场的理解。研究结果通过四步因子-项目混合细分方法阐明了该市场的异质性和特点, 为肢残游客的行为和体验研究提供了新见解。

Este estudio explora la heterogeneidad del mercado turístico de las personas con discapacidad física (PcDF) en función de las barreras percibidas para viajar, con el fin de prestarles un mejor servicio desde una perspectiva de marketing turístico.

Se realizó un análisis de segmentación de mercado en una muestra de 480 PcDF en Sichuan, China, en función de las barreras que percibían para viajar. Los datos se obtuvieron a través de tres encuestas in situ y cuatro encuestas en línea. Se aplicó una segmentación mixta factor-ítem de cuatro pasos que incluye análisis factorial, análisis de conglomerados, análisis discriminante y pruebas de chi-cuadrado para examinar las diferencias entre los segmentos del mercado turístico de PcDF, en términos de diversas características demográficas, condiciones de discapacidad (por ejemplo, duración de la discapacidad, causas de la discapacidad) y características de los viajes (por ejemplo, frecuencia de viaje, destinos turísticos).

Este estudio reveló que el mercado turístico de las PcDF es heterogéneo debido a las barreras de viaje percibidas por cada individuo. Se identificaron tres segmentos de mercado, a saber, el grupo de Exploradores Moderados, el grupo de Exploradores Mínimos y el grupo de Exploradores Intensivos. Los tres segmentos de mercado presentaban diferencias significativas en cuanto a las barreras para viajar, las características demográficas, las características del viaje y las condiciones de discapacidad.

Este estudio es el primero en aplicar las barreras percibidas para viajar como criterio de segmentación de mercado para comprender a las PcDF como un mercado turístico heterogéneo. Los hallazgos de este estudio amplían inicialmente el ámbito de aplicación del modelo de barreras para viajar y profundizan en la comprensión del mercado turístico chino de PcDF desde una perspectiva de marketing. Los resultados de nuestro estudio explicaron la heterogeneidad y las características de este mercado a través de un enfoque de segmentación mixta factor-ítem de cuatro pasos, contribuyendo a la literatura sobre el comportamiento y las experiencias de los viajeros con discapacidad.

Esta investigación proporciona sugerencias para que las autoridades y las entidades privadas puedan optimizar la disposición de instalaciones accesibles en zonas públicas en beneficio de todos. También ofrece implicaciones importantes a los comercializadores turísticos para que determinen aspectos clave del marketing, a los organizadores de viajes para que evolucionen en la organización de grupos de viaje para PcDF y a los profesionales para que presten servicios turísticos personalizados.

The purpose of this study is to empirically examine knowledge management (KM) practices as a moderator in the relationship between organizational culture and performance. The effect of four types of organizational culture on organizational performance was studied. In addition to direct effects, most importantly, KM practices as a moderator in strengthening the culture-performance relationship were empirically examined.

A carefully crafted survey instrument was distributed and data was collected from 1,255 respondents from 10 information technology companies in India. After checking the psychometric properties of the instrument, this paper performs hierarchical regression to test hypotheses.

The results reveal that: cooperative culture, innovative culture, consistent culture and effectiveness culture were all positively and significantly related to organizational performance; KM practices were positively and significantly related to organizational performance, KM practices moderate the relationship between various dimensions of organizational culture and organizational performance.

As with any survey-based research, the present study suffers from the problems associated with self-report measures. These are common method bias and social desirability bias. However, this study attempts to minimize these limitations by following appropriate statistical techniques.

This study contributes to both practicing managers and the literature on KM and organizational culture. The study suggests that managers use KM practices, which are all-pervasive and very important for improving organizational performance. The results highlight the importance of implementing KM practices in organizations.

This study provides new insights into the importance of KM practices in achieving sustained competitive advantage by achieving organizational effectiveness. To the knowledge, the importance of KM practices is underemphasized in organizational culture research.

This study aims to empirically examine the relationship of ethical leadership and psychological capital with knowledge creation. It also investigates the effect of psychological capital as a mediator in the relationship between ethical leadership and knowledge creation.

This study is based on quantitative research methodology. The data was gathered using a survey questionnaire from 286 members of public-sector research organizations (PSROs) in India. Structural equation modelling (SEM) was used for hypotheses testing.

The findings of this study show that ethical leadership and psychological capital have a positive influence on knowledge creation. Further, psychological capital mediated the relationship between ethical leadership and knowledge creation.

This study is a quantitative cross-sectional study. However, future researchers may use qualitative research methodology and longitudinal data collection to supplement this study.

This study provides new understanding into the creation of knowledge by emphasizing on the critical role played by ethical leadership and psychological capital and, thus, makes significant theoretical contribution. It emphasizes that managers should not only be ethical but also use interventions to strengthen psychological capital of employees to strengthen knowledge creation.

To the best of authors’ knowledge, this is the first study to examine the underlying mechanism of psychological capital in explaining the links of ethical leadership with knowledge creation.

As of 2022, the scope of the engagement and interest of debt capital providers in ESG reporting is mainly untapped. However, a vast amount of literature has produced conflicting findings about the importance of debt capital (leverage) as a factor in sustainability reporting (SR). This is the first meta-analysis reconciling the mixed results of 85 single country studies containing 131 effect sizes across 24,482 firms conducted over past three decades (1999–2022) investigating the influence of leverage on SR. The study emphasizes the significance of contextualizing research by identifying the macro-environmental elements modifying debt's impact on SR, through the use of the institutional theory. Eleven country variables were tested on the collected dataset, spread across 36 countries.

Meta-analysis technique for aggregation of existing extant empirical work. Continuous and categorical variable-based moderator analysis to demystify the influence of country characteristics affecting the leverage–SR relationship.

Results show positive significant impact of debt capital providers on SR. Country's level of development, GDP, extent of capital constraints in a country, financial sector development within a nation, country governance factors and corruption levels, country's culture, number of sustainability reporting instruments operational in a country and geographical location proved to be significant moderators.

The study details relevant meaningful research gaps, worthy of uptake by researchers to produce targeted research.

Governments must increasingly go beyond their mandated disclosure role and acknowledge the important institutional factors that have contributed to the expansion of ESG reporting through the creation of nation-specific tools, incentive structures and disclosure-encouraging regulations. To secure a steady flow of funding and prevent negative effects on company value and cost of capital in the midst of prolonged global economic upheaval, businesses must address the information requirements of lenders. The limited total effect size emphasizes the necessity for debt providers to step up their ESG activism and exercise their maximum power and potential in stimulating extensive SR firm-level practices.

The present study is the first meta-analysis reconciling the mixed results of 85 single-country studies containing 131 effect sizes across 24,482 firms conducted over the past three decades (1999–2022) investigating the influence of leverage on SR and demystifying the macro-environmental factors affecting the leverage–SR association.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

The purpose of this study is to examine the impact of carbon performance on carbon disclosure among nonfinancial French-listed firms, while also considering the corporate board’s characteristics as a secondary objective.

This study uses a sample of Société des Bourses Françaises 120 Index (SBF-120) French-listed firms to investigate the effect of multiple carbon performance proxies on carbon disclosure based on random effects models for the period 2010–2021. Generalized method of moments regressions are used to encounter endogeneity problems.

Drawing on stakeholder theory, this paper finds that greater carbon performance leads to greater carbon disclosure. Given the growing societal awareness about climate-change issues, carbon-responsible firms are likely to disseminate relevant carbon-related information through disclosures to respond to the information demands of a varied stakeholder group. Coherent with signaling theory, large firms that undertake carbon-reduction initiatives tend to disclose more information about their enhanced carbon performance to equity participants to distinguish themselves and highlight their decarbonization efforts.

This study offers significant insights given that SBF-120 firms are involved in climate-change activities as a response to the growing institutional and societal pressure to perform better and disclose reliable environmental information in their sustainability reports.

Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA).

MLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments.

ANN shows more accuracy than MLR with an R²-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R²-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing.

ANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.

This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.