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The purpose of this paper is to provide a picture of the water situation of the states of India and to identify key areas in which intervention is necessary for sustainable development and poverty elevation.

To understand the trend and situation of water across the states, Water Poverty Index (WPI) has been constructed. WPI has been computed for the years 2012 and 2018 to get a picture of temporal change happening in the region. Further, descriptive statistics were used to show the required changes.

Jharkhand and Rajasthan continue to be the worst performer in both time periods. Water poverty was the least in the states of Goa and Chandigarh for both time periods. Although owing to improvement in access and capacity component, the water status of India as a whole improved from 2012 to 2018 but few states have witnessed a decline in their water situation mainly due to deterioration in the environment and resource components.

This paper adds to the relatively scarce literature on the water situation conducted for the states of India. The findings of the paper provide insights into the lacking areas responsible for the deterioration in water poverty status. The results can be utilized for framing proper policies to combat the water woes of the country.

Given the remarkable increase in entrepreneurial activities initiated by women in the Egyptian context in addition to the scarcity of empirical studies on digital self-employment there, the authors of the present paper aim to identify what motivates women to engage in digital entrepreneurship, and to identify how those women establish their digital entrepreneurial activities.

The authors employed a qualitative research method through semi-structured interviews with 30 women entrepreneurs who own and manage digital businesses. Thematic analysis was subsequently used to determine the main ideas in the transcripts.

The authors have found that enjoying absolute independence, securing more time for family, guaranteeing an independent source of income in addition to the ease of accessing extensive online markets are the main motives behind the engagement of women in the Egyptian context in digital entrepreneurship activities. Moreover, the authors have also asserted that the minimal training and government support stimulate women entrepreneurs there to start and continue their digital business activities informally.

This paper contributes by filling a gap in entrepreneurship studies in which empirical studies on establishing and managing digital entrepreneurship among women in developing economies has been limited so far.

This study aims to demonstrate how the process of quality function deployment (QFD) is used to identify the basic requirements of the customers in designing and executing the commercial business center.

This study was considered with the aim of determining the approach of QFD methodology used in the planning and designing of commercial business centers. The methodology used in the study is a customer-driven process that includes customer requirements in each and every aspect of the planning and designing of the project. The main focus of this study is to understand the requirements of the customers and to design and execute a commercial business project.

This study illustrates the quality requirements of the projects that benefit from the QFD process to obtain customer requirements for the planning and designing of commercial business centers. A case study is used to demonstrate the use of QFD process. This helps to explain the effective application of QFD in the planning and designing of business centers and similar constructions.

The planning and designing of the commercial business center using the QFD process were challenging and hence it is limited to the design part. The strategic objectives are not taken into account while performing QFD in this case study and the risk of market research is lacking. House of quality (HOQ) can be too complicated at times; hence, the adaptability in the traditional QFD is lacking. Most of the work in the HOQ matrix is done through subjective evaluation. Therefore, this research is mostly useful for a single party responsible for all phases of the planning and designing of the project.

In the construction industry, the use of the QFD process for project performance analysis and application is restricted. As a result of the scarcity of studies on the planning and design of construction projects, this study on the planning and design of a construction project was inspired.

This study aims to create an efficient approach to validate building energy simulation models amidst challenges from time-intensive data collection. Emphasizing precision in model calibration through strategic short-term data acquisition, the systematic framework targets critical adjustments using a strategically captured dataset. Leveraging metrics like Mean Bias Error (MBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)), this methodology aims to heighten energy efficiency assessment accuracy without lengthy data collection periods.

A standalone school and a campus facility were selected as case studies. Field investigations enabled precise energy modeling, emphasizing user-dependent parameters and compliance with standards. Simulation outputs were compared to short-term actual measurements, utilizing MBE and CV(RMSE) metrics, focusing on internal temperature and CO₂ levels. Energy bills and consumption data were scrutinized to verify natural gas and electricity usage against uncertain parameters.

Discrepancies between initial simulations and measurements were observed. Following adjustments, the standalone school 1’s average internal temperature increased from 19.5 °C to 21.3 °C, with MBE and CV(RMSE) aiding validation. Campus facilities exhibited complex variations, addressed by accounting for CO₂ levels and occupancy patterns, with similar metrics aiding validation. Revisions in lighting and electrical equipment schedules improved electricity consumption predictions. Verification of natural gas usage and monthly error rate calculations refined the simulation model.

This paper tackles Building Energy Simulation validation challenges due to data scarcity and time constraints. It proposes a strategic, short-term data collection method. It uses MBE and CV(RMSE) metrics for a comprehensive evaluation to ensure reliable energy efficiency predictions without extensive data collection.

This study aims to address the question of why managers make different decisions in employee downsizing when their firms face external threats. Our research intends to shed light on whether and how CEOs' cognition (motivational attributes associated with regulatory focus) influences their decision-making and firms’ strategic actions on downsizing under high resource scarcity in the industry environment.

We used a longitudinal panel of 5,544 firm-year observations of US firms from 2003 to 2015 to test our conceptual model. The data was obtained from various sources, including corporate earnings call transcripts and archival databases. We used panel logistic regressions with both fixed and random effects in our research design.

Our results suggest that CEOs' motivational attributes could influence their employee downsizing decisions in response to external threats. We find that CEOs who are more promotion-focused (a stronger drive towards achieving ideals) are less likely to lay off employees during high resource scarcity. Conversely, CEOs with a higher prevention focus (a greater concern for security) do not have a meaningful impact on employee downsizing during periods of external resource scarcity.

Previous research has argued that a significant external threat would diminish individuals' impact on firm strategies and outcomes. Our findings challenge this idea, indicating that CEOs with a stronger drive towards achieving ideals are less inclined to lay off employees when resources are scarce in the environment. This study contributes to behavioral strategy research by providing new insights into how upper echelons’ cognition can influence their decision-making and firms’ employee downsizing.

This study aims to inspect the impact of earnings quality on corporate cash holdings of Jordanian companies listed on the Amman Stock Exchange.

This study examines a large sample of (98) Jordanian companies listed on the Amman Stock Exchange during the period that ranges from 2009 to 2019. Earnings quality was computed using two different methods; firstly, through the absolute abnormal discretionary accruals (as an inverse measure of earnings quality), which were estimated using the Dechow et al.’s (1995) cross-sectional version of the Modified Jones model and the Kothari et al. (2005) model; and secondly, through earnings persistence as a direct measure of earnings quality.

The empirical results of this study reveal that poor accounting quality (high levels of abnormal discretionary accruals) is associated with higher levels of cash holdings, implying that as the quality of earnings decreases, the harmful effects of information asymmetry and adverse selection costs will increase, leading, therefore, Jordanian companies to increase their corporate cash holdings levels to act as a buffer against any cash shortages. Further, the authors document that higher accounting quality (more persistent earnings) is associated with lower levels of cash holdings. In addition, this study found that earnings quality negatively and significantly affects the cash holdings of profitable companies in Jordan. Thus, earnings quality appeared to be a significant determinant of cash holdings for profit-making companies but not for companies enduring losses.

This study contributes to the limited evidence that investigates the relationship between earnings quality and corporate cash holdings. Where the majority of previous studies have focused on developed economies, to the best of the authors’ knowledge, this study is the first in Jordan to comprehensively explore the relationship between earnings quality, computed by the absolute abnormal discretionary accruals and earnings persistence, and corporate cash holdings. Also, it is the first to explore the nature of the earnings quality-cash holding nexus in loss-making companies compared with their profit-making counterparts to the best of the authors’ knowledge. The results of this study have important policy implications for managers, creditors, investors and academics in Jordan and other emerging economies that share similar characteristics.

This research proposes a novel risk assessment model to elucidate the risk propagation process of industrial safety accidents triggered by natural disasters (Natech), identifies key factors influencing urban carrying capacity and mitigates uncertainties and subjectivity due to data scarcity in Natech risk assessment.

Utilizing disaster chain theory and Bayesian network (BN), we describe the cascading effects of Natechs, identifying critical nodes of urban system failure. Then we propose an urban carrying capacity assessment method using the coefficient of variation and cloud BN, constructing an indicator system for infrastructure, population and environmental carrying capacity. The model determines interval values of assessment indicators and weights missing data nodes using the coefficient of variation and the cloud model. A case study using data from the Pearl River Delta region validates the model.

(1) Urban development in the Pearl River Delta relies heavily on population carrying capacity. (2) The region’s social development model struggles to cope with rapid industrial growth. (3) There is a significant disparity in carrying capacity among cities, with some trends contrary to urban development. (4) The Cloud BN outperforms the classical Takagi-Sugeno (T-S) gate fuzzy method in describing real-world fuzzy and random situations.

The present research proposes a novel framework for evaluating the urban carrying capacity of industrial areas in the face of Natechs. By developing a BN risk assessment model that integrates cloud models, the research addresses the issue of scarce objective data and reduces the subjectivity inherent in previous studies that heavily relied on expert opinions. The results demonstrate that the proposed method outperforms the classical fuzzy BNs.

Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.

A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.

It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.

The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

Growing food insecurity is a leading cause of fatalities, particularly in developing nations like Sub-Saharan Africa and Southeast Asia. However, the rising energy consumption and carbon dioxide (CO2) emissions are mostly associated with food production. Balancing the trade-offs between energy intensity and food security remains a top priority for environmentalists. Despite the critical role of the environment in food security, there is a scarcity of substantial studies that explore the statistical connections among food security, CO2 emissions, energy intensity, foreign direct investment (FDI) and per capita income. Therefore, this study aims to provide more precise and consistent estimates of per capita CO2 emissions by considering the interplay of food security and energy intensity within the context of emerging economies.

To examine the long-term relationships between CO2 emissions, food security, energy efficiency, FDI and economic development in emerging economies, this study employs correlated panel-corrected standard error, regression with Newey–West standard error and regression with Driscoll–Kraay standard error models (XTSCC). The analysis utilizes data spanning from 1980 to 2018 and encompasses 32 emerging economies.

The study reveals that increasing food security in a developing economy has a substantial positive impact on both CO2 emissions and energy intensity. Each model, on average, demonstrates that a 1 percent improvement in food security results in a 32% increase in CO2 levels. Moreover, the data align with the Environmental Kuznets Curve (EKC) theory, as it indicates a positive correlation between gross domestic product (GDP) in developing nations and CO2 emissions. Finally, all experiments consistently demonstrate a robust correlation between the Food Security Index (FSI), energy intensity level (EIL) and exchange rate (EXR) in developing markets and CO2 emissions. This suggests that these factors significantly contribute to environmental performance in these countries.

This study introduces novelty by employing diverse techniques to uncover the mixed findings regarding the relationship between CO2 emissions and economic expansion. Additionally, it integrates energy intensity and food security into a new model. Moreover, the study contributes to the literature by advocating for a sustainable development goal (SDG)-oriented policy framework that considers all variables influencing economic growth.

The purpose of this study is to investigate how the price delay of cryptocurrencies to market news affects the herding behavior of investors, particularly during turbulent events such as the COVID-19 period.

The paper investigates the presence of herding behavior by using Cross-Sectional Absolute Deviation (CSAD) measures. We also investigate the herding activity in the crypto traders’ behavior during up and down-market movements periods and under investor extreme sentiment conditions. The speed of cryptocurrencies’ price response to the information embedded in the market is assessed based on the price delay measure proposed by Hou and Moskowitz (2005).

Our findings suggest that cryptocurrencies characterized by high price delays exhibit more herding among investors, thereby highlighting higher degrees of market inefficiencies. This is also apparent during periods of extreme investor sentiment. We also document an asymmetric herding behavior across cryptocurrencies that present different levels of price speed adjustments to market news during bullish and bearish market conditions. Our results are consistent and robust across different sub-periods, various market return estimations and different price delay frequencies.

The study provides crucial guidelines for investors’ asset allocation and risk management strategies. This study is also valuable to regulators and policymakers, particularly in light of the increasing importance of financial reforms aimed at mitigating market distortions and enhancing the resilience of the cryptocurrency market. More specifically, regulations that improve the market’s information efficiency should be prioritized to speed up the response time of cryptocurrency prices to market information, which can help reduce the investors' herding behavior.

This paper makes a novel contribution to the academic literature by investigating the unexplored relationship between cryptocurrency price delays and the presence of herding behavior among investors, especially in times of uncertainty such as the COVID-19 pandemic.