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A progressive hybrid censoring scheme (PHCS) becomes impractical for ensuring dependable outcomes when there is a low likelihood of encountering a small number of failures prior to the predetermined terminal time T. The generalized progressive hybrid censoring scheme (GPHCS) efficiently addresses to overcome the limitation of the PHCS.

In this article, estimation of model parameter, survival and hazard rate of the Unit-Lindley distribution (ULD), when sample comes from the GPHCS, have been taken into account. The maximum likelihood estimator has been derived using Newton–Raphson iterative procedures. Approximate confidence intervals of the model parameter and their arbitrary functions are established by the Fisher information matrix. Bayesian estimation procedures have been derived using Metropolis–Hastings algorithm under squared error loss function. Convergence of Markov chain Monte Carlo (MCMC) samples has been examined. Various optimality criteria have been considered. An extensive Monte Carlo simulation analysis has been shown to compare and validating of the proposed estimation techniques.

The Bayesian MCMC approach to estimate the model parameters and reliability characteristics of the generalized progressive hybrid censored data of ULD is recommended. The authors anticipate that health data analysts and reliability professionals will get benefit from the findings and approaches presented in this study.

The ULD has a broad range of practical utility, making it a problem to estimate the model parameters as well as reliability characteristics and the significance of the GPHCS also encourage the authors to consider the present estimation problem because it has not previously been discussed in the literature.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

Drawing on organismic integration theory, we aim to examine whether the reasons independent contractors choose contract work are related to their on-the-job motivation and job satisfaction and whether their perceived support enhances positive (or buffers negative) effects.

We collected data at three separate time points from 241 adjunct instructors to test a moderated mediation model using bootstrapping analyses.

The positive relationship between pull factors (e.g. autonomy) and job satisfaction is fully mediated by the autonomous motivation contractors experienced at work. The inverse relationship between push factors (e.g. inability to secure desired work role) and job satisfaction is not mediated by autonomous nor controlled motivation experienced at work. Contractors' perceived organizational support does not moderate the relationship between either push or pull factors and autonomous motivation. Post hoc analysis shows a moderating effect of perceived supervisor support on the nonlinear relationship between push factors and autonomous motivation.

Recruiting individuals drawn to the benefits of contract work may have important implications for worker motivation, job satisfaction and potentially beyond. Moreover, organizations may consider whether existing support resources and infrastructure are appropriate for contractors.

Despite the abundance of evidence demonstrating the benefits of organizational and supervisor support among traditional employee populations, such support may be of limited value to those drawn to contract work.

Entrepreneurship institutions exhibit substantial gender discrimination despite worldwide efforts to decrease the phenomenon. The MENA area has a low percentage of women entrepreneurs since little is known about women’s desire to start their businesses. The authors use the theory of planned behavior (TPB) to explain what influences women's propensity toward entrepreneurship and what factors discourage them.

TPB is a psychological theory explaining how individuals act in certain situations. The authors created their database by using a systematic questionnaire. Overall, 350 women entrepreneurs contributed to their dataset. Finally, the authors used structural equation modeling to verify their hypotheses.

This study helps them to shed light to better understand the dynamics of Entrepreneurial Intention, in women from Lebanon. The authors do not find any relationship between lack of knowledge, funding, networking and entrepreneurial startup intention for Lebanese women. The role of dynamic capabilities in the entrepreneurial landscape of Lebanon, particularly for women, is substantially highlighted by the full mediation observed in the relationship between lack of knowledge and entrepreneurial start-up intentions. The findings discovered that these capabilities could fully mediate the negative impact of lack of networking on the intention to commence entrepreneurial ventures.

This research illustrates and explains how dynamic capabilities mediate the relationship between women entrepreneurs' challenges and their intention to start a business in the Lebanese context.

The effective prediction of crude oil futures prices can provide a reference for relevant enterprises to make production plans and investment decisions. To the nonlinearity, high volatility and uncertainty of the crude oil futures price, a matrixed nonlinear exponential grey Bernoulli model combined with an exponential accumulation generating operator (MNEGBM(1,1)) is proposed in this paper.

First, the original sequence is processed by the exponential accumulation generating operator to weaken its volatility. The nonlinear grey Bernoulli and exponential function models are combined to fit the preprocessed sequence. Then, the parameters in MNEGBM(1,1) are matrixed, so the ternary interval number sequence can be modeled directly. Marine Predators Algorithm (MPA) is chosen to optimize the nonlinear parameters. Finally, the Cramer rule is used to derive the time recursive formula.

The predictive effectiveness of the proposed model is verified by comparing it with five comparison models. Crude oil futures prices in Cushing, OK are predicted and analyzed from 2023/07 to 2023/12. The prediction results show it will gradually decrease over the next six months.

Crude oil futures prices are highly volatile in the short term. The use of grey model for short-term prediction is valuable for research. For the data characteristics of crude oil futures price, this study first proposes an improved model for interval number prediction of crude oil futures prices.

With the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.

The proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.

The empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.

This study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.

The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

This study aims to investigate the moderating effect of the COVID-19 epidemic on the relationship between key audit matter (KAM) and auditing quality.

The authors use the ordinary least squares regression on data from 942 firm-year observations of Jordanian non-financial institutions across the period (2017–2022) to test the hypotheses. The authors use content analysis method to measure levels of KAM disclosure.

The investigation’s findings highlight the importance of KAM disclosure in achieving audit quality in line with international standard on auditing no. 701 (ISA-701) requirements. COVID-19 is also found to have a positive relationship with audit quality, further confirming the crisis’s devastating impact on audit complexity and risks and providing evidence for the need for supplementary, high-quality audit services. Due to the correlation between KAM disclosure and increased auditor workload and responsibility, the analysis reveals that the COVID-19 factor strengthens the link between KAM disclosure and audit quality.

This study has the potential to be used as a basis for the creation of a new regulation or standard regarding the reporting of unfavourable events in financial filings. This study’s findings provide standard-setters, regulators and policymakers with current empirical data on the effects of implementing ISA-701’s mandate for external auditors to provide more information on KAM. The COVID-19 crisis offers a suitable setting in which to examine the value of precautionary disclosures in times of economic uncertainty, as well as the significance of confidence interval disclosures and the role of external auditing in calming investor fears. This analysis is helpful for stakeholders, regulatory agencies, standard-setters and readers of audit reports who are curious about the current state of KAM disclosures and the implementation of ISA-701. The results may have ramifications for academia in the form of a call for more evidence expanding this data to other burgeoning fields to have a clear explanation of the real impact of reporting KAM on audit practices.

To the authors’ awareness, this research is one of the few empirical studies on the effect of the COVID-19 crisis on auditing procedures, and more specifically, the effect of disclosures on KAM by external auditors on audit quality. This study’s findings represent preliminary scientific evidence linking the pandemic to business performance. Minimal research has been done on how auditors in developing nations react to pandemic investor protection and how auditors’ enlarged reporting responsibilities affect them. The vast majority of auditing studies have been conducted in a highly regulated system, so this research contributes by examining audit behaviour in a weak legal context.

Environmental, social and governance (ESG) issues have become the cornerstone of investment decisions in firms today. With that, publicly traded ESG indices (like the BSE ESG 100 index in India) have come into existence. The existing literature signifies that ESG generates financial implications and induces stability. The current study aims to test whether the firms listed on the ESG index (ESG-sensitive firms) face less financial distress than those not listed on such an index.

The study applies panel data difference-in-differences (DID) regression by considering ESG as an unstaggered treatment to 74 non-financial firms listed on India's Bombay Stock Exchanges (BSE) 100 index. In total, 42 firms are ESG treated as they got listed on the BSE ESG 100 index, formed in 2017. The remaining 32 firms form the control group. The confidence intervals and standard errors are estimated using clustered robust errors and the Donald and Lang method.

Listing on the ESG index matters for financial stability; differences in financial distress are significant on financial distress. ESG-sensitive firms face less financial distress than non-ESG firms (or firms not perceived as ESG-sensitive). The results are consistent across two financial distress measures, Altman z-scores for emerged and emerging markets. Thus, the DID in distress status between ESG-sensitive and non-ESG firms matter.

The study creates vibrant implications for practitioners using ESG to reduce financial distress.

The study is one of its kind to test the treatment effects of ESG on firm value and quantify treatment effects on financial distress.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.