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The purpose of this study is to propose a systematic model selection procedure for long-term load forecasting (LTLF) for ex-ante and ex-post cases considering uncertainty in exogenous predictors.

The different variants of regression models, namely, Polynomial Regression (PR), Generalised Additive Model (GAM), Quantile Polynomial Regression (QPR) and Quantile Spline Regression (QSR), incorporating uncertainty in exogenous predictors like population, Real Gross State Product (RGSP) and Real Per Capita Income (RPCI), temperature and indicators of breakpoints and calendar effects, are considered for LTLF. Initially, the Backward Feature Elimination procedure is used to identify the optimal set of predictors for LTLF. Then, the consistency in model accuracies is evaluated using point and probabilistic forecast error metrics for ex-ante and ex-post cases.

From this study, it is found PR model outperformed in ex-ante condition, while QPR model outperformed in ex-post condition. Further, QPR model performed consistently across validation and testing periods. Overall, QPR model excelled in capturing uncertainty in exogenous predictors, thereby reducing over-forecast error and risk of overinvestment.

These findings can help utilities to align model selection strategies with their risk tolerance.

To propose the systematic model selection procedure in this study, the consistent performance of PR, GAM, QPR and QSR models are evaluated using point forecast accuracy metrics Mean Absolute Percentage Error, Root Mean Squared Error and probabilistic forecast accuracy metric Pinball Score for ex-ante and ex-post cases considering uncertainty in the considered exogenous predictors such as RGSP, RPCI, population and temperature.

This study aims to examine the predictive power of childhood adversity and severity of positive symptoms on suicidality, controlling for selected sociodemographics factors, among hospitalized patients diagnosed with schizophrenia in Jordan.

This study used a descriptive-explorative design. The study was conducted at two major psychiatric hospitals in Jordan. The targeted sample was 66 patients diagnosed with schizophrenia. Data was collected using a structured format in the period February–April 2024.

A two-step multiple hierarchical regression analysis was conducted. In the first model, childhood adversity and the severity of positive symptoms were entered. In the second model, sociodemographic variables were entered. The analysis revealed that the first model (F = 5.35, p = 0.007) was statistically significant. The second model (F = 717, p < 0.001) was statistically significant. Furthermore, the analysis revealed that childhood adversity was not a significant predictor for suicidality. However, positive symptoms and patients’ demographics (age, number of hospitalizations and length of being diagnosed with schizophrenia) were significant predictors of suicidality. The analysis revealed that childhood adversity was not a significant predictor of suicidality. However, positive symptoms and patients’ demographics (age, number of hospitalizations and length of being diagnosed with schizophrenia) were significant predictors of suicidality.

One limitation of this study is related to the sample and the setting where there were only 66 patients recruited from governmental hospitals within inpatient wards. Thus, the upcoming studies should include more participants from private hospitals and different hospital settings including outpatient and emergency departments.

The research provides empirical insights that positive symptoms, age hospitalization and schizophrenia diagnosis length were significant predictors of suicidality. At the same time, childhood adversity was not a significant predictor of suicidality.

The current research contributes to expanding mental health studies. Moreover, this study enlarges the body of knowledge in the academic world and clinical settings. It supports the disciplines of psychology, mental health and social sciences by increasing knowledge of the complicated relationships among childhood adversity, positive symptoms and suicidality.

This paper fulfills an identified need to study childhood adversity with comorbid psychiatric disorders such as schizophrenia, as well as psychiatric mental health covariates.

The purpose of this study is to test whether the prediction of firm performance can be enhanced by incorporating nonfinancial disclosures, such as narrative disclosure tone and corporate governance indicators, into financial predictive models.

Three predictive models are developed, each with a different set of predictors. This study utilises two machine learning techniques, random forest and stochastic gradient boosting, for prediction via the three models. The data are collected from a sample of 1,250 annual reports of 125 nonfinancial firms in Pakistan for the period 2011–2020.

Our results indicate that both narrative disclosure tone and corporate governance indicators significantly add to the accuracy of financial predictive models of firm performance.

Our results offer implications for the restoration of investor confidence in the highly uncertain Pakistani market by establishing nonfinancial disclosures as reliable predictors of future firm performance. Accordingly, they encourage investors to pay more attention to these disclosures while making investment decisions. In addition, they urge regulators to promote and strengthen the reporting of such nonfinancial information.

This study addresses the neglect of nonfinancial disclosures in the prediction of firm performance and the scarcity of corporate governance literature relevant to the use of machine learning techniques.

The current paper aims to provide insights into the determinants associated with job satisfaction among police administrative (personnel) officers in Taiwan, especially both internal organizational predictors and unique external predictors related to the Chinese cultural context.

Data were gathered from police administrative officers across major and medium-sized police agencies in Taiwan. Multiple regression models were employed to analyze the relationship between both internal factors to the organization (e.g. workplace fairness, supervisor support, self-efficacy) and external factors (related to traditional Chinese culture and its expectations) and job satisfaction.

The external factors of work-family life balance and financial benefits are strong predictors, emphasizing the cultural significance of family harmony and financial stability in Taiwanese society. This finding challenges the prevailing notion in the literature that the primary source of job satisfaction among police officers is derived from internal organizational factors. Collectively, the findings concluded the multi-faceted determinants of job satisfaction among administrative officers in Taiwan, intertwining both individual and internal organizational factors with broader external cultural influences.

This study investigated the job satisfaction among administrative officers who play a key role in a police department. The findings showed that external factors exert a significant impact on job satisfaction. This offers a new frontier to examine job satisfaction among not only administrative officers but also patrol officers in Taiwan and Asian countries. In addition, training courses can be developed and focus on work-family relations when officers are off duty.

While previous research has extensively explored job satisfaction among police officers in various roles and countries, by integrating internal organizational and external predictors, this study pioneers the focus on “police administrative officers” within Taiwanese police agencies.

The current study aims to examine if the internal marketing and employees’ personal traits can predict their green innovative behavior at the workplace.

A survey was conducted with 683 frontline employees working in four- and five-star hotels in Serbia. Zero-order bivariate correlations among variables and linear multiple regression were conducted to predict green innovative behavior based on internal marketing, personality traits and psychological capital. Binary genetic algorithms were used to segregate the subset of predictors that would be most suitable to describe variance in the outcome.

The results showed that internal communication, incentive and reward systems, work support, work environment, openness and creative self-efficacy were the most important predictors of almost all the phases of green innovative behavior.

The research showed that a multidimensional approach in analyzing green innovative behavior is necessary as some factors can be significant or not so significant predictors. Acknowledging that innovation is a multistage process, entailing distinct activities and requiring varied individual behaviors to accomplish each task, amplifies the importance of this inquiry. Employees’ personal characteristics have direct impact on green innovative behavior in hospitality. Further, the results gave an insight into the possible mix of elements of internal marketing that can be used for boosting employees’ green innovative behavior in hospitality. This is important as implementing effective internal marketing practices empowers organizations to motivate employees to invest discretionary efforts.

本研究旨在探讨内部营销和员工个人特质是否能预测他们在工作场所的绿色创新行为。

在塞尔维亚的四星和五星级酒店中, 对683名一线员工进行了调查。在变量之间进行了零阶双变量相关性和线性多元回归, 以预测基于内部营销、个性特质和心理资本的绿色创新行为。使用二元遗传算法(GAs)将适用于描述结果变异性的预测子集进行分离。

结果显示, 内部沟通、激励和奖励制度、工作支持、工作环境、开放性和创造力自效能是几乎所有绿色创新行为阶段的最重要的预测因素。

研究表明, 分析绿色创新行为需要采用多维度的方法, 因为某些因素可能是更或更少决定性的预测因素。承认创新是一个多阶段的过程, 涉及到不同的活动, 并要求采用不同的个体行为来完成每个任务, 这加强了对这一调查的重要性。员工的个人特征直接影响了酒店业的绿色创新行为。此外, 结果揭示了可以用于促进酒店业员工绿色创新行为的内部营销元素可能的混合。这是重要的, 因为实施有效的内部营销实践使组织能够激励员工投入可自由支配的努力。

El presente estudio examina si el marketing interno y los rasgos de personalidad de los empleados pueden predecir su comportamiento innovador ecológico en el lugar de trabajo.

Se realizó una encuesta a 683 empleados de primera línea que trabajan en hoteles de cuatro y cinco estrellas en Serbia. Se llevaron a cabo correlaciones bivariadas de orden cero y regresiones lineales múltiples (LM) para predecir el comportamiento innovador ecológico en función del marketing interno, los rasgos de personalidad y el capital psicológico. Se utilizaron algoritmos genéticos binarios (AGs) para segregar el subconjunto de predictores más adecuado para describir la variabilidad en el resultado.

Los resultados mostraron que la comunicación interna, los sistemas de incentivos y recompensas, el apoyo en el trabajo, el entorno laboral, la apertura y la autoeficacia creativa eran los predictores más importantes en casi todas las fases del comportamiento innovador ecológico.

La investigación demostró que es necesario un enfoque multidimensional para analizar el comportamiento innovador ecológico, ya que algunos factores pueden o no ser predictores significativos. Reconocer que la innovación es un proceso de múltiples etapas, que implica actividades distintas y requiere comportamientos individuales variados para realizar cada tarea, amplifica la importancia de esta investigación. Las características personales de los empleados influyen directamente en el comportamiento innovador ecológico en la industria hotelera. Además, los resultados ofrecen una visión de la posible combinación de elementos de marketing interno que se pueden utilizar para impulsar el comportamiento innovador ecológico de los empleados en la hotelería. Esto es importante ya que la implementación de prácticas eficaces de marketing interno permite a las organizaciones motivar a los empleados para que inviertan esfuerzos discrecionales.

This paper aims to introduce a new perspective on long-term stock return predictability by focusing on the relative (individual and hybrid) informative power of a wide range of accounting (firm-related), technical and macroeconomic factors while considering the past performance of the stocks using machine learning algorithms.

The sample includes a panel data set of 94 non-financial firms listed in Egyptian Exchange 100 index from 2014: Q1 to 2019: Q4. Relativity has been investigated by comparing relevant factors’ individual and combined informative power and differentiating between losers and winners based on historical stock returns. To predict the quarterly stock returns, Gaussian process regression (GPR) has been used. The robustness of the results is examined through the out-of-sample test. This study also uses linear regression (LR) as a benchmark model.

The past performance and the presence of other predictors influence the informative power of relevant factors and hence their predictive ability. The out-of-sample results show a trade-off between GPR and LR with proven superiority to GPR in limited experiments. The individual informative power outperforms the hybrid power, in which macroeconomic indicators outperform the remaining sets of indicators for losers, while winners show mixed results in terms of various performance evaluation metrics. Prediction accuracy is generally higher for losers than for winners.

This study provides interesting insight into the dynamic nature of the predictor variables in terms of stock return predictability. Hence, this study also deepens the understanding of asset pricing in a way that directly contributes to practitioners’ portfolio diversification strategies.

In concern of the chaos of factors in the literature and its accompanying misleading conclusions, this study takes another look at the approach that studies stock return predictability. To the best of the authors’ knowledge, this is the first study in the Egyptian context that re-examines the predictive power of the previously discovered factors from a different perspective that highlights their relative nature.

The purpose of this study is to assess psychological distress among parents of children with autism spectrum disorder (ASD), self-esteem as a predictor of such distress and the effect of coping strategies.

A descriptive correlational study design was conducted using a convenient sample of parents of ASD children (N = 93).

This study revealed that the parents of an ASD child experienced a high level of anxiety (M = 15.89), a moderate level of depression (M = 15.85) and a mild level of stress (M = 16.86). Parents of ASD children also reported a low self-esteem score (M= 13.27). Mothers of ASD children reported higher levels of psychological distress, lower levels of self-esteem and more frequent utilisation of maladaptive coping strategies than fathers of ASD children.

Parents of children with ASD experience a significant level of psychological distress; however, this may be improved by developing programmes and psychological interventions focused on improving parents’ self-esteem and using more active coping strategies.

To the best of the authors’ knowledge, this is the first study conducted in Saudi Arabia that predict the psychological status among family caregivers of an autistic child.

This research paper aims to investigate the predictors and outcomes of immersive technology adoption in tourism.

PLS-SEM is used for data collected from tourists visiting the UAE and Egypt to examine predictors and consequences of adoption.

It is revealed that perceived ease of use, enjoyment, immersion, usefulness and attitude towards technology predict immersive technology adoption. It is also revealed that the adoption affects tourists’ perceived value and engagement, which, in turn, affects tourists’ satisfaction and loyalty.

The study has integrated a research model that combines both antecedents and consequences of immersive technology adoption where few empirical investigations were revealed to draw conclusions on this research area. Also, missing relations have been included and tested in the research model.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

This study aims to examine the impact of autonomous vehicles adoption motivations (i.e. technological, ecological and intrinsic motivation) on tourists’ pro-environmental behavior and verify the mediating role of tourists’ green self-image between the relationship of eco-friendly attitudes and autonomous vehicles adoption motivations.

The data from 586 national and international tourists were analyzed using the partial least squares method.

The findings revealed that eco-friendly attitude is a significant predictor of tourists’ green self-image; tourists’ green self-image is a significant predictor of autonomous vehicles adoption motivations; and autonomous vehicles adoption motivations are significant predictors of tourists’ pro-environmental behavior. In addition, tourists’ green self-image mediated the relationship between eco-friendly attitudes and autonomous vehicles adoption motivations.

These outcomes provide valuable guidance for the future development of green destination tourism and allow interesting implications for the tourism industry and autonomous vehicles adoption.

本研究探讨自动驾驶汽车采纳动机（即技术、生态和内在动机）对游客环保行为的影响, 并验证游客绿色自我形象在环保态度和自动驾驶汽车采纳动机之间的中介作用。

收集586份来自中国国内外游客的数据, 采用偏最小二乘法进行分析。

研究结果表明, 环保态度显著影响游客绿色自我形象, 进而影响自动驾驶汽车采纳动机, 带来游客的环保行为。此外, 游客的绿色自我形象在环保态度与自动驾驶汽车采纳动机之间起到中介作用。

本研究提出了游客绿色自我形象的概念, 将游客与环保人士的日常行为进行区分。研究结果为绿色目的地旅游业的未来发展提供了方向, 对旅游业和自动驾驶汽车的采纳产生影响。

Este estudio tiene como objetivo examinar el impacto de las motivaciones para la adopción de vehículos autónomos (es decir, motivaciones tecnológicas, ecológicas e intrínsecas) en el comportamiento proambiental de los turistas y verificar el papel mediador de la autoimagen ecológica de los turistas en la relación entre las actitudes ecológicas. y las motivaciones para la adopción de vehículos autónomos.

Se analizaron los datos de 586 turistas nacionales e internacionales mediante el método de mínimos cuadrados parciales.

Los hallazgos revelaron que la actitud ecológica es un predictor importante de la autoimagen ecológica de los turistas; la autoimagen ecológica de los turistas es un predictor importante de las motivaciones para la adopción de vehículos autónomos; y las motivaciones para la adopción de vehículos autónomos son predictores importantes del comportamiento proambiental de los turistas. Además, la autoimagen ecológica de los turistas medió la relación entre las actitudes ecológicas y las motivaciones para la adopción de vehículos autónomos.

Estos resultados proporcionan una orientación valiosa para el desarrollo futuro del turismo de destino ecológico y permiten implicaciones interesantes para la industria turística y la adopción de vehículos autónomos.