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The objective of this paper is to investigate the direction of the causal relationship between dividend policy (DP) and earnings management (EM).

This research utilizes the panel data analysis to investigate the causal relationship between EM and DP. It provides empirical insights based on a sample of 280 French nonfinancial companies listed on the CAC All-Tradable index during the period of 2008–2015. The study initiates with a Granger causality examination on the unbalanced panel data and employs a dynamic panel approach with the generalized method of moments (GMM). It further estimates the empirical models simultaneously using the three-stage least squares (3SLS) method and the iterative triple least squares (iterative 3SLS) method.

The estimation of our various empirical models confirms the presence of a bidirectional causal relationship between DP and EM.

Our study highlights the prevalence of EM in the French context, particularly within DP. It underscores the need for regulatory bodies, the Ministry of Finance, external auditors and stock exchange organizers to prioritize governance mechanisms for improving the quality of financial information disclosed by companies.

This research is, to the best of our knowledge, the first is to extensively investigate the reciprocal causal relationship between DP and EM in France. Previous studies have not placed a significant emphasis on exploring this bidirectional link between these two variables.

This study aims to emphasize the integration of lean start-up and design thinking approaches and investigate how they may be used together.

The report uses a systematic literature review methodology to analyze and summarize previous research on combining lean start-up and design thinking. Inferences were discovered and analyzed after relevant publications were chosen based on predetermined inclusion criteria.

The research shows that combining lean start-up and design thinking significantly impacts entrepreneurship. Start-ups can efficiently uncover consumer needs, reduce risks and improve their product or service offerings by combining the client-centricity of design thinking with the iterative and data-driven concepts of lean start-up. This integration promotes an innovative culture, gives teams the freedom to try new things and learn from mistakes and raises the possibility of start-up success.

The dependence on pre-existing literature, which might cover only some potential uses and circumstances, is a weakness of this research. It is advised that more empirical research be conducted to determine the precise circumstances in which the integrated strategy performs best. Future studies should also explore the difficulties and drawbacks of using these approaches to offer suggestions for overcoming them and maximizing their advantages.

The findings have significant ramifications for business owners and other professionals working in the start-up environment. The combination of lean start-up and design thinking emphasizes the relevance of early customer interaction and empathy-driven design. To foster creativity and hasten the expansion of start-ups, practitioners are urged to create a comprehensive strategy that integrates the advantages of both techniques. Through this integration, business owners may develop solutions that appeal to their target market, increasing adoption rates and market competitiveness.

This study is interesting in comparing lean start-up and design thinking, emphasizing the overlaps and benefits of their application to entrepreneurship. This study discusses successful start-up methods by offering suggestions for future research and practice. It also provides a basis for further developing and adopting the integrated approach.

Users often struggle to select choosing among similar online services. To help them make informed decisions, it is important to establish a service reputation measurement mechanism. User-provided feedback ratings serve as a primary source of information for this mechanism, and ensuring the credibility of user feedback is crucial for a reliable reputation measurement. Most of the previous studies use passive detection to identify false feedback without creating incentives for honest reporting. Therefore, this study aims to develop a reputation measure for online services that can provide incentives for users to report honestly.

In this paper, the authors present a method that uses a peer prediction mechanism to evaluate user credibility, which evaluates users’ credibility with their reports by applying the strictly proper scoring rule. Considering the heterogeneity among users, the authors measure user similarity, identify similar users as peers to assess credibility and calculate service reputation using an improved expectation-maximization algorithm based on user credibility.

Theoretical analysis and experimental results verify that the proposed method motivates truthful reporting, effectively identifies malicious users and achieves high service rating accuracy.

The proposed method has significant practical value in evaluating the authenticity of user feedback and promoting honest reporting.

The purpose of this study is to solve two unique but difficult partial differential equations: the foam drainage equation and the nonlinear time-fractional fisher’s equation. Through our methods, we aim to provide accurate solutions and gain a deeper understanding of the intricate behaviors exhibited by these systems.

In this study, we use a dual technique that combines the Aboodh residual power series method and the Aboodh transform iteration method, both of which are combined with the Caputo operator.

We develop exact and efficient solutions by merging these unique methodologies. Our results, presented through illustrative figures and data, demonstrate the efficacy and versatility of the Aboodh methods in tackling such complex mathematical models.

Owing to their fractional derivatives and nonlinear behavior, these equations are crucial in modeling complex processes and confront analytical complications in various scientific and engineering contexts.

The study’s aim was to design and test a leadership development approach using blended learning, to equip leaders for strengthening their own resilience and that of their teams.

A contextualised leadership development intervention was produced and evaluated following the principles of design-based research. Participants were from three organisations that work internationally to address the impact of economic disadvantage. Initial research used the behavioural event interview technique. Online assessment incorporated measures of situational judgement, emotion recognition and attributional style. Validity measures were multi-rater feedback (criterion), and NEO-PI 3 (construct). Individual feedback and a simulation-based peer workshop were followed by a four-to-six month period of experience-driven development and a final peer workshop for consolidating and evaluating learning outcomes.

The online assessment was a valid measure of leaders’ personal resilience resources and their resilience-building capability. Overall, the intervention improved participants’ understanding of, and engagement with, the processes of strengthening individual and collective (team) resilience.

The target sample size for the study was relatively small, to ensure it would be practical to replicate the approach when designing similar interventions for a senior leadership population in other contexts. Significant results provided robust evidence for the validity of the assessment approach. Findings for the workshops and experience-driven development phase were more tentative, but the value of the design iterations was clearly demonstrated.

The leadership development approach is suitable for application in other organisations, if similar principles are followed to produce and evaluate materials relevant to each broad sector context. Roll-out is cost-effective, with relatively few hours of blended or virtual delivery supporting experience-driven learning.

The impact leaders have on the wellbeing of those who report to them is well established, but less has been done to develop and formally evaluate practical, cost-effective interventions to improve this impact. The approach validated in this study can be applied more widely to benefit employee wellbeing as well as performance.

The study developed and evaluated a new approach to preparing leaders for the challenge of building team resilience, an aspect of leadership capability that has been given relatively little attention to date.

For ranking aggregation in crowdsourcing task, the key issue is how to select the optimal working group with a given number of workers to optimize the performance of their aggregation. Performance prediction for ranking aggregation can solve this issue effectively. However, the performance prediction effect for ranking aggregation varies greatly due to the different influencing factors selected. Although questions on why and how data fusion methods perform well have been thoroughly discussed in the past, there is a lack of insight about how to select influencing factors to predict the performance and how much can be improved of.

In this paper, performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task is studied. An influencing factor optimization selection method based on stepwise regression (IFOS-SR) is proposed to screen the optimal influencing factors. A working group selection model based on the optimal influencing factors is built to select the optimal working group with a given number of workers.

The proposed approach can identify the optimal influencing factors of ranking aggregation, predict the aggregation performance more accurately than the state-of-the-art methods and select the optimal working group with a given number of workers.

To find out under which condition data fusion method may lead to performance improvement for ranking aggregation in crowdsourcing task, the optimal influencing factors are identified by the IFOS-SR method. This paper presents an analysis of the behavior of the linear combination method and the CombSUM method based on the optimal influencing factors, and optimizes the task assignment with a given number of workers by the optimal working group selection method.

The transient loads on the spherical hybrid sliding bearings (SHSBs) rotor system during the process of accelerating to stable speed are related to time, which exhibits a complex transient response of the rotor dynamics. The current study of the shaft center trajectory of the SHSBs rotor system is based on the assumption that the rotational speed is constant, which cannot truly reflect the trajectory of the rotor during operation. The purpose of this paper truly reflects the trajectory of the rotor and further investigates the stability of the rotor system during acceleration of SHSBs.

The model for accelerated rotor dynamics of SHSBs is established. The model is efficiently solved based on the fourth-order Runge–Kutta method and then to obtain the shaft center trajectory of the rotor during acceleration.

Results show that the bearing should choose larger angular acceleration in the acceleration process from startup to the working speed; rotor system is more stable. With the target rotational speed increasing, the changes in the shaft trajectory of the acceleration process are becoming more complex, resulting in more time required for the bearing stability. When considering the stability of the rotor system during acceleration, the rotor equations of motion provide a feasible solution for the simulation of bearing rotor system.

The study can simulate the running stability of the shaft system from startup to the working speed in this process, which provides theoretical guidance for the stability of the rotor system of the SHSBs in the acceleration process.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

Cotton fibre lots are graded and selected for yarn spinning based on their quality value which is a function of certain fibre properties. Cotton grading and selection has created a domain of emerging interest among the researchers. Several researchers have addressed the said issue using a few exponents of multi-criteria decision-making (MCDM) technique. The purpose of this study is to demonstrate a cotton selection problem using a recently developed measurement of alternatives and ranking according to compromise solution (MARCOS) method which can handle almost any decision problem involving a finite number of alternatives and multiple conflicting decision criteria.

The MARCOS method of the MCDM technique was deployed in this study to rank 17 cotton fibre lots based on their quality values. Six apposite fibre properties, namely, fibre bundle strength, elongation, fineness, upper half mean length, uniformity index and short fibre content are considered as the six decision criteria assigning weights previously determined by an earlier researcher using analytic hierarchy process.

Among the 17 alternatives, C9 secured rank 1 (the best lot) with the highest utility function (0.704) and C7 occupied rank 17 (the worst lot) with the lowest utility function (0.596). Ranking given by MARCOS method showed high degree of congruence with the earlier approaches, as evidenced by high rank correlation coefficients (Rs > 0.814). During sensitivity analyses, no occurrence of rank reversal is observed. The correlations between the quality value-based ranking and the yarn tenacity-based rankings are better than many of the traditional methods. The results can be improved further by adopting other efficient method of weighting the criteria.

The properties of raw cotton have significant impact on the quality of final yarn. Compared to the traditional methods, MCDM is reported as the most viable solution in which fibre parameters are given their due importance while formulating a single index known as quality value. The present study demonstrates the application of a recently developed exponent of MCDM in the name of MARCOS for the first time to address a cotton fibre selection problem for textile spinning mills. The same approach can also be extended to solve other decision problems of the textile industry, in general.

Novelty of the present study lies in the fact that the MARCOS is a very recently developed MCDM method, and this is a maiden application of the MARCOS method in the domain of textile, in general, and cotton industry, in particular. The approach is very simple, highly effective and quite flexible in terms of number of alternatives and decision criteria, although highly robust and stable.

This paper shares findings from a constructivist grounded theory study, exploring Trainee Teachers’ perceptions of their teaching and learning experienced during university-based teacher education programmes, specifically the theoretical components. Findings led to the development of a model of program design, pedagogy and teaching strategies that were successful in creating opportunities to build Professional Capital. This paper aims to share this model, highlighting the significance of Professional Capital amidst challenges in English Teacher Education, and to suggest implications for application of the model within broader workforce development.

Semi-structured interviews were conducted with 18 trainee teachers from four English universities. To support the development of the theoretical framework, researchers employed inductive and iterative constant comparative methods aligned with constructivist grounded theory to sensitise concepts and codes, which were verified using theoretical sampling.

Informed by the findings of this study, a model is presented which highlights that participants developed human, social and decisional capital during their academic programs helping them to widen their perceptions of what counts as educationally important, beyond narrow performativity measures that are pervasive in a school system. By actively adopting a transformative pedagogy and employing constructivist approaches to curriculum design and delivery, optimal learning environments for learners to build their professional capital can be provided.

These findings may prove valuable to Higher Education academics as a model when designing and delivering professional, student-centred programmes. There are also implications for policymakers seeking to redesign initial teacher education towards schools-led and practice-oriented approaches, who wish to consider the perceptions, values and motivations of trainee teachers.

The findings highlight the significance of teacher trainees’ active engagement with academic literature and theory, in terms of contributing to the development of their professional capital, resilience and professional commitment.