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Destinations to be able to compete with each other need to equip themselves with as many competitive advantages as possible. Tourists' loyalty to a destination is considered as a prominent competitive tool for destinations. Tourists' loyalty manifests itself in recommendation of the destination to others, repeat visit of the destination and willingness to revisit the destination. Although a plethora of studies have tried to define models to show the relation between loyalty and the antecedent factors leading up to it, few of them have tried to integrate these models with mathematical approaches for better understanding of loyalty behavior. The purpose of this paper is to integrate a tourist destination model with Bayesian Network in order to predict the behaviour of destination loyalty and its antecedent factors.

This paper has developed a probability model by the integration of a destination loyalty model with a Bayesian network (BN) which enables to predict and analyze the behavior of loyalty and its influential factors. To demonstrate the application of this framework, Tehran, the capital of Iran, was chosen as a destination case study.

The outcome of this research will assist in identifying the weak key points in the tourist destination area for giving insights to the marketers, businesses and policy makers for making better decisions related to destination loyalty. In the analysis process, the most influential factors were recognized as the travel environment image, natural/historical attractions and, with a lower degree, infrastructure image which help the decision maker to detect and reinforce the weak factors and put more effort in focusing on improving the necessary parts rather than the irrelevant parts.

The research identified all critical factors that have the most influence on destination loyalty while driving the associate uncertainty which is significant for the tourism industry. This resulted in better decision-making which is used to identify the impact of tourism destination loyalty.

Consumers increasingly rely on organisations for online services and data storage while these same institutions seek to digitise the information assets they hold to create economic value. Cybersecurity failures arising from malicious or accidental actions can lead to significant reputational and financial loss which organisations must guard against. Despite having some critical weaknesses, qualitative cybersecurity risk analysis is widely used in developing cybersecurity plans. This research explores these weaknesses, considers how quantitative methods might address the constraints and seeks the insights and recommendations of leading cybersecurity practitioners on the use of qualitative and quantitative cyber risk assessment methods.

The study is based upon a literature review and thematic analysis of in-depth qualitative interviews with 16 senior cybersecurity practitioners representing financial services and advisory companies from across the world.

While most organisations continue to rely on qualitative methods for cybersecurity risk assessment, some are also actively using quantitative approaches to enhance their cybersecurity planning efforts. The primary recommendation of this paper is that organisations should adopt both a qualitative and quantitative cyber risk assessment approach.

This work provides the first insight into how senior practitioners are using and combining qualitative and quantitative cybersecurity risk assessment, and highlights the need for in-depth comparisons of these two different approaches.

Crowdfunding platforms are important innovations that allow nascent entrepreneurs to gain access to financial resources and crowd inputs to better refine and develop their business idea. The purpose of this paper is to investigate user-generated content (UGC) from both reward-based and equity-based crowdfunding platforms, in order to determine its implications for open and user innovation.

A total sample of 200 most funded technology products was extracted from four distinct crowdfunding platforms. A latent Dirichlet allocation (LDA) analysis was performed in an attempt to identify critical latent factors. The analysis was carried out through the theoretical lens of innovation literature, in an attempt to uncover the implications for open and user innovation.

The authors were able to highlight the implications of crowd inputs for open and user innovation, as backers provided nascent entrepreneurs with several types of feedback, ranging from product co-development to strategy and marketing. Furthermore, the study provided an overview of the key differences emerging between reward-based and equity-based crowdfunding platforms in terms of crowd inputs.

The present study features intrinsic limitations of the LDA approach being adopted. More specifically, it only provides a “snapshot” in time of the current sample, rather than investigating its development over time.

The present study solidifies the value of UGC as a resource to mine for trends and feedback.

The study contributes to both the innovation literature and the crowdfunding literature. It bridges several gaps found in both literature streams, by providing empirical evidence to test and verify pre-existing exploratory research.

The South African wool industry is integral to the country's agricultural sector, particularly sheep farming and wool production. Small-scale farmers play a vital role in this industry and contribute to employment and food security in rural communities. However, these farmers face numerous challenges, including a lack of funding, poor farming practices and difficulty selling their wool at fair prices. This study aims to address these challenges, the University of Free State launched a wool value chain project for small-scale farmers.

In this project, one of the studies conducted assessed the effectiveness of different detergents suitable for traditional wool scouring methods for small-scale farmers who lack access to sophisticated machinery. The investigation was conducted by scouring 160 wool samples using three different detergents and filtered water as a control. The wool samples were then evaluated for their cleanliness, brightness and fibre properties through a combination of scanning electron microscopy, spectrophotometry and statistical analysis at different scouring times (3, 10, 15 and 20 min, respectively).

The results showed that the combination of scouring time and the type of scouring solution used could significantly impact wool quality. It was found that using a combination of standard detergent or Woolwash as a scouring solution with a scouring time of 10–15 min resulted in the best outcome in terms of fibre property, wool colour and scouring loss.

This study demonstrated that traditional wool scouring methods could be an option for small-scale farmers and anyone who want to learn how to scour wool without expensive machinery to make wool products.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

This paper aims to propose a novel way of using textual clustering as a feature selection method. It is applied to identify the most important keywords in the profile classification. The method is demonstrated through the problem of sick-leave promoters on Twitter.

Four machine learning classifiers were used on a total of 35,578 tweets posted on Twitter. The data were manually labeled into two categories: promoter and nonpromoter. Classification performance was compared when the proposed clustering feature selection approach and the standard feature selection were applied.

Radom forest achieved the highest accuracy of 95.91% higher than similar work compared. Furthermore, using clustering as a feature selection method improved the Sensitivity of the model from 73.83% to 98.79%. Sensitivity (recall) is the most important measure of classifier performance when detecting promoters’ accounts that have spam-like behavior.

The method applied is novel, more testing is needed in other datasets before generalizing its results.

The model applied can be used by Saudi authorities to report on the accounts that sell sick-leaves online.

The research is proposing a new way textual clustering can be used in feature selection.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

This paper aims to examine the influence of the interplay between servant leadership behaviors and Machiavellianism on leader effectiveness.

Drawing on trait activation theory and motivation to lead theory, the authors hypothesize that the effect of servant leadership behaviors on perceived leadership effectiveness is manifested differently in teams with high-Machiavellian vs. low-Machiavellian leaders. In teams with low-Machiavellian leaders, servant leadership behaviors are expected to be associated with a cooperative way of handling team conflicts, which enhances employees' leader effectiveness ratings. In contrast, in teams with high-Machiavellian leaders, this mediation role vanishes due to the incongruency between Machiavellian traits and the cooperative context. The authors conducted a two-wave survey-based study and tested the hypotheses with a matched supervisor-employee sample from 310 employees and their leaders in 91 teams.

The results showed that servant leadership behaviors positively impact leadership effectiveness and that this effect takes place through cooperative team conflict management (TCM) without controlling for leaders' Machiavellian trait. Further analysis shows this mediation mechanism is only strong and significant in teams led by low-Machiavellian leaders, but not high-Machiavellian leaders.

To the authors’ best knowledge, this is the first study that examines the interplay of servant leadership behaviors and Machiavellianism on perceived leader effectiveness.

本文旨在探討僕人式領導行為與馬基雅維利主義之間的相互作用會如何影響領導效能。

我們根據特質激活理論和領導動機理論、建立了一個假設，這個假設就是： 僕人式領導行為對感知的領導效能所產生並顯示出來的影響、是會視乎團隊是高/強馬基雅維利主義，還是低/弱馬基雅維利主義而有所分別的。若團隊的領導者是低/弱馬基雅維利主義的話，僕人式領導行為大概會與使用合作的方式去處理團隊衝突有相互之關聯，這會提高僱員對領導效能的評分。與此相反，若團隊的領導者是高/強馬基雅維利主義的話，這調節作用和角色將會因馬基雅維利主義的特質與合作的環境之間存在著不協調而消失。我們進行了一個兩波的、以及基於調查的研究，在這研究中，我們利用管理者和員工相應的樣本來測試各個假設;這些樣本包括91個團隊內的310名員工及其領導者。

研究結果顯示、僕人式領導行為對領導效能會產生積極的影響，而這影響是透過以合作方式管理團隊衝突而產生的，亦沒有對領導者的馬基雅維利主義特質加以管控。我們進一步的分析顯示、這調節機制只會在由低/弱馬基雅維利主義的領導者領導的團隊內顯得強烈和顯著，但若領導者是高/強馬基雅維利主義的話，情況就不一樣了。

盡我們所知，本研究為首個研究、去探討僕人式領導行為與馬基雅維利主義之間的相互作用會如何影響感知的領導效能。

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.

This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.