Noura Metawa, Saad Metawa, Maha Metawea and Ahmed El-Gayar
This paper deeply investigates the herd behavior of the Egyptian mutual funds under changing and different conditions of the market pre- and post-events and compares the impact of…
Abstract
Purpose
This paper deeply investigates the herd behavior of the Egyptian mutual funds under changing and different conditions of the market pre- and post-events and compares the impact of asymmetric risk conditions on the herding behavior of the Egyptian mutual funds in both up and down markets.
Design/methodology/approach
We test for the existence of herding for the whole period from 2003 to 2022, as well as for the pre-and post-different Egyptian uprising periods. We employ two well-known models, namely the cross-sectional standard deviation (CSSD) and cross-sectional absolute deviation (CSAD) models. Additionally, we use the quantile regression approach.
Findings
We find that the behavior of mutual funds does not change following the different political and social events. For the whole period, we find evidence of herding behavior using only the model of CSAD in down-market conditions. We generalize our finding to be evidence of the existence herding behavior in different quantiles, under only the down market in specific points’ pre, post or both given events throughout the whole series. Conversely, during the upper market, we show a full absence of herding behavior considering all different quantiles. When the market is down, managers are afraid of the condition of uncertainty, neglecting their own private information, avoid acting independently and consequently, following other mutual funds. When the market is up, managers become rational and act fully independent.
Research limitations/implications
Future research should delve deeper into the drivers of herding behavior, assess its longer-term effects, develop risk management strategies and consider regulatory measures to mitigate the potential negative impact on mutual fund performance and investor outcomes.
Practical implications
The study reveals that the behavior of mutual funds remains consistent despite various political and social events, suggesting a degree of resilience in their investment strategies. The research uncovers evidence of herding behavior in both high and low quantiles, but exclusively in down markets. In such conditions of market decline, fund managers appear to forsake their private information, exhibiting a tendency to follow the crowd rather than acting independently.
Social implications
The study reveals that the behavior of mutual funds remains consistent despite various political and social events, suggesting a degree of resilience in their investment strategies. The research uncovers evidence of herding behavior in both high and low quantiles, but exclusively in down markets. In such conditions of market decline, fund managers appear to forsake their private information, exhibiting a tendency to follow the crowd rather than acting independently. Future research should delve deeper into the drivers of herding behavior, assess its longer-term effects, develop risk management strategies and consider regulatory measures to mitigate the potential negative impact on mutual fund performance and investor outcomes.
Originality/value
The paper investigates the herd behavior of the Egyptian mutual funds under asymmetric risk conditions, the study follows the spectrum of the herding behavior analysis and Egyptian mutual funds, extending the research with imperial analysis of market conditions pre- and post-events including currency floating, COVID-19 and political elections. The study gives substantial recommendations for policymakers and investors in emerging markets mutual funds.
Details
Keywords
Neamat Farouk El Gayar, Mohamed Saleh, Amir Atiya, Hisham El‐Shishiny, Athanasius Alkes Youhanna Fayez Zakhary and Heba Abdel Aziz Mohammed Habib
This paper aims to present an integrated framework for hotel revenue room maximization. The revenue management (RM) model presented in this work treats the shortcomings in…
Abstract
Purpose
This paper aims to present an integrated framework for hotel revenue room maximization. The revenue management (RM) model presented in this work treats the shortcomings in existing systems. In particular, it extends existing optimization techniques for hotel revenue management to address group reservations and uses “forecasted demand” arrivals generated from the real data.
Design/methodology/approach
The proposed forecasting module attempts to model the hotel reservation process from first principles. In particular, it models hotel arrivals as an interrelated process of stochastic parameters like reservations, cancellations, duration of stay, no shows, seasonality, trend, etc. and simulates forward in time the actual process of reservations to obtain the forecast. On the other hand, the proposed optimization module extends existing optimization techniques for hotel revenue management to address group reservations, while including integrality constraints and using “forecasted demand” arrivals generated from the data. The optimization model is based on large‐scale integer programming model to optimize decision rules for accepting reservations.
Findings
A case study based on three different sets of reservation records of simulated hotel data was conducted to test the operation of the system on real data. Results showed that the model is able to generate effective recommendations to maximize revenue.
Originality/value
The main value of this paper is that it presents an integrated framework for hotel room revenue maximization. The novelty introduced in this approach is that it is based on an advanced room demand forecast model that simulated the reservation process from its first principles and produces demand scenarios that are used by an optimization model to generate proper recommendations.
Details
Keywords
Muhammad Zaheer Asghar, Elena Barbera, Samma Faiz Rasool, Pirita Seitamaa-Hakkarainen and Hana Mohelská
This research paper aims to explore the influence of social media–based knowledge-sharing intentions (SMKI) on prospective authentic leadership development (ALD) to deal with the…
Abstract
Purpose
This research paper aims to explore the influence of social media–based knowledge-sharing intentions (SMKI) on prospective authentic leadership development (ALD) to deal with the future crisis. In the existing literature, to the best of the authors’ knowledge, there is no significant empirical evidence to test the relationship between SMKI and ALD. Thus, this study contributes to the growing literature regarding the role of SMKIs, ALD, social media–based knowledge-sharing behavior (SMKB) and facilitating conditions (FCs). However, in this study, the authors developed a conceptual framework based on technology adoption and leadership theory. It was used to identify preservice educational leaders’ SMKIs and their effect on ALD to deal with an educational crisis during the COVID-19 pandemic. Furthermore, SMKIs are strengthening ALD, directly and indirectly, using SMKB and FCs.
Design/methodology/approach
In this study, the higher education students are considered preservice leaders who were enrolled in educational leadership and management programs. However, this study’s target population and sample are students enrolled in educational leadership and management programs. Therefore, higher education students are considered preservice educational leaders. Therefore, a multilevel questionnaire survey approach was adopted to collect data from preservice educational leaders (n = 451 at Time 1 and n = 398 at Time 2) enrolled in education departments in the selected universities in Pakistan. A total of 398 survey questionnaires were finalized with a return ratio of 89%. The partial least square structural equation modeling with SmartPLS 3.2.8 was used for the data analysis.
Findings
This research found that SMKIs are positively and significantly connected with ALD. This study also confirms that SMKB significantly and positively mediates the relationship between SMKIs and ALD. Therefore, this study concludes that preservice educational leaders were ready to adopt SMKB.
Practical implications
Social media–based knowledge sharing can be helpful to develop authentic leadership among preservice educational leaders during a crisis. Preservice educational leaders as authentic leaders can prove to be an asset in dealing with the COVID-19 pandemic crisis.
Originality/value
This research integrated the technology adoption model and leadership theory to provide empirical evidence of SMKIs’ direct and indirect influence on ALD through social media–based knowledge-sharing actual use behavior by preservice educational leaders during the COVID-19 pandemic. Moreover, the moderated mediating effect of the FCs was also studied in the relationship between SMKIs and actual user behavior as well as ALD.
Details
Keywords
Shamima Yesmin and Ayesha Akhter
A shared set of moral standards, ethical principles and behavioral norms of social structure can be referred to as culture. Many health problems are strongly influenced by one’s…
Abstract
Purpose
A shared set of moral standards, ethical principles and behavioral norms of social structure can be referred to as culture. Many health problems are strongly influenced by one’s cultural background. The purpose of the paper is to examine the scientific explanation of indigenous norms and practice of health healing.
Design/methodology/approach
This qualitative study considered in-person interviews to know the Tribals’ indigenous healing practice in Bangladesh. A focus group discussion with five tribal students was conducted to form a baseline on Tribals’ norms, rituals and information-sharing behavior. Around 35 tribal students were interviewed to find out their healing practices, norms and rituals on health issues. All these practicing indigenous knowledge were documented instantly. Peer-reviewed scientific papers from renowned databases were searched to have scientific evidence on each case. All the studies having negative or positive evidence were mentioned with each case.
Findings
The findings showed more indigenous knowledge with scientific disagreements on health aspects among the Tribals’ health practice in Bangladesh. However, the positive impact of such knowledge is not negligible. Therefore, showcasing the scientific tribals’ indigenous knowledge to a global audience is a strong recommendation.
Originality/value
Health and health care-seeking behavior among the tribal population in Bangladesh is not a new area of research, few studies have focused on the context, reasons and choices in patterns of health care-seeking behavior; obstacles and challenges faced in accessing health-care provision in the tribal areas in the country. However, research attempts to show the relationship between ecological knowledge and scientific indication is new in nature.
Details
Keywords
The purpose of this paper is to autoregressively model the net occupancy rate of beds and bedrooms in hotels and similar accommodations and the nights spent at these…
Abstract
Purpose
The purpose of this paper is to autoregressively model the net occupancy rate of beds and bedrooms in hotels and similar accommodations and the nights spent at these accommodations of Spain for the period of 1990–2023 using monthly data.
Design/methodology/approach
The monthly occupancy rate of hotels and the total number of hotel nights data of Spain for the 1990M01–2023M09 range is considered. An autoregressive deep learning network is developed for the modeling of both metrics. Moreover, the results of the proposed autoregressive deep learning method are compared to those of a classical artificial neural network.
Findings
The actual occupancy rate, total night data and the deep learning model results are compared showing the accuracy of the developed model. Moreover, the R2, mean absolute error, root mean square error and mean absolute percentage error of the models are calculated further demonstrating the high performance of the developed model. The R2 values higher than 0.9 are achieved for both occupancy rate and total number of hotel nights data.
Practical implications
The modeling results given in this paper demonstrate that the previous values of the net occupancy rate and the total number of nights can be used as inputs of a deep learning network model by which accurate forecasts can be made for the future values of the occupancy rate and the total number of hotel nights. This modeling approach possesses importance from the practical viewpoint considering that the accurate planning and forecast of the net occupancy rate and the total number of nights affect the tourism income.
Originality/value
This study differs from existing literature by attempting to model the occupancy rate and the total number of hotel nights data autoregressively using deep learning networks.
研究目的
本研究旨在通过自回归方式对西班牙酒店及类似住宿的床位和房间的净入住率以及这些住宿的过夜天数进行建模, 时间范围为1990年至2023年, 使用月度数据。
研究方法
本研究使用了1990年1月至2023年9月期间西班牙酒店的月度入住率和总过夜天数数据, 开发了一个用于这两个指标建模的自回归深度学习网络。此外, 提出的自回归深度学习方法的结果与经典的人工神经网络进行了对比。
研究发现
实际的入住率和总过夜天数数据与深度学习模型的结果进行了对比, 显示出所开发模型的准确性。此外, 计算了模型的R2、MAE、RMSE和MAPE, 进一步证明了所开发模型的高性能。对于入住率和总过夜天数数据, R2值均超过0.9。
研究创新
本研究与现有文献的不同之处在于, 尝试使用深度学习网络自回归建模入住率和总过夜天数数据。
实践意义
本文给出的建模结果表明, 净入住率和总过夜天数的先前值可作为深度学习网络模型的输入, 进而对未来的入住率和总过夜天数进行准确预测。考虑到净入住率和总过夜天数的准确规划和预测会影响旅游收入, 从实际角度来看, 这种建模方法具有重要意义。
Details
Keywords
Mona Jami Pour and Fatemeh Taheri
Over the past decade, social media have significantly changed the way people communicate and interact with one another, which might result in positive or negative consequences…
Abstract
Purpose
Over the past decade, social media have significantly changed the way people communicate and interact with one another, which might result in positive or negative consequences. Every day, people use these technologies to share knowledge in the form of short messages, articles, images, videos and voice. Universities use social media to better connect the learners and educational communities. Previous studies have reported the positive impact of using social media by students to share knowledge. Despite the significance of social media usage in educational activities, there still remain limitations. Few studies have empirically investigated drivers related to knowledge sharing behavior in social media, and there are some inconsistent findings concerning effective factors. Therefore, the purpose of this study is to empirically examine the effect of personality traits on knowledge sharing behavior in social media among students by the mediating role of trust and subjective well-being (SWB).
Design/methodology/approach
To obtain this aim, cross-sectional survey was conducted. Convenience sampling technique was used to select the sample of 527 Iranian students, out of which 425 were used in the final analysis. Regression analysis and bootstrap method were used to test the research hypotheses.
Findings
The research findings revealed that the big five personality traits are associated with SWB, perceived trust and knowledge sharing behavior among students. With the exception of conscientiousness trait, all the traits used in this study lead to a significant change of the knowledge sharing behavior among students.
Practical implications
The findings offer further understanding about the mechanisms by which personality traits lead to knowledge sharing behavior through trust and SWB. They suggest the students to enhance personality profile and improve SWB for the benefit of these new educational platforms. Also, policymakers are encouraged to create trustworthy social media platforms to increase perceived trust and eventually knowledge sharing behavior among students.
Originality/value
Little is known about the effect of personality traits, as well as trust and SWB on knowledge sharing behavior among students. The study contributes to the related literature through empirically indicating how personality traits influence knowledge sharing behavior by the mediating role of trust and SWB.
Details
Keywords
Shenlong Wang, Kaixin Han and Jiafeng Jin
In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of…
Abstract
Purpose
In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years.
Design/methodology/approach
First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared.
Findings
The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR.
Originality/value
A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.
Details
Keywords
Vinay Singh, Iuliia Konovalova and Arpan Kumar Kar
Explainable artificial intelligence (XAI) has importance in several industrial applications. The study aims to provide a comparison of two important methods used for explainable…
Abstract
Purpose
Explainable artificial intelligence (XAI) has importance in several industrial applications. The study aims to provide a comparison of two important methods used for explainable AI algorithms.
Design/methodology/approach
In this study multiple criteria has been used to compare between explainable Ranked Area Integrals (xRAI) and integrated gradient (IG) methods for the explainability of AI algorithms, based on a multimethod phase-wise analysis research design.
Findings
The theoretical part includes the comparison of frameworks of two methods. In contrast, the methods have been compared across five dimensions like functional, operational, usability, safety and validation, from a practical point of view.
Research limitations/implications
A comparison has been made by combining criteria from theoretical and practical points of view, which demonstrates tradeoffs in terms of choices for the user.
Originality/value
Our results show that the xRAI method performs better from a theoretical point of view. However, the IG method shows a good result with both model accuracy and prediction quality.
Details
Keywords
Mostafa El Habib Daho, Nesma Settouti, Mohammed El Amine Bechar, Amina Boublenza and Mohammed Amine Chikh
Ensemble methods have been widely used in the field of pattern recognition due to the difficulty of finding a single classifier that performs well on a wide variety of problems…
Abstract
Purpose
Ensemble methods have been widely used in the field of pattern recognition due to the difficulty of finding a single classifier that performs well on a wide variety of problems. Despite the effectiveness of these techniques, studies have shown that ensemble methods generate a large number of hypotheses and that contain redundant classifiers in most cases. Several works proposed in the state of the art attempt to reduce all hypotheses without affecting performance.
Design/methodology/approach
In this work, the authors are proposing a pruning method that takes into consideration the correlation between classifiers/classes and each classifier with the rest of the set. The authors have used the random forest algorithm as trees-based ensemble classifiers and the pruning was made by a technique inspired by the CFS (correlation feature selection) algorithm.
Findings
The proposed method CES (correlation-based Ensemble Selection) was evaluated on ten datasets from the UCI machine learning repository, and the performances were compared to six ensemble pruning techniques. The results showed that our proposed pruning method selects a small ensemble in a smaller amount of time while improving classification rates compared to the state-of-the-art methods.
Originality/value
CES is a new ordering-based method that uses the CFS algorithm. CES selects, in a short time, a small sub-ensemble that outperforms results obtained from the whole forest and the other state-of-the-art techniques used in this study.