EuroMed Journal of Business最新文献

Purpose

This study investigates the current trends in CEO compensation by applying a bibliometric technique from the Scopus database.

Design/methodology/approach

The bibliometric analysis examines CEO compensation trends through the Scopus database. Frequency analysis is conducted using Microsoft Excel, while data visualisation is performed using VOSviewer. Finally, citation metrics are carried out using Harzing's Publish or Perish.

Findings

The results reveal that research into CEO compensation has been consistently increasing since 1976. This study identifies the most popular publication trends, the most active institutions, the top funding institutions, annual publication growth, document and source type, publishers, subject area, author co-citations, highly cited articles, top publishing countries, and keyword co-occurrences. Notably, Arizona State University in the United States (US) is the leading institution with the most prominent publications on CEO compensation. The US is the most active country involved in CEO compensation research.

Research limitations/implications

The current research only derived articles from the Scopus database. Nonetheless, the findings provide an in-depth comprehension of the meaning of “CEO compensation” and present the outline of the research trends on the concept, thus significantly facilitating further studies.

Practical implications

The findings can assist researchers in comprehending and classifying the drivers of CEO compensation previously identified and proven in past studies. Additionally, the findings also create opportunities for new researchers to study CEO compensation.

Originality/value

This study is among the pioneering research investigating CEO compensation-related publications and utilising the Scopus database from an international perspective. This study also offers a historical view of CEO remuneration, a summary of the most significant nations, journals, and writers on the topic, and an outline of how CEO remuneration studies have changed over time. These contributions will enable other researchers to focus on this subject.

Purpose

This study aims to provide a new overview and opportunities of blockchain (BCT), financial technology (fintech) and knowledge management (KM) over the past ten years. Its focus is on their potential to drive new value creation and innovation processes within the digital landscape of the tourism and hospitality.

Design/methodology/approach

This systematic literature review and sociotechnical approach employs a literature analysis, analyzing and synthesizing 62 relevant articles published in the past decade form e-databases (Web of Science and Scopus).

Findings

This study reveals that researchers frequently discuss the potential advantages and challenges of BCT, fintech and KM in this industry. These include establishing systems that prioritize transparency and traceability, addressing blockchain security concerns, enhancing financial transaction efficiency and trustworthiness, and promoting innovation and improvement through KM strategies. Furthermore, this review suggests that the application of blockchain, fintech and KM has the potential to create new markets and opportunities in the tourism and hospitality industry. This study provides insights into the state and implementation of technology-based and knowledge-based for tourism and hospitality in times of crisis and digitization era.

Practical implications

Shifting to new lens (refers to sociotechnical theory), from technology adoption strategy, it is important to stay updated with emerging technologies such as BCT and fintech and upcoming technologies trends must align with tourism and hospitality business objectives, customer expectations and market demands. From the socio-dimension, KM is not confined to technological tools alone. Instead, it is a strategic approach that emphasizes fostering a culture of open communication, collaboration and knowledge sharing within the team of tourism and hospitality industry.

Originality/value

Through a literature review approach, this study establishes a new foundation in tourism and hospitality such as analyzing research gaps, understanding benefits and challenges, supporting methodologies/theoretical frameworks and informing the future research opportunities. Additionally, a novel contribution is the inclusion of sociotechnical approach that is allocated into socio or knowledge resources perspective (knowledge management), and technical or technology perspective (blockchain and fintech) that drives tourism and hospitality innovation.

Purpose

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Design/methodology/approach

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

Findings

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

Research limitations/implications

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

Originality/value

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.