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Defining and validating a map of related technologies is critical for managers, investors and inventors. Because of the increase in the applications of and demand for semiconductor lasers, analyzing the technological position of developers has become increasingly critical. Therefore, the purpose of this study is to adopt the technological position analysis to identify mainstream technologies and developments relevant to semiconductor lasers.

Correspondence analysis and k-means cluster analysis, which are data mining techniques, are used to reveal strategic groups of major competitors in the semiconductor laser market according to their Patent Cooperation Treaty (PCT) patent applications.

The results of this study reveal that PCT patent applications are generally obtained for masers, optical elements, semiconductor devices and methods for measuring and that technology developers have varying technological positions.

Through position analysis, this study identifies the technological focuses of different manufacturers to obtain information that can guide the allocation of research and development resources.

The purpose of this paper is to develop a software-library in the R programming language that implements the concepts of the interpretive coordinate, interpretive axis and interpretive plane. This allows for the automatic and reliable interpretation of results from the multiple correspondence analysis (MCA) as previously proposed and published. Consequently, the users can seamlessly apply these concepts to their data, both via R commands and a corresponding graphical interface.

Within the context of this study, and through extensive literature review, the advantages of developing software using the Shiny library were examined. This library allows for the development of full-stack applications for R users without the need for knowledge of the corresponding technologies required for the development of complex applications. Additionally, the structural components of a Shiny application were presented, leading ultimately to the proposed software application.

Software utilizing the Shiny library enables nonexpert developers to rapidly develop specialized applications, either to present or to assist in the understanding of objects or concepts that are scientifically intriguing and complex. Specifically, with this proposed application, the users can promptly and effectively apply the scientific concepts addressed in this study to their data. Additionally, they can dynamically generate charts and reports that are readily available for download and sharing.

The proposed package is an implementation of the fundamental concepts of the exploratory MCA method. In the next step, discoveries from the geometric data analysis will be added as features to provide more comprehensive information to the users.

The practical implications of this work include the dissemination of the method’s use to a broader audience. Additionally, the decision to implement it with open-source code will result in the integration of the package’s functions by other third-party user packages.

The proposed software introduces the initial implementation of concepts such as interpretive coordination, the interpretive axis and the interpretive plane. This package aims to broaden and simplify the application of these concepts to benefit stakeholders in scientific research. The software can be accessed for free in a code repository, the link to which is provided in the full text of the study.

Although Islamic banks offer superior financial services than other interest-based conventional banks, they could not expand their share and dominate the markets in several Islamic countries. This problem could be attributed to some causes not addressed. The current study proposes Islamic financial literacy as an important factor that could help aggress this problem. Due to a wide variety of Islamic financial services and the lack of understanding of these services, the banks' small business customers are indifferent between Islamic and interested-based conventional services to finance their business.

This study uses the exploratory technique of multiple correspondence analysis to detect any potential role of Islamic financial literacy in customers' preference for Islamic banks over conventional ones. The potential effect was tested with other essential factors, such as the customers' age, gender, and educational level. This analysis was conducted on a data set from 2061 banks' small businesses customers using the mall-intercept survey method.

The study shows a low level of Islamic financial literacy among Yemeni banks' small business owners' customers. Furthermore, despite integrating some critical factors that could influence the actual bank selection process among Yemini banks' customers, the authors found a decisive potential role of Islamic financial literacy as one of the key determinants of bank selection preferences.

This is the first paper to explore the potential role of Islamic financial literacy on the actual selection between Islamic Banks and their counterparts: the conventional banks in Yemen. The research results could build a more comprehensive theoretical model on Islamic banks' customer behavior.

The sharing economy (SE) is significantly affecting traditional companies, which have felt a need to adapt their business model. The aim of this study is to identify the different types of adaptation developed by companies within a SE context, and to examine how they relate to their characteristics.

A content analysis involving 149 real-world adaptation cases was carried out, after which a Kruskal–Wallis test and a multiple correspondence analysis were used to explore the relationships between the types of adaptation identified, the business characteristics and the strategic decisions taken for these adaptations.

Through the analyses proposed in the study, the main conclusions suggest that the way companies adapt to SE is related to business characteristics and the strategic decisions taken for these actions, demonstrating throughout the article what types of adaptations are made depending on variables such as sector of activity or business orientation.

This study is the first to examine the variables affecting the decisions among traditional companies in response to the SE. In addition, this work explores the SE from the business point of view, shedding light on the participation in SE by traditional companies.

共享經濟現時正顯著地影響著感到需要改變它們的商業模式的傳統公司。本文旨在確定在共享經濟的背景裡, 公司為適應有關的環境而進行的各種改變; 研究亦擬探討這些改變與公司特徵之間的關係。

研究人員對149個真實世界的改變個案進行內容分析, 繼而進行克拉斯卡 - 瓦立斯檢定 (Kruskal-Wallis test) 和多重應對分析 (Multiple Correspondence Analysis) , 以探究被確定的改變的種類、企業的特徵與採用這些改變的策略性決策之間的關係。

研究人員、透過本研究建議的分析取得結論; 主要的結論似顯示、企業為應對共享經濟所作的改變、與它們的企業特徵和採用哪些行動的策略性決策是有關聯的。整篇論文, 顯示了企業所採用的改變種類、均取決於像活動領域和企業經營理念等的變數。

本研究為首個研究、去探討影響傳統公司回應共享經濟所作的決策的變數。再者, 本研究探究了以商業理念的觀點來考慮的共享經濟, 這使我們更容易理解傳統公司參與共享經濟的課題。

This paper aims to investigate the difference between the impacts of indicators of trade barriers (TBs) on bankrupt enterprises (BEs), new enterprises (NEs) and other enterprises (OEs).

The paper has used a multiple-step approach. At the first stage, the initial data has been collected from interviews with 164 top managers of SMEs in West Azerbaijan in Iran during two periods of 2013–2015 and 2017–2019. At the second step, multiple correspondence analysis has been used to summarize the relationships between variables and construct indices for different groups of TBs. Finally, the generalized structural equation model method was used to examine the impact of export barriers.

The results showed that the political legal index is the main TBs for BEs and NEs, but it had a more significant impact on BEs; the financial index was the second major TBs factor for BEs, while OEs did not have a problem in performance index, and the financial index was classified as a minor obstacle for them. All indicators of marketing barriers (except production index) had a negative and significant effect on all enterprises; the most important TBs for NEs was the information index.

The results indicated that if enterprises have a strong financial system and function, they can lessen the impact of sanctions and keep themselves in the market.

To understand the specificities of Cash-flow bullwhip in the context of major crises similar to that of COVID-19, to identify its financial impacts on the Moroccan FMCG companies, to establish the profile of the companies most affected by this CFB and finally to propose internal control mechanisms that should be put in place to mitigate the effects of Cash flow Bullwhip in such a context.

The authors chose to conduct descriptive research on companies operating in the fast-moving consumer goods sector in Morocco. For this purpose, a survey was conducted on a target population during the period from December 2020 to March 2021. To answer the different research questions, a multiple correspondence analysis (MCA) has been conducted on the 21 variables obtained from the survey questions.

Small and medium-sized companies are those that have been the most financially impacted. Indeed, the instability of the cash flow conversion cycle increased their working capital requirements and limited their self-financing capacity. To face this situation, those companies used alternative means to finance their operational activity by using their equities or bank loans.

Due to the originality of the COVID 19 context, this study gives a different angle of view to analyze the cash flow bullwhip and its implications on the financial health of companies.

Evaluating existing literature can lead to a better understanding of a scientific journal's state of the art. In this sense, this study aims to analyze the global research evolution of the Revista Europea de Dirección y Economia de la Empresa (REDEE) and the European Journal of Management and Business Economics (EJMBE).

A bibliometric analysis was conducted to acknowledge the most contributing authors, impactful articles, publication trends, keyword analysis, co-occurrence networks and collaboration networks. A total of 454 articles published between 2006 and 2022 were analyzed.

The results suggest that the international strategy set in 2014 has resulted in a steadily growing number of publications and a significant increment in citations. Relationship marketing and the connections between innovation, performance and entrepreneurship are topics of interest for the EJMBE.

Mapping existing EJMBE research through identifying the contributing authors, most impactful articles, publication trends, keyword analysis, co-occurrence networks and collaboration networks is missing to encourage new research projects.

This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.

A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.

The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.

This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.

The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.

This research addresses the pressing need for comprehensive studies in the rapidly evolving field of city tourism. This study aims to understand the overall performance of the International Journal of Tourism Cities (IJTC), the structure of knowledge in city tourism research and the prevalent themes and trends arising from IJTC.

A bibliometric analysis was conducted to scrutinize the publication patterns in IJTC. This involved examining parameters such as the annual count of published articles, the keywords used in them and their respective authors.

The findings reveal that IJTC has a growing and diverse publication output, establishing itself as a reputable and influential publication within urban tourism research. The results reflect various aspects and themes in city tourism research.

The study has certain limitations. The data used for analysis was obtained exclusively from the Scopus database. The analysis was conducted using only one software package, Bibliometrix. Other software packages may offer different features for bibliometric analysis. The study relied exclusively on quantitative methods for data analysis. Qualitative methods could have provided more nuanced interpretations of the data.

Comparative analyses could be conducted between IJTC and other journals within urban tourism or related disciplines. Such research would yield valuable insights into the current state of the field and aid in identifying areas warranting further investigation.

The findings from this study can inform the decisions and actions of various stakeholders involved in urban tourism. Practitioners and policymakers can draw from this research to make informed decisions. Existing and emerging authors can identify relevant topics for their research. Readers can access pertinent information for their needs.

This study offers a unique contribution by thoroughly comprehending the performance of IJTC between 2015 and 2023. It progresses the existing body of knowledge on city tourism research by examining its current status and future trends.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.