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The purpose of this research is to analyse the accessibility of Spanish beaches for blind people and their guide dogs as an inclusive tourist destination for national and international tourists.

This empirical research used a quantitative and qualitative methodology from public and private organizations, which intends to give voice to blind people to improve their quality of life.

This study presents a new and surprising contribution to the issue of accessibility of beaches for blind people and future studies related to this topic. Findings revealed that Spain has only nine adapted beaches for blind people, and most Spanish tourist destinations exclude blind people and its guide dogs from enjoying the beach and the sun and their facilities. In Spain there are in total 3,547 beaches; 10.3% of these are adapted beaches for people with reduced mobility, whereas only 0.25% are adapted beaches for blind people. Indeed, results also suggest that more efforts are required in response to the needs of blind people in tourism and transport activities by researchers, academics and government bodies.

The contribution of this paper is especially important for the management of the accessibility of Spanish beaches for blind people and their guide dogs by public and private organizations.

这项实证研究采用了来自公共和私人组织的定量和定性方法, 旨在让盲人发声以改善他们的生活质量。

本研究的目的是分析作为国内和国际游客包容性旅游目的地的西班牙海滩对盲人及其导盲犬的可达性。

这项研究对盲人的海滩可达性和与该主题相关的未来研究提出了新的和令人惊讶的贡献。 调查结果显示, 西班牙只有9个适合盲人的海滩, 大多数西班牙旅游目的地都将盲人及其导盲犬排除在享受海滩和阳光及其设施外。 西班牙共有 3,547 个海滩, 其中 10.3% 是适合行动不便人士使用的海滩, 而只有 0.25% 是适合盲人使用的海滩。 事实上, 结果还表明, 研究人员、学术界、政府机构、私营企业以及一个更加公平、团结和慷慨的社会需要做出更多努力来满足盲人在旅游和交通活动中的需求。

本文的贡献对于公共和私人组织管理盲人及其导盲犬在西班牙海滩的可达性尤为重要。

El objetivo de esta investigación es analizar la accesibilidad de las playas españolas para personas ciegas y sus perros guía como destino turístico inclusivo para turistas nacionales e internacionales.

Esta investigación utilizó una metodología cuantitativa y cualitativa apoyada con datos de organizaciones públicas y privadas, con la finalidad de dar voz a las personas ciegas para mejorar su calidad de vida.

Este estudio presenta una nueva y sorprendente contribución sobre la accesibilidad de las playas para personas ciegas y futuros estudios relacionados con este tema. Los resultados revelaron que España tiene solo 9 playas adaptadas para invidentes, la mayoría de los destinos turísticos españoles están excluyendo a las personas invidentes y sus perros guía para disfrutar de la playa y el sol, y de sus instalaciones. En España hay un total de 3.547 playas, el 10,3% son playas adaptadas para personas con movilidad reducida, mientras que solo el 0,25% son playas adaptadas para invidentes. De hecho, los resultados también sugieren que se requieren más esfuerzos en respuesta a las necesidades de las personas ciegas en las actividades de turismo y transporte por parte de investigadores, académicos, organismos gubernamentales, empresas privadas y una sociedad más justa, solidaria y generosa.

La contribución de este trabajo es especialmente importante para la gestión de la accesibilidad de las playas españolas para personas ciegas y sus perros guía por parte de organismos públicos y privados.

This paper aims to provide a critical reflection on diversity and inclusion research from the hospitality and tourism literature.

Through conducting a critical reflection, this paper used a thematic analysis focused on integrating the scholarly literature that has developed separately: one focusing on the human resources perspective and another concentrating on customer behavior. This critical reflection bridges the gap between these two perspectives.

The authors develop and offer a research agenda for future research drawing from three areas ripe for future research: human resources management, diversity resistance and marketing. They focus on theory-driven research that has practical applications to make hospitality and tourism more inclusive for both the workforce and consumers.

Meaningful research must be translated into practice, and by addressing these research gaps, organizations can gain insights into diverse worker and customer experiences and create more effective diversity initiatives.

The current literature often lacks an integrated approach that bridges the gap between the two reviewed perspectives: the human resources management and marketing perspectives. A holistic understanding of diversity and inclusion is vital, as it recognizes the interconnectedness between employees and customers within the context of the hospitality and tourism sector is important for several reasons.

Selecting the optimal supplier is a challenging managerial decision that involves several dimensions that vary over time. Despite the considerable attention devoted to this issue, knowledge is required to be updated and analyzed in this field. This paper reveals new opportunities to advance supplier selection (SS) research from a multidimensional perspective. Moreover, this study aims to formalise SS knowledge to enable the appropriate selection of sustainable, resilient and circular criteria.

This study is developed in two stages: first, a systematic literature review is conducted to select relevant papers. Descriptive and thematic analyses are employed to analyze criteria, solving approaches and case studies. Second, a criterion knowledge-based framework is developed and validated by experts to be implemented as ontology using Protégé software.

Evaluating the viability of suppliers need further studies to integrate other criteria and to align SS objectives with research advancement. Artificial intelligence tools are needed to revolutionize and optimize the traditional techniques used to solve this problem. Literature lucks frameworks for specific sectors. The proposed ontology provides a consistent criteria knowledge base.

For academics, the results of this study highlight opportunities to improve the viable SS process. From a managerial perspective, the proposed ontology can assist managers in selecting the appropriate criteria. Future works can enrich the proposed ontology and integrate this knowledge base into an information system.

This study contributes to promoting knowledge about viable SS. Capitalizing the knowledge base of criteria in a computer-interpretable manner supports the digitalization of this critical decision.

Testing business processes is crucial to assess the compliance of business process models with requirements. Automating this task optimizes testing efforts and reduces human error while also providing improvement insights for the business process modeling activity. The primary purposes of this paper are to conduct a literature review of Business Process Model and Notation (BPMN) testing and formal verification and to propose the Business Process Evaluation and Research Framework for Enhancement and Continuous Testing (bPERFECT) framework, which aims to guide business process testing (BPT) research and implementation. Secondary objectives include (1) eliciting the existing types of testing, (2) evaluating their impact on efficiency and (3) assessing the formal verification techniques that complement testing.

The methodology used is based on Kitchenham's (2004) original procedures for conducting systematic literature reviews.

Results of this study indicate that three distinct business process model testing types can be found in the literature: black/gray-box, regression and integration. Testing and verification approaches differ in aspects such as awareness of test data, coverage criteria and auxiliary representations used. However, most solutions pose notable hindrances, such as BPMN element limitations, that lead to limited practicality.

The databases selected in the review protocol may have excluded relevant studies on this topic. More databases and gray literature could also be considered for inclusion in this review.

Three main originality aspects are identified in this study as follows: (1) the classification of process model testing types, (2) the future trends foreseen for BPMN model testing and verification and (3) the bPERFECT framework for testing business processes.

Waste generated from electrical and electronic equipment, collectively known as E-waste, remains a persistent environmental, economic and social problem. Sustainable E-waste management (EWM) has numerous benefits, such as preventing electronic waste from entering landfills, reducing the need for virgin materials by recovering valuable materials from recycling and lowering greenhouse gas emissions. Circular economy (CE) practices are considered the initial steps toward sustainable EWM, but some hurdles have been reported in the adoption of these practices. Therefore, the current study aims to identify the common CE practices, sustainability of the EWM process and the challenges in EWM, and to develop a conceptual framework for effective EWM.

Very few studies have proposed frameworks that acknowledge the challenges and CE practices of EWM. To fill this gap, a systematic literature review (SLR) was performed, and 169 research articles were explored.

A total of seven challenges in the adoption of effective EWM were identified: rules and policy, infrastructure, consumer behaviour, informal sectors, community culture, technology and economy. Eight common CE practices were also found for effective EWM: reuse, recycle, remanufacturing, refurbishment, repair, reduce, recover and repurpose.

A conceptual framework guiding sustainable EWM was proposed, which includes solutions for the identified challenges, and CE practices with sustainable benefits.

This study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.

For conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.

Based on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.

Papers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.