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This paper aims to investigate the effects of virtual reality experiences (VREs) and emotions on holiday destination visit intentions by applying the stimulus-organism-response (S-O-R) framework. It also examined the role of amateur photography as a moderator in the relationship between VREs, emotions and visit intentions.

Primary data were collected through a structured survey questionnaire administered in India, and structural equation modelling (SEM) was used to examine relationships among variables. The formal survey was preceded by a pilot study. The partial least squares-SEM product indicator technique was applied to measure the moderation effect of amateur photography.

When people have high levels of positive emotions in the pre-travel stage with VRE participation, their intentions to visit destinations are stronger. Amateur photography moderated the relationships between user interactivity, memorable experiences, mental imagery and visit intentions. No significant moderating effect was found between emotional experiences and visit intentions.

The results indicate that creators of virtual reality (VR) communications should concentrate on producing interactive and visually striking VR content, as the research found that VR experiences with tourism-related activities have a significant impact on potential visitor emotions. Therefore, VR producers should incorporate authentic and distinctive elements into contents, giving viewers realistic and trustworthy VR tourism experiences. Destination marketers should evoke emotions in people to increase motivations to visit the VR-presented places. Destination marketers should create VR content with hedonic elements, fostering a connection and desire to visit the destination presented in the VR. For example, destination marketers can enhance the sensory aspects of VR content with audio, video, haptics and artificial intelligence, encouraging people to be emotionally captivated and fully immersed. The findings indicate that amateur photography has a significant moderating effect on prospective tourists’ intentions to visit a place presented in VR. Thus, destination marketers are advised to provide photogenic and relevant content to users. By using amateur photography, destinations can market their attractions to potential visitors in a more interactive and relatable manner. Showcasing images taken by amateur photographers presents a genuine perspective of the offerings, thereby increasing visit intentions among prospective visitors. Additionally, amateur photography helps promote the destination in a positive light, emphasizing the attractions and other offerings, and this encourages potential visitors to consider the destination as a travel option, thereby increasing visit intentions. Encouraging amateur photographers to take and share images also increases their engagement with the destination, creating a sense of community and connection and ultimately driving visit intentions. Destination marketers should embrace VR tourism content as a means of promoting their destinations to potential customers by considering the three crucial elements of memorable experiences, emotional experiences and mental imagery. In essence, destination management organizations and other decision makers should develop complete VR communication plans that incorporate interactive and visually striking activities. This can be done by creating VR experiences for prospective tourists, which will enable them to pick their preferred destinations at the pre-travel stage. Through this approach, an effective destination marketing strategy can be established.

This research has value in making several potential contributions to the tourism applications of VR. It is the first study to use and test the S-O-R framework to analyse tourist behaviour from the perspective of emotions generated by VR applications. The analysis focuses on India’s tourism market, which has not been explored much in the context of VR and from the perspective of a developing country. The research emphasizes the use of VR as a tool to understand tourist behaviour rather than just focusing on visit intentions. The findings provide valuable insights into the importance of VR in tourism. The knowledge generated will contribute to the understanding of the impact of VR experiences on emotions and visit intentions and the moderating effect of amateur photography. It has practical implications for destination marketers in developing effective marketing strategies that enhance tourists’ emotions and motivate visit intentions.

本研究运用刺激-机体-反应(S-O-R)框架, 探讨虚拟现实体验（VREs）和情绪对度假目的地旅游意向的影响。还研究了业余摄影在VREs、情感和访问意图之间的关系中的调节作用。

通过在印度进行的结构化调查问卷收集原始数据, 并使用结构方程模型(SEM)来检查变量之间的关系。在正式调查之前进行了一项试点研究。采用PLS-SEM结果指标方法测量业余摄影的调节效果。

当人们在旅行前的VRE参与阶段拥有高水平的积极情绪时, 他们访问目的地的意愿会更强。业余摄影调节了用户交互性、纪念体验、心理意象和访问意图之间的关系。情绪体验与访问意向之间无显著调节作用。

本研究对虚拟现实的旅游应用有几个可能的贡献。这是第一个使用并测试刺激-生物-反应(S-O-R)框架的研究, 从VR应用产生的情感角度分析游客行为。分析集中于印度的旅游市场, 而在VR的背景下和从一个发展中国家的角度来看, 印度的旅游市场还没有得到太多的探索。该研究强调将VR作为一种了解游客行为的工具, 而不仅仅是关注游客的意图。这些发现为VR在旅游业中的重要性提供了有价值的见解。所产生的知识将有助于理解VR体验对情绪和访问意图的影响以及业余摄影的调节作用。这对目的地营销人员制定有效的营销策略, 增强游客的情绪和激发旅游意图具有实际启示。

Esta investigación analizó los efectos de las experiencias de realidad virtual (VREs) y las emociones en las intenciones de visita a destinos vacacionales aplicando el modelo estímulo-organismo-respuesta (S-O-R). También examinó el papel de la fotografía amateur como moderador en la relación entre las VREs, las emociones y las intenciones de visita.

Los datos primarios se recogieron mediante una encuesta con cuestionario estructurado administrado en la India, y se utilizó la modelización de ecuaciones estructurales (SEM) para examinar las relaciones entre las variables. La encuesta principal fue precedida de un estudio piloto. Se aplicó la técnica PLS-SEM para medir el efecto moderador de la fotografía amateur.

Cuando las personas poseen un alto nivel de emociones positivas en la etapa previa al viaje con participación en VRE, sus intenciones de visitar destinos son más fuertes. La fotografía amateur moderó las relaciones entre la interactividad del usuario, las experiencias memorables, las imágenes mentales y las intenciones de visita. No se encontró ningún efecto moderador significativo entre las experiencias emocionales y las intenciones de visita.

Esta investigación tiene el valor de hacer varias contribuciones potenciales a las aplicaciones turísticas de la realidad virtual. Es el primer estudio que utiliza y pone a prueba el marco estímulo-organismo-respuesta (S-O-R) para analizar el comportamiento de los turistas desde la perspectiva de las emociones generadas por las aplicaciones de RV. El análisis se centra en el mercado turístico de la India, poco explorado en el contexto de la RV y desde la perspectiva de un país en desarrollo. La investigación hace hincapié en el uso de la RV como herramienta para comprender el comportamiento de los turistas, en lugar de centrarse únicamente en las intenciones de visita. Las conclusiones aportan valiosas ideas sobre la importancia de la RV en el turismo. Los conocimientos generados contribuirán a comprender el impacto de las experiencias de RV en las emociones y las intenciones de visita, así como el efecto moderador de la fotografía amateur. Se presentan implicaciones prácticas para los responsables de marketing de los destinos turísticos a la hora de desarrollar estrategias de marketing eficaces que potencien las emociones de los turistas y motiven las intenciones de visita.

The purpose of this research is to provide an introduction into Hoshin Kanri and how it has been used with other methodologies and frameworks. Additionally, factors that impact successful Hoshin Kanri implementation and future research opportunities are explored.

A systematic literature review was conducted to determine the methodologies and frameworks that have previously been explored and identify factors that lead to successful Hoshin Kanri implementation as it relates in the Western world.

Even though Hoshin Kanri has been used to a limited extent for decades in the West, there is still relatively little published research on the topic. In the period reviewed most published research was performed in the UK or USA. In addition, most years had less than two publications (the exception being five in 2020).

This literature review was primarily focused on the manufacturing sector, publications from 1990–2021, and available in English.

Current practitioners may use the research in this review to improve their current Hoshin Kanri practices using the techniques mentioned as a means of continuous improvement or to build a new framework for their own needs.

This paper makes an important contribution to the study of Hoshin Kanri as it provides: (1) an updated literature review regarding the study and application of Hoshin Kanri, (2) a qualitative analysis of alternative methodologies and frameworks to traditional Hoshin Kanri and (3) success factors when considering implementation of Hoshin Kanri within an organization and researchers with potential ideas for future research.

Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

Gazelle companies are characterized by rapid growth in a short time. Identifying the determinants of this exponential expansion is important as these firms have a significant impact on the economy. They generate increased employment and investment by investors interested in new opportunities. Previous studies have failed to reach a consensus about what fosters high growth in gazelle companies as each firm’s geographical, political and economic context is different. The present research uses cognitive mapping and interpretive structural modeling (ISM) to overcome the limitations of prior investigations and identify factors that can potentially accelerate growth in gazelle companies.

Two sessions were held with an expert panel with knowledge about and experience with these firms. In the first session, data were collected to create a group cognitive map, while the second meeting comprised ISM-based analyses of the high-growth determinants identified and the causal relationships between them. A final consolidation session was held to discuss the results with two members of the Committee for Central Region Coordination and Development (i.e. Comissão de Coordenação e Desenvolvimento Regional do Centro – a public entity that grants gazelle awards in Portugal).

The analysis system created was tested, and the results demonstrate that the dual methodology used can increase our understanding of the dynamics of high-growth determinants and lead to more informed and potentially better evaluations of gazelle companies. Indeed, once high-growth determinants in gazelle companies are understood, this information can help other firms implement the same business model to achieve similarly rapid growth. The strengths and shortcomings of this new structured analysis model are also analyzed.

The authors know of no prior work reporting the integrated use of cognitive mapping and ISM in this study context.

Our goals were to explore the pedagogies applied by instructors that supported Black, Indigenous and People of Color (BIPOC) student learning in a leadership course and the leadership behaviors BIPOC students identified as being applicable after the course.

Through survey research and qualitative data analysis, three prominent themes emerged.

High-quality, purposeful pedagogy created opportunities for students to learn. Second, a supportive, interactive community engaged students with the instructor, each other and the course material to support participation in learning. As a result, students reported experiencing big shifts, new growth and increased confidence during their leadership courses.

We discuss our findings and offer specific recommendations for leadership educators to better support BIPOC students in their leadership courses and classrooms and for further research with BIPOC students.

This study aims to explore an emerging collection of smart building technologies, known as smart workplace solutions (SWS), in the context of facilities management (FM).

This study is based on semi-structured interviews with facility managers in Finland, Norway and Sweden who have deployed SWSs in their organizations. SWS features, based on empirical data from a previous study, were also used to further analyse the interviews.

It analyses the benefits that SWSs bring from the facility management point of view. It is clear that the impetus for change and for deploying SWS in the context of FM is primarily driven by cost savings related to reductions in office space.

This research has been conducted with a focus on office buildings only. However, other building types can learn from the benefits that facility managers receive in the area of user-centred smart buildings.

SWSs are often seen as employee experience solutions that are only related to “soft” elements such as collaboration, innovation and learning. Understanding the FM business case can help make a more practical case for their deployment.

SWSs are an emerging area, and this study has collected data from facility managers who use them daily.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

With the continuous development of the wind power industry, wind power plant (WPP) has become the focus of resource development within the industry. Site selection, as the initial stage of WPP development, is directly related to the feasibility of construction and the future revenue of WPP. Therefore, the purpose of this paper is to study the siting of WPP and establish a framework for siting decision-making.

Firstly, a site selection evaluation index system is constructed from four aspects of economy, geography, environment and society using the literature review method and the Delphi method, and the weights of each index are comprehensively determined by combining the Decision-making Trial and Evaluation Laboratory (DEMATEL) and the entropy weight method (EW). Then, prospect theory and the multi-criteria compromise solution ranking method (VIKOR) are introduced to rank the potential options and determine the best site.

China is used as a case study, and the robustness and reliability of the methodology are demonstrated through sensitivity analysis, comparative analysis and ablation experiment analysis. This paper aims to provide a useful reference for WPP siting research.

In this paper, DEMATEL and EW are used to determine the weights of indicators, which overcome the disadvantage of single assignment. Prospect theory and VIKOR are combined to construct a decision model, which also considers the attitude of the decision-maker and the compromise solution of the decision result. For the first time, this framework is applied to WPP siting research.

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.