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The purpose of this paper is to (1) characterize representational and nonrepresentational images; (2) review the literature on representational and nonrepresentational images; (3) introduce the theory of consumption values (TCV) framework vis-à-vis representational and nonrepresentational images; and (4) generate propositions and questions for future research based on that intersection.

Conceptual/literature review with propositions and future research directions.

The authors offer nine propositions and several associated example research questions to explore and document the important ways in which representational and nonrepresentational images can affect the five dimensions of value as outlined in the TCV.

It is the hope that this work serves as a theoretical starting point – surely, there are other theories and frameworks beyond the TCV that may share ties with types of images, which scholars should be encouraged to explore, but if the authors had attempted to document every possible theory, the result would be a limitless document. As such, the authors have honed the efforts on a broad-reaching framework, the TCV, in the attempt to balance theoretical insights with parsimony. Through exploration of these and other avenues, the authors hope that scholars and practitioners alike will benefit from elucidation of theories and effects around representational and nonrepresentational images.

Several practical implications flow from the dimensions and propositions within this work.

Representational and nonrepresentational images have featured prominently throughout visual content and communications for centuries, yet the current body of literature remains scant and underdeveloped in its relationship to marketing. The present work addresses this gap by using the TCV as an overarching framework to generate propositions and future research questions.

The present study aims to examine and explain cultural heritage destination by applying multiple dimension image model (cognitive–affective–conative aspects).

A total of 233 international heritage-based tourists were surveyed on-site at some of the most prominent historical attractions in Malacca, one of the first cities in Malaysia declared as a UNESCO World Heritage Site. The covariance-based structural equation modelling was applied to examine the hypotheses.

The structural equation modelling validated that cognitive image aspects such as living culture, intangible and tangible are affected positively. The effects are statistically significant for conative and affective images. On the other hand, cognitive tangible image is an aspect that impacted affective image to a lesser degree than conative image. Whereas affective image attributes were found to have significant and positive influence on conative image.

This study enriches the limited empirical research study on heritage image conceptualisation by expanding into tri-component model. The destination image has garnered a great deal of attention, particularly due to its significant and impactful influence on the decision-making and the sustainable behaviour of tourists, and it has since become the subject of many studies in the tourism and hospitality literatures. But most research concerning heritage image for destinations has considered the construct uni-dimensionally.

The paper examines the role and significance of gastronomic manifestations in the context of destination branding, within the framework of image transfer mechanisms and the Associative Network Memory Model.

The research was conducted on a sample of 53 gastronomic events in the tourist destination of Vojvodina.

The results indicate that gastronomic manifestations image has a positive impact on the brand image and brand identity of the destination, as well as the destination's overall image. Furthermore, the study found that the food experience has a positive influence on the image of gastronomic events and the destination.

The study contributes to the advancement of research on tourist destination branding.

Tourists with different cultural backgrounds think and behave differently. Accurately capturing and correctly understanding cultural differences will help tourist destinations in product/service planning, marketing communication and attracting and retaining tourists. This research employs Hofstede's cultural dimensions theory to analyse the variations in destination image perceptions of Chinese-speaking and English-speaking tourists to Xiamen, a prominent tourist attraction in China.

The evaluation utilizes a two-stage approach, incorporating LDA and BERT-BILSTM models. By leveraging text mining, sentiment analysis and t-tests, this research investigates the variations in tourists' perceptions of Xiamen across different cultures.

The results reveal that cultural disparities significantly impact tourists' perceived image of Xiamen, particularly regarding their preferences for renowned tourist destinations and the factors influencing their travel experience.

This research pioneers applying natural language processing methods and machine learning techniques to affirm the substantial differences in the perceptions of tourist destinations among Chinese-speaking and English-speaking tourists based on Hofstede's cultural theory. The findings furnish theoretical insights for destination marketing organizations to target diverse cultural tourists through precise marketing strategies and illuminate the practical application of Hofstede's cultural theory in tourism and hospitality.

The purpose of this study is to use structural equation modeling to examine the interaction between the variables of corporate image, credibility, trust and satisfaction in the context of a virtual sporting event. The aim is to determine whether these variables have significant relationships with each other and which of them has the greatest influence on the prediction of participants' satisfaction.

A structured questionnaire was used, based on previously validated scales. The survey was administered using the LimeSurvey platform. The sample consisted of a total of 588 participants of the Medellín virtual marathon.

The results of the study reveal significant findings regarding the relationships between the variables of corporate image, credibility, trust, and satisfaction in virtual sporting events. In particular, it is highlighted that trust emerges as the most influential factor in participants' satisfaction, which offers an insightful understanding of the importance of this variable in the user experience in virtual sporting events.

This study emphasizes the importance of brand analysis in the sports environment, stressing that the actions undertaken by managers should highlight both the corporate image and the connections with users, given their fundamental role in customer satisfaction. Likewise, the study of these variables within the sports context provides new knowledge and fills existing gaps within the academy. Limitations include the sample and the lack of consideration of all brand variables.

The need to cultivate a strong and well-managed image to build trust with participants is emphasized for organizers of virtual sporting events. It is crucial to work on establishing long-term credibility, especially in the relatively new context of virtual racing. Maintaining, and building the virtual career offering is essential to strengthening relationships, demonstrating a robust corporate image. In addition, since trust and credibility have a significant impact on participant satisfaction in this type of event, managers must communicate the assurance that virtual careers offer an experience free of uncertainty and risk, which is particularly attractive to a new customer base interested in this format.

This article presents an original contribution by investigating the relationships between corporate image, credibility, trust, and satisfaction in the context of virtual sporting events. It employs a structural equation model to assess the significance and predictive capacity of these variables. Notably, the study identifies trust as the most influential factor in predicting participant satisfaction. These findings offer valuable insights into the relative importance of brand variables in shaping user satisfaction within the virtual sporting event domain. By shedding light on these dynamics, the research aids event managers in making informed resource allocation decisions, contributing to a nuanced understanding of brand impact in this context.

As museums and other memory institutions continue to invest considerably in mass-digitising collections and participating in large search portals, it is essential to understand existing and potential users, their motivations and search needs to inform collections’ documentation. In this article, we discuss insights from a collaborative project with National Museums Scotland, set up to enhance the findability of collection images and inform documentation practices by understanding the collections users and their search terms.

The research involved interviews with National Museums Scotland staff, users and non-users of the Museums’ Search our Collections portal encompassing a concept mapping and card sort activity; online surveys and content analysis of user search queries.

The project revealed that participants are interested in searching the online collections by terms often not represented in collections metadata, including terms related to identity (their own but also others’) and social context (e.g. through seasonal and social events); emotional and sensory interests (e.g. visual characteristics) and narrative themes (e.g. on under-represented histories).

Based on the findings, we further theorise the semantic gap in online museum collection metadata. To bridge this gap and cater to how users search, we argue for a paradigm shift in documentation practices: suggesting practitioners should not only view collection images as information but also tap into their rich potential for enabling social and affective connections.

Powder bed additive manufacturing has recently seen substantial growth, yet consistently producing high-quality parts remains challenging. Recoating streaking is a common anomaly that impairs print quality. Several data-driven models for automatically detecting this anomaly have been proposed, each with varying effectiveness. However, comprehensive comparisons among them are lacking. Additionally, these models are often tailored to specific data sets. This research addresses this gap by implementing and comparing these anomaly detection models for recoating streaking in a reproducible way. This study aims to offer a clearer, more objective evaluation of their performance, strengths and weaknesses. Furthermore, this study proposes an improvement to the Line Profiles detection model to broaden its applicability, and a novel preprocessing step was introduced to enhance the models’ performances.

All found anomaly detection models have been implemented along with several preprocessing steps. Additionally, a new universal benchmarking data set has been constructed. Finally, all implemented models have been evaluated on this benchmarking data set and the effect of the different preprocessing steps was studied.

This comparison shows that the improved Line Profiles model established it as the most efficient detection approach in this study’s benchmark data set. Furthermore, while most state-of-the-art neural networks perform very well off the shelf, this comparison shows that specialised detection models outperform all others with the correct preprocessing.

This comparison gives new insights into different recoater streaking (RCS) detection models, showcasing each one with its strengths and weaknesses. Furthermore, the improved Line Profiles model delivers compelling performance in detecting RCS.

This study aims to explore image-based visual price determinants (image features and visual aesthetic perception) and how image features affect Airbnb listing price on a sharing accommodation platform.

The study uses an SOR model and a hedonic price model to examine the connections between the characteristics of image features, visual aesthetic perception and Airbnb listing prices. The model is then examined by an econometric model using data from Insideairbnb.com.

Empirical results revealed that image features have a significant positive effect on visual aesthetic perception, visual aesthetic perception has a significant positive effect on Airbnb listing price and visual aesthetic perception has a significant mediating effect between image features and Airbnb listing price.

This study contributes to the relationship and effect mechanism among image features, visual aesthetic perception and Airbnb listing price and has some implications for both property operators and the sharing accommodation platform.

本研究探讨了基于图像的视觉价格决定因素（图像特征和视觉美学感知）以及图像特征如何影响共享住宿平台Airbnb价格。

本研究采用SOR模型和hedonic价格模型来检验图像特征特征、视觉美感与Airbnb房源价格之间的关系。然后使用Insideairbnb.com上的数据, 通过计量经济学模型对该模型进行检验。

实证结果显示：1）图像特征对视觉美学感知有显著的正向影响; 2）视觉美学感知对Airbnb价格有显著的正向影响; 3）视觉美学感知在图像特征和Airbnb价格之间有显著的中介效应。

本研究有助于探讨图像特征、视觉美学感知和Airbnb价格之间的关系和影响机制, 对房源经营者和共享住宿平台都有一定的借鉴意义。

Este estudio explora los determinantes visuales del precio basados en las imágenes (características de las imágenes y percepción estética visual) y cómo afectan las características de las imágenes al precio de los anuncios de Airbnb en una plataforma de alojamiento compartido.

El estudio emplea un modelo SOR y un modelo de precios hedónicos para examinar las conexiones entre las características de los rasgos de la imagen, la percepción estética visual y los precios de Airbnb. A continuación, se examina el modelo mediante un modelo econométrico utilizando datos de Insideairbnb.com.

Los resultados empíricos revelan que 1) las características de la imagen tienen un efecto positivo significativo sobre la percepción estética visual, 2) la percepción estética visual tiene un efecto positivo significativo sobre el precio de los anuncios de Airbnb, y 3) la percepción estética visual tiene un efecto mediador significativo entre las características de la imagen y el precio de los anuncios de Airbnb.

Este estudio contribuye al mecanismo de relación y efecto entre las características de la imagen, la percepción estética visual y el precio del anuncio de Airbnb, y tiene algunas implicaciones tanto para los operadores inmobiliarios como para la plataforma de alojamiento compartido.

Image change through visit experience with a destination is a common assumption, yet with rare empirical support to date. Thus, this study treats destination image as an interactive system of cognitive and affective components that are dynamic and changing through the course of an experience with a destination.

This longitudinal study tracks the change in images of 162 European visitors to the Gran Canaria Island one month before a two-week trip, on the last day of the trip, one month after the trip, and one year after the trip. A one-way repeated-measures analysis of variance with Bonferroni post hoc tests was conducted to evaluate the change in visitors’ cognitive, affective, or holistic images of Gran Canaria Island.

The Gran Canaria Island’s one-month post-visit image was more positive than the pre-visit image reflecting an improvement because of visitation. However, this improvement did not last long since the one-year post-visit image was not as positive as the one-month post-visit image. Surprisingly though, principle component analysis revealed more factors with more image items in the pre-visit image, whereas post-visit image factors were fewer in numbers and more generic, reflecting a reduction of details in the image after the visit.

This study is one of the very few efforts that contribute empirical evidence for the role of visitation in destination image evolution. It focuses on the pre and post-visit changes in the cognitive image and pre-, during, and post-visit changes in affective and overall images of a tourist destination in Europe.

The purpose of this paper is to analyse whether publicly shared images on Instagram are representative of tourist behaviour in a destination. This aspect is crucial for destination image management, as it can influence the way tourists perceive the destination.

The research compares three different factors: the route followed by a group of tourists, the whole set of photographs taken by them and the images that they made publicly available on Instagram. It relies on a field work done by a group of 122 tourists in Turin (Italy). At a qualitative level, the answers given by tourists to the motivations that led them to share some of the image are analysed.

The results showed how the spatial distribution of the images shared publicly on Instagram only partially coincides with the whole set of images taken by tourists.

It is important to avoid basing marketing and management policies on just the places featured in publicly shared images. If not, there is a risk of taking decisions based on the behaviour of some, rather than all, tourists.

Many papers claim to be based on the Instagram image as elements to study tourism. However, most of these papers only analyse public images. This fact can affect the results as there may be, for example, areas visited by tourists where photos are not taken. This paper therefore contributes to a better understanding of Instagram as a tool for the study of urban tourism.