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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.

This study aims to investigate the impact of destination accessibility on pro-tourism behaviour directly and indirectly through environmental impacts and/or affective destination image from residents’ point of view. It also investigates the moderated mediation effect of residency length in the indirect relationships between destination accessibility and pro-tourism behaviour through the negative environmental impacts or affective destination image.

A quantitative method was applied. More specifically, the survey approach was found to be the most effective method of data collection for this research. In total, 351 questionnaires were collected through an online survey from people who currently live in Alexandria. The questionnaire was translated into Arabic while adhering to back-translation guidelines. SPSS and AMOS software were used to analyse the collected data.

The results showed that residents’ destination accessibility affects both destination image and destination environment but does not affect the pro-tourism behaviour. It indirectly affects the pro-tourism behaviour through only affective destination image. Residency length plays a moderation role in this indirect relationship.

The current study can help policymakers, governments and tourism institutions to make decisions. Policymakers can use these results to adopt a comprehensive policy that encompasses different interested parties in tourism, in order to ensure that tourism development efforts will succeed. The government can also join hands with tourism institutions to mitigate the negative impacts of tourism in order to balance the negative and positive impacts of tourism.

The study contributes to the existing literature by highlighting the importance of destination accessibility to support tourism behaviours in domestic tourism.

The digital marketing landscape is rapidly evolving, but the integration of visual content still heavily depends on human expertise. Driven by the quest for innovative marketing strategies that resonate with family-oriented consumers, this study seeks to bridge this gap by applying machine learning to analyze visual content in the maternity and baby care product sector.

This study incorporates a range of machine learning techniques – including open science framework feature detection, panoptic segmentation, customized instance segmentation, and face detection calculation methods – to analyze and predict the appeal of images, thereby enhancing user engagement and parent-child intimacy.

The exploration of various ML models, such as DT, LightGBM, RIPPER algorithm, and CNNs, has offered a comparative analysis that addresses a methodological gap in the existing literature, which frequently depends on isolated model evaluations. According to our quadrant analysis with respect to engagement rate and parent-child intimacy, the selection of a model for real-world applications depends on balancing performance and interpretability.

The proposed system offers a series of actionable recommendations designed to enhance customer engagement and foster brand loyalty. This study contributes to image design in maternity and baby care marketing and provides analytical insights for recommendation systems.

Taking the lens of a cue diagnosticity framework and affective primacy theory, this study aims to examine the relative effects of cognitive and affective country image on consumer cognitive judgement, affective evaluation and behavioural tendency in one integrated model. It also explores how the direct effects may vary with the intra-valence nature (ambivalent vs. univalent) of cognition-affect.

The proposed research model was tested using data from a large Chinese sample and consumer responses to products from four countries − the USA, Japan, Brazil and India.

The results show that the relative effects of cognitive and affective country image are complex and differ by the intra-valence nature of cognition-affect. On a general level, cognitive and affective country image exert equal influence on affective evaluation and behavioural tendency. In contrast, cognitive country image demonstrates a more prominent effect than affective country image on cognitive judgement. Compared with univalent, ambivalent cognition-affect strengthens the positive impact of affective country image but does not significantly alter the positive impact of cognitive country image on consumer reactions.

This research contributes to the ongoing debate regarding implications of two focal aspects of macro country image by revealing their relative importance in an integrated framework and enriches country-of-origin research through unveiling the uni/ambivalent cognition-affect as a moderator of the relationship between cognitive/affective country image and consumer reactions. The research findings provide implications as to whether and when marketing strategies should focus on leveraging positive (negative) cognitive or affective country image.

Visually impaired people usually struggle with doing daily tasks due to a lack of visual cues. For image captioning assistive applications, most applications require an Internet connection for the image captioning generation function to work properly. In this study, we developed MyUEVision, an application that assists visually impaired people by generating image captions that can work with and without the Internet. This work also involves reviewing some image captioning models for this application.

The author has selected and experimented with three image captioning models for online models and two image captioning models for offline models. The user experience (UX) design was designed based on the problems faced by visually impaired users when using mobile applications. The application is developed for the Android platform, and the offline model is integrated into the application for the image captioning generation function to work offline.

After conducting experiments for selecting online and offline models, ExpansionNet V2 is chosen for the online model and VGG16 + long short-term memory (LSTM) is chosen for the offline model. The application is then developed and assessed, and the results show that the application can generate image captions with or without the Internet, providing the best result when having an Internet connection, and the image is captured in good lighting with a few objects.

MyUEVision stands out for its both online and offline functionality. This approach ensures the image captioning generator works with or without the Internet, setting it apart as a unique solution to address the needs of visually impaired individuals.

This study aims to critically evaluate the impact of government tourism promotion methods on city image and visiting intentions and to explore the moderating effect of tourism experience on two of the conceptualized relationships, using Macao as a case study.

The research analyzed responses from 407 Macao visitors during the pandemic to determine the effects of tourism promotion methods on the city’s image and visiting intention using structural equation modeling (SEM) to test the hypotheses.

This study revealed that tourism promotion methods positively impact the city’s image and visiting intentions. City image also mediates the relationship between tourism promotion methods and visiting intentions. Furthermore, tourism experience moderates the relationships between promotion methods and city image and between promotion methods and visiting intentions.

The study’s findings on the effects of promotion methods utilized by local governments support and expand upon existing theoretical frameworks within the realm of nonprofit organizations’ management and marketing for destination marketing organizations (DMOs). However, the cross-sectional design limits causality, and findings may not be generalizable beyond Macao without further comparative research.

This study provides valuable insights for policymakers and marketers to design promotional campaigns that deliver authentic and memorable experiences, aligning with their promotional promises, especially in postpandemic contexts.

This research contributes to the theoretical and methodological advancement of management and marketing for DMOs from a public sector perspective, highlighting the critical role of government involvement in urban tourism promotion.

The research offers valuable insights into how promotional activities influence visiting intentions, advancing tourism management and marketing for DMOs from a nonprofit perspective. The study validates theories and adds unique insights by focusing on Macao’s postpandemic recovery, offering practical implications for similar urban settings.

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.

This study aims to explore the differences in the tourists’ perceived destination image on travel e-commerce platforms (e.g. Ctrip and Fliggy) and social media platforms (e.g. Xiaohongshu and Weibo).

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.

The distinct characteristics of slow tourism compared to mass tourism determine the differences between slow travelers and mass travelers. This transformation is essential for potential tourists to transition into slow travelers when promoting slow tourism to them. Can travel vlogs, a “fast” way of promoting, achieve such a “slow” transformation? To address this question, the purpose of this paper is to introduce the resonance theory and investigate the impact of resonance with slow tourism vlogs on potential travelers’ destination image and travel intention.

Through the distribution of online questionnaires on site, this study collected a total of 349 valid questionnaires. Subsequently, the researcher used partial least squares structural equation modeling to analyze the data and test the hypotheses.

The research findings indicate that cognitive resonance only positively affected cognitive image, while emotional resonance was positively associated with both cognitive image and affective image. In addition, it was observed that cognitive image and affective image played a positive mediating role between resonance with slow tourism vlogs and travel intention.

This research is pioneering in quantitatively validating the structure linking resonance, destination image and travel intention in the context of slow tourism. By validating the second-order structure of cognitive and emotional resonance, this study underscores the crucial role of resonance in shaping tourists’ transition toward a slow mindset in slow tourism.