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In keeping with recent body image social trends, consumer demand for the adoption of plus-size models is increasing, although the use of thin models remains prevalent. The current study explores how consumers process information about fashion products displayed on different sizes of models in advertisements, focusing on model and consumer body sizes and both genders. As an underlying mechanism explaining how the relationship between model and consumer body sizes shapes consumer purchase intention, this study explores the role of guilt, shame and mental imagery.

The current study uses a text analytics technique to identify female consumers' general opinions of thin models in advertising. Employing a 3 (consumer body size: normal, overweight, obese) × 2 (model body size: thin, plus-size) × 2 (gender: male, female) between-subjects online experiment (n = 718), the main study comparatively analyzes the influences of plus-size and thin models on consumer responses.

The results reveal that, despite body positivity movements, thin models still generate negative emotions among female consumers. For obese female consumers, advertisements featuring plus-size models produce fewer negative emotions but not more mental imagery than advertisements featuring thin models. Conversely, for obese male consumers, advertisements featuring plus-size models generate more mental imagery but not more negative emotions than advertisements featuring thin models. The results also reveal that the relationship between consumer body size and guilt is moderated by perceived model size, which is also moderated by gender in generating mental imagery. While guilt plays a mediating role in enhancing mental imagery, resulting in purchase intention, shame does not take on this role.

This study is the first to present an integrated model that elucidates how consumers with varying body sizes respond to different sizes of models in advertising and how these responses impact purchase intentions.

Our findings only apply to contexts where consumers purchase fashion clothing in response to advertisements featuring thin versus plus-size models.

Exposing normal-size consumers to plus-size models generates less mental imagery, and thus, practitioners should seek to match the body sizes of the models featured in advertising to the body sizes of their target audience or ad campaigns that include both plus-size and thin models may help improve message persuasiveness in fashion advertising. Moreover, guilt-appeal advertising campaigns using thin models would appeal more to thin consumers of both genders than shame-appeal advertising.

By integrating the Uses and Gratifications Theory and Flow Theory, this research seeks to untwine the veiled effects of interactive commerce enhancements (ICEs), specifically haptic imagery and social presence, in promoting user immersion and sustaining social commerce (SC) users' usage intention, considering the moderating role of autotelic personality.

The research utilized purposive sampling of Malaysian SC app users with recent transactions. A dual-source data collection approach, encompassing offline and online channels, was employed to ensure a broad and diverse respondent pool. Partial least squares–structural equation modeling was chosen for its adeptness in analyzing complex relationships in predictive studies.

The findings revealed the significant positive effects of haptic imagery and social presence on user immersion and continuance intention within SC apps. Social presence and immersion were found to mediate the proposed paths. Additionally, autotelic personality traits were identified as moderators, influencing the strength of these relationships.

This research makes a unique contribution by addressing critical gaps in SC environments, extending the concept of ICEs, understanding the impacts of underlying mediators and pioneering the examination of autotelic personality traits' moderating effects. It introduces a fresh perspective on how individual differences impact user engagement. This groundbreaking study benefits social media and interactive marketing literature by comprehensively understanding how ICEs elevate SC, fostering innovation and heightened engagement.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

The symbolic presentation of products through images in online environments allows consumers to use or experience products only through imagination. Existing literature has demonstrated that providing sensory cues is an effective way to promote imaginative use or experience. However, such an approach seems to have been proposed for product that requires the use of body-related information (e.g. sensory information) for evaluation (high body-involving product). There is less literature on how to facilitate consumers’ imaginative use of product that requires relatively less bodily information (low body-involving product). Considering this, this research proposes a factor that influences the imaginative use of both high and low body-involving products, the character cues in the product image.

In this paper, two studies are conducted to verify the matching effect about presence or absence of character cues with product type (high body-involving vs. low body-involving) in facilitating imaginative use and the downstream effect.

The experimental results indicate that high (low) body-involving product display images are suitable for present (absent) character cues, which can promote the mental imagery of use the product, increase perceived image attractiveness and ultimately increase purchase intentions. The research also verified the influence of distance between the product and the character cues on the above effects.

We expand on the importance of character cues in product display images in an e-commerce environment and enrich the research about imaginative use in online environment.

This study aims to investigate how image-presentation-order influences mental imagery (MI) processing and purchase intentions. This study also examines the moderating effect of a series of images on the relationship between image-presentation-order and MI processing.

This research conducted two studies using an experimental approach.

Two studies showed that MI processing was higher, when an apparel product image worn by a model with a background was shown after rather than before a simple product image (SPI), indicating the recency effect. In contrast, examining a series of images, consumers were more engaged in MI processing, when product image(s) worn by a model with a background were presented first, followed by the four SPIs, than the reversed order (primacy effect). The level of MI in two studies subsequently increased purchase intentions.

Results of this study have the potential to provide guidance to online retailers for how to best order their product images on a website to help consumers form elaborated MI about the product and thus increase purchasing intentions.

Although past research has examined presentation-order effect using textual information, very limited studies have explored presentation-order effect of pictorial information. To the best of the authors’ knowledge, this research is in the forefront of investigations about the joint effect of image-presentation-order and the number of images on individuals’ perceptions.

As innovations introduce novel benefits to customers, they would need to be positioned in a way that sets them apart in the market. The purpose of this paper is to propose a novel approach for the positioning of innovations with the use of the customer imagination and, specifically, mental movies.

Using the schema approach as this study’s theoretical framework, the author proposes that innovations could be positioned using moving pictures (i.e. mental movies) instead of mental pictures (the approach traditionally taken).

A new conceptual framework for the positioning of innovations using mental movies is presented. In the framework, this study outlines how innovations can be positioned with the use of mental movies, and why such an approach would be beneficial. The framework outlines mixed reality, i.e. augmented reality, augmented virtuality and virtuality, as well as the metaverse and gaming as avenues for positioning innovations using mental movies. On the benefit side, the framework identifies successful market introductions, engagement and stickiness, memorability and positive emotions, uniqueness and differentiation and market share as the concrete benefits that can be achieved with this type of positioning.

The framework provides a novel approach for the positioning of innovations. It departs from existing literature by proposing that innovations can be positioned using mental movies. The framework also identifies why this approach would be beneficial for marketers and managers and provides concrete guidelines for how such a positioning can be achieved in the market.

This paper aims to examine the image realism effect, studying how changing the front-of-package visual affects the acceptance of an insect-based food by consumers. By comparing reactions to realistic and less realistic images of an insect as an ingredient, this research investigated how visual imagery can affect consumers’ responses, reducing perceived disgust or increasing expected taste.

Three experiments studied the impact of realistic (photo) versus less realistic (drawing) images for two types of insects (mealworm, cricket) on consumers’ psychological distance from the image, perceived disgust, expected taste, willingness to eat, purchase intention and food choice.

Study 1 demonstrates that using a less realistic insect image reduces perceived disgust, with psychological distance from this image and perceived disgust mediating realism effect on willingness to eat. Study 2 shows that a less realistic insect image, perceived as more remote, improves expected taste and willingness to eat. Study 3 confirms the results by measuring behavior: consumers were more likely to choose the product with the less realistic image.

The research focused on one kind of product and two ways of depicting this product, limiting the generalizability of the findings for other visual representations and product categories.

The findings suggest how brand managers can use the image realism effect on the packaging of novel, sustainable products to influence consumers, reducing their disgust and increasing their expected taste.

This research breaks new ground by explaining how visual cues on packaging affect the acceptance of insect-eating, drawing on construal level theory.

The construction industry is an industry which has gained notoriety when it comes to both physical and mental health problems. Compared to other industries, the construction sector has a higher prevalence of many stressors and mental health concerns. This calls for mechanisms to cope with these concerns. One coping mechanism propounded to help cope and adapt in the face of pressures and challenges is “Mental Toughness (MT)”. While mental toughness has been widely studied in various fields, there is a paucity of comprehensive research examining its significance among construction professionals. Thus, the motivation of this study is to establish the mental toughness characteristics among construction professionals in Ghana.

The construction industry is an industry which has gained notoriety when it comes to both physical and mental health problems. Compared to other industries, the construction sector has a higher prevalence of mental health concerns. One coping mechanism propounded to help cope and adapt in the face of pressures and challenges is “Mental Toughness (MT)”. While mental toughness has been widely studied in various fields, there is a paucity of comprehensive research examining its significance among construction professionals. Thus, the motivation of this study is to establish the mental toughness characteristics among construction professionals.

the study's findings revealed 13-factor model characteristics of mental toughness with 43 variables for mentally tough performers in the construction environment. These 13-factor models include Pressure Management (PM), Motivation (M), Emotional Intelligence (EI), Interpersonal self-belief (SB), Tough Attitude (TA), Job-related self-belief (SBB), Ethical Values (EV), Commitment (C), Focus (F), Optimism (OP), Expertise and Competence (EC), Imagery Control (IC) and Resilient (R).

The outcome of this study has significant practical implications for various stakeholders. For construction professionals the identified factors provide valuable insights into the psychological attributes and behaviours that contribute to mental toughness among construction professionals. Understanding these characteristics can empower professionals to develop strategies for coping with stress, maintaining focus, and fostering resilience in challenging construction environments to achieve optimum performance levels consistently.

The originality of this study's findings stems from the fact that it is among the first to provide greater insight into mental toughness characteristics considered by professionals (quantity surveyors, construction managers, engineers, project managers, architects, estate managers) in the construction industry.

Achieving tenure security is a global challenge impacting cities of the global south. The purpose of this paper is to evaluate the role of technology-enabled solutions as an enabler for the tenure rights of slum dwellers.

In this paper, we adopted a case study approach to analyze the use cases for technologies aiding India’s securitization of land tenure. The flagship state mission of Odisha, named the Jaga Mission, and that of Punjab, named BASERA – the Chief Minister’s Slum Development Program – were used as cases for this paper.

It was found that technologies like drone imagery and digital surveys fast-tracked the data collection and helped in mapping the slums with accuracy, mitigating human errors arising during measurement – a necessary condition for ensuring de jure tenure security. The adoption of a technology-based solution, along with a suitable policy and legal framework, has helped in the distribution of secure land titles to the slum dwellers in these states.

Odisha’s and Punjab’s journey in using technology to enable tenure security for its urban poor residents can serve as a model for the cities of the global south, dealing with the challenges of providing secure tenure and property rights.

The purpose of this paper is to explore teacher candidates’ response to young adult literature (prose and comics) featuring fat identified protagonists. The paper considers the textual and embodied resources readers use and reject when imagining and interpreting a character’s body. This paper explores how readers’ meaning making was influenced when reading prose versus comics. This paper adds to a corpus of scholarship about the relationships between young adult literature, comics, bodies and reader response theory.

At the time of the study, participants were enrolled in a teacher education program at a Midwestern University, meeting monthly for a voluntary book club dedicated to reading and discussing young adult literature. To examine readers’ responses to comics and prose featuring fat-identified protagonists, the author used descriptive qualitative methodologies to conduct a thematic analysis of meeting transcripts, written participant reflections and researcher memos. Analysis was grounded in theories of reader response, critical fat studies and multimodality.

Analyses indicated many readers reject textual clues indicating a character’s body size and weight were different from their own. Readers read their bodies into the stories, regarding them as self-help narratives instead of radical counternarratives. Some readers were not able to read against their assumptions of thinness (and whiteness) until prompted by the researcher and other participants.

Although many reader response scholars have demonstrated readers’ tendencies toward personal identification in the face of racial and class differences, there is less research regarding classroom practices around the entanglement of physical bodies, body image and texts. Analyzing reader’s responses to the constructions of fat bodies in prose versus comics may help English Language Arts (ELA) educators and students identify and deconstruct ideologies of thin-thinking and fatphobia. This study, which demonstrates thin readers’ tendencies to overidentify with protagonists, suggests ELA classrooms might encourage readers to engage in critical literacies that support them in reading both with and against their identities.