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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.

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 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.

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.

The construction sector is recognized for subjecting its workers to various psychological pressures and mental stressors. As a result, it is crucial to explore and discover effective strategies to support these workers in managing and enduring these demanding circumstances. Mental toughness is a concept that has been advocated to aid individuals in coping and adapting to pressures and challenges. Therefore, it is imperative to explore strategies critical to developing mental toughness among construction professionals. Thus, the purpose of this study is to identify critical strategies for the development of mental toughness among construction professionals.

A comprehensive literature review was conducted using Google Scholar, Scopus, ScienceDirect and PubMed databases to identify the strategies for the development of mental toughness. These identified strategies were used to develop a structured closed-ended questionnaire to aid in data collection. Of the 317 questionnaires distributed, 207 were collected and used for data analysis. The data were analysed using mean score ranking and one sample t-test.

The study highlighted 12 critical strategies for the development of mental toughness among construction professionals. These include recognizing and rewarding achievements, taking time for self-care, organizing mental training programs, using positive self-talk, embracing a growth mindset, providing social support/developing a strong support network, creating a challenging training environment, engaging in regular physical exercise, fostering a mastery motivational climate, setting clear but challenging goals, practicing mental imagery and practicing reflection on setbacks and failures. The analysis results indicated that all of the strategies, except one (which involves practicing reflection on setbacks and failures), displayed significance. By incorporating these strategies, construction firms and the industry as a whole can empower their workforce to cope and/or overcome the various demands and stressors that work and life places on them.

The study highlights the significance of recognizing and rewarding achievements, fostering a growth mindset and implementing mental training programs to cultivate mental toughness among construction professionals. Moreover, promoting self-care practices and offering social support are essential strategies for enhancing mental resilience in the construction industry. By adopting these evidence-based approaches, construction organizations can create a mentally tough workforce capable of handling the challenges and demands of the industry effectively.

This study contributes significantly to the general body of knowledge in mental health management among employees in the construction industry. It does set the pace for the development of the general well-being of construction professionals in the achievement of mental toughness goals.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

This research extends self-congruity theory and assesses the predictive validity of the triad personality congruence among brand, brand-related sustainability initiatives (BSI) and self-concept (BSSC: brand-sustainability-self-congruence) on consumers’ brand evaluation.

Three studies assessed BSSC using the brand personality scale (BPS) and the direct congruence measure (DCM). Through moderated mediation analyses, BSSC effects were examined on consumers’ behavioral intention, behavior and subjective well-being (SWB). The mediating role of brand trust and the moderating role of brand/BSI types and consumer characteristics were also assessed.

BSSC increased consumers’ brand trust, behavioral intention, behavior and SWB. Data based on BPS revealed impactful attributes that increase/decrease BSSC levels across brand-BSI combinations and the moderating role of sustainability involvement and income to enhance BSSC effects. BSSC was particularly effective, according to DCM.

For BSI planning, strategic consideration of BSSC based on both BPS and DCM is recommended. Thus, managers may predict the psychological impact of BSI and align its attributes to increase consumers’ brand evaluation.

In the sustainable marketing context, this research discusses BSSC – triad personality congruence – based on BPS and DCM and its predictive effects on consumers’ short-term brand evaluation, their actual behavior and SWB, a long-term life evaluation. The results imply a possible variation in consumers’ information processing according to the congruence measurement approach. Thus, it is relevant to the research on self-congruity, sustainability, marketing, consumer psychology/behavior and well-being.

Knife crime and serious violent crime (SVC) among youth has been growing at an alarming rate in the UK (Harding and Allen, 2021). Community and school-based intervention and prevention services to tackle knife crime are being developed with some evaluation; however, these are independent and of varied quality and rigour. Therefore, the purpose of this study is to record the approaches being developed and synthesise existing evidence of the impact and effectiveness of programmes to reduce knife crime. In addition, the complex factors contributing to knife crime and SVC are discussed.

A systematic approach was used to conduct this knife crime intervention evidence review using two search engines and four databases. Inclusion and exclusion criteria were applied to ensure focus and relevance. The results of searches and decisions by the research team were recorded at each stage using Preferred Reporting Items for systematic reviews and meta-analyses (PRISMA).

Some evidence underpins the development of services to reduce knife crime. Much of the evidence comes from government funded project reports, intervention and prevention services reports, with few studies evaluating the efficacy of intervention programmes at present. Some studies that measured immediate impact in line with the programme’s aims were found and demonstrated positive results.

This systematic review specifically synthesised the evidence and data derived from knife crime and weapon carrying interventions and preventions, integrating both grey and published literature, with a novel discussion that highlights the importance of outcome evaluations and issues with measuring the success of individual level interventions and their contributions to the overall reduction of violence.

This study aims to compare the local climate characteristics of Angkor Wat, Borobudur and Prambanan parks and determine effective strategies for mitigating thermal conditions that could suit Borobudur and Angkor Wat.

The study employed local climate zone (LCZ) indicators and ten-year historical climate data to identify similarities and differences in local climate characteristics. Satellite imagery processing was used to create maps of LCZ indicators. Meanwhile, microclimate models were used to analyze sky view factors and wind permeability.

The study found that the three tropical large-scale archaeological parks have low albedo, a medium vegetation index and high impervious surface index. However, various morphological characteristics, aerodynamic properties and differences in temple stone area and altitude enlarge the air temperature range.

Based on the similarities and differences in local climate, the study formulated mitigation strategies to preserve the sustainability of ancient temples and reduce visitors' heat stress.

The local climate characterization of tropical archaeological parks adds to the number of LCZs. Knowledge of the local climate characteristics of tropical archaeological parks can be the basis for improving thermal conditions.

Building on associative priming, anthropomorphism and biophilia theories, this study aims to explain that a natural–organic (that shows a natural object) and an anthropomorphised natural–organic logo (that shows an anthropomorphised natural object) both act as primes and imbue specific product value perceptions, which subsequently influence willingness to pay a premium price when products have not been used by or are unfamiliar to consumers.

Two between-subjects experiments were conducted with different products (one with real, but unfamiliar to consumers brand and another with a fictitious brand). Structural equation modelling was used to test the hypotheses.

Experimental studies showed that natural–organic logos evoked stronger utilitarian (functional and economic) value perceptions, which triggered greater willingness to pay a premium price compared to anthropomorphised natural–organic logos. The effect of hedonic (emotional and novelty) values on willingness to pay a premium price was stronger when an anthropomorphised natural–organic logo was used.

This research offers novel theoretical contributions highlighting the importance of careful logo design to imbue desired value perceptions when products have not been consumed or trialled.

Anthropomorphised natural–organic and natural–organic logos can provide different benefits to brand managers and can be used strategically to form desired value perceptions before products are consumed. Brands that wish to enhance premium pricing via hedonic values should consider using an anthropomorphised natural–organic logo. Natural–organic logos may be more suitable for brands that want to emphasise superior utilitarian values.

To the best of the authors’ knowledge, this research provides the first empirical assessment of the differential effects of the two forms of natural–organic logos on value perceptions and willingness to pay premium price.