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This study intend to investigate a theoretical model looking at how particular tourist emotions, such as “joy,” “love,” and “positive surprise,” might predict their behavior by looking at how satisfied they are with their whole experience when visiting spas, and to examine the relationship of emotional experience, destination image, satisfaction and intention to revisit for spa tourism.

A sample of 345 individuals who traveled to Alleppey as domestic tourists participated in the research study. A non-probability (purposive) sampling method in this study. The structural model was analyzed using Structural Equation modeling (SEM), and the path coefficients were examined to test the hypotheses.

The results supported the hypotheses, indicating that specific emotions, image of the destination, and satisfaction significantly impacted tourists' intentions to revisit Alleppey as a spa tourism destination. This study demonstrated that “emotions of joy, love, and positive surprise” have a considerable influence on the image of the destination and satisfaction. The findings reveal a substantial correlation between satisfaction and behavioral intention (“Intention to revisit”). The research suggests that a higher degree of satisfaction would encourage visitors to revisit the location.

The research suggests that a higher degree of satisfaction would encourage visitors to revisit the location. This research offers vital information for developing, planning, and putting into practice tourism policies in the spa tourism sector. This article focuses on domestic travelers who travel to Alleppey, so the conclusions may not be relevant to research utilizing foreign tourists.

According to the literature study, and to the authors` knowledge, only limited number of studies that look at spa tourism from a wellness perspective. Additionally, Alleppey is used in the study as the study’s setting, providing insight into the visitor experiences of this expanding spa tourism business. This study gives understanding about how emotional experience predicts behavioral intentions.

This study aims to examine decision factors for global sourcing, differentiated into transcontinental and continental sourcing to obtain insight into locational aspects of sourcing decisions and global trends. This study analyzed various country perceptions to reveal their influence on sourcing decisions. The country of origin (COO) theory explains why certain country perceptions and images influence purchasing experts in their selection of suppliers.

This study used a two-study approach. In Study 1, the authors conducted discrete choice card experiments with 71 purchasing experts located in Europe and the USA to examine the importance of essential decision factors for global sourcing. Given the clear evidence that location is a factor in sourcing decisions, in Study 2 the authors investigated purchasers’ perceptions and images of countries, adding country ranking experiments on various perceived characteristics such as quality, price and technology.

Study 1 provides evidence that the purchasers’ personal relationship with the supplier plays a decisive role in the supplier selection process. While product quality and location impact sourcing decisions, the attraction of the buying company and cultural barriers are less significant. Interestingly, however, these factors seem as important as price to respondents. This implies that a strong relationship with suppliers and good quality products are essential aspects of a reliable and robust supply chain in the post-COVID-19 era. Examining the locational aspect in detail, Study 2 linked the choice card experiments with country ranking experiments. In this study, the authors found that purchasing experts consider that transcontinental countries such as Japan and China offer significant advantages in terms of price and technology. China has enhanced its quality, which is recognizable in the country ranking experiments. Therefore, decisions on global sourcing are not just based on such high-impact factors as price and availability; country perceptions are also influential. Additionally, the significance of the locational aspect could be linked to certain country images of transcontinental suppliers, as the COO theory describes.

The new approach divides global sourcing into transcontinental and European sourcing to evaluate special decision factors and link these factors to the locational aspect of sourcing decisions. To deepen the clear evidence for the locational aspect and investigate the possible influence of country perceptions, the authors applied the COO theory. This approach enabled authors to show the strong influence of country perception on purchasing departments, which is represented by the locational effect. Hence, the success of transcontinental countries relies not only on factors such as their availability but also on the purchasers’ positive perceptions of these countries in terms of technology and price.

Employer branding (EB) has become a powerful tool for organizations to attract employees. Recruitment communication ideally reveals the image that companies want to portray to potential employees to attract talents with the right skills and competences for the organization. This study explores the impact of EB on employer attractiveness by testing how pre-existing employee preferences interact with EB and how this interaction affects employer attractiveness.

A quasi-experiment among 289 final-year students was used to test the relationships between EB, perceived employer image, person-organization (P-O) fit and employer attractiveness, and the potential moderating variables of pre-existing preferences, in this case operationalized as locational preferences. Students are randomly assigned to four vacancies: one with and one without EB cues in two different locations: Groningen and Amsterdam. The authors used standard scales for attractiveness, perceptions of an employer and person-organization fit. The authors test the relationships using a regression analysis.

Results suggest that if respondents have previous predispositions, then their preference can be enhanced using an EB-targeted strategy. Based on these results, the authors can conclude that EB and related practices can be successful avenues for organizations in the war for talent, particularly if they reaffirm previous preferences of potential employees.

The research is original in the way it provides empirical evidence on the relationship between EB and attractiveness, particularly when previous employee preferences exist. This is of value to employers using EB as a tool to influence employer attractiveness.

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.

This paper presents an interpretation of freehand drawings produced by supply chain management undergraduates in response to the question: “What is sustainability?” Having to explain sustainability pictorially forced students to distill what the essence of sustainability meant to them and provided insights into how they perceived sustainability and their roles in achieving sustainability in the context of supply chain management.

Students were asked to draw and answer the question “What is sustainability?” These drawings were discussed/interpreted in class. All drawings were initially examined quantitatively, before a sample of four were selected for presentation here.

Freehand drawing can be used as part of a critical pedagogy to create a visual representation to bypass cognitive verbal processing routes. This allows students to produce clear, more critical and inclusive images of their understanding of a topic regardless of their vocabulary.

The authors offer this as a model for educators seeking alternative methods for engaging with sustainability and for creating a learning environment where students can develop their capacity for critical self-reflection.

This study shows how a collaborative learning experience facilitates learners demonstrating their level of understanding of sustainability.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0718

This article examines the effect of China's soft power projection through the Chinese media in Africa on the African audiences' perception of China's national image through a case study of the residents of Nairobi. It adopted Joseph Nye's soft power model and sought to address three fundamental questions : What is the extent and objective of China's media diplomacy in Africa? How has China's media “offensive” in Africa impacted African audiences' imagery of China? What are the implications, if any, of China's media diplomacy on the Kenyan public view of China?

This article used a mixed-methods research design, which deployed elements of positivism and interpretivism. It used a deductive approach and deployed the survey strategy, which entailed the collection of data from Nairobi city residents. The secondary data were collected from relevant academic literature sources. The primary data were analyzed empirically using the Statistical Package for the Social Sciences (SPSS), while the secondary data were analyzed using discourse analysis.

China's soft power projection through the Chinese media in Africa is aimed at addressing the “misinformation” about China. China seeks its share of representation among the African public to correct negative perceptions of China. Kenyans had a generally positive attitude toward China. South Africa and Angola have “Fairly” positive perceptions of the Chinese media. However, this study did not reveal whether the perception was due to the influence of Chinese media. These findings implied that the African public's positive imagery of China cannot be fully attributed to Chinese media's influence.

This study is groundbreaking in that it is one of the few studies that have focused on China's public diplomacy in Africa and assessed the impact of Chinese media on the African public’s perception of China.

This study aims to investigate the factors that impact the solo travel intentions of millennial women in rural and urban areas. By exploring these factors, this research also sheds light on the similarities and differences in travel behaviors and motivations of women in different geographical contexts within India.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

This study problematizes the prevailing normative and managerial-dominated view of self-initiated employee ambassadorship on social media from a power perspective. The aim is to provide a more nuanced and critical understanding of the negative aspects of this phenomenon.

The empirical material encompasses qualitative interviews with employees from 14 organizations and Foucault’s concept of disciplinary discursive power to analyze which and how discourses exert power over employee communication on social media and what role visibility plays in it.

This study indicates that employee ambassadors’ social media communication is governed by two discourses that create complex tensions, where ambassadors constantly must negotiate between self-branding requirements and an authenticity paradox. These tensions intensify through visibility on social media, where employees strategize and situationally silence their communication through self-monitoring and self-surveillance practices. Conclusively, the findings also outline the need for further critical research to offer a deeper understanding of power relations that influence the communication practices of organizational members.

The paper contributes to a more nuanced understanding of self-initiated employee ambassadorship on social media and highlights disciplinary power relations that go beyond organizational borders.

The findings underscore that organizations need to address the critical aspects of self-initiated employee ambassadorship and act as facilitators to support employees in their navigation process.

This paper contributes a new critical power perspective on employee ambassadorship on social media.

The “chiralisation” of Euclidean polygonal tessellations is a novel, recent method which has been used to design new auxetic metamaterials with complex topologies and improved geometric versatility over traditional chiral honeycombs. This paper aims to design and manufacture chiral honeycombs representative of four distinct classes of 2D Euclidean tessellations with hexagonal rotational symmetry using fused-deposition additive manufacturing and experimentally analysed the mechanical properties and failure modes of these metamaterials.

Finite Element simulations were also used to study the high-strain compressive performance of these systems under both periodic boundary conditions and realistic, finite conditions. Experimental uniaxial compressive loading tests were applied to additively manufactured prototypes and digital image correlation was used to measure the Poisson’s ratio and analyse the deformation behaviour of these systems.

The results obtained demonstrate that these systems have the ability to exhibit a wide range of Poisson’s ratios (positive, quasi-zero and negative values) and stiffnesses as well as unusual failure modes characterised by a sequential layer-by-layer collapse of specific, non-adjacent ligaments. These findings provide useful insights on the mechanical properties and deformation behaviours of this new class of metamaterials and indicate that these chiral honeycombs could potentially possess anomalous characteristics which are not commonly found in traditional chiral metamaterials based on regular monohedral tilings.

To the best of the authors’ knowledge, the authors have analysed for the first time the high strain behaviour and failure modes of chiral metamaterials based on Euclidean multi-polygonal tessellations.