Search results

This study aims to investigate the effect of speckle pattern on displacement measurements using different speckle diameters and coverage ratios.

In order to compare the coverage ratio and speckle diameter during the evaluation of the correlation of digital images (DIC) study, template speckle plates were produced on a computer numerical control (CNC) punch press with 600 punches per minute. After the speckle plates were manufactured, the speckled pattern was randomly painted on a plain white side through the manufactured template plates, and then tensile tests were performed under the same loading conditions for each sample to observe displacement variation via correlation parameters.

During the manufacturing of templates with thin plates, a punch diameter of less than 1.7 mm will cause tool failure; therefore, uniform speckle size can be assessed before operation. A higher coverage ratio resulted in more accurate and reliable results in displacement data. With smaller coverage, the facet size should be increased to achieve favorable results.

If thick template plates are selected, speckle painting cannot be done properly; therefore, template thickness shall also be assessed before operation.

For randomly distributed DIC templates, increasing coverage beyond 50% does not make sense due to difficulties in the production process in the punch press.

Evaluating DIC results via templates manufactured in a punch press with different speckle diameters and coverage ratios is a new topic in literature.

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.

Improving service quality, student satisfaction and student loyalty is important to higher education institutions’ sustainable growth. The objectives of this study are a twofold: first, the study seeks to determine the dimensions of higher education service quality with a specific focus on Vietnam. Second, it examines how the service quality dimensions impact student satisfaction and student loyalty, with the moderating role of the university image.

This study followed a rigorous procedure, including interviews, a survey, exploratory factor analysis (EFA) and reliability analysis to identify higher education service quality dimensions and their measures. After that, using the data obtained from 1,550 university students in Vietnam, confirmatory factor analysis was used to validate the identified dimensions and structural equation modeling was used to test a proposed model explaining the outcomes of higher education service quality.

The findings reveal five dimensions of higher education service quality: academic aspect, nonacademic aspect, programming issues, facilities and industry interaction. Most of these factors have a positive influence on student satisfaction. In addition, the university image moderates the positive relationship between student satisfaction and student loyalty.

This study’s findings highlight the complexity of service quality in the higher education context and encourage higher education institutions to improve their service quality in image to enhance student satisfaction and loyalty.

This study suggests a unique measure of higher education service quality dimensions and provides fresh insights into how they impact student satisfaction and loyalty in Vietnam.

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.

This paper aimed to assess the service quality of the main seaports in Oman, which were Sohar, Ad Duqm and Salalah. The aim was to come up with ways to enhance the port service quality (PSQ) in Oman so that it could align with the Sultanate of Oman Logistics Strategy (SOLS) 2040 goals and achieve excellent and efficient operations.

To evaluate the service quality level of the port operators, this paper used a descriptive research design with Resources, Outcome, Process, Management, Image/reputation and Social (ROPMIS) modelling.

The findings indicated that the overall PSQ rating was currently between “satisfactory” and “very satisfactory” levels. However, the study also found that by empowering resources, outcomes, processes, management, image and social responsibility aspects, the port operators could provide a “high” quality of service, making their seaport operations more effective and efficient.

The study offers recommendations for improving port services in Oman, including investment in modern seaports, upgrading infrastructure and facilities, ensuring safety and efficiency of cargo operations, meeting and exceeding customer expectations, adopting new technology and automation, hiring policies that attract diverse talents, implementing environmentally friendly practices and improving governance. Overall, this study contributes to the literature and managerial practices in PSQ aspects and its contribution to the SOLS 2040 in Oman.

The originality and novelty of this study lie in its comprehensive assessment of the service quality of Oman's ports and the identification of areas for improvement to achieve outstanding service levels.

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 uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies.

This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI.

By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI.

Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm.