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The paper examines the role and significance of gastronomic manifestations in the context of destination branding, within the framework of image transfer mechanisms and the Associative Network Memory Model.

The research was conducted on a sample of 53 gastronomic events in the tourist destination of Vojvodina.

The results indicate that gastronomic manifestations image has a positive impact on the brand image and brand identity of the destination, as well as the destination's overall image. Furthermore, the study found that the food experience has a positive influence on the image of gastronomic events and the destination.

The study contributes to the advancement of research on tourist destination branding.

The purpose of this study is to show that the neo-documentary – or complimentary – approach in Library and Information Science by no means is conservative, but highly necessary also in today's digitized media landscape. An example from a digitized photo archive is chosen to demonstrate the importance of a complimentary analysis that considers both material aspects as well as social and mental ones.

By taking Jenna Hartel's description of the neo-documentary turn as point of departure, the paper focuses on one case, the portrait of Johannes Abrahamsen Motka taken by Sophus Tromholt in 1883 and discusses different versions of the photograph from glass plate negatives to digitized versions in different contexts and media.

Many of the same paratextual elements can be found in different versions, also the digitized ones, to help the viewer to establish a historical context, but the images exhibited today are nevertheless no longer the same ones taken by Tromholt at the end of the 19th century. Not only have the material properties changed, but also – and probably even more important in most cases – the social and mental aspects. More re-contextualization is needed for today's audiences to recognize and understand a historical photograph taken in a colonial context. Focusing on document's material elements is not novel within the LIS-field, but the so-called neo-documentary turn was also a reaction on political and technological developments during the 1980s and 1990s. The increased focus on understanding a document in a complimentary way has demonstrated its impact during the last decades and is, at the same time, still work in progress.

As a scholar in the humanities the author can only relate to and therefore analyze what the author can experience and observe on screen level.

In providing a case study, this article illustrates the necessity of employing a complimentary approach when analyzing documents. This also implicates the claim that the neo-documentary turn – or complimentary as it rather should be called – by no means is a conservative one, but a highly necessary one in today's digitized media landscape.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

This study aims to investigate the influence of cognitive and affective trust on the revisit intention of medical tourists to a developing country like India, focusing on the role of country image as a moderator.

This study used partial least square-based structural equation modelling to test the hypotheses using data from 297 medical tourists visiting India from abroad for treatment.

The findings of this study confirmed that all the dimensions of cognitive trust, namely, perceived expertise, performance and reputation of the health-care service providers, have a positive and significant impact on the revisit intention of medical tourists to India. In contrast, none of the two dimensions of affective trust have a significant effect on the revisit intention of medical tourists to India. This study also confirmed that country image significantly moderates the relationship between performance dimensions of cognitive trust and medical tourists’ revisit intention to India.

The study’s findings have significant theoretical and managerial implications as they explore the effect of cognitive and affective trust on medical tourists’ the revisit intention to visit an emerging economy, considering the country’s image as a moderator.

本研究旨在调查认知和情感信任对印度等发展中国家医疗游客重游意愿的影响, 重点关注国家形象的调节作用。

本研究采用基于偏最小二乘的结构方程模型, 使用 297 名从国外到印度接受治疗的医疗游客的数据来检验假设。

本研究的结果证实, 认知信任的所有维度, 即医疗服务提供者的专业知识、绩效和声誉, 对印度医疗游客的重游意愿产生积极且显着的影响。相比之下, 情感信任的两个维度都没有对印度医疗游客的重游意愿产生显着影响。这项研究还证实, 国家形象显着调节认知信任绩效维度与医疗游客重访印度意愿之间的关系。

该研究的结果具有重要的理论和管理意义, 因为他们探讨了认知和情感信任对医疗游客重访新兴经济体的意愿的影响, 并考虑到该国作为调节者的形象。

Este estudio tiene como objetivo investigar la influencia de la confianza cognitiva y afectiva en la intención de volver a visitar a un país en desarrollo como la India por parte de turistas médicos, centrándose en el papel de la imagen del país como moderador.

Este estudio empleó un modelo de ecuaciones estructurales parcial basado en mínimos cuadrados para probar las hipótesis utilizando datos de 297 turistas médicos que visitaron la India desde el extranjero para recibir tratamiento.

Los hallazgos de este estudio confirmaron que todas las dimensiones de la confianza cognitiva, es decir, la experiencia percibida, el desempeño y la reputación de los proveedores de servicios de atención médica, tienen un impacto positivo y significativo en la intención de volver a visitar a la India por parte de los turistas médicos. Por el contrario, ninguna de las dos dimensiones de la confianza afectiva tiene un efecto significativo en la intención de volver a visitar la India por parte de los turistas médicos. Este estudio también confirmó que la imagen del país modera significativamente la relación entre las dimensiones de desempeño de la confianza cognitiva y la intención de los turistas médicos de volver a visitar la India.

Los hallazgos del estudio tienen importantes implicaciones teóricas y administrativas, ya que exploran el efecto de la confianza cognitiva y afectiva en la intención de los turistas médicos de visitar una economía emergente, considerando la imagen del país como moderador.

The halal cosmetics industry continues to grow significantly. Furthermore, using halal cosmetics is a must for Muslims. This study aims to analyze the factors influencing the switching intention to halal cosmetics in Indonesia.

This quantitative study uses a Partial Least Square-Structural Equation Modelling (PLS-SEM) on 214 respondents. The variables include halal certification, halal awareness, product image, perceived behavioral control, subjective norm, attitude, advertisement and switching intention.

The product image plays the most influential role in deriving the attitude toward switching intention to halal cosmetics, following perceived behavioral control, halal awareness and subjective norm, but not halal certification and advertisement. The result indicates that the image of halal cosmetics influences customers’ attitudes toward switching to using halal cosmetics. Indonesian customers know the obligation to use halal products because they are Muslim. However, the existence of halal certification does not derive the switching intention to halal cosmetics.

This study conducts research only in Indonesia. As a recommendation, further studies might conduct a comparative test using multicultural respondents in several countries. Other studies also suggested examining factors of switching intention through different generational, especially in countries with high individualism traits.

This study will encourage the halal industry, especially the halal cosmetics industry, to pay more attention to the product image. Meanwhile, the government can provide incentives or rewards to promote industry participation in halal cosmetics. The findings provide a more detailed understanding of how product image can influence someone to switch to halal cosmetics.

Research on switching intention to halal cosmetics is still limited. This study uses halal variables, while previous studies only used religiosity. This study also introduced the product images motivating customers’ switching intention to use halal cosmetics.

This paper aims to assess the sense-of-community role in affecting young Muslim loyalty towards Muslim-majority tourism destinations. Specifically, this research assesses the sense of community dimension in the halal tourism context and evaluates its effects on destination satisfaction, image and loyalty.

This research used a quantitative approach by using data from 376 young Indonesian Muslim tourists with past travel experiences to destinations where Muslims are the majority. The dimension of the sense of community was evaluated using exploratory factor analysis. The association between variables was tested using partial least square-structural equation modelling.

The finding exhibits three notable sense of community dimensions: membership, influence and need fulfilment and emotional connection. Emotional connection shapes, directly and indirectly, destination loyalty, while influence and need fulfilment affect destination loyalty by satisfaction and destination image mediating role. Lastly, membership has no impact on developing destination loyalty.

This study offers tourism destinations in Muslim-majority countries an opportunity to draw and create loyalty among young Muslim tourists. Besides offering superior halal services and products, Muslim-majority tourism destinations need to develop young Muslim tourists' emotional connection to the destinations.

To the best of the authors’ knowledge, this is the first empirical examination of the sense of community's role in influencing tourist loyalty, specifically in halal tourism.

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.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

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.

In the competitive realm of destination branding, understanding the factors that lead tourists to develop a deep emotional connection with a destination’s brand is essential. At the heart of this emotional bond lies destination brand love – an exceptionally profound sentiment transcending mere satisfaction or preference. This concept has recently garnered attention from both tourism academia and industry practice. However, there remains a void in exploring the factors and mechanisms that pave the way for tourists to develop deep affection for a destination’s brand. This study examines the influences of self-congruence and destination immersion on destination brand love. In addition, the linkages from the four facets of self-congruence to destination brand love via the mediating role of destination brand immersion are also scrutinized.

With data collected from 421 tourists in Vietnam, the partial least squares structural equation modeling (PLS-SEM) approach was applied to test the research model and hypotheses.

The results reveal that (1) ideal and ideal social self-congruence impact destination brand love, (2) all four facets of self-congruence impact destination brand immersion and (3) there is an indirect effect of the four facets of self-congruence on destination brand love, mediated by destination brand immersion.

The study’s findings provide evidence that destination characteristics must be symbolic of the destination brand and consistent with its image among tourists, which is essential in understanding the complex behavior of tourists. Indeed, the results indicate that facets of self-congruence significantly influence both destination brand immersion and destination brand love. Therefore, destination marketers should develop marketing strategies that emphasize personal relevance to the tourist destination to increase their sympathy and love for the destination.

Adapting self-congruence theory and the psychology of flow theory, this study presents a model that elucidates both the direct and indirect relationships among the four facets of self-congruence: destination brand immersion and destination brand love based on the aspects of congruence between tourists and destinations. From this, it helps to explore the factors and mechanisms that pave the way for tourists to develop deep affection for a destination’s brand.