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The purpose of this study is to examine the relationships between servicescape components, image and behavioral intentions within the framework of hotel businesses.

Relationships between specified variables were tested with structural equation modeling (SEM) using a sample of 410 hotel customers.

It was found that servicescape components had a positive effect on overall image, and overall image had the same effect on word-of-mouth (WOM) and re-visiting. It was also determined that overall image played a mediating role between servicescape and behavioral intention components. Overall, image played a partial mediating role between substantive staging of servicescape (SSoS) and WOM and a full mediating role among the other variables.

For a business to maintain a positive image perception, servicescape components must be positively perceived, as well. Therefore, managers who intend to present a positive business image should design servicescape components according to current trends or the fashion expectations of target consumers. This would by default increase the attractiveness of the business. Moreover, businesses can create unique concepts by designing and developing new servicescape components. This step can be beneficial, especially for hotel businesses wishing to establish a specific brand. In this regard, it is recommended that businesses employ an expert who can track and organize servicescape developments.

Although it has been found that there are specific relationships between servicescape, image and behavioral intentions, there has been a deficiency of empirical studies carried out within hotel industries. Furthermore, while SSoS has been investigated in the literature, communicative staging of servicescape (CSoS) has been neglected. In this regard, investigating the aforementioned relationships within the framework of the hotel industry contributes to the literature. Furthermore, in this study, an attempt has been made to stress the importance of servicescape, which is found to be essential for positive business image perception.

This paper presents a novel printed circuit board (PCB) film image alignment method based on distance context of image components, which can be directly used for PCB film inspection. PCB film inspection plays a very important role in PCB production.

First, image components of reference film image and inspected film image are extracted. Then, local distance context (LDC) and global distance context (GDC) are computed for each image component. Using LDC and GDC, the similarity of each pair of components between the reference film image and the inspected film image are computed, the component correspondences can be established accordingly and the parameters for aligning these two images can be eventually estimated.

LDC and GDC act as the local spatial distribution descriptor and the global relative position descriptor of the current component, and they are invariant to translation, rotating and scale. Experimental results on aligning real PCB film images against various rotations and scaling transformation show that the proposed algorithm is fast and accurate and is very suitable for PCB film inspection.

The proposed algorithm is suitable for aligning those images that have some isolated connected components, such as the PCB film images. It is not suitable for general image alignment.

We put forward to use LDC and GDC as the local descriptor and global descriptor of an image component, and designed a PCB film image alignment algorithm that can overcome the shortcomings of that image alignment algorithm that was based on local feature descriptors such as Fourier descriptor.

In order to offer a theoretical and empirical contribution to the theme, the purpose of this paper is to assess the tourist image of the destination Balneário Camboriú, Santa Catarina, Brazil, from the tourists’ perspective. The authors specify the latent dimensions involved in the formation of the cognitive, affective, and conative image of coastal tourism destination, through which the authors can analyze and measure the construct.

The research is exploratory, descriptive, and predominantly quantitative. It uses non-probability convenience sampling, consisting of a sample of 425 tourists. Data collection were conducted in the studied destination during the summer season – 2015/2016.

Through exploratory and confirmatory factor analysis, the authors identified and tested the factors that comprise the image. Structural equation model evaluated the relationship that theoretically exists between the components of the image and the weight of the various components (cognitive, affective, and conative) on the overall image of the destination.

This research contributes theoretically and empirically to the discussions on the components of the destination image, in as much as it analyzes and interprets the cognitive, affective, and conative components of the image of the tourism destination Balneário Camboriú, Santa Catarina, Brazil.

This paper aims to propose a deep learning model that can be used to expand the number of samples. In the process of manufacturing and assembling electronic components on the printed circuit board in the surface mount technology production line, it is relatively easy to collect non-defective samples, but it is difficult to collect defective samples within a certain period of time. Therefore, the number of non-defective components is much greater than the number of defective components. In the process of training the defect detection method of electronic components based on deep learning, a large number of defective and non-defective samples need to be input at the same time.

To obtain enough electronic components samples required for training, a method based on the generative adversarial network (GAN) to generate training samples is proposed, and then the generated samples and real samples are used to train the convolutional neural networks (CNN) together to obtain the best detection results.

The experimental results show that the defect recognition method using GAN and CNN can not only expand the sample images of the electronic components required for the training model but also accurately classify the defect types.

To solve the problem of unbalanced sample types in component inspection, a GAN-based method is proposed to generate different types of training component samples and then the generated samples and real samples are used to train the CNN together to obtain the best detection results.

This work centers on the study of the image of universities, with the aims of explaining the process of image forming by means of its different components, both in its cognitive and affective dimensions and of analyzing its relationship with the students’ satisfaction with the university. The results of the empirical work carried out on a representative sample of 6,775 students studying at a Spanish university demonstrate that the cognitive component of image is an antecedent of the affective component. In turn, both of those components influence the forming of the overall image of the university and the cognitive, affective and overall images statistically and significantly influence the students’ satisfaction with the university.

The purpose of this paper is to: distinguish between financial brand equity and perceptual brand equity; distinguish among different stakeholder perspectives on customer-based brand equity, consumer-based brand equity (CBBE) (consumer, customer, employee and resident); contrast the original definitions and measures of CBBE by Aaker and Keller; identify the deviations from the original conceptualizations and measures of CBBE by Aaker and Keller; discuss the evolution of the most commonly used CBBE components; analyze the structural relations of the most common CBBE components and generate conceptual, structural, relational and methodological suggestions for future research.

This critical review conducted a narrative analysis of the perceptual brand equity literature (CBBE) by inventorying about 200 empirical and conceptual CBBE studies in several different contexts. Studies that included CBBE, brand equity, customer-based brand equity and consumer-based brand equity were included in this review. Only 87 representative studies that either conceptually defined or empirically measured CBBE and its components were included in the deep analysis.

The review revealed that the literature is divergent in terms of CBBE components or structural relations among components in any context including tourism and hospitality. Even though about 40 different CBBE components exist in different contexts, the totality of the CBBE literature reflects a consensus on five components of CBBE, brand familiarity/brand awareness, brand image/brand associations, perceived quality, consumer value and brand loyalty, all of which collectively define the total meanings of a brand from consumer/customer perspective.

Keeping these five components intact, this study suggests a concise CBBE definition, conceptual clarifications for these components, a model reflecting their structural relations and a framework of parsimonious measures. The study makes future research recommendations in terms of using uniform CBBE components, measures and relational structure among components, identifying the relationship between CBBE and financial-based brand equity and comparing different stakeholder perspectives on CBBE in future research.

With these observations and suggestions, this critical review provides a guideline for more robust theory development of the CBBE construct. Additionally, it offers a parsimonious and practical CBBE blueprint for the practitioners who include CBBE as a strategic market metric in their marketing and research plans. Furthermore, it suggests standardization in CBBE research to enable systematic reviews with meta-analysis of the CBBE literature in the future. Using standard components, measures and relational models in CBBE research, as was suggested in this study, would allow meta-analysis for a meaningful comparison of results between different brands, products and even industries.

This paper presents work on design and development of a software platform in terms of virtual distributed control system (DCS) for the design of machine control systems. A component‐based framework has been adopted within the design avenue, due to the fact that machines or mechatronic systems are built from components: objects as in software engineering. Since machine control systems are distributed conformant, the main focus is given on DCS‐based approach. DCS inherits local intelligence to be incorporated at the field level (sensor and actuator level), unlike its centralised counterpart. The developed platform has been validated through a case study. The virtual platform can be useful for many kinds of event driven target platforms such as assembly lines, conveyor‐based material handling systems, production cells, and so on.

Components‐based approach virtual design and validation method computer assisted design.

Specification requirements for virtual design of mechatronic systems.

Prototype tool/approach with a case study example.

The purpose of this paper is to analyse quantitative and qualitative image aspects of destinations hosting a small‐scale sport event, as perceived by spectators and participants.

A survey was conducted at different sport events hosted by destinations in a rural setting. The data are analysed using confirmatory factor analyses and correspondence analyses.

The results suggest several differences between the image perception of participants and spectators. These can be attributed to different levels of involvement, which is very high for participants. The perception of the qualitative image aspects showed that unique features were associated with the destination, such as sport themes or organizational aspects of the sport event visited. Further, it is shown that the quantitatively measured indicators of affective destination image have a great influence on the image of rural sport event tourism destinations.

A research limitation could arise due to the sample, as almost all sport tourists were German. A more international sample might have shown different results. Future research should analyse samples of different sports, concluding whether the sport performed also influences behaviour.

For marketing communications it is essential to utilise emotions to promote the destination, as they are a very essential element of destinations hosting sport events.

This research contributes to the understanding of images held by spectators and participants of small‐scale sport events hosted in rural destinations. The value is the large sample, consisting of various rural destinations, which allows for general patterns of the perceived image to be drawn.

The purpose of this paper is to measure the image of surf tourism destination, Praia do Rosa, Santa Catarina, Brazil, in order to examine the complex relationship between destination image components and surfers’ behavior in relation to surfing itself and to the trips made to take part in surfing activities, in a surf spot.

From the tourist population in Praia do Rosa surf spot, in Brazil, a sample of 200 surfers was taken. Data analysis included the following multivariate techniques: exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling as a procedure for the development and reliability of the measurement models of destination image of surf tourism in Brazil. Data analysis was performed using PASW-SPSS 24 and Mplus 7.

Through EFA and CFA, it was possible to identify the attributes that make up the image of a surf destination and their relationship with the behavior profile of surfers. The results highlight the role of the different factors that make up the overall image of the destination, through the analysis of cognitive, affective and conative components.

The results confirm and provide theoretical and empirical support for the research, showing that the set of observable variables and the specified latent dimensions are reliable. However, it is necessary to expand the sample studied so that the measurement model has better fit indices and show convergent and discriminant validity.

This study is relevant as it provides information that can be used by destination managers, especially regarding surf tourism. In the face of strong growth, the sector could benefit from the identification of destination image attributes which can be used in marketing campaigns put forward by both the public and private sector.

The results of the current study provide both public and private tourism managers with insights into surf tourism demand useful in developing effective marketing and positioning strategies.

This study explored and tested the image perception in a surf destination. The results contribute theoretically and empirically to discussions about the components of destination image. Also, the findings add to the understanding of surfing behavior, one of the most popular sports in the world, with surfers willing to travel long distances in search of ideal surf conditions.

This study utilised Mehrabian and Russel's stimulus-organism-response (SOR) model to investigate the inter-relationship between perceived social media information qualities (IQ) of online UGC, destination image, perceived travel risk and behavioural intentions.

A total of 255 responses from international tourists were collected. The data collection via an online survey was performed from October 2020 to February 2021. The study model and hypotheses were examined using partial least squares-structural equation modelling (PLS-SEM).

The result indicated that social media intrinsic, representational, and social IQ significantly influenced tourists perceived cognitive image, while only contextual and social IQ significantly predicted the perceived affective image. This study also confirms the significant effect of cognitive image on the affective image. In addition, the authors found that both destination image components, cognitive and affective, significantly affect tourists' behavioural intentions. However, surprisingly, tourists' perceived travel risk did not moderate the effect of destination image components on behavioural intentions.

This study contributes to the body of knowledge by extending Mehrabian and Russel's SOR model in the tourism behaviour context.

From a practical perspective, this study proves that UGC significantly affects destination image and plays an integral part in luring tourists to visit a destination.

Previous research in this area is limited, making this study particularly novel. This study represents one of the initial attempts to evaluate the dimensions of information quality in UGC on social media and online review platforms, particularly within the field of tourism. Treating online travel UGC seriously could assist organisations in leveraging tourist behaviour and enhancing destination image.