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1 – 10 of over 1000Manuel Alonso Dos Santos, Manuel J. Sánchez-Franco, Eduardo Torres-Moraga and Ferran Calabuig Moreno
This study explores the effect of video assistant referee (VAR) sponsorship on spectator response and compares it with advertising and conventional sponsorship.
Abstract
Purpose
This study explores the effect of video assistant referee (VAR) sponsorship on spectator response and compares it with advertising and conventional sponsorship.
Design/methodology/approach
An experiment with 809 subjects is conducted by analyzing 20 one-minute video clip stimuli from a Premier League soccer game divided into four formats: two formats of VAR sponsorship, advertising, and conventional sponsorship.
Findings
The results show that the indicators of recall, credibility, and perceived congruence improve when the VAR sponsorship format is used.
Originality/value
This is the first manuscript to examine the effectiveness of a new type of sponsorship: VAR sponsorship. This manuscript provides metrics that will guide practitioners on whether to use this type of sponsorship.
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Far from all, football clubs can provide the same level of exposure effects as global football brands, even on local level, and many of these clubs also operate in a context of…
Abstract
Purpose
Far from all, football clubs can provide the same level of exposure effects as global football brands, even on local level, and many of these clubs also operate in a context of commercial immaturity. The purpose of this paper is to show what value a football club can provide for sponsors in a context of commercial immaturity with limited expected exposure effects.
Design/methodology/approach
The study is based on a case study approach, taking its point of departure in two sponsor brand management paradigms, the projective and relational paradigm. The case of Malmö FF in the Swedish top tier league and the club’s official partners has been chosen to exemplify the commercially immature context.
Findings
The study has shown that the most important value the club can provide for sponsors is to act as a mediator in sponsor–stakeholder relations. Exposure effects are subordinate to the relational effects sponsors achieve through their sponsorship.
Research limitations/implications
The study indicates that the relational construct in the sponsorship literature should to a greater extent include sponsor–stakeholder relations, beyond the sponsor–club dyad, in a context of commercial immaturity.
Practical implications
The results indicate that club management should engage in stakeholder management with a strong focus on stakeholders of sponsors to provide value for these sponsors.
Originality/value
This study explores a new dimension to the relational construct of sponsorship, using the relational paradigm of brand management in a context of commercial immaturity. The mediating effect of the club is a contribution to the discourse on the relational construct.
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Diabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for…
Abstract
Purpose
Diabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.
Design/methodology/approach
In this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.
Findings
By conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.
Originality/value
In this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.
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Adamantios Diamantopoulos, Ilona Szőcs, Arnd Florack, Živa Kolbl and Martin Egger
Drawing on the stereotype content model (SCM), the authors investigate the stereotype content transfer (in terms of warmth and competence) from country to brand and the…
Abstract
Purpose
Drawing on the stereotype content model (SCM), the authors investigate the stereotype content transfer (in terms of warmth and competence) from country to brand and the simultaneous impact of these two stereotypes on consumer responses toward brands.
Design/methodology/approach
The authors test a structural equation model conceptualizing brand stereotypes as full mediators between country stereotypes and consumer outcomes. In addition, in a moderated mediation analysis, the authors investigate the role of brand typicality and utilitarianism/hedonism in potentially moderating the country to brand stereotype content transfer.
Findings
Country warmth and competence, respectively, impact brand warmth and competence, thus confirming the hypothesized stereotype content transfer. This transfer is found to be robust and not contingent on brands' perceived typicality of their country of origin. However, brands' utilitarian nature amplifies the positive impact of country competence on brand competence. Finally, brand stereotypes fully mediate the impact of country stereotypes on consumers' brand attitudes and behavioral intentions.
Originality/value
The authors provide the first empirical attempt that (1) explicitly differentiates between consumers' stereotypical perceptions of countries and stereotypical perceptions of brands from these countries, (2) empirically examines the transfer of stereotypical dimensions of different targets (i.e. country to brand), (3) explores boundary conditions for such transfer and (4) simultaneously considers the impact of both kinds of stereotypes on managerially relevant consumer outcomes.
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Kittisak Chotikkakamthorn, Panrasee Ritthipravat, Worapan Kusakunniran, Pimchanok Tuakta and Paitoon Benjapornlert
Mouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently…
Abstract
Purpose
Mouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently, deep learning methods effectively solved mouth segmentation problems with state-of-the-art performances. This study presents a modified Mobile DeepLabV3 based technique with a comprehensive evaluation based on mouth datasets.
Design/methodology/approach
This paper presents a novel approach to mouth segmentation by Mobile DeepLabV3 technique with integrating decode and auxiliary heads. Extensive data augmentation, online hard example mining (OHEM) and transfer learning have been applied. CelebAMask-HQ and the mouth dataset from 15 healthy subjects in the department of rehabilitation medicine, Ramathibodi hospital, are used in validation for mouth segmentation performance.
Findings
Extensive data augmentation, OHEM and transfer learning had been performed in this study. This technique achieved better performance on CelebAMask-HQ than existing segmentation techniques with a mean Jaccard similarity coefficient (JSC), mean classification accuracy and mean Dice similarity coefficient (DSC) of 0.8640, 93.34% and 0.9267, respectively. This technique also achieved better performance on the mouth dataset with a mean JSC, mean classification accuracy and mean DSC of 0.8834, 94.87% and 0.9367, respectively. The proposed technique achieved inference time usage per image of 48.12 ms.
Originality/value
The modified Mobile DeepLabV3 technique was developed with extensive data augmentation, OHEM and transfer learning. This technique gained better mouth segmentation performance than existing techniques. This makes it suitable for implementation in further lip-reading applications.
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Warot Moungsouy, Thanawat Tawanbunjerd, Nutcha Liamsomboon and Worapan Kusakunniran
This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face…
Abstract
Purpose
This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.
Design/methodology/approach
The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.
Findings
The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.
Originality/value
The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.
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Oladosu Oyebisi Oladimeji and Ayodeji Olusegun J. Ibitoye
Diagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the…
Abstract
Purpose
Diagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.
Design/methodology/approach
To selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.
Findings
The ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.
Practical implications
Since ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.
Originality/value
This research has not been published anywhere else.
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Alessandra Lumini, Loris Nanni and Gianluca Maguolo
In this paper, we present a study about an automated system for monitoring underwater ecosystems. The system here proposed is based on the fusion of different deep learning…
Abstract
In this paper, we present a study about an automated system for monitoring underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different Convolutional Neural Network (CNN) models, fine-tuned on several datasets with the aim of exploiting their diversity. The aim of our study is to experiment the possibility of fine-tuning CNNs for underwater imagery analysis, the opportunity of using different datasets for pre-training models, the possibility to design an ensemble using the same architecture with small variations in the training procedure.
Our experiments, performed on 5 well-known datasets (3 plankton and 2 coral datasets) show that the combination of such different CNN models in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches in all the tested problems. One of the main contributions of this work is a wide experimental evaluation of famous CNN architectures to report the performance of both the single CNN and the ensemble of CNNs in different problems. Moreover, we show how to create an ensemble which improves the performance of the best single model. The MATLAB source code is freely link provided in title page.
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Luca Rampini and Fulvio Re Cecconi
This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM…
Abstract
Purpose
This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM models and using them inside a graphic engine to produce a photorealistic representation of indoor spaces enriched with facility-related objects. The virtual environment creates several images by changing lighting conditions, camera poses or material. Moreover, the created images are labeled and ready to be trained in the model.
Design/methodology/approach
This paper focuses on the challenges characterizing object detection models to enrich digital twins with facility management-related information. The automatic detection of small objects, such as sockets, power plugs, etc., requires big, labeled data sets that are costly and time-consuming to create. This study proposes a solution based on existing 3D BIM models to produce quick and automatically labeled synthetic images.
Findings
The paper presents a conceptual model for creating synthetic images to increase the performance in training object detection models for facility management. The results show that virtually generated images, rather than an alternative to real images, are a powerful tool for integrating existing data sets. In other words, while a base of real images is still needed, introducing synthetic images helps augment the model’s performance and robustness in covering different types of objects.
Originality/value
This study introduced the first pipeline for creating synthetic images for facility management. Moreover, this paper validates this pipeline by proposing a case study where the performance of object detection models trained on real data or a combination of real and synthetic images are compared.
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Stefan Scheidt, Carsten Gelhard, Juliane Strotzer and Jörg Henseler
While the branding of individuals has attracted increasing attention from practitioners in recent decades, understanding of personal branding still remains limited, especially…
Abstract
Purpose
While the branding of individuals has attracted increasing attention from practitioners in recent decades, understanding of personal branding still remains limited, especially with regard to the branding of celebrity CEOs. To contribute to this debate, this paper aims to explore the co-branding of celebrity CEOs and corporate brands, integrating endorsement theory and the concept of meaning transfer at a level of brand attributes.
Design/methodology/approach
A between-subjects true experimental design was chosen for each of the two empirical studies with a total of 268 participants, using mock newspaper articles about a succession scenario at the CEO level of different companies. The study is designed to analyse the meaning transfer from celebrity CEO to corporate brand and vice versa using 16 personality attributes.
Findings
This study gives empirical support for meaning transfer effects at the brand attribute level in both the celebrity-CEO-to-corporate-brand and corporate-brand-to-celebrity-CEO direction, which confirms the applicability of the concept of brand endorsement to celebrity CEOs and the mutuality in co-branding models. Furthermore, a more detailed and expansive perspective on the definition of endorsement is provided as well as managerial guidance for building celebrity CEOs and corporate brands in consideration of meaning transfer effects.
Originality/value
This study is one of only few analysing the phenomenon of meaning transfer between brands that focus on non-evaluative associations (i.e. personality attributes). It is unique in its scope, insofar as the partnering relationship between celebrity CEOs and corporate brands have not been analysed empirically from this perspective yet. It bridges the gap between application in practice and the academic foundations, and it contributes to a broader understanding and definition of celebrity endorsement.
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