Search results

The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.

High-stakes decision-makers, including human resource (HR) professionals, often exhibit accent biases against second language speakers in professional evaluations. We extend this work by investigating how HR students evaluate simulated job interview performances in English by first and second language speakers of English.

Eighty HR students from Calgary and Montreal evaluated the employability of first language (L1) Arabic, English, and Tagalog candidates applying for two positions (nurse, teacher) at four points in the interview (after reading the applicant’s resume, hearing their self-introduction, and listening to each of two responses to interview questions). Candidates’ responses additionally varied in the extent to which they meaningfully answered the interview questions.

Students from both cities provided similar evaluations, employability ratings were similar for both advertised positions, and high-quality responses elicited consistently high ratings while evaluations for low-quality responses declined over time. All speakers were evaluated similarly based on their resumes and self-introductions, regardless of their language background. However, evaluations diverged for interview responses, where L1 Arabic and Tagalog speakers were considered more employable than L1 English speakers. Importantly, students’ preference for L1 Arabic and Tagalog candidates over L1 English candidates was magnified when those candidates provided low-quality interview responses.

Results suggest that even in the absence of dedicated equity, diversity, and inclusion (EDI) training focusing on language and accent bias, HR students may be aware of second language speakers’ potential disadvantages in the workplace, rewarding them in the current evaluations. Findings also highlight the potential influence of contextual factors on HR students’ decision-making.

This study's goal is to look at how visitors' experiences affect the indirect links between the destination's extrinsic motivations (DEMs) and tourists' intrinsic motives (TIMs), on the one hand, and the perceived destination image (PDI), on the other.

Using structural equation modeling, 613 tourists from different nationalities were used to test the five hypotheses.

The research results revealed that second-order destinations' extrinsic motivations directly impact TIM and PDI. It also showed that tourists' experiences as moderators reduce the direct effect of DEM on PDI for first-time visitors compared to repeat visitors. Moreover, it increases the direct effect of TIM on PDI for repeated visitors.

Destination managers can fix the problems that hurt their reputations and images by hiring police officers in tourist areas and cleaning tourist places. In the same way, destination managers and travel agencies should use AI tools to create social media marketing campaigns focusing on natural and historical monuments. Also, the marketing plans should stress the value for money (for example, lodging, food and attractions’ cost). Finally, destination marketers can make programs for repeat visitors, focusing on DEM and TIM.

This article tries to fill a gap in the research on PDI formation in emerging markets as a modern technique in destination marketing by using the push-intrinsic and pull-extrinsic theories. It also looks at how the tourists' experiences moderate the direct link between DEM, TIM and PDI. Lastly, this study examines how TIM affects a destination's image in emerging markets.

As a distinctive form of communist heritage tourism, the ideology and government-led form of red tourism warrants an in-depth examination of how tourists consume and perceive it. This study aims to reveal tourists’ perception of red tourism through the lens of destination image.

This study collected 9,819 user-generated photographs within four types of red tourism destinations (RTDs) and used a computer visual and semiotic analysis approach to conduct photograph-based cognitive and affective attributes extraction. Network analysis further visualized the co-relations between cognitive images and affective images. ANOVA analysis compared the differences of the four types of destination images.

Ten dimensions of cognitive image and eight categories of affective image of red tourism were identified. It found that monuments, statues, memorial symbols were the distinctive cognitive features, and admiration was the most dominant emotion. Heterogeneity of destination images was identified among the four types of RTDs.

To the best of the authors’ knowledge, the study is one of the first to explore tourists’ consumption of red tourism through the lens of destination image, which reveals the inconsistencies between the officially projected images and tourists’ perceived images of red tourism. Using Plutchik’s model, it validates a series of positive and negative emotions contributing to the affective images of red tourism, which expands the findings of emotions within the extant red tourism research. Through combined applications of computer visual and semiotic analysis, ANOVA, network analysis and model visualization, the study provides an important methodological triangulation for photograph-based destination image studies.

红色旅游作为共产主义旅游的独特形式, 游客如何感知这种国家意识形态植入与政府主导型旅游值得深入研究。本研究旨在从目的地意象视角揭示游客红色旅游感知。

本研究收集四种类型的红色旅游地9819张用户生成照片, 利用计算机视觉-情感析法对照片进行认知和情感元素提取。复杂网络分析揭示了认知意象与情感意象之间的关联。方差分析比较了四种红色旅游地意象的差异。

本研究确定了红色旅游认知意象的十个维度和情感意象的八个类别。研究发现, 纪念碑、雕像、纪念符号是其独特的认知意象元素, 钦佩是其最主要的情感，四种类型红色旅游地意象存在差异性。

本文是同类研究中首次从目的地意象视角探索游客对红色旅游地感知, 揭示了红色旅游官方投射意象与游客感知意象之间的差异。利用Plutchik情感之轮模型, 验证了一系列积极和消极情绪构成红色旅游地情感意象, 拓展了红色旅游的情感发现。综合运用计算机视觉-情感分析、方差分析、网络分析和模型可视化等方法, 为基于照片的旅游目的地意象研究提供了一个重要方法。

Como forma distintiva del turismo del patrimonio comunista, la ideología y la forma gubernamental del turismo rojo justifican un examen en profundidad de cómo lo consumen y perciben los turistas. Este estudio pretende revelar la percepción que tienen los turistas del turismo rojo desde la perspectiva de la imagen del destino.

Este estudio recopiló 9.819 fotografías generadas por los usuarios dentro de cuatro tipos de destinos de turismo rojo, y utilizó un enfoque de análisis visual y semiótico por ordenador para llevar a cabo la extracción de atributos cognitivos y afectivos basados en fotografías. El análisis de redes visualizó además las correlaciones entre las imágenes cognitivas y las imágenes afectivas. El análisis ANOVA comparó las diferencias de los cuatro tipos de imágenes de destino.

Se identificaron diez dimensiones de imagen cognitiva y ocho categorías de imagen afectiva del turismo rojo. Se descubrió que los monumentos, las estatuas y los símbolos conmemorativos eran los rasgos cognitivos distintivos, y la admiración la emoción más dominante. Se identificó una heterogeneidad de imágenes de destino entre los cuatro tipos de destinos de turismo rojo.

El estudio es uno de los primeros en explorar el consumo de turismo rojo por parte de los turistas a través de la lente de la imagen del destino, lo que revela las incoherencias entre las imágenes proyectadas oficialmente y las imágenes percibidas por los turistas del turismo rojo. Utilizando el modelo de Plutchik, valida una serie de emociones positivas y negativas que contribuyen a las imágenes afectivas del turismo rojo, lo que amplía los hallazgos sobre las emociones dentro de la investigación existente sobre el turismo rojo. Mediante aplicaciones combinadas de análisis visual y semiótico por ordenador, ANOVA, análisis de redes y visualización de modelos, el estudio proporciona una importante triangulación metodológica para los estudios de la imagen del destino basados en fotografías.

Collaborating with consumers during new product development can provide companies with significant benefits and competitive advantages. Although several studies have been conducted on the design of co-innovation platforms, there is still a need for a more comprehensive understanding of the co-innovation phenomenon. To address this gap, this research aims to identify the critical success factors of co-innovation platforms and provide an extensive analysis of the variables that determine their effectiveness.

This study presents a systematic literature review of co-innovation platforms based on an analysis of 89 articles published in 50 scholarly journals in the disciplines of information systems, marketing and business, covering the years from 2006 to 2022.

The review synthesizes the current state of scientific knowledge and groups prior studies thematically as critical success factors of co-innovation platforms. As a result, eight success factors have been identified in terms of quantity and quality of contributions. These factors include product involvement, perceived fairness, sense of community, interactive environment, employee involvement, participant diversity, assessment structure and task design.

The study consolidates existing research about the critical success of co-innovation platforms. It also provides a research framework that incorporates a diverse set of variables that can be used to assess co-innovation performance in future studies.

This study explores the intricate relationship between inventory leanness, financial constraints and digital transformation in listed Chinese manufacturing firms.

Using a large panel data collected from 2,563 Chinese listed manufacturing enterprises over the period from 2012 to 2021, this research employs the instrumental variable method combined with two-stage least squares estimators to explore the U- shaped relationship between inventory leanness and financial constraints. Furthermore, the moderating role of digital transformation is demonstrated.

Contrary to traditional assumptions, our research uncovers a U-shaped relationship between inventory leanness and financial constraints, indicating that excessive inventory reduction can exacerbate financial constraints. Digital transformation plays a significant moderating role, particularly in highly digitalized environments.

Our findings have practical significance for top managers and policymakers. We advocate for a balanced approach to lean inventory management to mitigating financial constraints. The study emphasizes the pivotal role of digital transformation in alleviating the impact of inventory leanness on financial constraints, highlighting the need for digital transformation strategies.

This research provides a comprehensive analysis of inventory leanness, financial constraints and digital transformation dynamics. It challenges conventional thinking by revealing the nonlinear nature of the inventory leanness–financial constraints relationship. The concept of moderation highlights the moderating effect of digital transformation. This study offers practical guidance for practitioners and policymakers.

Social media facilitates the communication and the relationship between healthcare professionals and patients. However, limited research has examined the role of social media in a physicians' online return. This study, therefore, investigates physicians' online economic and social capital return in relation to physicians' use of social media and consumer engagement.

Using ordinary least squares (OLS) regression with fixed effects (FE) and panel data collected from Sina Weibo and Sina Health, this study analyzes the impact of physicians' social media use and consumer engagement on physicians' online return and the moderation effect of professional seniority.

The results reveal that physicians' use of social media and consumer sharing behavior positively affect physicians' online economic return. In contrast, consumer engagement positively impacts physicians' online social capital return. While professional seniority enhances the effect of physicians' social media use on online economic return, professional seniority only enhances the relationship between consumers' sharing behavior to the posts and physicians' online social capital return when professional seniority comes to consumer engagement.

This study reveals the different roles of social media use and consumer engagement in physicians' online return. The results also extend and examine the social media affordances theory in online healthcare communities and social media platforms.

Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing style and color design via computer language, which aims to edit and control the garment image based on the specified target attributes while preserving other details from the original image. The current image attribute editing model often generates images containing missing or redundant attributes. To address the problem, this paper aims for a novel design method utilizing the Fashion-attribute generative adversarial network (AttGAN) model was proposed for image attribute editing specifically tailored to women’s blouses.

The proposed design method primarily focuses on optimizing the feature extraction network and loss function. To enhance the feature extraction capability of the model, an increase in the number of layers in the feature extraction network was implemented, and the structure similarity index measure (SSIM) loss function was employed to ensure the independent attributes of the original image were consistent. The characteristic-preserving virtual try-on network (CP_VTON) dataset was used for train-ing to enable the editing of sleeve length and color specifically for women’s blouse.

The experimental results demonstrate that the optimization model’s generated outputs have significantly reduced problems related to missing attributes or visual redundancy. Through a comparative analysis of the numerical changes in the SSIM and peak signal-to-noise ratio (PSNR) before and after the model refinement, it was observed that the improved SSIM increased substantially by 27.4%, and the PSNR increased by 2.8%, serving as empirical evidence of the effectiveness of incorporating the SSIM loss function.

The proposed algorithm provides a promising tool for precise image editing of women’s blouses based on the GAN. This introduces a new approach to eliminate semantic expression errors in image editing, thereby contributing to the development of AI in clothing design.

User value co-creation behaviors are crucial for the sustainable development of Virtual Brand Communities. This research, grounded in social exchange theory, investigates the impact of community satisfaction and identification on customer value co-creation behaviors and further explores how the reciprocity norm moderates these relationships.

Our research data were collected from users across multiple brand communities, totaling 481 survey responses. Structural equation modeling was performed to test the research hypotheses.

These results provide in-depth insights into the nexus between user-community relationships and customer value co-creation behaviors. While community satisfaction and identification positively influence co-creation, their effects vary across different value co-creation behaviors. Notably, the reciprocity norm within the community dampens the relationship between community satisfaction and value co-creation behaviors.

Unlike previous studies focusing on customer value co-creation behaviors, our research emphasizes social exchange, unveiling the mechanisms behind customer value co-creation. Our findings not only enrich the body of knowledge on customer value co-creation but also deepen our understanding of online collective behavior and knowledge sharing, offering valuable insights for the development of virtual communities.