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This paper aims to answer the following four research questions: Where do tourists gaze at the destination? What do tourists gaze at the destination? How do tourists gaze differently? Why do tourists gaze differently referring to relevant theory?

With a computer vision approach, this study illustrated a series of maps that reflect where and what do tourists gaze at and compared the differences in the visual perceptions among Asian, European and North American tourists in Hong Kong.

The findings confirm that the “tourist gaze” is influenced by geographical and cultural conditions. The conclusions provided three types of implementations for destination management strategies and advocated a high engagement with computer vision technology.

In theory, this study proves that the “tourist gaze” is influenced by geographical and cultural conditions. The study’s methodological contribution lies in applying advanced technology of visual content analysis for big data relevant to the issue of the tourist gaze. Practically, the finding that has not been achieved via previous questionnaire surveys will serve as a reference for tourism recommendations and precision marketing. In addition, its practical contribution is that it offers a means by which to explore tourists’ perceptions of destinations and understand the attractiveness of destinations to tourists.

研究一方面使用计算机视觉深入学习模型对游客照片内容进行识别, 比较了亚洲、欧洲和北美游客在香港不同空间场景的视觉感知差异。另一方面, 研究借助ArcGIS软件对游客凝视地点和内容差异进行了具体可视化分析。

这项研究有四个研究子问题：

• （1） 游客在哪里凝视？

• （2） 游客凝视了什么？

• （3） 游客凝视内容有什么不同？

  （4） 为什么游客凝视不同？

（1） 游客在哪里凝视？

（2） 游客凝视了什么？

（3） 游客凝视内容有什么不同？

（4） 为什么游客凝视不同？

不同游客在旅游目的地的“凝视”存在差异, 差异表征具体体现在地点选择和内容偏好等维度。同时, 研究结果显示计算机视觉技术在旅游研究领域呈现较好的应用潜力。

理论上, 本研究佐证了”游客凝视”受地理和文化条件影响的理论。技术上, 本研究探索了视觉分析技术在游客凝视议题上应用, 为旅游目的地感知评估提供了新的视角。应用层面, 研究结论为旅游目的地精准营销提供了参考。

Resumen

Con un enfoque de visión artificial, este estudio ilustra una serie de mapas que reflejan dónde y qué miran los turistas, y compara las diferencias en las percepciones visuales entre los turistas asiáticos, europeos y norteamericanos en Hong Kong.

El estudio tiene cuatro preguntas de investigación:

• (1) ¿Dónde miran los turistas en el destino?

• (2) ¿Qué miran los turistas en el destino?

• (3) ¿Cómo miran los turistas de forma diferente?

• (4) ¿Por qué los turistas miran de forma diferente en referencia a la teoría pertinente?

(1) ¿Dónde miran los turistas en el destino?

(2) ¿Qué miran los turistas en el destino?

(3) ¿Cómo miran los turistas de forma diferente?

(4) ¿Por qué los turistas miran de forma diferente en referencia a la teoría pertinente?

Las conclusiones confirman que la “mirada del turista” está influida por las condiciones geográficas y culturales. Las conclusiones aportan tres tipos de aplicaciones para las estrategias de gestión de destinos y abogan por un alto compromiso con la tecnología de visión artificial.

En teoría, este estudio demuestra que la “mirada del turista” está influenciada por las condiciones geográficas y culturales. La contribución metodológica del estudio radica en la aplicación de tecnología avanzada de análisis de contenido visual para big data relevante para el tema de la mirada del turista. En la práctica, los hallazgos que no se han logrado a través de encuestas anteriores servirán de referencia para las recomendaciones turísticas y el marketing de precisión. Además, su contribución práctica es que ofrece un medio para explorar las percepciones de los turistas sobre los destinos, y comprender el atractivo de los mismos para los turistas.

This study aims to conduct an empirical investigation of differing perceptions of nine types of urban space and nine visual elements among tourists in destination using a computer vision (CV) approach.

The data for this study was extracted from YFCC 100 M dataset. Nine types of urban space in Beijing were initially identified using a scene recognition model. Subsequently, a semantic segmentation model was applied, which yielded substantial evidence relating to nine visual elements that were used to elicit differing perceptions among tourists from different continents.

Tourists from three continents had different perceptions about corridors, old buildings, overlooks and traffic spaces, reflecting their cultural convention. Asians, Europeans and North Americans diversely gazed at the landscape element of buildings, foliage, sky and people in urban space. All those provided evidence to contribute to the tourist gaze theory's construction.

This study firstly depicted how tourists perceive the tourism symbol of urban space. The novel approach of employing two CV models offer methodological insights to tourism research relevant to visual perception.

游客对城市空间的感知:计算机视觉途径

本研究采用计算机视觉方法, 探究游客对旅游目的地九种城市空间类型及九种视觉元素的感知差异。

本研究数据提取自YFCC 100M图片数据集。首先, 利用场景识别模型识别了游客图片中的九种城市空间类型。其次, 应用语义分割模型识别了游客图片的九个视觉元素。这些分析结果被用于探究不同大洲游客的视觉感知差异。

来自不同大洲的游客对城市空间有不同的感知偏好。亚洲人更喜欢拍摄自己与著名的城市建筑, 欧洲人和北美人更喜欢自然元素, 如水、树叶和天空。不同大洲游客对视觉元素的偏好佐证了旅游凝视理论。

本研究选取了独特的城市空间为研究对象, 来验证游客凝视理论。此外, 两种计算机视觉模型为旅游研究提供了新的方法论视角。

La percepción de los turistas del espacio urbano: Un enfoque de vision artificial

Resumen

Los datos para este estudio se extrajeron del conjunto de datos YFCC 100 M. Inicialmente se identificaron nueve tipos de espacio urbano en Pekín mediante un modelo de reconocimiento de escenas. Posteriormente, se aplicó un modelo de segmentación semántica, que aportó pruebas sustanciales en relación con nueve elementos visuales que se utilizaron para suscitar percepciones diferentes entre turistas de distintos continentes.

El objetivo de este estudio es llevar a cabo una investigación empírica sobre las diferentes percepciones de nueve tipos de espacio urbano y nueve elementos visuales entre los turistas en destino, utilizando un enfoque de visión artificial (CV).

Los turistas de tres continentes tenían percepciones diferentes sobre los pasillos, los edificios antiguos, los miradores y los espacios de tráfico, lo que refleja su convención cultural. Los asiáticos, los europeos y los norteamericanos observaron de forma diversa el elemento paisajístico de los edificios, el follaje, el cielo y las personas en el espacio urbano. Todos ellos aportaron pruebas para contribuir a la construcción de la teoría de la mirada turística.

Este estudio describe por primera vez cómo los turistas perciben el símbolo turístico del espacio urbano. El novedoso enfoque de emplear dos modelos de vision artificial ofrece conocimientos metodológicos para la investigación del turismo relacionados con la percepción visual.

This paper aims to track how ecotourism has been presented in a digital world over time using geotagged photographs and internet search data. Ecotourism photographs and Google Trends search data are used to evaluate tourist perceptions of ecotourism by developing a categorization of essential attributes, examining the relation of ecotourism and sustainable development, and measuring the popularity of the ecotourism sites.

The researchers collected geotagged photographs from Flickr.com and downloaded Google search data from Google Trends. An integrative approach of content, trend and spatial analysis was applied to develop ecotourism categories and investigate tourist perceptions of ecotourism. First, the authors investigate ecotourism geotagged photographs on a social media to comprehend tourist perceptions of ecotourism by developing a categorization of key ecotourism attributes and measuring the popularity of the ecotourism sites. Second, they examined how ecotourism has been related with sustainable development using internet search data and investigate the trends in search data. Third, spatial analysis using GIS maps was used to visualize the spatial-temporal changes of photographs and tourist views throughout the world.

This study identified three primary themes of ecotourism perceptions and 13 categories of ecotourism attributes. Interest over time about ecotourism was mostly presented as its definitions in Google Trends. The result indicates that tracked ecotourism locations and tourist footprints are not congruent with the popular regions of ecotourism Google search.

This research follows the changing trends in ecotourism over a decade using geotagged photographs and internet search data. The evaluation of the global ecotourism trend provides important insights for global sustainable tourism development and actual tourist perception. Analyzing the trend of ecotourism is a strategic approach to assess the achievement of UN sustainable development goals. Factual perspectives and insights into how tourists are likely to seek and perceive natural attractions are valuable for a range of audiences, such as tourism industries and governments.

研究目的本论文旨在探索生态旅游业在电子世界中是如何随着时间而显示出来的，文章样本为带有地理标记的图片和互联网搜索数据。本文使用生态旅游图片和谷歌趋势搜索数据来评估游客对生态旅游的感知，通过对关键要素的分类，审视生态旅游和可持续发展的关系，以及衡量生态旅游基地的受欢迎程度等方法。

本论文作者从Flickr.com上搜集地理标记图片以及从谷歌趋势上下载谷歌搜索数据。样本分析通过内容、趋势、空间上的综合分析，来开发生态旅游类别和游客对生态旅游的感知。首先，我们研究了社交媒体上的生态旅游地理标记图片以理解游客对生态旅游的感知情况，以此搭建了关键生态旅游要素的类别体系，和衡量生态旅游基地的受欢迎程度。第二，我们通过使用互联网搜索数据，检测了生态旅游如何与可持续发展相连接，以及研究了搜索数据中的趋势。第三，我们使用了GIS软件来操作空间分析，对图片的空间-时间改变和游客对世界的观点做了可视化处理。

本论文确立了三项生态旅游感知的基本主题以及13项生态旅游要素类别。生态旅游互联网随着时间演化，根据谷歌趋势上的定义，被大致地展现出来。本论文研究结果表示生态旅游地理位置和游客足迹与生态旅游谷歌搜索的热门区域不全是完全吻合的。

本论文使用地理标记图片和互联网搜索数据将生态旅游发展趋势近十年的变化描画出来。全球生态旅游趋势的评估对全球可持续旅游发展和实际游客感知方面做出重要见解启示。生态旅游趋势的分析作为一种战略方法，对UN可持续发展目标的时间起到评估作用。本论文针对游客的真实感知和意见，游客如何选择和感知自然景观，这对于很多群体，比如旅游行业和政府，都有着重要意义。

Arts-informed, visual research was conducted to document the pictorial metaphors that appear among original drawings of information. The purpose of this paper is to report the diversity of these pictorial metaphors, delineate their formal qualities as drawings, and provide a fresh perspective on the concept of information.

The project utilized pre-existing iSquare drawings of information that were produced by iSchool graduate students during a draw-and-write activity. From a data set of 417 images, 125 of the strongest pictorial metaphors were identified and subjected to cognitive metaphor theory.

Overwhelmingly, the favored source domain for envisioning information was nature. The most common pictorial metaphors were: Earth, web, tree, light bulb, box, cloud, and fishing/mining, and each brings different qualities of information into focus. The drawings were often canonical versions of objects in the world, leading to arrays of pictorial metaphors marked by their similarity.

Less than 30 percent of the data set qualified as pictorial metaphors, making them a minority strategy for representing information as an image. The process to identify and interpret pictorial metaphors was highly subjective. The arts-informed methodology generated tensions between artistic and social scientific paradigms.

The pictorial metaphors for information can enhance information science education and fortify professional identity among information professionals.

This is the first arts-informed, visual study of information that utilizes cognitive metaphor theory to explore the nature of information. It strengthens a sense of history, humanity, nature, and beauty in our understanding of information today, and contributes to metaphor research at large.

Despite the extensive academic interest in social media sentiment for financial fields, multimodal data in the stock market has been neglected. The purpose of this paper is to explore the influence of multimodal social media data on stock performance, and investigate the underlying mechanism of two forms of social media data, i.e. text and pictures.

This research employs panel vector autoregressive models to quantify the effect of the sentiment derived from two modalities in social media, i.e. text information and picture information. Through the models, the authors examine the short-term and long-term associations between social media sentiment and stock performance, measured by three metrics. Specifically, the authors design an enhanced sentiment analysis method, integrating random walk and word embeddings through Global Vectors for Word Representation (GloVe), to construct a domain-specific lexicon and apply it to textual sentiment analysis. Secondly, the authors exploit a deep learning framework based on convolutional neural networks to analyze the sentiment in picture data.

The empirical results derived from vector autoregressive models reveal that both measures of the sentiment extracted from textual information and pictorial information in social media are significant leading indicators of stock performance. Moreover, pictorial information and textual information have similar relationships with stock performance.

To the best of the authors’ knowledge, this is the first study that incorporates multimodal social media data for sentiment analysis, which is valuable in understanding pictures of social media data. The study offers significant implications for researchers and practitioners. This research informs researchers on the attention of multimodal social media data. The study’s findings provide some managerial recommendations, e.g. watching not only words but also pictures in social media.

The purpose of this paper is to examine the extent and quality of voluntary intellectual capital disclosures (ICD) by professional accounting firms (PAFs) in the UK.

The research method adopted for this study is content analysis considering the ICD in firms’ annual reports, corporate social responsibility reports, websites and recruitment materials. The sample for this research is based on 20 PAFs ranked by fee income. The paper employs institutional theory as its theoretical lens.

The findings of this paper show that ICDs vary across different forms of reports. The most frequently reported disclosure category is human capital, while the least reported category is internal capital. Monetary disclosures are most likely to relate to internal capital, while pictorial disclosures are most likely to relate to human capital.

The sample size of the study is relatively small reflecting the extreme market concentration of accounting services in the UK and internationally. Future research can conduct a longitudinal study to capture the trend of reporting practices and consider narrative and discursive approaches to ICD.

No previous studies of intellectual capital (IC) disclosure have considered ICDs in professional service firms that are in themselves rich sources of human capital. Furthermore, the investigation uses a wide range of communications and assesses monetary, non-monetary, narrative and pictorial disclosures. This research extends both the IC disclosure and PAFs’ literatures.

Pattern recognition systems often have to handle problem of large volume of training data sets including duplicate and similar training samples. This problem leads to large memory requirement for saving and processing data, and the time complexity for training algorithms. The purpose of the paper is to reduce the volume of training part of a data set – in order to increase the system speed, without any significant decrease in system accuracy.

A new technique for data set size reduction – using a version of modified frequency diagram approach – is presented. In order to reduce processing time, the proposed method compares the samples of a class to other samples in the same class, instead of comparing samples from different classes. It only removes patterns that are similar to the generated class template in each class. To achieve this aim, no feature extraction operation was carried out, in order to produce more precise assessment on the proposed data size reduction technique.

The results from the experiments, and according to one of the biggest handwritten numeral standard optical character recognition (OCR) data sets, Hoda, show a 14.88 percent decrease in data set volume without significant decrease in performance.

The proposed technique is effective for size reduction for all pictorial databases such as OCR data sets.

State-of-the-art algorithms currently used for data set size reduction usually remove samples near to class's centers, or support vector (SV) samples between different classes. However, the samples near to a class center have valuable information about class characteristics, and they are necessary to build a system model. Also, SV s are important samples to evaluate the system efficiency. The proposed technique, unlike the other available methods, keeps both outlier samples, as well as the samples close to the class centers.

The purpose of this research is to demonstrate the effectiveness of pictorial warning labels that leverage the risk of obesity as a deterrent against alcohol abuse. It evaluates the impact of three different kinds of warning labels that can potentially discourage alcoholic drinking: (1) a claim, in text format, that cautions consumers about the product (i.e. a responsibility warning statement); (2) a textual warning label, text-format information on the content of the product or the consequences of excessive consumption (i.e. a synthetic nutritional table); (3) a pictorial warning label, an image depicting a food product with a caloric content equivalent to that of an alcoholic beverage.

In Study 1, a 2 × 2 × 2 factorial design is used to evaluate the intention to buy different alcoholic cocktails. The stimuli comprised two cocktails that are similar in alcoholic volume, but different in their caloric content. The images of the products were presented across eight warning label conditions and shown to 480 randomly selected Italian respondents who quantified their intention to buy the product. In Study 2, a different sample of 34 Italian respondents was solicited with the same stimuli considered in Study 1, and neuropsychological measurements through Electroencephalography (EEG) were registered. A post hoc least significance difference (LSD) test is used to analyse data.

The results show that only the presence of an image representing an alcoholic beverage's caloric content causes a significant reduction in consumers' purchase intentions. This effect is due to the increase in negative emotions caused by pictorial warning labels.

The findings provide interesting insights on pictorial warning labels, which can influence the intention to purchase alcoholic beverages. They confirmed that the use of images in the warning labels has a greater impact than text, and that the risk of obesity is an effective deterrent in encouraging consumers to make healthier choices.

Product presentation plays a critical role in influencing consumers’ purchasing decisions in online shopping. The purpose of this study is to examine the effects of text-image information on online product presentation pages on sales. This study also specifically explores the moderating role of celebrity endorsements in the relationship between text-image presentation information and sales.

This study is based on regression analysis using data sets from the largest online book retailer in China, Dangdang.com.

The results of this study show that there is an inverse U-shaped relationship between word count and book sales. Conversely, image quantity has a positive linear effect on book sales. Furthermore, celebrity endorsements moderate the relationship between word count and sales in two distinct ways. First, the positive effect of increasing word count from low to medium is enlarged by increasing the number of celebrity endorsers. Second, the turning point of the inverse-U relationship between word count and sales rank moves to the right as the number of endorsers increases.

To the best of the authors’ knowledge, this study is among the first to examine the effects of text-image quantity on sales across the full continuum. This study adds understanding on how information load might have distinct consequences on the processing performance of text and images. Furthermore, this study investigates how sales are impacted by the quantity of celebrity endorsers in relation to textual and pictorial information in online shopping contexts, extending our knowledge of the effectiveness of celebrity endorsements.

Fierce competition in the crowdfunding market has resulted in high failure rates. Owing to their dedication and efforts, many founders have relaunched failed campaigns as a second attempt. Despite the need for a better understanding, the success of campaign relaunches has not been well-researched. To fill this research gap, this study first theorizes how founders’ learning may enhance their competencies and influence investors’ attribution of entrepreneurial failure. The study then empirically documents the extent and conditions under which such learning efforts impact campaign relaunch performance.

This study examines 5,798 Kickstarter-relaunched campaigns. The founders’ learning efforts are empirically captured by key changes in campaign design that deviate from past business practices. Word movers’ distances and perceptual hashing algorithms (pHash) are used separately to measure differences in campaign textual descriptions and pictorial designs.

Differences in textual descriptions and pictorial designs during campaign failure–relaunch are positively associated with campaign relaunch success. The impacts are further amplified when the previous failures are more severe.

This study is one of the first to examine the success of a campaign relaunch after an initial failure. This study contributes to a better understanding of founders’ learning in crowdfunding contexts and provides insights into the strategies founders can adopt to reap performance benefits.