Search results

Destination monitoring is crucial to understand performance and identify key points of differentiation. Visitor satisfaction is an essential driver of destination performance. With the fast-growing volume of user-generated content through social media, it is now possible to tap into very large amounts of data provided by travellers as they share their experiences. Analysing these data for consumer sentiment has become attractive for destinations and companies. The idea of drawing on social media sentiment for satisfaction monitoring aligns well with the broader move towards smart destinations and real-time information processing. Thus, this paper aims to examine whether the electronic word of mouth originating from Twitter posts offers a useful source for assessing destination sentiment. Importantly, this research examines what caveats need to be considered when interpreting the findings.

This research focusses on a prominent tourist destination situated on Australia’s East Coast, the Gold Coast. Using a geographically informed filtering process, a collection of tweets posted from within the Gold Coast destination was created and analysed. Metadata were analysed to assess the population of Twitter users, and sentiment analysis, using the Valence Aware Dictionary for Sentiment Reasoning algorithm, was performed.

Twitter posts provide considerable information, including about who is visiting and what sentiment visitors and residents express when sending tweets from a destination. They also uncover some challenges, including the “noise” of Twitter data and the fact that users are not representative of the broader population, in particular for international visitors.

This paper highlights limitations such as lack of representativeness of the Twitter data, positive bias and the generic nature of many tweets. Suggestions for how to improve the analysis and value of tweets as a data source are made.

This paper contributes to understanding the value of non-traditional data sources for destination monitoring, in particular by highlighting some of the pitfalls of using information sources, such as Twitter. Further research steps have been identified, especially with a view to improving target-specific sentiment scores and the future employment of big-data approaches that involve integrating multiple data sources for destination performance monitoring.

The identification of cost-effective ways of measuring and monitoring guest satisfaction can lead to improvements in destination management. This in turn will enhance customer experience and possibly even resident satisfaction. The social benefits, especially at times of considerable visitation pressure, can be important.

The use of Twitter data for the monitoring of visitor sentiment at tourist destinations is novel, and the analysis presented here provides unique insights into the potential, but also the caveats, of developing new, smart systems for tourism.

目的地监控对理解绩效和确立区别关键点至关重要。游客满意是目的地绩效的关键动力。随着社交媒体上用户生成内容的快速增长, 研究其游客提供的大量数据变成可能, 这些数据体现了游客的旅游体验。分析这些消费者情绪的数据对目的地和有关企业的吸引力巨大。研究社交媒介情绪数据和满意度与更广泛地对智慧旅游和实时信息处理等方面的研究和谐一致。因此, 本论文旨在检验Twitter帖子中的在线口碑效应是否成为测量目的地情绪的有用数据。更重要的是, 本论文检验在研究结果中哪些领域应该着重考虑研究。

本论文集中研究了澳大利亚东海岸的一处旅游目的地, 黄金海岸。本论文使用地理信息过滤的处理方式, 有关黄金海岸的tweets为样本, 进行分析。本论文分析了元数据, 使用VADER数算, 检测了Twitter用户人口和情绪分析。

Twitter帖子提供相当多的信息, 包括谁是游客, 当游客发布有关旅游目的地的tweets的时候, 拥有什么样的情绪。研究结果还指出了一些挑战, 包括twitter数据的“杂音”, 用户并不能代表广大研究对象的事实, 特别是国际游客。

本论文强调了几点限制, 如Twitter数据的代表性、积极偏见、大多数tweets千篇一律等。本论文对如何提高分析结果和使用tweets作为数据源的价值提出了几点建议。

本论文对非传统数据以对旅游目的地监控的价值做出贡献, 尤其是强调了使用信息数据的弊端, 如Twitter。未来研究方向应该着重研究目标明确的情绪指数, 以及运用大数据分析方法, 分析多个数据源来检测旅游目的地性能。

本论文确立的经济有效的方法以衡量和监控游客满意度, 对提高目的地管理有着巨大帮助。同时, 这也可以提高游客体验和甚至提高当地居民的满意度。社会利益, 特别有的时候很大的旅游压力, 是巨大的。

The study aims to provide a critical review of the origin, development and process of sentiment analysis (SA) and a demonstration for hospitality researchers and students on how to perform SA using a sample study.

A critical review and sample case demonstration approach was applied. The sample study used Leximancer to perform SA using TripAdvisor review data.

A critical evaluation of the most popular SA tools was provided, highlighting their advantages and disadvantages. A step-by-step demonstration with data provided makes it possible for readers to learn this technique at own pace.

By providing a critical review of SA supported with a demonstration case study, this study makes a timely contribution for broader awareness and understanding, as well as the application of SA in hospitality.

The purpose of this study is to discover the components of virtual leadership in universities and higher education institutions during Covid-19 pandemic.

This research uses the qualitative research design, and semi-structured interviews with senior leaders were conducted with non-random purposeful and snowball sampling methods. Also, grounded theory was used for analyzing, coding and classifying the data.

After analyzing data, five main components influencing virtual leadership including leader's personality traits, followers' characteristics, technological, environmental and organizational components were identified.

As leadership, particularly virtual leadership, highly depends on context such as the level of technology in that certain region, the results cannot be generalized to other sectors or countries.

The research advances leadership development theory by highlighting the main components of virtual leadership in educational sector as a comprehensive perspective and complement the other aspects of virtual leadership such as consequences and challenges investigated by other studies.