Search results

Due to its rich marine biological resources, Taiwan is a place worthy of developing marine ecotourism. This study explores the marine ecotourism intentions and marine ecotourism behaviors of tourists during the COVID-19 pandemic by using the unified theory of acceptance and use of technology (UTAUT) method. Under the UTAUT framework, this study considered the factor of environmental attitudes and issued questionnaires in four research areas, namely, Yilan Wushi Fishing Harbor, Port of Hualien, Hualien Stone Stairs Platform, and Taitung Chengfong Fishing Port. In total, 431 effective research samples were collected. First, this study verified the validity and reliability of the dimensions through confirmatory factor analysis. According to the results of structural equation modeling (SEM) analysis, performance expectancy, effort expectancy, social influence, and environmental attitude all have an influence on tourists' marine ecotourism intentions, while facilitating conditions had no influences on tourists' marine ecotourism intentions. Tourists' marine ecotourism intentions further influenced their marine ecotourism behaviors. In addition, this study evaluated the mediating effect of marine ecotourism intentions by nested-model analysis. Finally, this study provided substantive policy recommendations as a reference for tourism management units and local governments.

This book constitutes the refereed post-conference proceedings of the 15th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2020, held in Shenzhen, China, in November 2020. The 40 revised full papers and 15 revised short papers were carefully reviewed and selected from 137 submissions. The papers of this volume are organized in topical sections on: crowdsourcing, crowd intelligence, and crowd cooperative computing; domain-specific collaborative applications; collaborative mechanisms, models, approaches, algorithms, and systems; social media and online communities; and short papers.

The Belt and Road initiative (BRI) was announced in 2013 by the Chinese government. Its goal is to promote the cooperation between European and Asian countries, as well as enhancing the trust between members and unifying the market. Since its creation, more and more developing countries are joining the initiative. Based on the geographical location characteristics of the countries in this initiative, we propose an improvement of a popular recommendation algorithm that includes geographic location information. This recommendation algorithm is able to make suitable recommendations of products for countries in the BRI. Then, Fitness and Complexity metrics are used to evaluate the impact of the recommendation results and measure the country's competitiveness. The aim of this work is to provide countries' insights on the ideal development direction. By following the recommendations, the countries can quickly increase their international competitiveness.