TOURISM DEVELOPMENT WITH AN ENDOGENOUS APPROACH: A CASE STUDY OF XIDI, CHINA

With a critique of over-dependency on and vulnerability to externally controlled development, the endogenous approach has been increasingly accepted as a more effective way to animate sustainable socio-economic development in rural areas. Although tourism has been frequently taken as an alternative development strategy for rural economies, little academic effort has been made on examining the mechanism of how the endogenous approach can be operationalized in a tourism framework. Using qualitative methods, this study examined the experience of tourism development in one of China’s most famous rural cultural tourism destinations (Xidi). Based on the case analysis, a new communal endogenous approach for tourismdevelopment, which is prevailing in rural China, is summarized and its influences on community participation in tourism are discussed

Understanding DMOs’ Online Information Communication and Networking A New Zealand Case

Destination marketing organizations (DMOs) are the industry’s peak body in most destinations, as they are usually supported by sets of formal and informal networks spanning public and private sectors. Although studies show that DMOs are the most central stakeholder across tourism networks, very limited efforts have been made, if any, to examine the information exchange and communication behaviors of DMOs in cyberspace, despite the fact that the Internet has already been a prevalent tool for the daily operation of the tourism industry. DMOs are also called Regional Tourism Organizations (RTOs) in New Zealand. Using a New Zealand case, this study attempted to gain an exploratory understanding on the connectedness of DMO websites in cyberspace and their usage by other individuals and organizations online

SOCIAL NETWORKS IN THE TOURISM INDUSTRY: AN INVESTIGATION OF CHARLESTON, SOUTH CAROLINA

Over the past decades, increasing attention has been given to the networking in the tourism industry (Lynch, 2000; Pavlovich, 2003). The existing literature mainly focuses on the interrelationships among tourism stakeholders at sector level and the structure of the interorganizational networks in tourism industry. However, little research has been done to examine the possible antecedents and outcomes of the tourism networks and the interrelationships between the network structures at different subject level (i.e., interpersonal and interorganizational) and in different social contexts (i.e., online and offline). The purpose of this study is to address these research gaps by empirically examining the networks in a tourism destination. Choosing Charleston, South Carolina as the study area, this study included three phases of data collection and analysis. A series of in-depth interviews with the Charleston Area Convention and Visitor Bureau (CACVB) staff were first conducted for the development of the survey instrument. An online self-administrated survey was then conducted with 337 investors of the CACVB Travel Council to examine the scope and strength of the relationships between tourism professionals and tourism organizations. In addition, the Web sites of 745 tourism-related organizations located in Charleston were collected for generating an inter-hyperlink network in the tourism industry. Using network analysis techniques, the relational characteristics of the identified Web sites were measured, and their possible relationships with the organizations\u27 offline characteristics were also examined. The results confirmed the proposed influences of personality in individual\u27s social network structures in tourism business environment, and indicated that different personality traits contributed to different aspects of individual\u27s social networks characteristics (i.e. social network diversity and social network tie strength). At the organizational level, the study suggested that the interorganizational networks between tourism organizations were socially embedded in their boundary-spanning personnel\u27s social networks. In addition, market turbulence was found negatively related to tourism organization\u27s network diversity that had significant influence on their market performance. For the interorganizational network in cyberspace, the study revealed that tourism organization\u27s sector played an important role in their online network structures which were found correlated to tourism organization\u27s offline network structure as well as market performance

Knowledge Networks A Social Network Analysis of Dissertation Subjects

This paper presents a social network analysis of the subject areas of tourism dissertations in North America based on the ProQuest Dissertations and Theses-Full text database (1994-2008). This study demonstrates the openness and vibrancy of the tourism field from a network perspective. The longitudinal examinations revealed a U-shape pattern in the structural evolution of the knowledge network in tourism research. 21 key subjects were identified as the anchors of the dynamic tourism knowledge system, and their associations with each other were also examined. In addition, this article discusses the relations between the major subject areas and the faculty program distributions of doctoral tourism research at North American institutions

Dimensionality reduction of networked systems with separable coupling-dynamics: theory and applications

Complex dynamical systems are prevalent in various domains, but their analysis and prediction are hindered by their high dimensionality and nonlinearity. Dimensionality reduction techniques can simplify the system dynamics by reducing the number of variables, but most existing methods do not account for networked systems with separable coupling-dynamics, where the interaction between nodes can be decomposed into a function of the node state and a function of the neighbor state. Here, we present a novel dimensionality reduction framework that can effectively capture the global dynamics of these networks by projecting them onto a low-dimensional system. We derive the reduced system's equation and stability conditions, and propose an error metric to quantify the reduction accuracy. We demonstrate our framework on two examples of networked systems with separable coupling-dynamics: a modified susceptible-infected-susceptible model with direct infection and a modified Michaelis-Menten model with activation and inhibition. We conduct numerical experiments on synthetic and empirical networks to validate and evaluate our framework, and find a good agreement between the original and reduced systems. We also investigate the effects of different network structures and parameters on the system dynamics and the reduction error. Our framework offers a general and powerful tool for studying complex dynamical networks with separable coupling-dynamics.Comment: 15 pages, 5 figure

Is cannabis tourism deviant? A theoretical perspective

With the growth of cannabis tourism, destinations such as the Netherlands have begun to offer cannabis- related products and services to visitors, including tourists from countries where all drugs are strictly prohibited. Yet limited research has sought to understand cannabis-oriented tourists\u27 efforts to neutralize deviant connotations, namely by justifying or rationalizing misbehavior, when deciding to participate in cannabis tourism. This research note proposes a framework of deviant consumption behavior (DCB) constructed of geographic shifting, self-identity shifting, and moral identity shifting from the perspective of cannabis-oriented tourists to delineate tourists\u27 decision-making process around engaging in deviant behaviors. The proposed framework suggests that previously developed DCB frameworks in the marketing and consumer behavior literature should be adapted for use in outbound tourism research. This research note also highlights areas for debate and investigation regarding cannabis tourists\u27 deviant behavior. Future research directions are provided based on the proposed framework as it applies to deviant tourism research

Cover Crop Impacts on Soil Nutrient Cycling: Effects of Functional Diversity and Management History

Interest in increasing agroecosystem diversity through use of cover crops continues to rise. Cover crops are non-harvested crops that provide a range of ecosystem functions, and mixtures of cover crop species with complementary traits, such as legumes and grasses, may increase multiple functions at once. However, the performance of cover crops grown in monocultures and mixtures is expected to vary across farms with different levels of soil fertility, which result from unique management histories. Understanding the interactions of these two factors can help optimize the use of cover crops for more sustainable soil nutrient management. This study therefore addressed the following research questions: (1) Do legume-grass cover crop mixtures alter rates of decomposition compared to legume and grass cover crop monocultures? (2) Are the effects of litter type different in soils with different management histories? We incubated three litter treatments in two soils with contrasting fertility levels for 360 days, and measured decomposition dynamics through respired CO2, microbial extracellular enzyme activity, and inorganic N mineralization. As expected, new carbon inputs to soil increased microbial processes in the short term, but basically had no long-term effect on the measured responses. The lower fertility soil had a greater response to litter addition for both CO2 respiration and enzyme activities for enzymes that degrade labile organic carbon compounds. The total inorganic N release was higher on the high fertility soil. Overall, both cover crop litter addition and farm management history affect microbial decomposition dynamics. In this study, we found that cover crop litter addition had a stronger effect on soil biological processes compared to management history, however, the difference between litter mixture and monoculture treatments was not significant.Master of ScienceSchool for Environment and SustainabilityUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/145707/1/Ying_Tianyu_Thesis_HIDE_ONE_YEAR.pd

Learning from past crises: Evaluating hotels’ online crisis responses to health crises

Organizational learning is an important function of tourism crisis management. By examining and evaluating hotels’ responses to the 2010 bed bug crisis on social media, the purpose of this study was to provide insights into how to establish effective crisis responses. Situational crisis communication theory was used as the theoretical framework and a total of 136 management responses were included in the sample. Content analysis and co-occurrence analysis were conducted. The results revealed a learning curve of crisis management for hotels. Enhancing and Bolstering were the most commonly used strategies within the sample. Further analysis showed the inconsistencies between hotels’ crisis response strategies and the situational crisis communication theory guidelines, where instructing information were seldom included. Based on the findings, this study discussed the importance of creating effective crisis responses and future research directions

DeSCo: Towards Generalizable and Scalable Deep Subgraph Counting

Subgraph counting is the problem of counting the occurrences of a given query graph in a large target graph. Large-scale subgraph counting is useful in various domains, such as motif counting for social network analysis and loop counting for money laundering detection on transaction networks. Recently, to address the exponential runtime complexity of scalable subgraph counting, neural methods are proposed. However, existing neural counting approaches fall short in three aspects. Firstly, the counts of the same query can vary from zero to millions on different target graphs, posing a much larger challenge than most graph regression tasks. Secondly, current scalable graph neural networks have limited expressive power and fail to efficiently distinguish graphs in count prediction. Furthermore, existing neural approaches cannot predict the occurrence position of queries in the target graph. Here we design DeSCo, a scalable neural deep subgraph counting pipeline, which aims to accurately predict the query count and occurrence position on any target graph after one-time training. Firstly, DeSCo uses a novel canonical partition and divides the large target graph into small neighborhood graphs. The technique greatly reduces the count variation while guaranteeing no missing or double-counting. Secondly, neighborhood counting uses an expressive subgraph-based heterogeneous graph neural network to accurately perform counting in each neighborhood. Finally, gossip propagation propagates neighborhood counts with learnable gates to harness the inductive biases of motif counts. DeSCo is evaluated on eight real-world datasets from various domains. It outperforms state-of-the-art neural methods with 137x improvement in the mean squared error of count prediction, while maintaining the polynomial runtime complexity.Comment: 8 pages main text, 10 pages appendi