Search results

Coopetition (simultaneous cooperation and competition of actors) is still a relatively new concept in business, management, and tourism. However, several coopetition studies have focused on tourism and tourism destinations. Also, compilation literature reviews of tourism and tourism destinations have been published (Rusko, 2018). This chapter focuses on underlying coopetition networks of tourism and specifically of tourism destinations. Because of the typical features of tourism destinations, multifaceted connections in competition and cooperation – and coopetition – are present in everyday business and activities among actors of the destination. These coopetitive relationships cover several levels, they are present in micro, meso, macro, and meta level interplay of tourism destination. Furthermore, the analysis shows that several studies about coopetitive networks in tourism destinations do not use terms “macro” or “meta” though these seem to be the main levels of the studies. This only reveals the fertile dimensions of coopetitive networks in tourism. These various relationships form coopetitive networks that represent several dimensions and levels of actors, competition, cooperation, and coopetition. This chapter introduces these multifaceted perspectives of coopetition networks, which have been described in the contemporary literature about tourism and tourism destination.

The application of network theory and social network analysis (SNA) to tourism and hospitality is recent. Nonetheless, several authors have been applying the method contributing to regional planning, local-level tourism networks, tourism policy and governance, innovation, entrepreneurship, knowledge transfer, and learning. This chapter aims to characterize the use of SNA in tourism and hospitality research. Specifically, it intends to: (i) present the framework of SNA in a methodological perspective; (ii) perform a bibliometric analysis of SNA use in tourism and hospitality research; (iii) systematize the dimensions and metrics that researchers can use to apply SNA, namely the relevance for tourism; and (iv) present a case study analyzing tourism innovation networks. This chapter brings important contributions to tourism and hospitality research and practice, by focusing on the theoretical framework and practical application of SNA, providing relevant conceptual and practical knowledge that will empower researchers to use this method in tourism and hospitality studies.

Building on new trends in tourism and smart city governance, this study aims to examine the degree of interrelation between stakeholder networks involved in tourism governance and smart city development. A model describing the transition towards smart tourism city governance is proposed.

The proposed model is tested through a multiple case study of seven European cities. This choice of sample makes the study highly representative. Data collection is based on an exhaustive search and analysis of available data on smart city initiatives, destination management organisations and tourism plans. Social network analysis using Gephi software is used to build stakeholder networks.

Analysis of the stakeholder networks that shape tourism governance and smart initiatives in several cities reveals a disconnection between the two types of networks. The results show limited progress towards the expected synergies of true smart tourism city governance.

Theoretically, the study contributes to the debate on new forms of governance for the complex evolution of urban tourism. In practice, the relationship between tourism governance and smart city initiatives needs to be redefined to achieve synergies that increase the inclusiveness and efficiency of urban tourism policies.

This study examines the under-researched topic of the interrelation between tourism governance and smart city initiatives. By comparing the networks of actors resulting from these two processes, it assesses the extent to which this interrelation helps the emergence of new governance models (smart tourism city governance).

The purpose of this paper is to examine the information flows, in terms of content and process, underpinning the sharing of knowledge by managers and owners. Such an examination reveals similarities and differences that will influence the generation and dissemination of knowledge used in tourism business operations and contribute to innovation.

This paper examines information flows within the theoretical and methodological framework of social network analysis. The findings were derived from a quantitative study of tourism managers and owners of a tourism hub in South-West England.

The main finding was that network structure characteristics determine the flow of information within owners’ and managers’ social networks. The owners of smaller businesses received information from several sources and, therefore, had fewer structural constraints and reported larger structural holes. In comparison, the managers had more brokerage opportunities to disseminate the information within their social networks.

This paper highlights knowledge sharing between tourism business managers and owners in an open network structure. First, an open network structure builds innovation through the provision of nonredundant information. This is determined through the effective size of structural holes and the dissemination of information through brokerage roles. Second, the knowledge capability of a destination is built up through the social networking of managers and owners. The generation and dissemination of knowledge in a tourism destination are facilitated by the social networking activities of managers and owners.

Managers and owners of tourism businesses require knowledge through information to assist with innovative business practices. The practical implication of this is that the social networks of managers and owners have different network characteristics, and that these differences result in consequences for the innovation of business practices. Another practical implication relates to the importance of managers in knowledge dissemination based on having several brokerage roles in the tourism destination.

These findings are important because an understanding of social networks and the flow of information is one of the keys to determining the influences on knowledge sharing within tourism destination knowledge networks of owners or managers and their potential contributions to innovation.

Industrial tourists can contribute to the local sites income. Apart from large and well-known industrial units, small industrial units can also introduce their products to the market by organizing tourism tours. This study aims to analyze an industrial tourism business network established in Isfahan, a large, industrial and historic city in central of Iran, as a case study.

Participants were managers and experts of organizations operated in the Industrial Tourism Network in Isfahan city. A sample of 30 participates was selected through purposive and snowball sampling approaches. Data was analyzed by qualitative content analysis and social network analysis using UCINET software.

Twenty-eight organizations participated in the Industrial Tourism Business Network of Isfahan. Macro indices of the network revealed that: density is relatively low; most of the relationships among network member organizations are reciprocal; transitivity is relatively weak indicating low network stability against environmental player; clustering is low, indicating a relatively low tendency of members to form different clusters. Micro indices indicated that in-degree and out-degree centralizations are low; overall centralization is poor. In addition, six actors were in the center and 22 actors were in the peripheral. Finally, the number of interactions of central organizations was triple than the peripheral organizations.

Limitations of this study include: The data obtained in this study only reflected the relationships among the actors studied during a specific period of time; this paper only focused on actors engaged in an industrial tourism network. Therefore, the sample might involve some bias. The results of the study contribute to our understanding of the role of networking in tourism destinations, particularly industrial sites.

According to the results, the authors may be able to advise firms operating in the tourism industry, particularly actors operate in industrial tourism, regarding how to increase their collaboration with both internal and external environment to achieve better performance.

To the best of the authors’ knowledge, this is the first paper that has addressed an industrial tourism business network.

The purpose of this paper is to analyze the variety of relations existing in tourism networks, identified as complex and mutable entities, where a vast range of stakeholders coexist.

After a deep review on stakeholder theory, the research applies techniques of network analysis to a case study. Specifically, the analysis focuses on 354 hospitality firms acting in Molise Region (Italy). Each operator was asked to judge the importance to collaborate with other stakeholders to enhance the effectiveness of their management and marketing activities. The answers highlight the degree of preference among stakeholders and the resulting information is the level of confidence in the network.

Results confirm the importance of intensifying relationships between tourism companies themselves and between them and policy makers. It appears that public stakeholders are more important for both management and marketing activities than private sector, since they place a much higher position in the scale of preference.

The paper provides a starting‐point for further research about non‐quantitative destination performance measurement, such as trust and commitment between the stakeholders in tourism destination, and the use of network analysis' techniques.

Destination managers and policy‐makers may use techniques of network analysis to elaborate useful information for planning and managing the relationships inside the tourism network.

The paper offers a novel approach for developing network analysis in tourism network literature. It explores non‐quantitative destination performance measurements and uses management and marketing activities to analyze relationships between public and private stakeholders.

This research aims to analyze the relations between coopetition and innovation, by comparing two coopetitive tourism SMEs networks in Brazil.

The first network comprises 23 SMEs in Honey Island, a natural reserve, and the second network comprises 21 out of 25 SMEs in the Campos Gerais region, recognized by its strong agribusiness. Innovativeness variables included innovation inputs, capabilities, and outputs; and four types of relations that foster innovation were considered, namely, commercial, informational, knowledge, and partnerships. Social network analysis was employed as well as statistical analyses such as Kolmogorov–Smirnov, Mann–Whitney, Spearman correlation and Fischer's Z transformation.

Results show that coopetition is related to SMEs innovativeness. Commercial relations centralities correlated with many innovation outputs, information and knowledge centralities with some innovation inputs and outputs, and partnerships also with capabilities.

Besides contributing to the literature of innovation in tourism, this paper also contributes to the literature on coopetition and innovation by investigating how different types of coopetition relationships foster innovation inputs, capabilities, and outputs.

Managers may benefit from these findings by fostering specific innovation inputs, capabilities, or outputs by means of different coopetition relations. Similarly, regional tourism policy planners may also improve the innovativeness of tourism small businesses by fostering coopetition networks.

This paper not only compares the innovativeness of two small business coopetition networks in the tourism industry but also analyses quantitively in detail how different types of coopetition relations are related to different innovativeness variables.

This paper gathers tourism digital footprint from online travel platforms, choosing social network analysis method to learn the structure of destination networks and to probe into the features of tourist flow network structure and flow characteristics in Guilin of China.

The digital footprint of tourists can be applied to study the behaviors and laws of digital footprint. This research contributes to improving the understanding of demand-driven network relationships among tourist attractions in a destination.

(1) Yulong River, Yangshuo West Street, Longji Terraced Fields, Silver Rock and Four Lakes are the divergent and agglomerative centers of tourist flow, which are the top tourist attractions for transiting tourists. (2) The core-periphery structure of the network is clearly stratified. More specifically, the core nodes in the network are prominent and the core area of the network has weak interaction with the peripheral area. (3) There are eight cohesive subgroups in the network structure, which contains certain differences in the radiation effects.

This research aims at exploring the spatial network structure characteristics of tourism flows in Guilin by analyzing the online footprints of tourists. It takes a good try to analyze the application of network footprint with the research of tourism flow characteristics, and also provides a theoretical reference for the design of tourist routes and the cooperative marketing among various attractions.

Within a religious tourism destination, the study of business networks can be used to improve understanding of the interactions taking place within or among businesses, by exploring the informal and formal connections linking them together. Hence, the purpose of this study is to map and analyze strategic business networks operating in a religious destination.

This research explores the networks of stakeholders associated with a religious context in an intensive single case study on Al-Atabat Al-Aliyat in Iraq through qualitative analysis. Multiple methods of data collection were used to build strategic networks promoting religious tourism in the Middle-East region. Key analysis practices include content analysis and social network analysis (SNA). The identified interrelationships between stakeholders were analyzed by using UCINet, an SNA tool.

The study reveals three different types of cooperation networks in the case of Al-Atabat Al-Aliyat, namely, a 67-actor continuous network, a 69-actor non-Arbaeen network and an 89-actor Arbaeen network. The results also pinpoint the need for more collaborative activities on the different levels of cooperation in the networks.

This paper provides important guidelines on how to manage the interrelationships within tourism networks in a religious destination such as Iraq. The findings highlight the acute need for establishing strategic and effective networks of stakeholders in Al-Atabat Al-Aliyat. Non-tangible, long-term and quantitative benefits can be found for tourism businesses investing time and money in religious networks in Iraq.

The research contributes to religious tourism literature, filling the gaps about how the interrelationships among the actors involved in a business network can evolve over time and influence the success of a certain religious destination.

While the importance of interorganizational learning (IOL) as a resource for strategic renewal has been acknowledged in research, limited attention has been placed on exploring IOL in complex networks or ecosystems. This paper aims to bridge this gap in literature through conceptualizing IOL in the tourism industry at the micro, meso and macro levels of the ecosystem.

This paper applies a narrative literature review to explore the tourism industry and IOL. Through insights which evolved from literature, exploratory typologies and propositions were developed forming the basis of the multi-level typology of interorganizational learning in the tourism industry.

Based on the multi-level typology of interorganizational learning in the tourism industry, market actors may develop in isolation, in clusters or networks, with the aggregate output of these formations constituting the basis of the tourism ecosystem. Within the multi-layers of the tourism ecosystem, IOL may be subdivided into seven distinct typologies, with learning ranging from active, passive, interactive to asymmetric. Each typology of IOL is subsequently influenced by relational ties, which may range from collaborative to competitive, and power distribution, which may lead to symmetric or asymmetric relationships.

The novelty of this paper lies in an ecosystem perspective of IOL in the tourism industry, outlining the multi-level nature of IOL in a destination.