A social network perspective of tourism research collaborations

Racherla, Pradeep; Hu, Clark

doi:10.1016/j.annals.2010.03.008

Cited by 192 publications

(164 citation statements)

References 37 publications

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“…), but also the structure of the economy and how policy seeks shape it (Rita, 2000). Competitiveness is not a "zero sum game" (Racherla & Hu, 2010). In other words, an increase of this factor in one country, region or state, does not come at the expense of another (Crouch & Brent, 1999).…”

Section: Tourism Competitivenessmentioning

confidence: 99%

Improving Tourism Competitiveness: The Case of Mexico

Serrato

Valenzuela

Rayas

2013

Business and Economic Research

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Through this research, a categorization and analysis of resources and actions taken by each Mexican state that contribute towards tourism competitiveness is developed. Such competitiveness is evaluated through one hundred and twelve variables, which are grouped in ten categories defined as dimensions. An overall competitiveness index is developed for each state, as well as complementary indexes for each state under the ten dimensions considered. The results of the research are satisfactory, since governmental authorities reported improvements on the number of tourists and the average expenditure. Such results were Business and Economic Research ISSN 2162-4860 2013 www.macrothink.org/ber 389 achieved by developing new initiatives and projects defined by identifying the strengths and opportunity areas in each state regarding tourism competitiveness, which are outlined through this research.

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Section: Tourism Competitivenessmentioning

confidence: 99%

Improving Tourism Competitiveness: The Case of Mexico

Serrato

Valenzuela

Rayas

2013

Business and Economic Research

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“…This field has seen the development of numerous associations, journals and research consortia, and is linked to groups of researchers in other domains such as business and information technology (Racherla & Hu, 2010 Walle (1997) argued that formal scientific methods are needed in tourism research. This would ensure generality of the empirical outcomes as well as maintain the quality and credibility of research findings.…”

Section: Tourismmentioning

confidence: 99%

Novel Approach to Tourism Analysis with Multiple Outcome Capability Using Rough Set Theory

Huang¹,

Tseng²,

Chen³

2016

IJCIS

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To explore the relationship between characteristics and decision-making outcomes of the tourist is critical to keep competitive tourism business. In investigation of tourism development, most of the existing studies lack of a systematic approach to analyze qualitative data. Although the traditional Rough Set (RS) based approach is an excellent classification method in qualitative modeling, but it is can't deal with the case of multiple outcomes, which is a common situation in tourism. Consequently, the Multiple Outcome Reduct Generation (MORG) and Multiple Outcome Rule Extraction (MORE) approaches based on RS to handle multiple outcomes are proposed. This study proposes a ranking based approach to induct meaningful reducts and ensure the strength and robustness of decision rules, which helps decision makers understand tourist's characteristics in a tourism case.

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“…Zhao et al [27] proposed a topic-oriented community detection approach which combines both topic clustering and link analysis. Similarly, Racherla and Hu [28] identified topic-oriented communities by exploiting a topic similarity matrix and assigning a predefined research topic to each document and author. Differently from these approaches, we characterise topics through a formal ontology since this solution allows us to generate a structured set of unambiguous research topics linked by semantic relationships [18].…”

Section: Related Workmentioning

confidence: 99%

Forecasting the Spreading of Technologies in Research Communities

Osborne

Mannocci

Motta

2017

Proceedings of the Knowledge Capture Conference

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Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution.

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A social network perspective of tourism research collaborations

Cited by 192 publications

References 37 publications

Improving Tourism Competitiveness: The Case of Mexico

Improving Tourism Competitiveness: The Case of Mexico

Novel Approach to Tourism Analysis with Multiple Outcome Capability Using Rough Set Theory

Forecasting the Spreading of Technologies in Research Communities

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