Dataset Information

Towards the smart tourism destination: Key factors in information source use on the tourist shopping journey.

ABSTRACT: This paper aims to contribute to the knowledge on how the Smart Tourism Destination (STD) might enhance the Tourist Shopping Journey (TSJ) through offering information sources that meet visitors' needs and preferences. The CAN (Cognitive-Affective-Normative) model was employed to explore the antecedents of using information sources for purchases made in destinations. The importance of the cognitive variables performance and effort expectancy in the purchasing process are highlighted: tourists are pragmatic when consulting information sources in destinations. This study contributes to the knowledge of the role of information sources in TSJ behaviour, and can help managers in the development of STD strategies and services. It also opens new research lines by considering the TSJ as a hitherto unexamined holistic process.

SUBMITTER: Garcia-Milon A

PROVIDER: S-EPMC7402109 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Towards the smart tourism destination: Key factors in information source use on the tourist shopping journey.

García-Milon Alba A Juaneda-Ayensa Emma E Olarte-Pascual Cristina C Pelegrín-Borondo Jorge J

Tourism management perspectives 20200804

This paper aims to contribute to the knowledge on how the Smart Tourism Destination (STD) might enhance the Tourist Shopping Journey (TSJ) through offering information sources that meet visitors' needs and preferences. The CAN (Cognitive-Affective-Normative) model was employed to explore the antecedents of using information sources for purchases made in destinations. The importance of the cognitive variables performance and effort expectancy in the purchasing process are highlighted: tourists ar ...[more]

PMID: 32834961

Similar Datasets

Project description:User-generated content (UGC) is an important data source for tourism GIScience research. However, no effective approach exists for identifying hidden spatiotemporal patterns within multi-scale unstructured UGC. Therefore, we developed an algorithm to measure the tourist destination popularity (TDP) based on a multi-spatiotemporal text granular computing model, called TDPMTGC. To accurately granulate the spatial and temporal information of tourism text, tourism text data granules are used to represent landscape objects. These granules are unified objects that possess multiple attributes, such as spatial and temporal dimensions. The multi-spatiotemporal scales are characterized by the multi-hierarchical structure of granular computing, and transformations of granular layers and data granule size are achieved by scale selection in the spatial and temporal dimensions. Therefore, all scales between the spatial and temporal dimension are related, which allows for the comparability of the data granules of all spatial-spatial, temporal-temporal and spatial-temporal layers. This approach achieves a quantitative description and comparison of the popularity value of granules between adjacent scales and cross-scales. Therefore, the TDP with multi-spatiotemporal scales can be deduced and calculated in a systematic framework. We first introduce the conceptual framework of TDPMTGC to construct a quantitative measurement model of TDP at multi-spatiotemporal scales. Then, we present a dataset construction approach to support multi-spatiotemporal scale granular reorganization. Finally, TDPMTGC is derived to describe both the TDP at a single spatial or temporal scale and the patterns and processes of the TDP at multi-spatiotemporal scales. A case study from Jiuzhaigou shows that the TDP derived using TDPMTGC is consistent with the conclusions of existing studies. More importantly, TDPMTGC provides additional detailed characteristics, such as the contributions of different scenic spots in a tourist route or scenic area, the monthly anomalies and daily contributions of TDP in a specific year, the distinct weakening of tourist route scale in tourist cognition, and the daily variations of TDP during in-season and off-season times. This is the first time that a granular computing model has been introduced to tourism GIScience that provides a feasible scheme for reorganizing large-scale unstructured text and constructing public spatiotemporal UGC tourism datasets. TDPMTGC constitutes a new approach for exploring tourist behaviors and the driving mechanisms of tourism patterns and processes.

Dataset Information

Towards the smart tourism destination: Key factors in information source use on the tourist shopping journey.

Publications

Towards the smart tourism destination: Key factors in information source use on the tourist shopping journey.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets