Abstract
This article contains dataset on the behavior of international tourists when traveling is related to 1) tourist demographics, 2) things that affect tourists to choose travel destinations when planning, 3) use of mobile data while traveling, 4) how to get internet access while traveling, 5) social media used during traveling, and 6) behavior of smartphone use for tourists during traveling. The raw data presented here can be used as material to analyze the behavior of international tourists related to any media that affects international tourists in planning their trips, and how they behave during traveling. This data is a source of raw data from our research on smartphones and international tourist behavior, besides being used for various other research purposes.
Keywords: Information and communication technology, Smart tourism, Smartphone, Mobile computing, Tourist behavior
Specifications Table
| Subject | Computer Network and Communications |
| Specific subject area | ICT in Tourism |
| Type of data | Text in Data Sheet, Questionnaire Form, Tourist Response. |
| How data were acquired | The survey, analytics, self-report questionnaires. |
| Data format | Raw data in datasheet format, Excel compatible |
| Parameters for data collection | Demographic Behavior for international tourist while traveling |
| Description of data collection | Raw data collection through a questionnaire about the behavior of international tourists on smartphone use. |
| Data source location | Bandung, Indonesia. |
| Data accessibility | Repository name: Mandeley Data Data identification number: https://doi.org/10.17632/zwzb8hzc9j.1 Direct URL to data: https://data.mendeley.com/datasets/zwzb8hzc9j/1 |
Value of the Data
|
1. Data
The dataset is the result of the distribution of response from international tourists related to the ICT— a source of input to infer tourist behavior used this dataset. The data are mainly related to the use of information and communication technology [[1], [2], [3], [4], [5], [6], [7]]. The dataset consists of seven groups, as shown in Fig. 1.
Fig. 1.
Data group in the dataset.
Each group stores specific data fields within the groups. The criteria for each behavioral group stored in each record. Following Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7 in the Questionnaire form (Fig. 2) as an essence of fields used in storing the results of the questionnaire is acquired one by one.
Table 1.
Group fields of Tourist Personal Demographic.
| Field Name | Type | Description |
|---|---|---|
| p_age | Number | Age of tourist |
| p_gender | Options “M” or “F” | Gender of tourist |
| p_edu | Options “L”, “D”, “PG” or “ETC” | The education level of tourist. The contents are in the form of choices, L: under the university, D: Diploma, PG: Postgraduate, ETC: other options. |
| p_country | Text | Country origin of tourist |
Table 2.
Group fields of Tourist Pre-trip Source Information.
| Field Name | Type | Description |
|---|---|---|
| pre_OA | Boolean | Online Advertising, |
| pre_SM | Boolean | Social Media |
| pre_NP | Boolean | Newspaper |
| pre_Mg | Boolean | Magazine |
| pre_TV | Boolean | Television |
| pre_TA | Boolean | Tripadvisor |
| pre_AA | Boolean | Advice Agent |
| pre_Bl | Boolean | Blog |
| pre_SE | Boolean | Search Engine |
| pre_rec | Boolean | Recommendation |
| pre_desc | Boolean | Description additional information |
Table 3.
Group fields of Tourist Mobile Operator while traveling.
| Field Name | Type | Description |
|---|---|---|
| m_local | Boolean | Local operator usage by tourist |
| m_roaming | Boolean | Activates roaming facilities from the operator of the country of origin |
Table 4.
Fields in the group of Tourist Internet Usage Location.
| Field Name | Type | Description |
|---|---|---|
| internet_hotel | Boolean | Internet usage at the hotel by tourist |
| Internet_restaurant | Boolean | Internet usage at restaurant by tourist |
| Internet_tourist_attraction | Boolean | Internet usage at a tourist attraction by tourist |
Table 5.
Group fields of Tourist Internet-Connected and Usage Time While Traveling.
| Field Name | Type | Description |
|---|---|---|
| Internet_daily_usage | Number | Total hours of daily usage and connected to the internet while traveling by tourist |
Table 6.
Group fields of Tourist usage of Social Media While Traveling.
| Field Name | Type | Description |
|---|---|---|
| Soc_med | Text | Tourist social media name used while traveling |
Table 7.
Group fields of Tourist Smartphone Function While Traveling.
| Field Name | Type | Description |
|---|---|---|
| sp_TP | Boolean | Taking photos |
| sp_MF | Boolean | Map features |
| sp_RS | Boolean | Restaurant search |
| sp_SAA | Boolean | Search of attraction |
| sp_Tr | Boolean | Translator |
| sp_VC | Boolean | Video Call |
| sp_Tl | Boolean | Telephone |
| sp_CC | Boolean | Currency converter |
| sp_SMP | Boolean | Social media posting |
| sp_RN | Boolean | Reading news |
| sp_SP | Boolean | Share photos |
| sp_OB | Boolean | Online banking |
| sp_WA | Boolean | |
| sp_ATG | Boolean | As tour guide |
Fig. 2.
Questionnaire form.
Each group present of each topic. Personal data stored in a table based on these groups:
-
1.
Personal demographic. The demographics of each tourist participating in completing the questionnaire (see Table 1)
-
2.
Pre-trip source information. Pre-trip source information. Information used by tourists when planning a trip before actual (see Table 2)
-
3.
Mobile operator. The use of mobile operators by tourists to connect to the internet during the trip takes place (see Table 3)
-
4.
Internet access. The source of internet access during a trip (See Table 4)
-
5.
Internet usage time. Group fields of Tourist Internet-Connection and usage duration while traveling (See Table 5)
-
6.
Social media. Social media used by tourists during a trip (See Table 6)
-
7.
Smartphone Function. Selection of services from a smartphone by a tourist on a trip (See Table 7)
2. Experimental design, materials, and methods
Application programs that can be used to open, process, and display the query datasets are compatible with Microsoft programs that can open data in XLSX format. To use this data, the user can retrieve it from the dataset stored on Mendeley's repository [8].
Data material was obtained from the results of the distribution of questionnaires on foreigners and tourists in Bandung [9,10] in the period 11 April 2019 to 28 May 2019.
To process and experiment with data, including doing it by filtering, sorting by using the general formula that is in the data processor.
Based on existing data, data processors can find some results according to the wishes included in the dataset, for example, particular country tourist behavior, age-based behavior, gender-based behavior, or behavior based on education level. Processing is also combined based on several other categories.
Thus, the data contained in this dataset can be an input for various parties related to the behavior of international tourists to be able to travel [11]. This data is useful for a variety of research carried out in the field of ICT [12], especially in the area of tourism research.
Transparency document
As a form of transparency related to this article, data can be found online, namely through the repository provided by Mendeley with the address https://doi.org/10.17632/zwzb8hzc9j.1.
Acknowledgments
This research has been conducted by the Pervasive Computing & Educational Technology Research Group. C-ACT, Universiti Teknikal Malaysia Melaka (UTeM). The Indonesia Tourism Journalist Association (ITJA) which has provided access to various parties related to this research. Sekolah Tinggi Teknologi Bandung and PT Jackwisata (jacktour.com) has provided research materials related to technology and tourism.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2019.104610.
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
References
- 1.Xiang Z. From digitization to the age of acceleration: on information technology and tourism. Tourism Manag. Perspect. 2018;25:147–150. [Google Scholar]
- 2.Tan G.W.H., Ooi K.B. Gender and age: do they really moderate mobile tourism shopping behavior? Telematics Inf. 2018;35(6):1617–1642. [Google Scholar]
- 3.Brauer R., Dymitrow M., Tribe J. The impact of tourism research. Ann. Tourism Res. 2019;77:64–78. [Google Scholar]
- 4.Huang C.D., Goo J., Nam K., Yoo C.W. Smart tourism technologies in travel planning: the role of exploration and exploitation. Inf. Manag. 2017;54(6):757–770. [Google Scholar]
- 5.Arenas A.E., Goh J.M., Urueña A. How does IT affect design centricity approaches: evidence from Spain's smart tourism ecosystem. Int. J. Inf. Manag. 2019;45:149–162. [Google Scholar]
- 6.Assaf A.G., Tsionas M.G. A review of research into performance modeling in tourism research - launching the Annals of Tourism Research curated collection on performance modeling in tourism research. Ann. Tourism Res. 2019;76:266–277. [Google Scholar]
- 7.Kock F., Josiassen A., Assaf A.G. On the origin of tourist behavior. Ann. Tourism Res. 2018;73:180–183. [Google Scholar]
- 8.Rusdi J.F. Smartphone usage and international tourist behaviour. Mendeley Data. 2019;V1. doi: 10.1016/j.dib.2019.104610. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Rusdi J.F. Indonesia Tourism Journalist Association; 2017. Kolaborasi Pariwisata Bandung Raya. [Google Scholar]
- 10.Tarigan A.K.M., Sagala S., Samsura D.A.A., Fiisabiilillah D.F., Simarmata H.A., Nababan M. Bandung city, Indonesia. Cities. 2016;50:100–110. [Google Scholar]
- 11.Rusdi J.F., Salam S., Abu N.A., Sahib S., Naseer M., Abdullah A.A. Drone tracking modelling ontology for tourist behavior. J. Phys. Conf. Ser. 2019;1201(1):012032. [Google Scholar]
- 12.Rusdi J.F., Salam S., Abu N.A., Baktina T.G., Hadiningrat R.G., Sunaryo B., Rusmartiana A., Nashihuddin W., Fannya P., Laurenty F., Shanono N., Hardi R. ICT research in Indonesia. SciTech Framework. 2019;1:1–23. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.