Keywords

JEL Classification

1 Introduction

Nowadays, there is a lot of interest from researchers in Data Analytics due to the large amount of unstructured information generated. However, one of the most interesting areas is the use of social media, where there are 3196 millions of users, 13% higher than in 2017 (We are social 2008–2018.), and according to the same report will keep growing. Due to many people use social media to communicate, find relevant information or any recommendation, a very important area of research has emerged i.e. social media analytics where consists of: (i) capture a lot of conversations that occur naturally in social media, (ii) transform it into useful data, (iii) find ways to talk sociologically about them (Brooker et al. 2017) and exploit that information. The benefits of obtain useful information has been taken advantage in areas such as management, economy, politics, tourism, etc.; where some researcher have reached a consensus that social networks affects consumer choice and increase profits (Aral et al. 2013; Piccialli and Jung 2017). This work is focused on analyzing the most important studies about social media analytics and smart tourism in such way that current issues, opportunities and challenges can be found. According to a report of United Nations World Tourism Organization UNWTO, in the European Union in 2017, 538 millions of tourists have arrived, 8% more than 2016 (UNWTO 2018). However, there aren’t related works that makes a systematic literature review (SLR) that allows show the advantages, disadvantages, challenges and opportunities for researchers and professionals in the field of Social Media Analytics and Smart Tourism (SMAST). Therefore, there is a need to synthesize the research until now regarding the current issues related to SMAST.

The fundamental pillars of social media analytics are: social media, users or people and industry and technology that transforms conversations, comments, photos, videos, likes, blogs, tweets, etc., into data with a lot of value for analysts and marketing specialists whose aim is analyze and monitor user behavior, brand loyalty and other performance indicators, making these data effective (Misirlis and Vlachopoulou 2018). In this work, many issues discussed in the analyzed publications are identified to define future research in SMAST.

This study presents a complete analysis to understand and describe social media analytics focused on smart tourism through the review of relevant literature. The main aim of this paper is an extensive review of publications related to SMAST, creating a systematic map of publications found; for that it is essential to create a conceptual classification scheme (S3M) for the literature found, using four dimensions or categories, such us: research methodologies, type of analysis, current issues about tourism and type of social media platform (Misirlis and Vlachopoulou 2018), providing an overview of current research issues in SMAST. Additionally, this work benefits to researchers, people involved in tourism and governments where tourism is an essential part of their economies.

Thus, the following research questions are formulated for this systematic literature review:

RQ1::

what are the current issues related to SMAST?

RQ2::

what are the top ranking for future research topic in SMAST?

The structure of the paper is divided into five sections that are: the introduction, theoretical background, research methodology, the SLR result findings and conclusions, future research directions and limitations.

2 Theoretical Background

Smart Tourism

The term “smart” has become a buzzword to describe technological, economic and social developments using sensors-dependent technologies and large amount of data and information exchange (Gretzel et al. 2015). First of all, is necessary to have a clear idea about what is “Smart Tourism”, this term is derived from the concept of “Smart City” whose objective is improve the quality of life of all citizens. The term “Smart Tourism” refers to the activity where tourist apply new technologies in sectors related to touristic experience services, applications for reservations, accommodation, transportation and restaurants; in addition, it is related as a social phenomenon where the existing hospitality industry and tourism industry are integrated with the use of information and communication technologies (ICT) (Hunter et al. 2015; Lee 2017). Thus, it is clear that the tourist activity is unquestionably linked to ICT.

Social Media Analytics

The term of Social Media Analytics SMA refers to “an emerging field of interdisciplinary research that aims to combine, extend and adapt methods for the analysis of social networks” (Stieglitz et al. 2014). Another definition considers it as a set of tools for “collect, analyze, summarize and visualize social network data, generally driven by specific requirements of a target application”. (Zeng et al. 2010).

Applications and services related to tourism have been influenced by social networks that every year increase the number of users and its impact has been exploited by marketing companies in general. Social media analytics focused on tourism is based on the use of information and communication technology to collect, clean, process, analyze and visualize those data to transform it into useful information in order to improve both tourist services and tourist’ experience.

Thus, it is possible to define Social Media Analytics and Smart Tourism as an interdisciplinary set of methods and techniques that allows collect data from social media (i.e. blogs, review sites, media sharing, question-and-answer sites, social bookmarking, social networking, social news and wikis) using technological services provided by Smart Cities to process, analyze and visualize useful information in order to improve services and tourists applications.

3 Research Methodology

This work uses the methodological process based on Systematic Literature Review mentioned in Okoli and Schabram (2010) and conceptual classification scheme named S3M by using four distinct dimensions/categories/criteria of classification and in each category issues are defined. This hybrid methodology allows to make a systematic literature review using a classification to have a general vision of issues, platforms, types of analysis and methodologies in each paper. Articles that use S3M can be found in five types of journals: Marketing and e-Marketing, e-business and management, behavioral sciences, information systems and social media, Misirlis and Vlachopoulou (2018), thus, S3M can be used in this work. Okoli and Schabram (2010) makes a SLR where the research questions are first determined, then the search, selection, classification and analysis is carried out.

In this manner the research questions (RQs) which were mentioned in the introduction are mentioned below:

RQ1::

what are the current issues related to SMAST?

RQ2::

what are the top ranking for future research topic in SMAST?

Subsequently, the search’ information is performed. Thus, the search terms are chosen to answer the research questions and it’s are combined with the use of Boolean operators (AND, OR). Terms used are: (“social media” analytics OR analysis AND “smart tourism”). This process was done on the three academic databases, such as: Scopus, Science Direct and IEEE. Articles belonging a books, book chapters, articles in press and review are excluded from the research. In total, 398 papers between 2014 to April 2018 were found. The year with the largest number of papers was published was 2017 with 135 articles and until April 2018, the number amounts to 47. According to the domain for this work, 45 of them were selected, those that are repeated among the selected databases were discarded. Thus, each study was revised in such way that the appropriate classification for SMAST can be established.

The methodology used in this work can be synthesized as shown in the Fig. 1.

Fig. 1
figure 1

Source Own elaboration

Steps for selecting papers

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The papers, which paper’ title matches with the research questions are named as “found”, the papers which abstract of them match with the research questions are named as “candidate” and the papers which results of them match with the research questions are named as “selected”. Results are demonstrated in Table 1.

Table 1. Classification and selection of papers per database
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4 SLR and Findings

The 45 papers that have been selected after the analysis of title, abstract and results are used to answer the research question 1 (RQ1) (Table 2, sorted by id). Additional information as publisher, year, authors and source are presented.

Table 2. List selected studies table
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The 45 selected papers are distributed as follows: 4 published in 2014, with 8.8%, 7 published in 2015 with 15.5%, 15 published in 2016 with 33.3%, 17 published in 2017 with 37.7% and 2 published until April 2018 with 4.4%. Thus, the interest in SMAST is growing each year, being 2016 and 2017 the years with the largest number of studies were published.

Further, we proceed to classify according to four different dimensions. As mentioned initially, each article is subdivided according to: (i) the methodology of research, (ii) type of analysis, (iii) current issues in tourism and (iv) type of social media platform. The search, selection and classification of each work allows to summarize each publication, have a clear vision on topics on which each work is focused and find the current issues that generate more interest.

The term typology is used instead of taxonomy, the classification used in Misirlis and Vlachopoulou (2018) where this classification is adapted to this study. The categories of marketing and fields of study are discarded and a category based on tourism is added. Based on the analysis of works published and selected for this study, different subcategories specifically related to the tourism domain were identified where future studies and researchers can add, modify or eliminate issues or categories. Each work is related to the Smart Tourism, however; in the typology of “Tourism current issues” is added.

Based on the 45 papers analyzed, 6 current issues have been found in the field of tourism, which allows to answer the first research question RQ1: What are the current issues related to SMAST?; such as: (i) destination and attraction, (ii) decision making/marketing, (iii) travel satisfaction/tourism satisfaction, (iv) Mobility behavior/tourism movements, (v) travel information/search information/Electronic word of mouth (eWOM)/user-generated content (UGC). This current issues are related with the work of Shafiee and Ghatari (2016); they mention topics such as: service quality, reputation and destination image, UGC as eWOM, experiences, behaviors and movements patterns (Table 3).

Table 3. S3M typology for social media analytics and Smart Tourism
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Each study analyzed can be based on one or several issues in different categories or dimensions. Nevertheless, each work uses a methodology of research, one or many types of analysis and if required, the data of a social media platform for analysis. On the other hand, some papers uses surveys as source of information to perform analysis (e.g. to find the level of travel satisfaction some researchers use surveys to get the perceptions of users before, during and after about their travel and their relation with the use of social media). The paper relationship with the categories and issues can be seen in Table 4.

Table 4. Matrix by paper, categories and issues
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To answer RQ2: what are the top ranking for future research topic in SMAST?. According to the data collected from Table 2, the papers selected are analyzed according to the categories and issues. Table 3 is analyzed and grouped according to categories and issues, further. The number of papers for each issue that were discussed can be seen on the graph of Fig. 2.

Fig. 2
figure 2

Source Own elaboration from Microsoft Excel

Current SMAST Issues.

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5 Conclusions, Future Research Directions and Limitations

This study presents an overview of the current issues on which researchers at SMAST are based, through the systematic literature review about of published papers between 2014 and April 2018. After the search 389 papers were found, of them the candidate publications were 76 and of them, through a thorough review 45 were selected.

The 45 selected studies were categorized according to the recommendation of Misirlis and Vlachopoulou (2018), they use S3M; nevertheless, it was adapted to the present work where current issues in tourism were identified and for this six issues were identified: (i) destination and attraction, (ii) decision making/marketing, (iii) travel satisfaction/tourism satisfaction, (iv) Mobility behavior/tourism movements, (v) travel information/search information/Electronic word of mouth (eWOM)/user-generated content (UGC);are issues that researchers have put a lot of interest in the field of tourism or smart tourism; and, the rest of issues related with social media analytics. The results show the current issues about of SMAST that allows to answer the first research question. To answer the second research question, after analyze the results obtained in Table 3, the current issues with the most numbers of works was: (1) Literature review/Theoretical approach/Explorative analysis is in first place with 30 papers followed by (2) Travel information/Search information/Electronic word of mouth (eWOM), user-generated content (UGC) with 30 papers and (3) Social media activity analysis with 18 papers. It is clear that tourism researchers are based on testing from different areas and topics of sciences (e.g. mental accounting theories or prospect theory) in such way, that they can experiment with data generated from social media platforms. There is also, a lot of interest in the quality of the data generated from social media, because this influences other users to choose a destination for their holidays and can be used for marketing purposes and higher revenues for the tourism industry. Results presented in this work can help to researchers to better understand trends based on SMAST, and also, reveals the lack of interest in issues such as privacy of data in social media; this issue is a serious problem that could be addressed in future research. Another issue found that has not been addressed and has a great relevance in the sector is: Travel satisfaction; although, there are some research based on the use of surveys or questionnaires and their associated possible biases (e.g. social desirability biases, short-term recall biases, etc.) without taking into account data generated in social networks that can be processed with NLP techniques combined with machine and deep learning techniques, this generate new challenges and opportunities for future research. The present study is not without limitations; the search of information was made with terms such as: “social media analytics and smart tourism” and not separately (e.g. destinations, decision making, travel satisfaction, etc.) which would give a clearer idea of studies conducted in specific issues of tourism. This work may be used as a research reference for the next 3–5 years and can be used as a reference for future review studies in SMAST.