مروری نظام مند بر کاربرد کلان داده ها در صنعت گردشگری (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
با پیشرفت فنّاوری و افزایش استفاده از شبکه های اجتماعی و اینترنت، حجم تولید داده ها افزایش چشمگیری داشته و بهره گیری از مفهوم «کلان داده ها» نیز به تبع آن، به طور فزاینده ای در پژوهش های علمی مختلف به چشم می خورد؛ اما سازمان دهی و تحلیل ادبیات موجود با محوریت بهره گیری از کلان داده ها در حوزه گردشگری برای شناسایی وضعیت تحقیقات صورت گرفته در این عرصه، خود مسئله ای است که هدف این مطالعه است؛ ازاین رو این پژوهش به بررسی میزان بهره گیری از کلان داده در تحقیقات گردشگری منتشر شده از سال ۲۰۱۶ تا ۲۰۲۲، به شناسایی شکاف های پژوهشی و فرصت های پیشرفت های آتی پرداخته است. ازآنجاکه این پژوهش در پی ارائه تصویری کلی از کارهای انجام شده در مقالات منتشر شده در حوزه گردشگری است، از روش مطالعه نگاشت نظام مند استفاده شده است. مقاله ها براساس این ویژگی ها بررسی شدند: موضوع تحقیق، توصیف مفهومی و نظری، منابع داده، نوع داده و اندازه، روش های جمع آوری داده ها، روش های تجزیه وتحلیل داده ها و گزارش و تجسم داده ها. یافته های این مطالعه حاکی از افزایش تحقیقات انجام شده در حوزه مدیریت مهمان نوازی و گردشگری با استفاده از روش های تحلیلی برای کلان داده هاست. بااین حال، این حوزه پژوهشی از نظر دامنه نسبتاً پراکنده و در روش شناسی محدود است و شکاف های متعددی را نشان می دهد. نتیجه دیگر آن که پژوهش های گردآوری شده نشان می دهند هنوز از پتانسیل کلان داده ها به طور کامل استفاده نشده و فرصت های زیادی پیش روی صنعت گردشگری از بعد پژوهشی وجود دارد. همچنین آشنانبودن کامل پژوهشگران این حوزه با ابزارهای تحلیل و تصمیم گیری داده محور چالشی است که در این پژوهش نمایان شده است.A Systematic Review on the Use of Big Data in Tourism
As technology advances and the use of social networks and the Internet grows, there's a significant increase in the production of data. The concept of "big data" is becoming more prominent in various scientific studies. However, understanding how big data is used in tourism is a challenge addressed in this study. The research investigates the application of big data in tourism studies published from 2016 to 2022, aiming to identify research gaps and potential areas for future development. To provide an overview of published works in tourism, we used the systematic mapping study method. Articles were assessed based on various factors, including research topic, conceptual and theoretical descriptions, data sources, data types and sizes, data collection methods, data analysis techniques, and reporting and visualization methods. The findings indicate a growth in research within hospitality and tourism management using analytical techniques for big data. Despite this, the research in this field is scattered and limited in scope and methodology, revealing several gaps. Additionally, the study suggests that the full potential of big data remains untapped in tourism, offering numerous research opportunities for the industry. A notable challenge highlighted is the .researchers' incomplete knowledge of data-driven analysis and decision-making tools in this field Keywords: Big Data, Social Media, Machine Learning, Systematic Mapping Review 1. IntroductionThe speed of technological changes and their profound effects on various industries, coupled with unpredictable events such as the recent pandemic—having a significant impact on various sectors—requires these industries to cope with uncertainty (Piyush Sharma, 2020). Among them, dynamic, fluctuating, and time-sensitive industries like the tourism sector must respond promptly and more efficiently to emerging challenges and threats in order to survive (Vikrant Kaushal, 2021). Hence, the roles of technology and digitization are vital in helping industries, countries, and organizations overcome rapid challenges (Soluk, 2021).Digitization and the adoption of new technologies have been increasing dramatically in recent years (Ritter, 2020). This includes the use of methods that change the business model to create more effective and efficient processes toward technology—arguably the most crucial change in exploitation. One significant aspect is the analysis of consumer behavior, market dynamics, supply and delivery processes, and customer communication using data obtained from the digitization process (Youssef, 2021). Meanwhile, big data stands out as one of the most well-known paradigms of information analysis, attracting both researchers and experts (José Álvarez García, 2023).Methodologically, big-data-based approaches allow researchers to overcome the problems associated with traditional sampling methods because big data practically enables working with the entire population under investigation. This capability makes it possible to address any question related to the opinions, views, ideas, and behavior of all individuals. Simultaneously, it proves to be a powerful tool for addressing new research questions, developing innovative research designs useful in advancing knowledge, and ultimately providing support for policy and managerial decision-making (Gerard, 2016).The most crucial factor is that the tourism industry grows based on information. Thus, tourism research requires a large amount of strong, up-to-date, timely, and relevant information to support and assist decision-making processes. Big data can provide up-to-date and highly informed inferences about human behavior and activities that boost the tourism industry (Feifei Xu, 2020). Through the traveler, a large number of data sources can inform decisions made at different stages, such as before, during, and after the trip. One popular data source is social media, used by tourists and tourism businesses to communicate, find or provide relevant information or advice, and obtain information about important news and crises (Park, 2019). Therefore, considering the importance of using big data analysis in tourism studies and stating the issue that no monitoring and analysis of the state of research conducted in this field with this theme has been done so far, the upcoming research on the analysis of the use of big data is focused on tourism research by conducting a systematic mapping review. 2. Literature reviewSince no systematic mapping research has been conducted to investigate the status and process of research on the use of big data in tourism studies, the concern of this research is to use the opportunities created by big data and the need to use big data and analyze and make decisions on Its basis is in the tourism industry, by reviewing the literature and explaining the different dimensions of the needs of this industry, this concern and issue will become clearer, therefore, in this section, we will review the researches conducted in recent years that emphasized the necessity of using big data in the field of tourism and Each offered a solution for a different need.Describing the need to create a personal customer experience in the tourism industry, Chen et al. state that personal experiences lead to increased customer satisfaction and loyalty. Big data allows the tourism industry to analyze customer preferences, behaviors and feedback to provide recommendations (Chen, 2017). By describing the need for demand forecasting and price optimization in tourism, Jiang et al. believe that accurate demand forecasting helps tourism businesses to optimize pricing and resource allocation. Big data analytics enables the analysis of historical data, seasonal patterns and market trends for better decision making (Xiang, 2017). Referring to the need for operational efficiency and resource management in the tourism industry, Lee et al. have stated that big data helps simplify operations by providing insights into resource use, crowd management, and operational efficiency. This leads to cost savings and improved service quality (Li, 2018). Gretzel et al also stated the need for risk management and crisis response in this industry, stating that big data helps to identify potential risks and predict crises in the tourism sector. Analyzing data from various sources helps to develop effective risk management strategies (Gretzel, 2015). Stating the need to improve marketing and customer engagement in the tourism industry, Neuhofer et al. believe that big data enables targeted marketing campaigns based on customer insights and improves the effectiveness of promotional efforts. It also increases customer engagement through data-driven strategies (Neuhofer, 2015). Pointing to the need for tourism destination management and planning, Wang has stated that big data supports destination management by providing insight into tourist movements, preferences and impact on local infrastructure. This contributes to sustainable destination planning (Wang, 2016). Cigala also points to the need for customer feedback and reputation management in the tourism industry, noting that big data analytics enable monitoring of online comments and social media sentiment, helping businesses manage their reputation. and resolve customer concerns quickly (Sigala, 2017). With the mentioned sources, it can be concluded that the upcoming issue is the concern of knowing the amount of attention that is given to the use of big data in scientific researches in the field of tourism, and with the clarification of the necessity of using big data in tourism researches, it is necessary to address the theoretical definitions of this research. 3. Research MethodologyThe purpose of this research is to review the studies that have been done in recent years in the field of tourism using big data. According to the purpose of the research, which includes answers to questions focused on the classification and organization of research conducted in the field of tourism with a focus on big data, and in which the identification, classification and analysis of existing literature related to a specific topic will be done, Petersen et al. (2008) recommend the systematic mapping review method (SMS). Systematic mapping study is a special form of literature review that complements the systematic review method (Banaeianjahromi, 2016). While a systematic review examines the research question in detail (Petersen, 2008), a systematic mapping study is a means of categorizing and summarizing the available information about the research question in an unbiased way (Wendler, 2012). In other words, the study of systematic mapping provides an overview of a specific research field with the aim of examining issues related to the identification, analysis, and organization of primary objectives, methods, and content (Kitchenham et al., 2011). In this method, various consecutive activities by following a series of independent tasks lead to reaching the final goal in this system. In this method, we often collect and review scientific articles from a specific subject area to answer some predetermined questions. The strategy behind this method is to find and evaluate all applicable articles to address specific problems that exist around a specific topic (P. Di Francesco, 2017). Therefore, in this research, we reached six different stages to reach the desired result: 1- Determining the purpose of the research 2- Determining the research questions 3- Research strategy 4- Selection criteria 5- Selecting suitable researches 6- Analyzing the results. 4. ResultsBy examining the results obtained from the research questions, it was found that, firstly, the use of big data analysis in scientific articles has grown significantly, and secondly, in most of the researches conducted around the topic of tourism using big data analysis, the most source of big data collection is the data produced. by users (UGC) with the approach of statistical analysis and then analysis by artificial intelligence and machine learning. This means that social networks, which are responsible for the dissemination of user-generated data, can play a more prominent role in scientific research, and the traditional method of collecting questionnaires will give way to examining the real opinions of users on social networks about a specific topic. Therefore, in response to the first question of this research, which source of big data is often used by researchers in tourism studies, it should be said that the research findings show the interest of researchers in using big data produced by users in social networks, because access to them Easier and less challenging to analyze them by artificial intelligence. In response to the second question of this research, which approaches and tools are used to analyze the big data extracted, the findings of the research indicate the dominance of the statistical analysis approach on the content generated by the user. Because according to the answer to the first question, there is often big data produced by users in the form of text and comments in social networks, and the collection of these opinions can gradually take the place of scientific research questionnaires. In response to the third question of this research, which is the type and classification of big data that support this tool and methods of analysis, it should be said that the findings of the research show that the textual analysis of the data collected with the purpose of predictive analysis has been used the most in the selected articles. Therefore, one of the most used and challenging aspects in the management of tourist destinations, i.e. the management of the high volume of input in the peak seasons, will be somewhat predictable. On the other hand, the analysis of keywords confirms that these articles are often used in order to predict the tourism demand of a destination and arrive at a prediction model, and in the second step, to examine the conditions of sustainable development of tourist destinations by using big data analysis. In addition, the results show that most of the research in which the researcher has the desire to use big data has been used more than quantitative data analysis and less qualitative analysis has been used. 5. ConclusionThe purpose of this article was to investigate the amount of use of big data in scientific research in the field of tourism published from 2016 to 2022. Based on the purpose of this research, questions were designed and an attempt was made to answer these three questions in order to achieve the goal. The results obtained from the analysis of the findings of this research show that most of the researchers who have conducted research in the field of tourism using big data analysis are more interested in using the big data produced by users in the form of textual data and comments, as well as the purpose of their use. It has been used to forecast and predict the trend or volume of input or seasonal demand in the tourism industry. Perhaps the main reason for this is the ease of analyzing the data generated by users in the form of text in social networks. However, big data has different types and forms that can be used depending on the researcher's needs, but perhaps the researchers' lack of knowledge about other big data analysis methods makes them closer to the easiest method, which is qualitative big data analysis.Limitations: Although the use of big data in academic research has made significant progress, there are still challenges regarding data quality, data cost, and user privacy concerns. In addition, there are several issues regarding the reliability of user-generated data, such as faking reviews about a tourist attraction to reach more users. Also, due to privacy concerns, mobile phone roaming and user transaction data have not been widely used in tourism research, but despite their benefits, they are costly and difficult to access. Because usually mobile phone operator networks, as well as business solution providers, do not want to share private information of their users. To overcome these challenges, mutual cooperation between university, government and industries becomes necessary. This collaboration not only ensures data availability and data cost reduction for tourism research using big data, but also addresses practical issues in return. Another way that comes to mind to overcome this challenge is to use social media data, as it allows people to provide their views on events, products, tools, and other topics, and this data is easier to access and should be explored in future research. Let's take advantage of this huge amount of data.Suggestions for future research: Considering the diverse and extensive dimensions and forms of big data as well as various methods and tools that are added to their diversity day by day, the need to take advantage of this diversity in scientific research is felt, but the gap that is considered in this research It was found that researchers are not sufficiently informed about this tool and the variety of data types that are produced in large volumes in the field of information technology. One of the most important parts after collecting big data is analyzing this big data and then making data-driven decisions based on the results of these analyses. Therefore, it is suggested that researchers should get to know these methods before conducting research and choosing tools and methods, and after having sufficient mastery in the field of big data analysis, they should choose the most optimal method based on their needs and make the most of the available potential. To gain in the use of big data in the field of tourism.
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