شناخت نقشه مفهومی جمع‌سپاری در گردشگری هوشمند با رویکرد فراترکیبی

محورهای موضوعی : مربوط به گردشگری

فاطمه دانشور ¹ , احمد خادم الحسینی ² , امیر گندمکار ³ , محمدحسین ندیمی ⁴

1 - دانشجوی دکتری جغرافیا و برنامه‌ریزی شهری، گروه جغرافیا، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران.
2 - گروه جغرافیا، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
3 - مرکز تحقیقات گردشگری، گروه جغرافیا، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
4 - گروه مهندسی کامپیوتر، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران

تاریخ دریافت : 1401/04/13 تاریخ پذیرش : 1401/05/15 تاریخ انتشار : 1401/09/01

کلید واژه: صنعت گردشگری, نقشه مفهومی, فراترکیب, جمع‌سپاری,

چکیده مقاله :

اگرچه جمع سپاری به عنوان یک اصل تجاری جدید در بسیاری از برنامه های گردشگری در جهان مدرن ظاهر شده است، اما مکانیسم اساسی آن در صنعت گردشگری بویژه در کشور ایران ناشناخته باقی مانده است. هدف اصلی از این پژوهش، شناخت جامع از یک برنامه جمع سپاری در صنعت گردشگری در دو سطح فنی و مفهومی (هستی شناختی، چرایی، چالش ها و راهبردها) است. تجزیه و تحلیل خوشه‌ای در جهت شناخت چارچوب مفهومی چالش هایی که در زمینه جمع سپاری در صنعت گردشگری وجود دارد و در نهایت شناخت راهبردهای معرفی شده در زمینه جمع سپاری در صنعت گردشگری به محققان کمک می‌کند تا به یک چارچوب شناختی عمیق در زمینه جمع سپاری در صنعت گردشگری دست یابند. روش پژوهش،کیفی و نوعی از متامطالعه به نام فراترکیب است. برای گـردآوری دادهـا از روش کتابخانـه ای استفاده شده است. در راستای انجام پـژوهش پس از طراحی سؤالات پژوهش، جستجویی نظاممند بر اساس کلیدواژه های مرتبط، از پایگـاه هـای داده هدف شاملEBSCO Business Source Complete وWeb of Science صورت گرفته است. نتایج پژوهش نشان می دهد که الگوی تحقیقات در زمینه چالش های جمع سپاری در صنعت گردشگری در چهار خوشه فناوری، انسانی، استراتژیک و مطالعات نسل سوم قابل تعریف است و راهبردهای ارائه شده توسط محققین در اجرای موفق یک پروژه جمع سپاری در صنعت گردشگری نیز، در چهار دسته راهبردهای مرتبط با چشم انداز و استراتژی، سرمایه انسانی، زیرساخت و محیط خارجی قابل اجرا است. نتایج این پژوهش به عنوان یک نقشه راه برای تحقیقات آینده پزوهشگران قابل استفاده است و درک کامل تری از چگونگی پلتفرم های جمع سپاری ایجاد شده در صنعت گردشگری ایجاد می کند.

چکیده انگلیسی:

Although crowdsourcing is a new business principle in many tourism programs in the modern world, its basic mechanism remains unknown in the tourism industry, especially in Iran. This study aimed to comprehensively determine a crowdsourcing program in the tourism industry at both technical and conceptual levels (i.e., nature, reasons, challenges, and strategies). Cluster analysis for understanding the conceptual framework of the challenges that exist in the field of crowdsourcing in the tourism industry and finally knowing the strategies introduced in crowdsourcing in the tourism industry will help the researchers to develop a comprehensive cognitive framework in this field. This study is qualitative in nature and uses a meta-synthesis approach and a library-based method for collecting the required data. In order to carry out the research, after formulating the research questions, a systematic search based on the related key terms was conducted using the target databases including EBSCO Business Source Complete and Web of Science. The results of the study showed that the pattern of research in crowdsourcing challenges in the tourism industry can be determined in four clusters namely technology, human beings, strategies, and third-generation studies. Moreover, the strategies presented by the researchers in the successful implementation of a crowdsourcing project in the tourism industry can be implemented in four categories of strategies related to vision and strategy, human capital, infrastructure, and external environment. The results of this research can be used as a road map for future studies and provide a more comprehensive understanding of creating crowdsourcing platforms in the tourism industry.

منابع و مأخذ:

 

 

• Amador-Domínguez, E., Hohenecker, P., Lukasiewicz, T., Manrique, D., & Serrano, E. (2019, June). An ontology-based deep learning approach for knowledge graph completion with fresh entities. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 125-133)


• Ballatore, A., & Bertolotto, M. (2011, March). Semantically enriching VGI in support of implicit feedback analysis. In International Symposium on Web and Wireless Geographical Information Systems (pp. 78-93). Springer, Berlin, Heidelberg.


• Bench, S., & Day, T. (2010). The user experience of critical care discharge: a meta-synthesis of qualitative research. International journal of nursing studies, 47(4), 487-499.


• Boudreau, K. J., & Lakhani, K. R. (2013). Using the crowd as an innovation partner. Harvard business review, 91(4), 60-9.


• Brabham, D. C. (2008). Crowdsourcing as a model for problem solving: An introduction and cases. Convergence, 14(1), 75-90.


• Brabham, D. C. (2009). Crowdsourcing the public participation process for planning projects. Planning Theory, 8(3), 242-262.


• Brabham, D. C. (2012). Motivations for participation in a crowdsourcing application to improve public engagement in transit planning. Journal of Applied Communication Research, 40(3), 307-328.


• Buettner, R. (2015). A systematic literature review of crowdsourcing research from a human resource management perspective. In 2015 48th Hawaii International Conference on System Sciences(pp. 4609-4618). IEEE.


• Buhalis, D., & O'Connor, P. (2005). Information communication technology revolutionizing tourism. Tourism recreation research, 30(3), 7-16.


• Chandra, S., Verma, S., Lim, W. M., Kumar, S., & Donthu, N. (2022). Personalization in personalized marketing: Trends and ways forward. Psychology & Marketing.


• Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business Press


• Coleman, D., Georgiadou, Y., & Labonte, J. (2009). Volunteered geographic information: The nature and motivation of produsers. International journal of spatial data infrastructures research, 4(4), 332-358.


• De Longueville, B., Annoni, A., Schade, S., Ostlaender, N., & Whitmore, C. (2010). Digital earth's nervous system for crisis events: real-time sensor web enablement of volunteered geographic information. International Journal of Digital Earth, 3(3), 242-259.


• De Mattos, C. A., Kissimoto, K. O., & Laurindo, F. J. B. (2018). The role of information technology for building virtual environments to integrate crowdsourcing mechanisms into the open innovation process. Technological forecasting and social change, 129, 143-153.


• De Silva, M., Howells, J., & Meyer, M. (2018). Innovation intermediaries and collaboration: Knowledge–based practices and internal value creation. Research Policy, 47(1), 70-87.


• Dennis, S. F. Jr., Gaulocher, S., Carpiano, R. M., & Brown, D. (2009). Participatory photo mapping (PPM): Exploring an integrated method for health and place research with young people. Health & Place, 15(2), 466-473.


• Eger, C., Miller, G., & Scarles, C. (2019). Corporate philanthropy through the lens of ethical subjectivity. Journal of Business Ethics, 156(1), 141-153.


• Estellés-Arolas, Enrique, and Fernando González-Ladrón-de-Guevara. "Towards an integrated crowdsourcing definition." Journal of Information science2 (2012): 189-200.


• Feller, J., Finnegan, P., Hayes, J., & O’Reilly, P. (2012). ‘Orchestrating’sustainable crowdsourcing: A characterisation of solver brokerages. The Journal of Strategic Information Systems, 21(3), 216-232


• Fink, A. (2019). Conducting research literature reviews: From the internet to paper. Sage publications.


• Gefen, D., & Carmel, E. (2008). Is the world really flat? A look at offshoring at an online programming marketplace. MIS quarterly, 367-384.


• Goetz, M., & Zipf, A. (2012). Towards defining a framework for the automatic derivation of 3D CityGML models from volunteered geographic information. International Journal of 3-D Information Modeling (IJ3DIM), 1(2), 1-16.


• Goetz, M., Lauer, J., & Auer, M. (2012, January). An algorithm based methodology for the creation of a regularly updated global online map derived from volunteered geographic information. In Proceedings of the Fourth International Conference on Advanced Geographic Information Systems, Applications, and Services, Valencia, Spain (Vol. 30, pp. 50-58).


• Gol, E. S., Stein, M. K., & Avital, M. (2019). Crowdwork platform governance toward organizational value creation. The Journal of Strategic Information Systems, 28(2), 175-195


• Gómez, M., Rouvoy, R., Adams, B., & Seinturier, L. (2016, May). Reproducing context-sensitive crashes of mobile apps using crowdsourced monitoring. In 2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft) (pp. 88-99)


• Goodchild, M. F. (1997). Towards a geography of geographic information in a digital world. Computers, environment and urban systems, 21(6), 377-391.


• Goodchild, M. F. (2007). Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4), 211-221


• Grossner, K., & Glennon, A. (2007, December). Volunteered geographic information: Level III of a digital earth system. In Proceedings of the Position paper presented at the Workshop on Volunteered Geographic Information, Santa Barbara, CA, USA (Vol. 1314, p. 2).


• Gupta, S., & Vajic, M. (2000). The contextual and dialectical nature of experiences. New service development: Creating memorable experiences, 15, 33-51.


• Gursoy, D., & Chi, C. G. (2020). Effects of COVID-19 pandemic on hospitality industry: review of the current situations and a research agenda. Journal of Hospitality Marketing & Management, 29(5), 527-529.


• Howe, J. (2008). Crowdsourcing: How the power of the crowd is driving the future of business. Random House


• Hudson-Smith, A., Crooks, A., Gibin, M., Milton, R., & Batty, M. (2009). NeoGeography and Web 2.0: concepts, tools and applications. Journal of Location Based Services, 3(2), 118- 145.


• Jeppesen, L. B., & Lakhani, K. R. (2010). Marginality and problem-solving effectiveness in broadcast search. Organization science, 21(5), 1016-1033.


• Kaganer, E., Carmel, E., Hirschheim, R., & Olsen, T. (2013). Managing the human cloud. MIT Sloan Management Review, 54(2), 23.


• Kazai, G., Kamps, J., & Milic-Frayling, N. (2013). An analysis of human factors and label accuracy in crowdsourcing relevance judgments. Information retrieval, 16(2), 138-178.


• Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1-26.


• Kohler, T. (2015). Crowdsourcing-based business models: How to create and capture value. California management review, 57(4), 63-84


• Leung, R., Vu, H. Q., & Rong, J. (2017). Understanding tourists’ photo sharing and visit pattern at non-first tier attractions via geotagged photos. Information Technology & Tourism, 17(1), 55-74


• Lim, W. M., Kumar, S., Verma, S., & Chaturvedi, R. (2022). Alexa, what do we know about conversational commerce? Insights from a systematic literature review. Psychology & Marketing, 39(6), 1129-1155.


• Lu, W., & Stepchenkova, S. (2015). User-generated content as a research mode in tourism and hospitality applications: Topics, methods, and software. Journal of Hospitality Marketing & Management, 24(2), 119-154.


• Malone, T. W., Laubacher, R., & Dellarocas, C. (2010). The collective intelligence genome. MIT Sloan management review, 51(3), 21.


• Mergel, I. (2015). Opening government: Designing open innovation processes to collaborate with external problem solvers. social science computer review, 33(5), 599-612.


• Moon, M. J. (2018). Evolution of co-production in the information age: crowdsourcing as a model of web-based co-production in Korea. Policy and Society, 37(3), 294-309.


• Neuhofer, B., & Buhalis, D. (2014). Experience, co-creation and technology: Issues, challenges and trends for technology enhanced tourism experiences. The Routledge handbook of tourism marketing, 124-139.


• Niu, X., Qin, S., Zhang, H., Wang, M., & Wong, R. (2018). Exploring product design quality control and assurance under both traditional and crowdsourcing-based design environments. Advances in Mechanical Engineering, 10(12), 1687814018814395.


• Pe-Than, E. P. P., Goh, D. H. L., & Lee, C. S. (2017). Analyzing Crowdsourced Mobile Content: Do Games Make a Difference?. Journal of Information Science Theory and Practice, 5(2), 6-16.


• Piccoli, G., O’Connor, P., Capaccioli, C., & Alvarez, R. (2003). Customer relationship management—A driver for change in the structure of the US lodging industry. Cornell Hotel and Restaurant Administration Quarterly, 44(4), 61-73.


• Prpić, J., Taeihagh, A., & Melton, J. (2015). The fundamentals of policy crowdsourcing. Policy & Internet, 7(3), 340-361.


• Rogova, А. (2017). Аssessment of awareness of the population of the region about the possibilities of crowdsourcing in regional management. Global scientific potential, 2(47), 39-43.


• Rowe, F. (2014). What literature review is not: diversity, boundaries and recommendations. European Journal of Information Systems, 23(3), 241-255.


• Samara, D., Magnisalis, I., & Peristeras, V. (2020). Artificial intelligence and big data in tourism: a systematic literature review. Journal of Hospitality and Tourism Technology, 11(2), 343-367.


• Saxton, G. D., Oh, O., & Kishore, R. (2013). Rules of crowdsourcing: Models, issues, and systems of control. Information Systems Management, 30(1), 2-20.


• Sigala, M., Christou, E., & Gretzel, U. (Eds.). (2012). Social media in travel, tourism and hospitality: Theory, practice and cases. Ashgate Publishing, Ltd..


• Silva, C. E. P. D., & Ramos, I. (2012). Crowdsourcing innovation intermediaries Functions. Association for Information Systems.


• Söderholm, P., & Karim, R. (2010). An enterprise risk management framework for evaluation of eMaintenance. International Journal of System Assurance Engineering and Management, 1(3), 219-228.


• Sukhu, A., Zhang, T., & Bilgihan, A. (2015). Factors influencing information-sharing behaviors in social networking sites. Services Marketing Quarterly, 36(4), 317-334.


• Taeihagh, A. (2017). Crowdsourcing, sharing economies and development. Journal of Developing Societies, 33(2), 191-222.


• Tulloch, D. L. (2008). Is VGI participation? From vernal pools to video games. GeoJournal, 72(3), 161-171.


• Verma, S. (2022). Sentiment analysis of public services for smart society: Literature review and future research directions. Government Information Quarterly, 101708.


• Wand, Y., & Weber, R. (1993). On the ontological expressiveness of information systems analysis and design grammars. Information systems journal, 3(4), 217-237.

61- Webster, J., & Watson, R. T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Quarterly, 26(2), xiii–xxiii. http://www.jstor.org/stable/4132319