IDEAS home Printed from https://ideas.repec.org/a/rbs/ijbrss/v5y2016i1p01-14.html
   My bibliography  Save this article

Gossip Management at Universities using Big Data Warehouse Model Integrated with a Decision Support System

Author

Listed:
  • Pelin Vardarlier

    (Istanbul Medipol University)

  • Gokhan Silahtaroglu

    (Istanbul Medipol University)

Abstract

Big Data has recently been used for many purposes like medicine, marketing and sports. It has helped improve management decisions. However, for almost each case a unique data warehouse should be built to benefit from the merits of data mining and Big Data. Hence, each time we start from scratch to form and build a Big Data Warehouse. In this study, we propose a Big Data Warehouse and a model for universities to be used for information management, to be more specific gossip management. The overall model is a decision support system that may help university administraitons when they are making decisions and also provide them with information or gossips being circulated among students and staff. In the model, unsupervised machine learning algorithms have been employed. A prototype of the proposed system has also been presented in the study. User generated data has been collected from students in order to learn gossips and students’ problems related to school, classes, staff and instructors. The findings and results of the pilot study suggest that social media messages among students may give important clues for the happenings at school and this information may be used for management purposes.The model may be developed and implemented by not only universities but also some other organisations. Key Words: Big Data, Data Collection, University Management, Gossip

Suggested Citation

  • Pelin Vardarlier & Gokhan Silahtaroglu, 2016. "Gossip Management at Universities using Big Data Warehouse Model Integrated with a Decision Support System," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 5(1), pages 01-14, January.
  • Handle: RePEc:rbs:ijbrss:v:5:y:2016:i:1:p:01-14
    as

    Download full text from publisher

    File URL: http://www.ssbfnet.com/ojs/index.php/ijrbs/article/view/122/125
    Download Restriction: no

    File URL: http://www.ssbfnet.com/ojs/index.php/ijrbs/article/view/122
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bang Nguyen & Xiaoyu (allen) Yu & T. C. Melewar & Junsong Chen, 2015. "Brand innovation and social media : Knowledge acquisition from social media, market orientation, and the moderating role of social media strategic capability," Post-Print hal-02312301, HAL.
    2. Luo, Qiuju & Zhong, Dixi, 2015. "Using social network analysis to explain communication characteristics of travel-related electronic word-of-mouth on social networking sites," Tourism Management, Elsevier, vol. 46(C), pages 274-282.
    3. Vu, Huy Quan & Li, Gang & Law, Rob & Ye, Ben Haobin, 2015. "Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos," Tourism Management, Elsevier, vol. 46(C), pages 222-232.
    4. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    5. Mynttinen, S. & Logrén, J. & Särkkä-Tirkkonen, M. & Rautiainen, T., 2015. "Perceptions of food and its locality among Russian tourists in the South Savo region of Finland," Tourism Management, Elsevier, vol. 48(C), pages 455-466.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tihana Škrinjarić, 2021. "Ranking Environmental Aspects of Sustainable Tourism: Case of Selected European Countries," Sustainability, MDPI, vol. 13(10), pages 1-19, May.
    2. Yamilé Pérez Guilarte & Daniel Barreiro Quintáns, 2019. "Using Big Data to Measure Tourist Sustainability: Myth or Reality?," Sustainability, MDPI, vol. 11(20), pages 1-19, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Brewis, Claire & Dibb, Sally & Meadows, Maureen, 2023. "Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    2. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    3. Emilio V. Carral & Marisa del Río & Zósimo López, 2020. "Gastronomy and Tourism: Socioeconomic and Territorial Implications in Santiago de Compostela-Galiza (NW Spain)," IJERPH, MDPI, vol. 17(17), pages 1-25, August.
    4. Ladi Daodu & Prof. Dr. Amiya Bhaumik, 2024. "Impacts of Innovation and Business Analytics on the Performance of the Service Sector in Nigeria," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(6), pages 77-91, June.
    5. Dominik M. Wielgos & Christian Homburg & Christina Kuehnl, 2021. "Digital business capability: its impact on firm and customer performance," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 762-789, July.
    6. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    7. Irina Maiorescu & Mihaela Bucur & Bogdan Georgescu & Daniel Moise & Vasile Alecsandru Strat & Ion Daniel Zgură, 2020. "Social Media and IOT Wearables in Developing Marketing Strategies. Do SMEs Differ From Large Enterprises?," Sustainability, MDPI, vol. 12(18), pages 1-18, September.
    8. Constant Berkhout & Abhi Bhattacharya & Carlos Bauer & Ross W. Johnson, 2024. "Revisiting the construct of data-driven decision making: antecedents, scope, and boundaries," SN Business & Economics, Springer, vol. 4(10), pages 1-23, October.
    9. Sidney Anderson, 2024. "Expanding data literacy to include data preparation: building a sound marketing analytics foundation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 227-234, June.
    10. Carmela Iorio & Giuseppe Pandolfo & Antonio D’Ambrosio & Roberta Siciliano, 2020. "Mining big data in tourism," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(5), pages 1655-1669, December.
    11. Leogrande, Angelo, 2021. "The Destruction of Price-Representativeness," MPRA Paper 111239, University Library of Munich, Germany.
    12. Kumar, V. & Ramachandran, Divya & Kumar, Binay, 2021. "Influence of new-age technologies on marketing: A research agenda," Journal of Business Research, Elsevier, vol. 125(C), pages 864-877.
    13. Raphaël Maucuer & Alexandre Renaud & Sébastien Ronteau & Laurent Muzellec, 2022. "What can we learn from marketers? A bibliometric analysis of the marketing literature on business model research," Post-Print hal-03718522, HAL.
    14. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    15. Jingmei Gao & Zahid Sarwar, 2024. "How do firms create business value and dynamic capabilities by leveraging big data analytics management capability?," Information Technology and Management, Springer, vol. 25(3), pages 283-304, September.
    16. Boccali, Filippo & Mariani, Marcello M. & Visani, Franco & Mora-Cruz, Alexandra, 2022. "Innovative value-based price assessment in data-rich environments: Leveraging online review analytics through Data Envelopment Analysis to empower managers and entrepreneurs," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    17. Son, Youngdoo & Kim, Wonjoon, 2023. "Development of methodology for classification of user experience (UX) in online customer review," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    18. Eleonora Di Maria & Marco Bettiol & Mauro Capestro, 2023. "How Italian Fashion Brands Beat COVID-19: Manufacturing, Sustainability, and Digitalization," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    19. Caputo, Andrea & Pizzi, Simone & Pellegrini, Massimiliano M. & Dabić, Marina, 2021. "Digitalization and business models: Where are we going? A science map of the field," Journal of Business Research, Elsevier, vol. 123(C), pages 489-501.
    20. Gupta, Shivam & Justy, Théo & Kamboj, Shampy & Kumar, Ajay & Kristoffersen, Eivind, 2021. "Big data and firm marketing performance: Findings from knowledge-based view," Technological Forecasting and Social Change, Elsevier, vol. 171(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rbs:ijbrss:v:5:y:2016:i:1:p:01-14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Umit Hacioglu (email available below). General contact details of provider: https://edirc.repec.org/data/ssbffea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.