IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v154y2023ics0148296322007809.html
   My bibliography  Save this article

Leveraging user behavior and data science technologies for management: An overview

Author

Listed:
  • Blasco-Arcas, Lorena
  • Kastanakis, Minas N.
  • Alcañiz, Mariano
  • Reyes-Menendez, Ana

Abstract

This article introduces the special section on leveraging user behavior and data science technologies for management. It reviews 12 articles and discusses their contribution towards establishing a new dynamic paradigm of leveraging user behavior and data science technologies for management. User data has become a promising and relevant area to explore in order to improve decision-making. However, and despite increasing access to this kind of data, several challenges remain related to how to successfully collect, manage and incorporate user data to managerial decisions. In this special issue, we focus on exploring different facets related to impactful data practices in management as well as envisaging future developments related to new sources of user data and methods. Overall, the special issue contributes to deepening the understanding of data usage and management for business through a series of articles that highlight promising further developments in areas such as data collection, data disclosure and privacy, data usage and data analysis methods.

Suggested Citation

  • Blasco-Arcas, Lorena & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2023. "Leveraging user behavior and data science technologies for management: An overview," Journal of Business Research, Elsevier, vol. 154(C).
  • Handle: RePEc:eee:jbrese:v:154:y:2023:i:c:s0148296322007809
    DOI: 10.1016/j.jbusres.2022.113325
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296322007809
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jbusres.2022.113325?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Crivellari, Alessandro & Beinat, Euro & Caetano, Sandor & Seydoux, Arnaud & Cardoso, Thiago, 2022. "Multi-target CNN-LSTM regressor for predicting urban distribution of short-term food delivery demand," Journal of Business Research, Elsevier, vol. 144(C), pages 844-853.
    2. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    3. Julien Cloarec, 2022. "Privacy controls as an information source to reduce data poisoning in artificial intelligence-powered personalization," Post-Print hal-03816601, HAL.
    4. Cloarec, Julien, 2022. "Privacy controls as an information source to reduce data poisoning in artificial intelligence-powered personalization," Journal of Business Research, Elsevier, vol. 152(C), pages 144-153.
    5. 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.
    6. Grigorios, Lamprinakos & Magrizos, Solon & Kostopoulos, Ioannis & Drossos, Dimitrios & Santos, David, 2022. "Overt and covert customer data collection in online personalized advertising: The role of user emotions," Journal of Business Research, Elsevier, vol. 141(C), pages 308-320.
    7. Ho, Kung-Cheng & Yang, Lu & Luo, Sijia, 2022. "Information disclosure ratings and continuing overreaction: Evidence from the Chinese capital market," Journal of Business Research, Elsevier, vol. 140(C), pages 638-656.
    8. Halliday, Sue Vaux, 2016. "User-generated content about brands: Understanding its creators and consumers," Journal of Business Research, Elsevier, vol. 69(1), pages 137-144.
    9. Ana Babić Rosario & Kristine Valck & Francesca Sotgiu, 2020. "Conceptualizing the electronic word-of-mouth process: What we know and need to know about eWOM creation, exposure, and evaluation," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 422-448, May.
    10. Vanhala, Mika & Lu, Chien & Peltonen, Jaakko & Sundqvist, Sanna & Nummenmaa, Jyrki & Järvelin, Kalervo, 2020. "The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research," Journal of Business Research, Elsevier, vol. 106(C), pages 46-59.
    11. Loutfi, Ahmad Amine, 2022. "A framework for evaluating the business deployability of digital footprint based models for consumer credit," Journal of Business Research, Elsevier, vol. 152(C), pages 473-486.
    12. Poulis, Konstantinos & Kastanakis, Minas, 2020. "On theorizing and methodological fetishism," European Management Journal, Elsevier, vol. 38(5), pages 676-683.
    13. Sykora, Martin & Elayan, Suzanne & Hodgkinson, Ian R. & Jackson, Thomas W. & West, Andrew, 2022. "The power of emotions: Leveraging user generated content for customer experience management," Journal of Business Research, Elsevier, vol. 144(C), pages 997-1006.
    14. Yang, Shuai & Wang, Yizhe & Li, Zhen & Chen, Chiyin & Yu, Ziyue, 2022. "Time-of-day effects on (un)healthy product purchases: Insights from diverse consumer behavior data," Journal of Business Research, Elsevier, vol. 152(C), pages 447-460.
    15. Chopdar, Prasanta Kr & Paul, Justin & Korfiatis, Nikolaos & Lytras, Miltiadis D., 2022. "Examining the role of consumer impulsiveness in multiple app usage behavior among mobile shoppers," Journal of Business Research, Elsevier, vol. 140(C), pages 657-669.
    16. Chen, Li-Fei & Tsai, Chih-Tsung, 2016. "Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain," Tourism Management, Elsevier, vol. 53(C), pages 197-206.
    17. Blasco-Arcas, Lorena & Lee, Hsin-Hsuan Meg & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2022. "The role of consumer data in marketing: A research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 436-452.
    18. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    19. Han, Shuihua & Jia, Xinyun & Chen, Xinming & Gupta, Shivam & Kumar, Ajay & Lin, Zhibin, 2022. "Search well and be wise: A machine learning approach to search for a profitable location," Journal of Business Research, Elsevier, vol. 144(C), pages 416-427.
    20. Gouthier, Matthias H.J. & Nennstiel, Carina & Kern, Nora & Wendel, Lars, 2022. "The more the better? Data disclosure between the conflicting priorities of privacy concerns, information sensitivity and personalization in e-commerce," Journal of Business Research, Elsevier, vol. 148(C), pages 174-189.
    21. Giray, Caner & Yon, Belma & Alniacik, Umit & Girisken, Yener, 2022. "How does mothers’ mood matter on their choice of organic food? Controlled eye-tracking study," Journal of Business Research, Elsevier, vol. 144(C), pages 1175-1185.
    Full references (including those not matched with items on IDEAS)

    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. Mariani, Marcello M. & Borghi, Matteo & Laker, Benjamin, 2023. "Do submission devices influence online review ratings differently across different types of platforms? A big data analysis," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    2. Blasco-Arcas, Lorena & Lee, Hsin-Hsuan Meg & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2022. "The role of consumer data in marketing: A research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 436-452.
    3. Loutfi, Ahmad Amine, 2022. "A framework for evaluating the business deployability of digital footprint based models for consumer credit," Journal of Business Research, Elsevier, vol. 152(C), pages 473-486.
    4. Ciechanowski, Leon & Jemielniak, Dariusz & Gloor, Peter A., 2020. "TUTORIAL: AI research without coding: The art of fighting without fighting: Data science for qualitative researchers," Journal of Business Research, Elsevier, vol. 117(C), pages 322-330.
    5. Cloarec, Julien & Cadieu, Charlotte & Alrabie, Nour, 2024. "Tracking technologies in eHealth: Revisiting the personalization-privacy paradox through the transparency-control framework," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    6. 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.
    7. 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).
    8. Leogrande, Angelo, 2021. "The Destruction of Price-Representativeness," MPRA Paper 111239, University Library of Munich, Germany.
    9. Reyes-Menendez, Ana & Clemente-Mediavilla, Jorge & Villagra, Nuria, 2023. "Understanding STI and SDG with artificial intelligence: A review and research agenda for entrepreneurial action," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    10. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
    11. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    12. Nguyen Anh Khoa Dam & Thang Le Dinh & William Menvielle, 2019. "A systematic literature review of big data adoption in internationalization," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 182-195, September.
    13. Liedong, Tahiru Azaaviele & Rajwani, Tazeeb & Lawton, Thomas C., 2020. "Information and nonmarket strategy: Conceptualizing the interrelationship between big data and corporate political activity," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    14. Holmlund, Maria & Van Vaerenbergh, Yves & Ciuchita, Robert & Ravald, Annika & Sarantopoulos, Panagiotis & Ordenes, Francisco Villarroel & Zaki, Mohamed, 2020. "Customer experience management in the age of big data analytics: A strategic framework," Journal of Business Research, Elsevier, vol. 116(C), pages 356-365.
    15. Shet, Sateesh.V. & Poddar, Tanuj & Wamba Samuel, Fosso & Dwivedi, Yogesh K., 2021. "Examining the determinants of successful adoption of data analytics in human resource management – A framework for implications," Journal of Business Research, Elsevier, vol. 131(C), pages 311-326.
    16. Vanhala, Mika & Lu, Chien & Peltonen, Jaakko & Sundqvist, Sanna & Nummenmaa, Jyrki & Järvelin, Kalervo, 2020. "The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research," Journal of Business Research, Elsevier, vol. 106(C), pages 46-59.
    17. Luigi M. De Luca & Dennis Herhausen & Gabriele Troilo & Andrea Rossi, 2021. "How and when do big data investments pay off? The role of marketing affordances and service innovation," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 790-810, July.
    18. Shaw, F. Atiyya & Wang, Xinyi & Mokhtarian, Patricia L. & Watkins, Kari E., 2021. "Supplementing transportation data sources with targeted marketing data: Applications, integration, and internal validation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 149(C), pages 150-169.
    19. J. Piet Hausberg & Kirsten Liere-Netheler & Sven Packmohr & Stefanie Pakura & Kristin Vogelsang, 2019. "Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis," Journal of Business Economics, Springer, vol. 89(8), pages 931-963, December.
    20. Keshav Singh Rawat & Sandeep Kumar Sood, 2021. "Emerging trends and global scope of big data analytics: a scientometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(4), pages 1371-1396, August.

    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:eee:jbrese:v:154:y:2023:i:c:s0148296322007809. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    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.