IDEAS home Printed from https://ideas.repec.org/a/pal/jmarka/v7y2019i4d10.1057_s41270-019-00064-5.html
   My bibliography  Save this article

Strength in diversity: methods and analytics

Author

Listed:
  • Maria Petrescu

    (ICN Business School Artem, CEREFIGE Laboratoire
    Colorado State University Global)

  • Anjala S. Krishen

    (University of Nevada)

Abstract

No abstract is available for this item.

Suggested Citation

  • Maria Petrescu & Anjala S. Krishen, 2019. "Strength in diversity: methods and analytics," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(4), pages 203-204, December.
  • Handle: RePEc:pal:jmarka:v:7:y:2019:i:4:d:10.1057_s41270-019-00064-5
    DOI: 10.1057/s41270-019-00064-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41270-019-00064-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1057/s41270-019-00064-5?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. Arifine, Ghizlane & Felix, Reto & Furrer, Olivier, 2019. "Multi-Brand Loyalty in Consumer Markets: A Qualitatively-Driven Mixed Methods Approach," FSES Working Papers 501, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    2. Shalini Bahl & George R. Milne, 2010. "Talking to Ourselves: A Dialogical Exploration of Consumption Experiences," Journal of Consumer Research, Oxford University Press, vol. 37(1), pages 176-195, June.
    3. Mikalef, Patrick & Boura, Maria & Lekakos, George & Krogstie, John, 2019. "Big data analytics and firm performance: Findings from a mixed-method approach," Journal of Business Research, Elsevier, vol. 98(C), pages 261-276.
    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. John F. Riggs & Dena Hale & Scott Widmier & Sonya Tidwell-Riggs, 2023. "Randomized, Multicenter, Parallel-Arm (RMPA) research trial design: a potential solution to survey length, response rate and data quality in social science research," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 577-586, December.
    2. Michael A. Levin & John T. Gironda, 2023. "New frontiers in forecasting, predicting, and explaining: an introduction to the special issue," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 559-560, December.
    3. Munten, Pauline & Vanhamme, Joëlle, 2023. "To reduce waste, have it repaired! The quality signaling effect of product repairability," Journal of Business Research, Elsevier, vol. 156(C).

    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. Luther Yuong Qai Chong & Thien Sang Lim, 2022. "Pull and Push Factors of Data Analytics Adoption and Its Mediating Role on Operational Performance," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    2. Raed A.I. Abueed & Mehmet Aga, 2019. "Sustainable Knowledge Creation and Corporate Outcomes: Does Corporate Data Governance Matter?," Sustainability, MDPI, vol. 11(20), pages 1-15, October.
    3. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    4. Yuelin Gao & Kaiguang Wang & Chenyang Gao & Yulong Shen & Teng Li, 2019. "Application of Differential Evolution Algorithm Based on Mixed Penalty Function Screening Criterion in Imbalanced Data Integration Classification," Mathematics, MDPI, vol. 7(12), pages 1-36, December.
    5. Jiang, Kangqi & Du, Xinyi & Chen, Zhongfei, 2022. "Firms' digitalization and stock price crash risk," International Review of Financial Analysis, Elsevier, vol. 82(C).
    6. Shah, Tushar R., 2022. "Can big data analytics help organisations achieve sustainable competitive advantage? A developmental enquiry," Technology in Society, Elsevier, vol. 68(C).
    7. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    8. Maya Vachkova & Arsalan Ghouri & Haidy Ashour & Normalisa Binti Md Isa & Gregory Barnes, 2023. "Big data and predictive analytics and Malaysian micro-, small and medium businesses," SN Business & Economics, Springer, vol. 3(8), pages 1-28, August.
    9. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    10. Daniel Villanova, 2019. "The extended self, product valuation, and the endowment effect," AMS Review, Springer;Academy of Marketing Science, vol. 9(3), pages 357-371, December.
    11. Tsionas, Mike & Patel, Pankaj C. & Guedes, Maria João, 2022. "Endogenous efficiency of the dynamic profit maximization in the intertemporal production models of venture behavior," International Journal of Production Economics, Elsevier, vol. 246(C).
    12. Castell, Carolin & Kiefer, Jasmin & Schubach, Sebastian & Schumann, Jan H. & Graf-Vlachy, Lorenz & König, Andreas, 2023. "Integrating digital platform dynamics into customer orientation research: A systematic review and research agenda," Journal of Business Research, Elsevier, vol. 163(C).
    13. Elisabetta Raguseo & Claudio Vitari & Federico Pigni, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," Post-Print hal-03032504, HAL.
    14. Yuksel, Mujde & McDonald, Mark A. & Milne, George R. & Darmody, Aron, 2017. "The paradoxical relationship between fantasy football and NFL consumption: Conflict development and consumer coping mechanisms," Sport Management Review, Elsevier, vol. 20(2), pages 198-210.
    15. Adriana Manolica & Marius-Iulian Cluci & Teodora Roman, 2021. "The Consumer Explained through the Extended-Self," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 572-581, August.
    16. Schoenherr, Tobias, 2023. "Supply chain management professionals’ proficiency in big data analytics: Antecedents and impact on performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    17. James A. Cunningham & Nadja Damij & Dolores Modic & Femi Olan, 2023. "MSME technology adoption, entrepreneurial mindset and value creation: a configurational approach," The Journal of Technology Transfer, Springer, vol. 48(5), pages 1574-1598, October.
    18. AlNuaimi, Bader Khamis & Khan, Mehmood & Ajmal, Mian M., 2021. "The role of big data analytics capabilities in greening e-procurement: A higher order PLS-SEM analysis," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    19. Huynh, Minh-Tay & Nippa, Michael & Aichner, Thomas, 2023. "Big data analytics capabilities: Patchwork or progress? A systematic review of the status quo and implications for future research," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    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:pal:jmarka:v:7:y:2019:i:4:d:10.1057_s41270-019-00064-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    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.