IDEAS home Printed from https://ideas.repec.org/a/sae/entthe/v43y2019i5p843-854.html
   My bibliography  Save this article

A New Methodological Frontier in Entrepreneurship Research: Big Data Studies

Author

Listed:
  • Andreas Schwab
  • Zhu Zhang

Abstract

The emergence of “big data†and related analytic techniques are creating opportunities to advance empirical entrepreneurship theory and practice. This editorial focuses on the implications for the design and execution of empirical studies. It offers guidance on how to navigate related methodological challenges and outlines what editors, professional associations, research-method teachers, and administrators can do to enable high-quality big data research.

Suggested Citation

  • Andreas Schwab & Zhu Zhang, 2019. "A New Methodological Frontier in Entrepreneurship Research: Big Data Studies," Entrepreneurship Theory and Practice, , vol. 43(5), pages 843-854, September.
    • Handle: RePEc:sae:entthe:v:43:y:2019:i:5:p:843-854
    DOI: 10.1177/1042258718760841
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1042258718760841
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1042258718760841?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
    ---><---

    References listed on IDEAS

    as
    1. Guzzo, Richard A. & Fink, Alexis A. & King, Eden & Tonidandel, Scott & Landis, Ronald S., 2015. "Big Data Recommendations for Industrial–Organizational Psychology," Industrial and Organizational Psychology, Cambridge University Press, vol. 8(4), pages 491-508, December.
    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. Jana Diesner & Terrill L. Frantz & Kathleen M. Carley, 2005. "Communication Networks from the Enron Email Corpus “It's Always About the People. Enron is no Different”," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 201-228, October.
    4. Goldberg, Amir & Srivastava, Sameer B & Manian, Govind & Monroe, William & Potts, Christopher, 2016. "Fitting In or Standing Out? The Tradeoffs of Structural and Cultural Embeddedness," Institute for Research on Labor and Employment, Working Paper Series qt9bf631rg, Institute of Industrial Relations, UC Berkeley.
    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. Carlo Corradini & Emma Folmer & Anna Rebmann, 2022. "Listening to the buzz: Exploring the link between firm creation and regional innovative atmosphere as reflected by social media," Environment and Planning A, , vol. 54(2), pages 347-369, March.
    2. Francesco Perugini, 2023. "Space–time analysis of entrepreneurial ecosystems," The Journal of Technology Transfer, Springer, vol. 48(1), pages 240-291, February.
    3. Bernd Wurth & Erik Stam & Ben Spigel, 2022. "Toward an Entrepreneurial Ecosystem Research Program," Entrepreneurship Theory and Practice, , vol. 46(3), pages 729-778, May.
    4. Markku Maula & Wouter Stam, 2020. "Enhancing Rigor in Quantitative Entrepreneurship Research," Entrepreneurship Theory and Practice, , vol. 44(6), pages 1059-1090, November.
    5. Ari Hyytinen & Petri Rouvinen & Mika Pajarinen & Joosua Virtanen, 2023. "Ex Ante Predictability of Rapid Growth: A Design Science Approach," Entrepreneurship Theory and Practice, , vol. 47(6), pages 2465-2493, November.
    6. Guéneau, Grégory & Chabaud, Didier & Sauvannet, Marie-Christine Chalus, 2023. "Sticky ties: Quest for structural inter-organizational configurations in entrepreneurial ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    7. Schade, Philipp & Schuhmacher, Monika C., 2023. "Predicting entrepreneurial activity using machine learning," Journal of Business Venturing Insights, Elsevier, vol. 19(C).
    8. Diego Corrales-Garay & Eva-María Mora-Valentín & Marta Ortiz-de-Urbina-Criado, 2020. "Entrepreneurship Through Open Data: An Opportunity for Sustainable Development," Sustainability, MDPI, vol. 12(12), pages 1-25, June.
    9. Williamson, Amanda J. & Short, Jeremy C. & Wolfe, Marcus T., 2021. "Standing out in crowdfunded microfinance: A topic modeling approach examining campaign distinctiveness and prosocial performance," Journal of Business Venturing Insights, Elsevier, vol. 16(C).
    10. Jabeur, Sami Ben & Ballouk, Houssein & Mefteh-Wali, Salma & Omri, Anis, 2022. "Forecasting the macrolevel determinants of entrepreneurial opportunities using artificial intelligence models," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    11. Graham, Byron & Bonner, Karen, 2022. "One size fits all? Using machine learning to study heterogeneity and dominance in the determinants of early-stage entrepreneurship," Journal of Business Research, Elsevier, vol. 152(C), pages 42-59.
    12. Brown, Ross & Rocha, Augusto, 2020. "Entrepreneurial uncertainty during the Covid-19 crisis: Mapping the temporal dynamics of entrepreneurial finance," Journal of Business Venturing Insights, Elsevier, vol. 14(C).
    13. Lucas, Benjamin & Francu, R. Elena & Goulding, James & Harvey, John & Nica-Avram, Georgiana & Perrat, Bertrand, 2021. "A Note on Data-driven Actor-differentiation and SDGs 2 and 12: Insights from a Food-sharing App," Research Policy, Elsevier, vol. 50(6).

    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. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    2. Ahmad Ibrahim Aljumah & Mohammed T. Nuseir & Md. Mahmudul Alam, 2021. "Traditional marketing analytics, big data analytics and big data system quality and the success of new product development," Post-Print hal-03538161, HAL.
    3. Aaltonen, Aleksi Ville & Alaimo, Cristina & Kallinikos, Jannis, 2021. "The making of data commodities: data analytics as an embedded process," LSE Research Online Documents on Economics 110296, London School of Economics and Political Science, LSE Library.
    4. Anke Joubert & Matthias Murawski & Markus Bick, 2023. "Measuring the Big Data Readiness of Developing Countries – Index Development and its Application to Africa," Information Systems Frontiers, Springer, vol. 25(1), pages 327-350, February.
    5. Rampersad, Giselle, 2020. "Robot will take your job: Innovation for an era of artificial intelligence," Journal of Business Research, Elsevier, vol. 116(C), pages 68-74.
    6. Chiara Mio & Silvia Panfilo & Benedetta Blundo, 2020. "Sustainable development goals and the strategic role of business: A systematic literature review," Business Strategy and the Environment, Wiley Blackwell, vol. 29(8), pages 3220-3245, December.
    7. Myra Sader & Barthélemy Chollet & Sébastien Brion & Olivier Trendel, 2021. "Supported, detached, or marginalized? The ambivalent role of social capital on stress at work," Post-Print hal-03167159, HAL.
    8. Magdalena Rusch & Josef‐Peter Schöggl & Rupert J. Baumgartner, 2023. "Application of digital technologies for sustainable product management in a circular economy: A review," Business Strategy and the Environment, Wiley Blackwell, vol. 32(3), pages 1159-1174, March.
    9. Michela Arnaboldi, 2018. "The Missing Variable in Big Data for Social Sciences: The Decision-Maker," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    10. Anike Sult & Janice Wobst & Rainer Lueg, 2024. "The role of training in implementing corporate sustainability: A systematic literature review," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(1), pages 1-30, January.
    11. 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).
    12. Mohamed Gaber & Edward J. Lusk, 2019. "A Vetting Protocol for the Analytical Procedures Platform for the AP-Phase of PCAOB Audits," Accounting and Finance Research, Sciedu Press, vol. 8(4), pages 1-43, November.
    13. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
    14. Leogrande, Angelo, 2021. "The Destruction of Price-Representativeness," MPRA Paper 111239, University Library of Munich, Germany.
    15. Damminda Alahakoon & Rashmika Nawaratne & Yan Xu & Daswin Silva & Uthayasankar Sivarajah & Bhumika Gupta, 2023. "Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities," Information Systems Frontiers, Springer, vol. 25(1), pages 221-240, February.
    16. Taiwen Feng & Hongyan Sheng, 2023. "Identifying the equifinal configurations of prompting green supply chain integration and subsequent performance outcome," Business Strategy and the Environment, Wiley Blackwell, vol. 32(8), pages 5234-5251, December.
    17. 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.
    18. Correa, Juan C. & Garzón, Wilmer & Brooker, Phillip & Sakarkar, Gopal & Carranza, Steven A. & Yunado, Leidy & Rincón, Alejandro, 2019. "Evaluation of collaborative consumption of food delivery services through web mining techniques," Journal of Retailing and Consumer Services, Elsevier, vol. 46(C), pages 45-50.
    19. Sehrish Atif, 2023. "Mapping circular economy principles and servitisation approach in business model canvas: an integrated literature review," Future Business Journal, Springer, vol. 9(1), pages 1-21, December.
    20. Mazanec, Josef A., 2020. "Hidden theorizing in big data analytics: With a reference to tourism design research," Annals of Tourism Research, Elsevier, vol. 83(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:sae:entthe:v:43:y:2019:i:5:p:843-854. 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: SAGE Publications (email available below). General contact details of provider: .

    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.