IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v117y2020icp322-330.html
   My bibliography  Save this article

TUTORIAL: AI research without coding: The art of fighting without fighting: Data science for qualitative researchers

Author

Listed:
  • Ciechanowski, Leon
  • Jemielniak, Dariusz
  • Gloor, Peter A.

Abstract

In this tutorial, we show how to scrape and collect online data, perform sentiment analysis, social network analysis, tribe finding, and Wikidata cross-checks, all without using a single line of programming code. In a step-by-step example, we use self-collected data to perform several analyses of the glass ceiling. Our tutorial can serve as a standalone introduction to data science for qualitative researchers and business researchers, who have avoided learning to program. It should also be useful for experienced data scientists who want to learn about the tools that will allow them to collect and analyze data more easily and effectively.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jbrese:v:117:y:2020:i:c:p:322-330
    DOI: 10.1016/j.jbusres.2020.06.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296320303854
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jbusres.2020.06.012?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. 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.
    2. Przegalinska, Aleksandra & Ciechanowski, Leon & Stroz, Anna & Gloor, Peter & Mazurek, Grzegorz, 2019. "In bot we trust: A new methodology of chatbot performance measures," Business Horizons, Elsevier, vol. 62(6), pages 785-797.
    3. 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.
    4. 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.
    5. Dariusz Jemielniak & Maciej Wilamowski, 2017. "Cultural diversity of quality of information on Wikipedias," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(10), pages 2460-2470, October.
    6. Agata Stasik, 2018. "Global controversies in local settings: anti-fracking activism in the era of Web 2.0," Journal of Risk Research, Taylor & Francis Journals, vol. 21(12), pages 1562-1578, December.
    7. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    8. Vicente-Saez, Ruben & Martinez-Fuentes, Clara, 2018. "Open Science now: A systematic literature review for an integrated definition," Journal of Business Research, Elsevier, vol. 88(C), pages 428-436.
    9. Jose Ramon Saura & Pedro Palos-Sanchez & Antonio Grilo, 2019. "Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining," Sustainability, MDPI, vol. 11(3), pages 1-14, February.
    10. Rudolf R. Sinkovics & Elfriede Penz & Pervez N. Ghauri, 2008. "Enhancing the Trustworthiness of Qualitative Research in International Business," Management International Review, Springer, vol. 48(6), pages 689-714, December.
    11. Harrison, Robert L., 2013. "Using mixed methods designs in the Journal of Business Research, 1990–2010," Journal of Business Research, Elsevier, vol. 66(11), pages 2153-2162.
    12. Bernard Cova & Veronique Cova, 2002. "Tribal marketing: The tribalisation of society and its impact on the conduct of marketing," Post-Print hal-01822665, HAL.
    13. Polsa, Pia, 2013. "The crossover-dialog approach: The importance of multiple methods for international business," Journal of Business Research, Elsevier, vol. 66(3), pages 288-297.
    14. Tadeusz Chełkowski & Peter Gloor & Dariusz Jemielniak, 2016. "Inequalities in Open Source Software Development: Analysis of Contributor’s Commits in Apache Software Foundation Projects," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-19, April.
    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. Haenlein, Michael & Kaplan, Andreas, 2021. "Artificial intelligence and robotics: Shaking up the business world and society at large," Journal of Business Research, Elsevier, vol. 124(C), pages 405-407.
    2. Katarzyna Sanak-Kosmowska & Jan W. Wiktor, 2021. "The Morphology and Differentiation of the Content of International Debate on Renewable Energy. A Bibliometric Analysis of Web of Science, Scopus, and Twitter," Energies, MDPI, vol. 14(21), pages 1-23, October.
    3. Villarroel Ordenes, Francisco & Silipo, Rosaria, 2021. "Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications," Journal of Business Research, Elsevier, vol. 137(C), pages 393-410.
    4. Chowdhury, Soumyadeb & Budhwar, Pawan & Dey, Prasanta Kumar & Joel-Edgar, Sian & Abadie, Amelie, 2022. "AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework," Journal of Business Research, Elsevier, vol. 144(C), pages 31-49.
    5. Boegershausen, Johannes & Datta, Hannes & Borah, Abhishek & Stephen, Andrew, 2022. "Fields of Gold: Web Scraping and APIs for Impactful Marketing Insights," Other publications TiSEM 5f1ed70a-48c3-422c-bc10-0, Tilburg University, School of Economics and Management.

    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. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. Ghasemaghaei, Maryam & Calic, Goran, 2019. "Does big data enhance firm innovation competency? The mediating role of data-driven insights," Journal of Business Research, Elsevier, vol. 104(C), pages 69-84.
    7. 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.
    8. 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).
    9. Sun, Pengfei & Yuan, Chunhui & Li, Xiaolong & Di, Jia, 2024. "Big data analytics, firm risk and corporate policies: Evidence from China," Research in International Business and Finance, Elsevier, vol. 70(PB).
    10. Côrte-Real, Nadine & Ruivo, Pedro & Oliveira, Tiago & Popovič, Aleš, 2019. "Unlocking the drivers of big data analytics value in firms," Journal of Business Research, Elsevier, vol. 97(C), pages 160-173.
    11. Ghasemaghaei, Maryam & Calic, Goran, 2020. "Assessing the impact of big data on firm innovation performance: Big data is not always better data," Journal of Business Research, Elsevier, vol. 108(C), pages 147-162.
    12. Alberto Bertello & Alberto Ferraris & Stefano Bresciani & Paola Bernardi, 2021. "Big data analytics (BDA) and degree of internationalization: the interplay between governance of BDA infrastructure and BDA capabilities," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 25(4), pages 1035-1055, December.
    13. Sleep, Stefan & Gala, Prachi & Harrison, Dana E., 2023. "Removing silos to enable data-driven decisions: The importance of marketing and IT knowledge, cooperation, and information quality," Journal of Business Research, Elsevier, vol. 156(C).
    14. 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.
    15. 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.
    16. 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).
    17. Leogrande, Angelo, 2021. "The Destruction of Price-Representativeness," MPRA Paper 111239, University Library of Munich, Germany.
    18. 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.
    19. 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.
    20. 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).

    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:117:y:2020:i:c:p:322-330. 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.