IDEAS home Printed from https://ideas.repec.org/a/bas/econst/y2024i2p153-174.html
   My bibliography  Save this article

Assessing the Significance of Intellectual Capital in Retail Trade through Conjoint Analysis

Author

Listed:
  • Ivanka Nikolova

Abstract

In the conditions of widespread application of information and communication technologies, intellectual resources as a result of human intelligence, knowledge and experience are the leading source of development and growth. Intellectual capital, encompassing all the intellectual resources from which businesses derive growth in various forms, is emerging as the leading form of capital today. The aim of this publication is to assess the significance of joint impact and the extent to which elements of intellectual capital contribute to value creation and the establishment of conditions for manifestations of competitive advantages in retail trade. The study was carried out using conjoint analysis. After a brief historical overview of the development of the conjoint analysis methodology, its relationship with the hierarchical process analysis developed by Thomas Saaty is shown and a full profile conjoint analysis is conducted. The necessary data were obtained by carrying out surveys with managers from consumer goods retail chains. Based on the obtained ratings - partial, average, and overall - of the 'utility' and 'importance' categories, conclusions have been drawn regarding the joint impact and the extent to which the elements of intellectual capital contribute to value creation in retail trade.

Suggested Citation

  • Ivanka Nikolova, 2024. "Assessing the Significance of Intellectual Capital in Retail Trade through Conjoint Analysis," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 153-174.
    • Handle: RePEc:bas:econst:y:2024:i:2:p:153-174
    as

    Download full text from publisher

    File URL: https://www.iki.bas.bg/Journals/EconomicStudies/2024/2024-2/09_Ivanka-Nikolova.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vithala R. Rao, 2014. "Applied Conjoint Analysis," Springer Books, Springer, edition 127, number 978-3-540-87753-0, April.
    2. Nikolinka Ignatova, 2021. "Main Trends in the Development of Human Resources in Trade," Nauchni trudove, University of National and World Economy, Sofia, Bulgaria, issue 2, pages 279-294, April.
    3. Simeonka Petrova, 2021. "Challenges Facing Retailers’ Assortment Supply In The Omnichannel Age," Business Management, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 4 Year 20, pages 42-57.
    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. Bodo Herzog, 2018. "Valuation of Digital Platforms: Experimental Evidence for Google and Facebook," IJFS, MDPI, vol. 6(4), pages 1-13, October.
    2. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    3. Mengelkamp, Esther & Schönland, Thomas & Huber, Julian & Weinhardt, Christof, 2019. "The value of local electricity - A choice experiment among German residential customers," Energy Policy, Elsevier, vol. 130(C), pages 294-303.
    4. Maksim Godovykh, 2024. "Hospitality Art Experience Model: The Effects of Visual Art on Guests’ Attitudes and Behavior," Tourism and Hospitality, MDPI, vol. 5(2), pages 1-9, May.
    5. Christian P Theurer & Andranik Tumasjan & Isabell M Welpe, 2018. "Contextual work design and employee innovative work behavior: When does autonomy matter?," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-35, October.
    6. Anoek Castelein & Dennis Fok & Richard Paap, 2020. "A multinomial and rank-ordered logit model with inter- and intra-individual heteroscedasticity," Tinbergen Institute Discussion Papers 20-069/III, Tinbergen Institute.
    7. Xiang Wu & Bin Hu & Jie Xiong, 2020. "Understanding Heterogeneous Consumer Preferences in Chinese Milk Markets: A Latent Class Approach," Post-Print hal-02489646, HAL.
    8. Isihara, Paul & Shi, Chaojun & Ward, Jonathan & O'Malley, Leo & Laney, Skyler & Diedrichs, Danilo & Flores, Gabriel, 2020. "Identifying most typical and most ideal attribute levels in small populations of expert decision makers: Studying the Go/No Go decision of disaster relief organizations," Journal of choice modelling, Elsevier, vol. 35(C).
    9. Poulissen, Davey & de Grip, Andries & Fouarge, Didier & Künn-Nelen, Annemarie, 2021. "Employers' Willingness to Invest in the Training of Temporary Workers: A Discrete Choice Experiment," IZA Discussion Papers 14395, Institute of Labor Economics (IZA).
    10. Hein, Maren & Goeken, Nils & Kurz, Peter & Steiner, Winfried J., 2022. "Using Hierarchical Bayes draws for improving shares of choice predictions in conjoint simulations: A study based on conjoint choice data," European Journal of Operational Research, Elsevier, vol. 297(2), pages 630-651.
    11. Knoblauch, Theresa A.K. & Trutnevyte, Evelina & Stauffacher, Michael, 2019. "Siting deep geothermal energy: Acceptance of various risk and benefit scenarios in a Swiss-German cross-national study," Energy Policy, Elsevier, vol. 128(C), pages 807-816.
    12. Alexey Zalozhnev & Dmitriy Chistov, 2022. "Models For Making Decisions On The Volume And Price Of Purchases In Wholesale Trade," Business Management, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 2 Year 20, pages 59-70.
    13. Basem Al-Omari & Joviana Farhat & Mujahed Shraim, 2023. "The Role of Web-Based Adaptive Choice-Based Conjoint Analysis Technology in Eliciting Patients’ Preferences for Osteoarthritis Treatment," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    14. Sunghi An & Daisik Nam & R. Jayakrishnan & Soongbong Lee & Michael G. McNally, 2021. "A Study of the Factors Affecting Multimodal Ridesharing with Choice-Based Conjoint Analysis," Sustainability, MDPI, vol. 13(20), pages 1-14, October.
    15. Carsten Herbes & Johannes Dahlin & Peter Kurz, 2020. "Consumer Willingness To Pay for Proenvironmental Attributes of Biogas Digestate-Based Potting Soil," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
    16. Paolo Mariani & Andrea Marletta & Mauro Mussini & Mariangela Zenga, 2021. "Professional Profiles and Job Requirements in Labour Demand: An Analysis of the Italian Information Technology Sector," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 651-667, August.
    17. Yegoryan, Narine & Guhl, Daniel & Klapper, Daniel, 2018. "Inferring Attribute Non-Attendance Using Eye Tracking in Choice-Based Conjoint Analysis," Rationality and Competition Discussion Paper Series 111, CRC TRR 190 Rationality and Competition.
    18. Scharwenka, Nina & Mattes, Martin & Kröll, Markus & Giese, Moritz, 2018. "Mehrwert durch nutzenbasierte Preissetzung bei Konsumgütern," Marketing Review St.Gallen, Universität St.Gallen, Institut für Marketing und Customer Insight, vol. 35(5), pages 46-53.
    19. Eunae Son & Song Soo Lim, 2021. "Consumer Acceptance of Gene-Edited versus Genetically Modified Foods in Korea," IJERPH, MDPI, vol. 18(7), pages 1-17, April.
    20. Côté, Elizabeth & Đukan, Mak & Pons-Seres de Brauwer, Cristian & Wüstenhagen, Rolf, 2022. "The price of actor diversity: Measuring project developers’ willingness to accept risks in renewable energy auctions," Energy Policy, Elsevier, vol. 163(C).

    More about this item

    JEL classification:

    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

    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:bas:econst:y:2024:i:2:p:153-174. 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: Diana Dimitrova (email available below). General contact details of provider: https://edirc.repec.org/data/ikbasbg.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.