IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i18p10352-d636805.html
   My bibliography  Save this article

Evaluating the Service Operating Efficiency and Its Determinants in Global Consulting Firms: A Metafrontier Analysis

Author

Listed:
  • Gowangwoo Park

    (Department of Smart Convergence Consulting, Hansung University, Seoul 02876, Korea)

  • Seok-Kee Lee

    (Department of Computer Engineering, Hansung University, Seoul 02876, Korea)

  • Kanghwa Choi

    (Department of Business, Hansung University, Seoul 02876, Korea)

Abstract

Knowledge consulting services are one of the fastest growing fields in the knowledge service industry since the 2010s and have been emerging as a core area of the knowledge economy. Accordingly, consulting services are actively sought and provided in various fields, including business strategy and management, accounting, and ICT, and global consulting firms have experienced rapid growth. However, previous research evaluating the performance or service quality of knowledge consulting services is relatively scarce. In particular, there are barely any studies that apply the data envelopment analysis (DEA) model to measure the relative operating efficiencies of consulting firms in the global consulting service field. This study measured the operating efficiency of 27 global consulting firms using DEA. As global consulting firms are managed differently depending on the characteristics of the country in which they operate, the 27 global consulting firms were classified into three groups by region (USA, Europe, Asia) to measure their meta-efficiency (ME), group efficiency (GE), and technology gap ratio (TGR) and identify the causes of inefficiency at global consulting firms. The contextual variables within consulting firms that affect efficiency were analyzed using Tobit regression. Based on the analysis results, this study suggests strategies for enhancing the operating efficiency and realizing sustainable growth in global consulting firms.

Suggested Citation

  • Gowangwoo Park & Seok-Kee Lee & Kanghwa Choi, 2021. "Evaluating the Service Operating Efficiency and Its Determinants in Global Consulting Firms: A Metafrontier Analysis," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10352-:d:636805
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/18/10352/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/18/10352/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yasar A. Ozcan, 2014. "Performance Measurement Using Data Envelopment Analysis (DEA)," International Series in Operations Research & Management Science, in: Health Care Benchmarking and Performance Evaluation, edition 2, chapter 0, pages 15-47, Springer.
    2. Ming-Miin Yu & Li-Hsueh Chen, 2020. "Evaluation of efficiency and technological bias of tourist hotels by a meta-frontier DEA model," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(5), pages 718-732, May.
    3. Yi-Lung Lee & Shew-Huei Kuo & Mei-Yi Jiang & Yang Li, 2019. "Evaluating the Performances of Taiwan’s International Tourist Hotels: Applying the Directional Distance Function and Meta-Frontier Approach," Sustainability, MDPI, vol. 11(20), pages 1-11, October.
    4. Yixiong He & Weiming Song & Fan Yang, 2021. "Research on the Supply Efficiency of Marine Ecological Products in the Yangtze River Delta Costal Urban Agglomerations Based on DEA-Tobit Model," Sustainability, MDPI, vol. 13(12), pages 1-17, June.
    5. Siran Fang & Xiaoshan Xue & Ge Yin & Hong Fang & Jialin Li & Yongnian Zhang, 2020. "Evaluation and Improvement of Technological Innovation Efficiency of New Energy Vehicle Enterprises in China Based on DEA-Tobit Model," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
    6. Hoegl, Martin & Proserpio, Luigi, 2004. "Team member proximity and teamwork in innovative projects," Research Policy, Elsevier, vol. 33(8), pages 1153-1165, October.
    7. Back, Yujin & Praveen Parboteeah, K. & Nam, Dae-il, 2014. "Innovation in Emerging Markets: The Role of Management Consulting Firms," Journal of International Management, Elsevier, vol. 20(4), pages 390-405.
    8. Woo, Ka-shing & Ennew, Christine T., 2005. "Measuring business-to-business professional service quality and its consequences," Journal of Business Research, Elsevier, vol. 58(9), pages 1178-1185, September.
    9. Chieh-Wen Chang & Kun-Shan Wu & Bao-Guang Chang & Kuo-Ren Lou, 2019. "Measuring Technical Efficiency and Returns to Scale in Taiwan’s Baking Industry―A Case Study of the 85 °C Company," Sustainability, MDPI, vol. 11(5), pages 1-14, February.
    10. Hao Zhang & Jianxin You & Xuekelaiti Haiyirete & Tianyu Zhang, 2020. "Measuring Logistics Efficiency in China Considering Technology Heterogeneity and Carbon Emission through a Meta-Frontier Model," Sustainability, MDPI, vol. 12(19), pages 1-18, October.
    11. Assaf, A., 2009. "Accounting for size in efficiency comparisons of airports," Journal of Air Transport Management, Elsevier, vol. 15(5), pages 256-258.
    12. Momparler, Alexandre & Carmona, Pedro & Lassala, Carlos, 2015. "Quality of consulting services and consulting fees," Journal of Business Research, Elsevier, vol. 68(7), pages 1458-1462.
    13. McDonald, John, 2009. "Using least squares and tobit in second stage DEA efficiency analyses," European Journal of Operational Research, Elsevier, vol. 197(2), pages 792-798, September.
    14. Bloom, Paul N. & Reve, Torger, 1990. "Transmitting signals to consumers for competitive advantage," Business Horizons, Elsevier, vol. 33(4), pages 58-66.
    15. Hoff, Ayoe, 2007. "Second stage DEA: Comparison of approaches for modelling the DEA score," European Journal of Operational Research, Elsevier, vol. 181(1), pages 425-435, August.
    16. Wang, Nannan & Chen, Ji & Yao, Shengnan & Chang, Yen-Chiang, 2018. "A meta-frontier DEA approach to efficiency comparison of carbon reduction technologies on project level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2606-2612.
    17. Creplet, F. & Dupouet, O. & Kern, F. & Mehmanpazir, B. & Munier, F., 2001. "Consultants and experts in management consulting firms," Research Policy, Elsevier, vol. 30(9), pages 1517-1535, December.
    18. Doo-Young Park & Kanghwa Choi & Dae-Han Kang, 2020. "Measuring the Meta Efficiency and Its Determinants on Efficiency in the Korean Coffee Shop Franchise," Sustainability, MDPI, vol. 12(6), pages 1-20, March.
    19. Ming-Miin Yu & Li-Hsueh Chen, 2020. "A meta-frontier network data envelopment analysis approach for the measurement of technological bias with network production structure," Annals of Operations Research, Springer, vol. 287(1), pages 495-514, April.
    20. Michael Fritsch & Viktor Slavtchev, 2010. "How does industry specialization affect the efficiency of regional innovation systems?," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 45(1), pages 87-108, August.
    21. Dong Jin Shin & Byung Sub Cha & Brian H.S. Kim, 2020. "Efficient Expenditure Allocation for Sustainable Public Services?—Comparative Cases of Korea and OECD Countries," Sustainability, MDPI, vol. 12(22), pages 1-19, November.
    22. Wang, Qunwei & Zhao, Zengyao & Zhou, Peng & Zhou, Dequn, 2013. "Energy efficiency and production technology heterogeneity in China: A meta-frontier DEA approach," Economic Modelling, Elsevier, vol. 35(C), pages 283-289.
    23. Navarro, Susana & Llinares, Carmen & Garzon, Dolores, 2016. "Exploring the relationship between co-creation and satisfaction using QCA," Journal of Business Research, Elsevier, vol. 69(4), pages 1336-1339.
    24. Gronroos, Christian & Ojasalo, Katri, 2004. "Service productivity: Towards a conceptualization of the transformation of inputs into economic results in services," Journal of Business Research, Elsevier, vol. 57(4), pages 414-423, April.
    25. Yasar A. Ozcan, 2014. "Evaluation of Performance in Health Care," International Series in Operations Research & Management Science, in: Health Care Benchmarking and Performance Evaluation, edition 2, chapter 0, pages 3-14, Springer.
    26. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    27. George E. Battese & D. S. Prasada Rao, 2002. "Technology Gap, Efficiency, and a Stochastic Metafrontier Function," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(2), pages 87-93, August.
    28. Liliana Pérez‐Nordtvedt & Ben L. Kedia & Deepak K. Datta & Abdul A. Rasheed, 2008. "Effectiveness and Efficiency of Cross‐Border Knowledge Transfer: An Empirical Examination," Journal of Management Studies, Wiley Blackwell, vol. 45(4), pages 714-744, June.
    29. Yasar A. Ozcan, 2014. "Health Care Benchmarking and Performance Evaluation," International Series in Operations Research and Management Science, Springer, edition 2, number 978-1-4899-7472-3, September.
    30. Chiu, Yung-Ho & Lee, Jen-Hui & Lu, Ching-Cheng & Shyu, Ming-Kuang & Luo, Zhengying, 2012. "The technology gap and efficiency measure in WEC countries: Application of the hybrid meta frontier model," Energy Policy, Elsevier, vol. 51(C), pages 349-357.
    31. Banker, Rajiv D. & Lee, S.-Y.Seok-Young & Potter, Gordon & Srinivasan, Dhinu, 2000. "An empirical analysis of continuing improvements following the implementation of a performance-based compensation plan," Journal of Accounting and Economics, Elsevier, vol. 30(3), pages 315-350, December.
    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. Víctor Giménez & Jorge R. Keith & Diego Prior, 2019. "Do healthcare financing systems influence hospital efficiency? A metafrontier approach for the case of Mexico," Health Care Management Science, Springer, vol. 22(3), pages 549-559, September.
    2. Doo-Young Park & Kanghwa Choi & Dae-Han Kang, 2020. "Measuring the Meta Efficiency and Its Determinants on Efficiency in the Korean Coffee Shop Franchise," Sustainability, MDPI, vol. 12(6), pages 1-20, March.
    3. Chin‐wei Huang & Hsiao‐Yin Chen, 2023. "Using nonradial metafrontier data envelopment analysis to evaluate the metatechnology and metafactor ratios for the Taiwanese hotel industry," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 1904-1919, June.
    4. Kim, Youngjune & Chen, Bowen & Featherstone, Allen M. & Pendell, Dustin L., 2017. "Are Efficient Farms and Inefficient Farms Heterogeneous?," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252830, Southern Agricultural Economics Association.
    5. Fang-Rong Ren & Ze Tian & Yu-Ting Shen & Yung-Ho Chiu & Tai-Yu Lin, 2019. "Energy, CO 2 , and AQI Efficiency and Improvement of the Yangtze River Economic Belt," Energies, MDPI, vol. 12(4), pages 1-17, February.
    6. Peter Dohmen & Martin Ineveld & Aniek Markus & Liana Hagen & Joris Klundert, 2023. "Does competition improve hospital performance: a DEA based evaluation from the Netherlands," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(6), pages 999-1017, August.
    7. He, Yan & Chiu, Yung-ho & Zhang, Bin, 2015. "The impact of corporate governance on state-owned and non-state-owned firms efficiency in China," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 252-277.
    8. Girma Jirata Duguma & Jiqin Han, 2021. "Effect of deposit mobilization on the technical efficiency of rural saving and credit cooperatives: Evidence from Ethiopia," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 92(4), pages 621-647, December.
    9. Xi Chen & Mingzhe Pu & Yu Zhong, 2022. "Evaluating China Food’s Fertilizer Reduction and Efficiency Initiative Using a Double Stochastic Meta-Frontier Method," IJERPH, MDPI, vol. 19(12), pages 1-21, June.
    10. Md. Abul Kalam Azad & Susila Munisamy & Abdul Kadar Muhammad Masum & Paolo Saona & Peter Wanke, 2017. "Bank efficiency in Malaysia: a use of malmquist meta-frontier analysis," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 7(2), pages 287-311, August.
    11. J. David Cummins & María Rubio-Misas, 2022. "Integration and convergence in efficiency and technology gap of European life insurance markets," Annals of Operations Research, Springer, vol. 315(1), pages 93-119, August.
    12. Sun, Chuanwang & Liu, Xiaohong & Li, Aijun, 2018. "Measuring unified efficiency of Chinese fossil fuel power plants: Intermediate approach combined with group heterogeneity and window analysis," Energy Policy, Elsevier, vol. 123(C), pages 8-18.
    13. Chen Kaihua & Kou Mingting, 2014. "Staged efficiency and its determinants of regional innovation systems: a two-step analytical procedure," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(2), pages 627-657, March.
    14. Xiangyu Teng & Danting Lu & Yung-ho Chiu, 2019. "Emission Reduction and Energy Performance Improvement with Different Regional Treatment Intensity in China," Energies, MDPI, vol. 12(2), pages 1-18, January.
    15. Jung Ho Park & Kwangsoo Shin, 2018. "Efficiency of Government-Sponsored R&D Projects: A Metafrontier DEA Approach," Sustainability, MDPI, vol. 10(7), pages 1-17, July.
    16. Wang, Qunwei & Chiu, Yung-Ho & Chiu, Ching-Ren, 2017. "Non-radial metafrontier approach to identify carbon emission performance and intensity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 664-672.
    17. Mansour Zarrin & Jan Schoenfelder & Jens O. Brunner, 2022. "Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework," Health Care Management Science, Springer, vol. 25(3), pages 406-425, September.
    18. Yu, Yantuan & Peng, Chong & Li, Yushuang, 2019. "Do neighboring prefectures matter in promoting eco-efficiency? Empirical evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 456-465.
    19. Tsekouras, Kostas & Chatzistamoulou, Nikos & Kounetas, Kostas & Broadstock, David C., 2016. "Spillovers, path dependence and the productive performance of European transportation sectors in the presence of technology heterogeneity," Technological Forecasting and Social Change, Elsevier, vol. 102(C), pages 261-274.
    20. Sadaf Hafeez & Noreen Izza Arshad & Lukman Bin A B Rahim & Muhammad Farooq Shabbir & Jawad Iqbal, 2020. "Innovation in Chinese internet companies: A meta-frontier analysis," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-15, May.

    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:gam:jsusta:v:13:y:2021:i:18:p:10352-:d:636805. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.