IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i4p1637-d492716.html
   My bibliography  Save this article

Mechanism Underlying the Formation of Virtual Agglomeration of Creative Industries: Theoretical Analysis and Empirical Research

Author

Listed:
  • Xu Chen

    (Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China)

  • Chunhong Liu

    (Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China)

  • Changchun Gao

    (Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China)

  • Yao Jiang

    (School of Management, Shanghai University of Engineering Science, Shanghai 201620, China)

Abstract

Industrial agglomeration serves as an effective model for developing the creative economy and manifests itself as the interdependence of creative subjects in geographical space. The traditional methods of resource agglomeration have undergone tremendous changes due to the development of digital technology. These transformations have given birth to a new organizational form of the virtual agglomeration of creative industries. The present work uses field interviews and grounded theoretical research methods to construct a theoretical model of this new organizational phenomenon. Questionnaire surveys and empirical testing using structural equation models are here combined to systematically analyze the formation mechanism of the virtual agglomeration of creative industries. The results show that digital technology, virtual platforms, digital creative talents, digitization of cultural resources, and government policies have driven the formation of the virtual agglomeration of creative industries. This has been achieved through network collaboration, freedom of participation, and trust guarantee mechanisms. The effect of emerging consumer demand on the virtual agglomeration of creative industries is not significant. In addition, the implications of this research are also considered and discussed.

Suggested Citation

  • Xu Chen & Chunhong Liu & Changchun Gao & Yao Jiang, 2021. "Mechanism Underlying the Formation of Virtual Agglomeration of Creative Industries: Theoretical Analysis and Empirical Research," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:1637-:d:492716
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/4/1637/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/4/1637/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ester Alba Pagán & María del Mar Gaitán Salvatella & María Dolores Pitarch & Arabella León Muñoz & María del Mar Moya Toledo & José Marin Ruiz & Maurizio Vitella & Georgia Lo Cicero & Franz Rottenstei, 2020. "From Silk to Digital Technologies: A Gateway to New Opportunities for Creative Industries, Traditional Crafts and Designers. The SILKNOW Case," Sustainability, MDPI, vol. 12(19), pages 1-37, October.
    2. Xuefang Xie & Xuemei Xie & Carla Martínez-Climent, 2019. "Identifying the factors determining the entrepreneurial ecosystem of internet cultural industries in emerging economies," International Entrepreneurship and Management Journal, Springer, vol. 15(2), pages 503-522, June.
    3. Sarstedt, Marko & Ringle, Christian M. & Smith, Donna & Reams, Russell & Hair, Joseph F., 2014. "Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers," Journal of Family Business Strategy, Elsevier, vol. 5(1), pages 105-115.
    4. Wen-Jie Yan & Shang-Chia Chiou, 2020. "Dimensions of Customer Value for the Development of Digital Customization in the Clothing Industry," Sustainability, MDPI, vol. 12(11), pages 1-27, June.
    5. Peter Martey Addo & Dominique Guégan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Documents de travail du Centre d'Economie de la Sorbonne 18003, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    6. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835164, HAL.
    7. Satish Nambisan, 2017. "Digital Entrepreneurship: Toward a Digital Technology Perspective of Entrepreneurship," Entrepreneurship Theory and Practice, , vol. 41(6), pages 1029-1055, November.
    8. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Post-Print halshs-01889154, HAL.
    9. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Working Papers 2018:08, Department of Economics, University of Venice "Ca' Foscari".
    10. Wanyue Wei & Zheng He & Lez Rayman-Bacchus & GuangMing Xiang, 2019. "Do Industrial Clusters Still Matter to Trust-Building in the Internet Era? A Network Embeddedness Perspective," SAGE Open, , vol. 9(3), pages 21582440198, August.
    11. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01719983, HAL.
    12. Christian Peukert, 2019. "The next wave of digital technological change and the cultural industries," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 43(2), pages 189-210, June.
    13. Jimmyn Parc & Shin Dong Kim, 2020. "The Digital Transformation of the Korean Music Industry and the Global Emergence of K-Pop," Sustainability, MDPI, vol. 12(18), pages 1-16, September.
    14. Hsin-Pin Fu & Hsiaoping Yeh & Rong-Ling Ma, 2018. "A study of the CSFs of an e-cluster platform adoption for microenterprises," Information Technology and Management, Springer, vol. 19(4), pages 231-243, December.
    15. Heidi Wiig Aslesen & Roman Martin & Stefania Sardo, 2019. "The virtual is reality! On physical and virtual space in software firms’ knowledge formation," Entrepreneurship & Regional Development, Taylor & Francis Journals, vol. 31(9-10), pages 669-682, October.
    16. Hao Ren & Rongrong Wang & Suopeng Zhang & An Zhang, 2017. "How Do Internet Enterprises Obtain Sustainable Development of Organizational Ecology? A Case Study of LeEco Using Institutional Logic Theory," Sustainability, MDPI, vol. 9(8), pages 1-21, August.
    17. Nicos Komninos & Christina Kakderi & Antonio Collado & Ilektra Papadaki & Anastasia Panori, 2021. "Digital Transformation of City Ecosystems: Platforms Shaping Engagement and Externalities across Vertical Markets," Journal of Urban Technology, Taylor & Francis Journals, vol. 28(1-2), pages 93-114, April.
    18. Stepan Zemtsov, 2020. "New technologies, potential unemployment and ‘nescience economy’ during and after the 2020 economic crisis," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(4), pages 723-743, August.
    19. Hsin‐Hann Tsai & Hong‐Yuh Lee & Hsiao‐Cheng Yu, 2008. "Developing the Digital Content Industry in Taiwan," Review of Policy Research, Policy Studies Organization, vol. 25(2), pages 169-188, March.
    20. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Post-Print halshs-01835164, HAL.
    21. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01889154, HAL.
    22. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Post-Print halshs-01719983, HAL.
    23. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, 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. Weisbrod, Glen & Hensher, David A., 2023. "Improving transportation project evaluation by recognizing the role of spatial scale and context in measuring non-user economic benefits," Transport Policy, Elsevier, vol. 144(C), pages 80-89.
    2. Xu Chen & Chunhong Liu & Yao Jiang & Changchun Gao, 2021. "What Causes the Virtual Agglomeration of Creative Industries?," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    3. Jiaoping Yang & Shujun Wang & Shan Sun & Jianhua Zhu, 2022. "Influence Mechanism of High-Tech Industrial Agglomeration on Green Innovation Performance: Evidence from China," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    4. Ti-An Chen, 2022. "Business Performance Evaluation for Tourism Factory: Using DEA Approach and Delphi Method," Sustainability, MDPI, vol. 14(15), pages 1-19, July.

    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. Dan Wang & Zhi Chen & Ionut Florescu, 2021. "A Sparsity Algorithm with Applications to Corporate Credit Rating," Papers 2107.10306, arXiv.org.
    2. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    3. Gunnarsson, Björn Rafn & vanden Broucke, Seppe & Baesens, Bart & Óskarsdóttir, María & Lemahieu, Wilfried, 2021. "Deep learning for credit scoring: Do or don’t?," European Journal of Operational Research, Elsevier, vol. 295(1), pages 292-305.
    4. Roy Cerqueti & Francesca Pampurini & Annagiulia Pezzola & Anna Grazia Quaranta, 2022. "Dangerous liasons and hot customers for banks," Review of Quantitative Finance and Accounting, Springer, vol. 59(1), pages 65-89, July.
    5. Theuri, Joseph & Olukuru, John, 2022. "The impact of Artficial Intelligence and how it is shaping banking," KBA Centre for Research on Financial Markets and Policy Working Paper Series 61, Kenya Bankers Association (KBA).
    6. José Américo Pereira Antunes, 2021. "To supervise or to self-supervise: a machine learning based comparison on credit supervision," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-21, December.
    7. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    8. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
    9. Salima Smiti & Makram Soui, 2020. "Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE," Information Systems Frontiers, Springer, vol. 22(5), pages 1067-1083, October.
    10. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
    11. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan-level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    12. Seyyide Doğan & Yasin Büyükkör & Murat Atan, 2022. "A comparative study of corporate credit ratings prediction with machine learning," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(1), pages 25-47.
    13. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    14. Nenad Milojević & Srdjan Redzepagic, 2021. "Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(3), pages 41-57.
    15. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.
    16. Irving Fisher Committee, 2019. "The use of big data analytics and artificial intelligence in central banking," IFC Bulletins, Bank for International Settlements, number 50.
    17. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    18. A. R. Provenzano & D. Trifir`o & A. Datteo & L. Giada & N. Jean & A. Riciputi & G. Le Pera & M. Spadaccino & L. Massaron & C. Nordio, 2020. "Machine Learning approach for Credit Scoring," Papers 2008.01687, arXiv.org.
    19. Yaseen Ghulam & Kamini Dhruva & Sana Naseem & Sophie Hill, 2018. "The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans," Risks, MDPI, vol. 6(3), pages 1-21, September.
    20. Li-Chen Cheng & Wei-Ting Lu & Benjamin Yeo, 2023. "Predicting abnormal trading behavior from internet rumor propagation: a machine learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.

    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:4:p:1637-:d:492716. 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.