IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i8p3386-d348521.html
   My bibliography  Save this article

Environmental Activism and Big Data: Building Green Social Capital in China

Author

Listed:
  • Jing Xu

    (Center for Energy and Environmental Policy, University of Delaware, Newark, DE 19716, USA)

  • Huijun Zhang

    (Department of Physics, The Hong Kong university of Science and Technology, Clear Water Bay, Hong Kong 87P8+H5, China)

Abstract

The rapid development of information and communication technologies, coupled with the significant progress in the areas of environmental policy and public participation, has led to the advent of environmental big data in China recently. This article applies social capital theory as an analytical lens to shed light on how Chinese environmental non-government organizations (ENGOs) adopt big data to promote environmental governance. This study conducts case studies focusing on two ENGOs: The Institute of Public and Environmental Affairs (IPE) and Green Hunan. Combining a qualitative approach with quantitative analysis, this research examines two big data-induced initiatives: The first involves green supply chain management in the IPE, brand-sensitive multinational corporations (MNCs), and Chinese suppliers of the MNCs, while the second involves the mobile data-based Riverwatcher Action Network of Green Hunan and numerous volunteers nationwide. This study found that big data adoption by ENGOs contributes effectively to building green social capital, including social networks and pro-environmental social norms. Green social capital has important implications for governance in terms of fostering coordination and cooperation across the boundaries of the public, private, and voluntary sectors. This study highlighted the finding that empowerment by big data helps Chinese ENGOs play the role of a change agent in sustainability transitions.

Suggested Citation

  • Jing Xu & Huijun Zhang, 2020. "Environmental Activism and Big Data: Building Green Social Capital in China," Sustainability, MDPI, vol. 12(8), pages 1-24, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3386-:d:348521
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/8/3386/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/8/3386/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    3. Bernhard Swoboda & Cathrin Puchert & Dirk Morschett, 2016. "Explaining the differing effects of corporate reputation across nations: a multilevel analysis," Journal of the Academy of Marketing Science, Springer, vol. 44(4), pages 454-473, July.
    4. Tao Tao & Kunlun Xin, 2014. "Public health: A sustainable plan for China's drinking water," Nature, Nature, vol. 511(7511), pages 527-528, July.
    5. Hans WESTLUND & Malin GAWELL, 2012. "Building Social Capital For Social Entrepreneurship," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 83(1), pages 101-116, March.
    6. Marta Portela & Isabel Neira & Maria del Salinas-Jiménez, 2013. "Social Capital and Subjective Wellbeing in Europe: A New Approach on Social Capital," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 114(2), pages 493-511, November.
    7. Suebvises, Ploy, 2018. "Social capital, citizen participation in public administration, and public sector performance in Thailand," World Development, Elsevier, vol. 109(C), pages 236-248.
    8. Martin Hilbert, 2016. "Big Data for Development: A Review of Promises and Challenges," Development Policy Review, Overseas Development Institute, vol. 34(1), pages 135-174, January.
    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. Zhang, Mingming & Wong, Wing-Keung & Kim Oanh, Thai Thi & Muda, Iskandar & Islam, Saiful & Hishan, Sanil S. & Abduvaxitovna, Shamansurova Zilola, 2023. "Regulating environmental pollution through natural resources and technology innovation: Revisiting the environment Kuznet curve in China through quantile-based ARDL estimations," Resources Policy, Elsevier, vol. 85(PA).
    2. Jing Xu & Huijun Zhang, 2022. "Activating beyond Informing: Action-Oriented Utilization of WeChat by Chinese Environmental NGOs," IJERPH, MDPI, vol. 19(7), pages 1-15, March.

    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. Andrea De Mauro & Marco Greco & Michele Grimaldi, 2019. "Understanding Big Data Through a Systematic Literature Review: The ITMI Model," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1433-1461, July.
    2. Jiming Hu & Yin Zhang, 2017. "Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 91-109, July.
    3. 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.
    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. Stefan Sleep & John Hulland & Richard A. Gooner, 2019. "THE DATA HIERARCHY: factors influencing the adoption and implementation of data-driven decision making," AMS Review, Springer;Academy of Marketing Science, vol. 9(3), pages 230-248, December.
    6. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    7. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    8. Merendino, Alessandro & Dibb, Sally & Meadows, Maureen & Quinn, Lee & Wilson, David & Simkin, Lyndon & Canhoto, Ana, 2018. "Big data, big decisions: The impact of big data on board level decision-making," Journal of Business Research, Elsevier, vol. 93(C), pages 67-78.
    9. Braganza, Ashley & Brooks, Laurence & Nepelski, Daniel & Ali, Maged & Moro, Russ, 2017. "Resource management in big data initiatives: Processes and dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 328-337.
    10. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    11. 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.
    12. Mariani, Marcello M. & Borghi, Matteo & Laker, Benjamin, 2023. "Do submission devices influence online review ratings differently across different types of platforms? A big data analysis," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    13. Elisabetta Raguseo & Claudio Vitari, 2017. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," Grenoble Ecole de Management (Post-Print) halshs-01923259, HAL.
    14. Francesco Badia & Fabio Donato, 2022. "Opportunities and risks in using big data to support management control systems: A multiple case study," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(3), pages 39-63.
    15. 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).
    16. Thomas Niebel & Fabienne Rasel & Steffen Viete, 2019. "BIG data – BIG gains? Understanding the link between big data analytics and innovation," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 28(3), pages 296-316, April.
    17. Li, Francis G.N. & Bataille, Chris & Pye, Steve & O'Sullivan, Aidan, 2019. "Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?," Applied Energy, Elsevier, vol. 239(C), pages 991-1002.
    18. 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.
    19. Dong-Hui Jin & Hyun-Jung Kim, 2018. "Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    20. 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.

    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:12:y:2020:i:8:p:3386-:d:348521. 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.