IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v25y2023i10d10.1007_s10668-022-02529-7.html
   My bibliography  Save this article

Effective end‑of‑life (EOL) products management in mobile phone industry with using Twitter data analysis perspective

Author

Listed:
  • Seyed Hamed Ghanadpour

    (Shahid Beheshti University)

  • Sajjad Shokouhyar

    (Shahid Beheshti University)

  • Mohadeseh Pourabbasi

    (Shahid Beheshti University)

Abstract

In today’s life, with the rapid improvement of the electronic industry, and the ever-increasing use of mobile phones, the tremendous amount of end-of-life (EOL) mobile phones around the world needs a sustainable management system to decide on mobile phone waste. In this regard, manufacturers around the world need to plan for better management of products at the EOL phase. Therefore, this paper introduces a novel framework for the iPhone mobile phone waste in the EOL phase that its purpose is determining the EOL option to reduce mobile phone waste. The proposed framework uses the Twitter database as a rich data source to extract mobile phone defects from customer opinions using a data mining technique. Finally, a multi-objective mathematical framework is developed to make efficient EOL decisions based on mobile phone defects obtained from Twitter data. The findings of this study can help mobile phone manufacturers investigate mobile phone defects by using customer opinions on social media platforms. Therefore, they design appropriate strategic programs to decide on appropriate EOL processes for returned products. This study sheds new light on the importance of social media data in solving waste management problems by demonstrating the impact of customer opinions.

Suggested Citation

  • Seyed Hamed Ghanadpour & Sajjad Shokouhyar & Mohadeseh Pourabbasi, 2023. "Effective end‑of‑life (EOL) products management in mobile phone industry with using Twitter data analysis perspective," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 11337-11366, October.
  • Handle: RePEc:spr:endesu:v:25:y:2023:i:10:d:10.1007_s10668-022-02529-7
    DOI: 10.1007/s10668-022-02529-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-022-02529-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10668-022-02529-7?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. Dehghanian, Farzad & Mansour, Saeed, 2009. "Designing sustainable recovery network of end-of-life products using genetic algorithm," Resources, Conservation & Recycling, Elsevier, vol. 53(10), pages 559-570.
    2. Thavalingam, Vyshnavi & Karunasena, Gayani, 2016. "Mobile phone waste management in developing countries: A case of Sri Lanka," Resources, Conservation & Recycling, Elsevier, vol. 109(C), pages 34-43.
    3. Ruomeng Cui & Santiago Gallino & Antonio Moreno & Dennis J. Zhang, 2018. "The Operational Value of Social Media Information," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1749-1769, October.
    4. Nishikant Mishra & Akshit Singh, 2018. "Use of twitter data for waste minimisation in beef supply chain," Annals of Operations Research, Springer, vol. 270(1), pages 337-359, November.
    5. Ahluwalia, Poonam Khanijo & Nema, Arvind K., 2007. "A life cycle based multi-objective optimization model for the management of computer waste," Resources, Conservation & Recycling, Elsevier, vol. 51(4), pages 792-826.
    6. Chae, Bongsug (Kevin), 2015. "Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research," International Journal of Production Economics, Elsevier, vol. 165(C), pages 247-259.
    7. Mattias Strand & Anna Syberfeldt & André Geertsen, 2017. "A Decision Support System for Sustainable Waste Collection," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 9(4), pages 49-65, October.
    8. Darby, Lauren & Obara, Louise, 2005. "Household recycling behaviour and attitudes towards the disposal of small electrical and electronic equipment," Resources, Conservation & Recycling, Elsevier, vol. 44(1), pages 17-35.
    9. Ameli, Mariam & Mansour, Saeed & Ahmadi-Javid, Amir, 2016. "A multi-objective model for selecting design alternatives and end-of-life options under uncertainty: A sustainable approach," Resources, Conservation & Recycling, Elsevier, vol. 109(C), pages 123-136.
    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. Mihalis Giannakis & Rameshwar Dubey & Shishi Yan & Konstantina Spanaki & Thanos Papadopoulos, 2022. "Social media and sensemaking patterns in new product development: demystifying the customer sentiment," Annals of Operations Research, Springer, vol. 308(1), pages 145-175, January.
    2. Chen, Xi & Wong, Tse Chiu, 2021. "Application of social media data in supply chain management : A systematic review," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 499-523, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2020. "The interconnectedness of the economic content in the speeches of the US Presidents," Papers 2002.07880, arXiv.org.
    4. Choi, Tsan-Ming & Guo, Shu & Luo, Suyuan, 2020. "When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    5. Borthakur, Anwesha & Govind, Madhav, 2017. "Emerging trends in consumers’ E-waste disposal behaviour and awareness: A worldwide overview with special focus on India," Resources, Conservation & Recycling, Elsevier, vol. 117(PB), pages 102-113.
    6. Sajjad Shokouhyar & Sina Shokoohyar & Shima Mirzaei, 2023. "Stakeholders' engagement through social media analytics in promoting sustainable development practices in the mobile supply chain: A cross‐country analysis," Business Strategy and the Environment, Wiley Blackwell, vol. 32(8), pages 5807-5820, December.
    7. Shima Mirzaei & Sajjad Shokouhyar, 2023. "Applying a thematic analysis in identifying the role of circular economy in sustainable supply chain practices," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(5), pages 4691-4722, May.
    8. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2021. "The interconnectedness of the economic content in the speeches of the US Presidents," Annals of Operations Research, Springer, vol. 299(1), pages 593-615, April.
    9. Purva Grover & Arpan Kumar Kar & Yogesh K. Dwivedi, 2022. "Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions," Annals of Operations Research, Springer, vol. 308(1), pages 177-213, January.
    10. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    11. Manomaivibool, Panate & Vassanadumrongdee, Sujitra, 2012. "Buying back household waste electrical and electronic equipment: Assessing Thailand's proposed policy in light of past disposal behavior and future preferences," Resources, Conservation & Recycling, Elsevier, vol. 68(C), pages 117-125.
    12. Chae, Bongsug (Kevin), 2018. "The Internet of Things (IoT): A Survey of Topics and Trends using Twitter Data and Topic Modeling," 22nd ITS Biennial Conference, Seoul 2018. Beyond the boundaries: Challenges for business, policy and society 190376, International Telecommunications Society (ITS).
    13. Lei Wang & Ram Gopal & Ramesh Shankar & Joseph Pancras, 2022. "Forecasting venue popularity on location‐based services using interpretable machine learning," Production and Operations Management, Production and Operations Management Society, vol. 31(7), pages 2773-2788, July.
    14. Phillips, Paul S. & Tudor, Terry & Bird, Helen & Bates, Margaret, 2011. "A critical review of a key Waste Strategy Initiative in England: Zero Waste Places Projects 2008–2009," Resources, Conservation & Recycling, Elsevier, vol. 55(3), pages 335-343.
    15. Pardis Pourmohammadi & Reza Tavakkoli-Moghaddam & Yaser Rahimi & Chefi Triki, 2023. "Solving a hub location-routing problem with a queue system under social responsibility by a fuzzy meta-heuristic algorithm," Annals of Operations Research, Springer, vol. 324(1), pages 1099-1128, May.
    16. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.
    17. Ray Qing Cao & Dara G. Schniederjans & Vicky Ching Gu, 2021. "Stakeholder sentiment in service supply chains: big data meets agenda-setting theory," Service Business, Springer;Pan-Pacific Business Association, vol. 15(1), pages 151-175, March.
    18. Ashish Kumar Rathore & Santanu Das & P. Vigneswara Ilavarasan, 2018. "Social Media Data Inputs in Product Design: Case of a Smartphone," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(3), pages 255-272, September.
    19. Eva Labro & Mark Lang & Jim Omartian, 2019. "Predictive Analytics and Organizational Architecture: Plant-Level Evidence from Census Data," Working Papers 19-02, Center for Economic Studies, U.S. Census Bureau.
    20. Prabhsimran Singh & Yogesh K. Dwivedi & Karanjeet Singh Kahlon & Ravinder Singh Sawhney & Ali Abdallah Alalwan & Nripendra P. Rana, 0. "Smart Monitoring and Controlling of Government Policies Using Social Media and Cloud Computing," Information Systems Frontiers, Springer, vol. 0, pages 1-23.

    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:spr:endesu:v:25:y:2023:i:10:d:10.1007_s10668-022-02529-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.