IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i4p1362-d320014.html
   My bibliography  Save this article

Social Expectations and Market Changes in the Context of Developing the Industry 4.0 Concept

Author

Listed:
  • Sebastian Saniuk

    (Department of Engineering Management and Logistics Systems, University of Zielona Gora, 65-417 Zielona Góra, Poland)

  • Sandra Grabowska

    (Department of Production Engineering, Silesian University of Technology, 40-019 Katowice, Poland)

  • Bożena Gajdzik

    (Department of Industrial Informatics, Silesian University of Technology, 40-019 Katowice, Poland)

Abstract

There are more and more talks in the community of scientists and business practitioners about new challenges for industry in connection with the fourth industrial revolution. Industry 4.0 is the result of the development of cyber-physical generation systems as part of the fourth industrial revolution. Industry 4.0 sets new areas of change in the sphere of production and management but also exerts an impact on various aspects of society’s life. It is a transformational challenge for enterprises of the present age. Industry 4.0 is present in economic studies at the macroeconomic level and business at the microeconomic level. Scientists discuss the essence of change, and specialized research centers and consulting companies carry out research on various aspects of this industrial revolution. The article presents the range of expectations and changes in society towards the development of the concept of Industry 4.0. The work was based on a literature study and direct research in the field of social change in the Industry 4.0 era. The aim of the article is to identify social expectations of development changes related to the implementation of the Industry 4.0 concept. The article devotes a lot of attention to customization because it is one of the keys of Industry 4.0, leading to a change of the paradigm from mass production to personalized production. This simple change will affect customers, producers, and employees. Based on the synthesis of literature and secondary research, authors identify opportunities and threats to the broadly understood society functioning in the Industry 4.0 environment. Social conditions were analyzed from the point of view of the consumer, producer, and employee. In the cited direct studies, the basic area of analysis was product personalization and pre-recognition of the opinions of potential consumers about customization in Industry 4.0. The limitation of the research area to the consumer segment resulted from the importance of product personalization in Industry 4.0 and its impact on producer behavior and effects for employees.

Suggested Citation

  • Sebastian Saniuk & Sandra Grabowska & Bożena Gajdzik, 2020. "Social Expectations and Market Changes in the Context of Developing the Industry 4.0 Concept," Sustainability, MDPI, vol. 12(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1362-:d:320014
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/4/1362/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/4/1362/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ying-Ju Chen & Mingcherng Deng, 2015. "Information Sharing in a Manufacturer–Supplier Relationship: Suppliers' Incentive and Production Efficiency," Production and Operations Management, Production and Operations Management Society, vol. 24(4), pages 619-633, April.
    2. Christian Arnold & Daniel Kiel & Kai-Ingo Voigt, 2016. "How The Industrial Internet Of Things Changes Business Models In Different Manufacturing Industries," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 20(08), pages 1-25, December.
    3. Joachim Klewes & Robert Wreschniok, 2009. "Reputation capital Building and maintaining trust in the 21st century," Springer Books, in: Joachim Klewes & Robert Wreschniok (ed.), Reputation Capital, pages 1-8, Springer.
    4. Markus Lang & Patricia Deflorin & Helmut Dietl & Eric Lucas, 2014. "The Impact of Complexity on Knowledge Transfer in Manufacturing Networks," Production and Operations Management, Production and Operations Management Society, vol. 23(11), pages 1886-1898, November.
    5. Radosław Wolniak, 2019. "The Level of Maturity of Quality Management Systems in Poland—Results of Empirical Research," Sustainability, MDPI, vol. 11(15), pages 1-17, August.
    6. Bin Hu & Dimitris Kostamis, 2015. "Managing Supply Disruptions when Sourcing from Reliable and Unreliable Suppliers," Production and Operations Management, Production and Operations Management Society, vol. 24(5), pages 808-820, May.
    7. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    8. Klassen, Robert D. & Vereecke, Ann, 2012. "Social issues in supply chains: Capabilities link responsibility, risk (opportunity), and performance," International Journal of Production Economics, Elsevier, vol. 140(1), pages 103-115.
    9. Sabrina Schneider & Patrick Spieth, 2013. "Business Model Innovation: Towards An Integrated Future Research Agenda," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-34.
    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. Ciampi, Francesco & Demi, Stefano & Magrini, Alessandro & Marzi, Giacomo & Papa, Armando, 2021. "Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation," Journal of Business Research, Elsevier, vol. 123(C), pages 1-13.
    2. Yi Tao & Loo Hay Lee & Ek Peng Chew, 2016. "Quantifying the Effect of Sharing Information in a Supply Chain Facing Supply Disruptions," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(04), pages 1-28, August.
    3. Ancillai, Chiara & Sabatini, Andrea & Gatti, Marco & Perna, Andrea, 2023. "Digital technology and business model innovation: A systematic literature review and future research agenda," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    4. Daniel Kiel & Julian M. Müller & Christian Arnold & Kai-Ingo Voigt, 2017. "Sustainable Industrial Value Creation: Benefits And Challenges Of Industry 4.0," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 21(08), pages 1-34, December.
    5. Jan F. Tesch & Anne-Sophie Brillinger & Dominik Bilgeri, 2017. "Internet Of Things Business Model Innovation And The Stage-Gate Process: An Exploratory Analysis," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 21(05), pages 1-19, June.
    6. Julian Marius Müller & Daniel Kiel & Kai-Ingo Voigt, 2018. "What Drives the Implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability," Sustainability, MDPI, vol. 10(1), pages 1-24, January.
    7. Bhatti, Sabeen Hussain & Santoro, Gabriele & Khan, Jabran & Rizzato, Fabio, 2021. "Antecedents and consequences of business model innovation in the IT industry," Journal of Business Research, Elsevier, vol. 123(C), pages 389-400.
    8. Muhammad Anwar, 2018. "BUSINESS MODEL INNOVATION AND SMEs PERFORMANCE — DOES COMPETITIVE ADVANTAGE MEDIATE?," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 22(07), pages 1-31, October.
    9. Müller, Julian Marius & Buliga, Oana & Voigt, Kai-Ingo, 2018. "Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 2-17.
    10. Weking, Jörg & Stöcker, Maria & Kowalkiewicz, Marek & Böhm, Markus & Krcmar, Helmut, 2020. "Leveraging industry 4.0 – A business model pattern framework," International Journal of Production Economics, Elsevier, vol. 225(C).
    11. Manjot Singh Bhatia & Saurabh Kumar, 2022. "Linking stakeholder and competitive pressure to Industry 4.0 and performance: Mediating effect of environmental commitment and green process innovation," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 1905-1918, July.
    12. Kiel, Daniel & Arnold, Christian & Voigt, Kai-Ingo, 2017. "The influence of the Industrial Internet of Things on business models of established manufacturing companies – A business level perspective," Technovation, Elsevier, vol. 68(C), pages 4-19.
    13. Christian Arnold & Kai-Ingo Voigt, 2019. "Determinants of Industrial Internet of Things Adoption in German Manufacturing Companies," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 1-21, October.
    14. Mariani, Marcello M. & Fosso Wamba, Samuel, 2020. "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, Elsevier, vol. 121(C), pages 338-352.
    15. Schmidt, Christoph G. & Wuttke, David A. & Heese, H. Sebastian & Wagner, Stephan M., 2023. "Antecedents of public reactions to supply chain glitches," International Journal of Production Economics, Elsevier, vol. 259(C).
    16. Jie Wu & Zhixin Chen & Xiang Ji, 2020. "Sustainable trade promotion decisions under demand disruption in manufacturer-retailer supply chains," Annals of Operations Research, Springer, vol. 290(1), pages 115-143, July.
    17. Scott, James & Ho, William & Dey, Prasanta K. & Talluri, Srinivas, 2015. "A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments," International Journal of Production Economics, Elsevier, vol. 166(C), pages 226-237.
    18. Thorsten Schoormann & Julia Schweihoff & Ilka Jussen & Frederik Möller, 2023. "Classification tools for business models: Status quo, comparison, and agenda," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-36, December.
    19. Christian Rammer & Gastón P Fernández & Dirk Czarnitzki, 2021. "Artificial Intelligence and Industrial Innovation: Evidence from Firm-Level Data," Working Papers of Department of Economics, Leuven 674605, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    20. Ahmad Ibrahim Aljumah & Mohammed T. Nuseir & Md. Mahmudul Alam, 2021. "Traditional marketing analytics, big data analytics and big data system quality and the success of new product development," Post-Print hal-03538161, HAL.

    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:4:p:1362-:d:320014. 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.