IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i13p7911-d851262.html
   My bibliography  Save this article

Supporting Decision-Making in the Technical Equipment Selection Process by the Method of Contradictory Evaluations

Author

Listed:
  • Marek Gaworski

    (Institute of Mechanical Engineering, Warsaw University of Life Sciences—SGGW, 02-787 Warsaw, Poland)

  • Piotr F. Borowski

    (Institute of Mechanical Engineering, Warsaw University of Life Sciences—SGGW, 02-787 Warsaw, Poland)

  • Łukasz Kozioł

    (Technical Department, Cedrus S.A., 95-060 Brzeziny, Poland)

Abstract

Creating new research concepts is an important element in supporting the development of production systems and their components, including technical equipment. Many technical devices are characterized by a high degree of complexity, which justifies the need for a specific approach to their assessment and becomes an inspiration to search for innovative methods of assessment. As part of the proposed new approach to the assessment of technical objects, studies were carried out in which users of chainsaws assessed their technical and functional features, taking into account the options: advantage and disadvantage. The number of positive and negative assessments of a given feature was used in the developed methodology to calculate the utility potential index. The proposed formula for calculating the utility potential index is a contribution to the current state of knowledge in the field of evaluation of technical objects and their features, as well as comparisons between technical objects. Based on the definition of the utility potential index, its value ranges from 0.0 to 1.0. The conducted research and calculation results indicated the accumulation of the lowest and the highest values of the utility potential index for one of the functional features, i.e., the price of the equipment. The utilitarian effect of the study is the creation of a tool supporting the selection of equipment for the needs of its users. The research results can also be used as a suggestion for the improvement of the indicated features of technical devices by their manufacturers.

Suggested Citation

  • Marek Gaworski & Piotr F. Borowski & Łukasz Kozioł, 2022. "Supporting Decision-Making in the Technical Equipment Selection Process by the Method of Contradictory Evaluations," Sustainability, MDPI, vol. 14(13), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7911-:d:851262
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/13/7911/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/13/7911/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jaekyeong Kim & Ilyoung Choi & Qinglong Li, 2021. "Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    2. Siskos, Eleftherios & Tsotsolas, Nikos, 2015. "Elicitation of criteria importance weights through the Simos method: A robustness concern," European Journal of Operational Research, Elsevier, vol. 246(2), pages 543-553.
    3. Piotr F. Borowski, 2021. "Digitization, Digital Twins, Blockchain, and Industry 4.0 as Elements of Management Process in Enterprises in the Energy Sector," Energies, MDPI, vol. 14(7), pages 1-20, March.
    4. Rosa Maria Fanelli & Luca Romagnoli, 2020. "Customer Satisfaction with Farmhouse Facilities and Its Implications for the Promotion of Agritourism Resources in Italian Municipalities," Sustainability, MDPI, vol. 12(5), pages 1-21, February.
    5. Purnawirawan, Nathalia & Eisend, Martin & De Pelsmacker, Patrick & Dens, Nathalie, 2015. "A Meta-analytic Investigation of the Role of Valence in Online Reviews," Journal of Interactive Marketing, Elsevier, vol. 31(C), pages 17-27.
    6. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    7. Latruffe, Laure & Diazabakana, Ambre & Bockstaller, Christian & Desjeux, Yann & Finn, John & Kelly, Edel & Ryan, Mary & Uthes, Sandra, 2016. "Measurement of sustainability in agriculture: a review of indicators," Studies in Agricultural Economics, Research Institute for Agricultural Economics, vol. 118(3), pages 1-8, December.
    8. Floyd, Kristopher & Freling, Ryan & Alhoqail, Saad & Cho, Hyun Young & Freling, Traci, 2014. "How Online Product Reviews Affect Retail Sales: A Meta-analysis," Journal of Retailing, Elsevier, vol. 90(2), pages 217-232.
    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. Suchocka, Marzena & Heciak, Jakub & Błaszczyk, Magdalena & Adamczyk, Joanna & Gaworski, Marek & Gawłowska, Agnieszka & Mojski, Jacek & Kalaji, Hazem M. & Kais, Karolina & Kosno-Jończy, Joanna & Heciak, 2023. "Comparison of Ecosystem Services and Replacement Value calculations performed for urban trees," Ecosystem Services, Elsevier, vol. 63(C).

    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. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
    2. Elvira Ismagilova & Emma L. Slade & Nripendra P. Rana & Yogesh K. Dwivedi, 2020. "The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis," Information Systems Frontiers, Springer, vol. 22(5), pages 1203-1226, October.
    3. Agnieszka Zablocki & Bodo Schlegelmilch & Michael J. Houston, 2019. "How valence, volume and variance of online reviews influence brand attitudes," AMS Review, Springer;Academy of Marketing Science, vol. 9(1), pages 61-77, June.
    4. Nguyen, Hang T. & Chaudhuri, Malika, 2019. "Making new products go viral and succeed," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 39-62.
    5. Weiwei Zhang & Austin Williams & Nicole Griffith & Jessica Gaskins & P Brandon Bookstaver, 2020. "Online availability of fish antibiotics and documented intent for self-medication," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-12, September.
    6. Hongpeng Wang & Rong Du & Jin Li & Weiguo Fan, 2020. "Subdivided or aggregated online review systems: Which is better for online takeaway vendors?," Electronic Commerce Research, Springer, vol. 20(4), pages 915-944, December.
    7. Marchand, André & Hennig-Thurau, Thorsten & Wiertz, Caroline, 2017. "Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 336-354.
    8. Ismagilova, Elvira & Dwivedi, Yogesh K. & Slade, Emma, 2020. "Perceived helpfulness of eWOM: Emotions, fairness and rationality," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    9. Verma, Deepak & Prakash Dewani, Prem & Behl, Abhishek & Pereira, Vijay & Dwivedi, Yogesh & Del Giudice, Manilo, 2023. "A meta-analysis of antecedents and consequences of eWOM credibility: Investigation of moderating role of culture and platform type," Journal of Business Research, Elsevier, vol. 154(C).
    10. Ravula, Prashanth & Bhatnagar, Amit & Gauri, Dinesh K, 2023. "Role of gender in the creation and persuasiveness of online reviews," Journal of Business Research, Elsevier, vol. 154(C).
    11. Mardumyan, Anna & Siret, Iris, 2023. "When review verification does more harm than good: How certified reviews determine customer–brand relationship quality," Journal of Business Research, Elsevier, vol. 160(C).
    12. Tamaddoni, Ali & Seenivasan, Satheesh & Pallant, Jason I. & Skiera, Bernd, 2023. "Investigating the effect of status changes in review platforms," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 189-209.
    13. Ismagilova, Elvira & Slade, Emma & Rana, Nripendra P. & Dwivedi, Yogesh K., 2020. "The effect of characteristics of source credibility on consumer behaviour: A meta-analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    14. Mican Daniel & Sitar-Taut Dan-Andrei, 2020. "Analysis of the Factors Impacting the Online Shopping Decision-Making Process," Studia Universitatis Babeș-Bolyai Oeconomica, Sciendo, vol. 65(1), pages 54-66, April.
    15. Elvira Ismagilova & Emma L. Slade & Nripendra P. Rana & Yogesh K. Dwivedi, 0. "The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis," Information Systems Frontiers, Springer, vol. 0, pages 1-24.
    16. Wang, Fang & Karimi, Sahar, 2019. "This product works well (for me): The impact of first-person singular pronouns on online review helpfulness," Journal of Business Research, Elsevier, vol. 104(C), pages 283-294.
    17. Kordrostami, Elika & Rahmani, Vahid, 2020. "Investigating conflicting online review information:evidence from Amazon.com," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    18. Li, Kunlin & Chen, Yuhan & Zhang, Liyi, 2020. "Exploring the influence of online reviews and motivating factors on sales: A meta-analytic study and the moderating role of product category," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    19. Moon, Sangkil & Kim, Seung-Wook & Iacobucci, Dawn, 2024. "Dynamic relationship changes between reviewers and consumers in online product reviews," Journal of Retailing, Elsevier, vol. 100(1), pages 70-84.
    20. Zablocki, Agnieszka & Makri, Katerina & Houston, Michael J., 2019. "Emotions Within Online Reviews and their Influence on Product Attitudes in Austria, USA and Thailand," Journal of Interactive Marketing, Elsevier, vol. 46(C), pages 20-39.

    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:14:y:2022:i:13:p:7911-:d:851262. 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.